Version 4.5.5: Fix critical protobuf dependency conflict - all engines now load successfully on Python 3.13

Fix protobuf conflict: Move descript-audiotools to --no-deps list
- descript-audiotools forces protobuf downgrade from 6.32.0 to 3.19.6 - This breaks google.protobuf.internal.builder import needed by engines - Install descript-audiotools with --no-deps to prevent protobuf conflicts - Maintains compatibility while fixing ChatterBox and F5-TTS engine failures
2025-08-22 14:31:21 -03:00 · 2025-08-22 14:24:14 -03:00 · 2025-08-22 14:12:28 -03:00 · 2025-08-22 13:55:09 -03:00 · 2025-08-22 13:46:26 -03:00 · 2025-08-22 13:37:42 -03:00
167 changed files with 31209 additions and 250 deletions
@@ -1,3 +1,7 @@
+# Reference ComfyUI folders (not part of this project)
+ComfyUI/
+ComfyUI_frontend/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -5,6 +5,116 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

+## [4.5.5] - 2025-08-22
+
+### Fixed
+
+- Fix critical protobuf dependency conflict preventing ChatterBox, F5-TTS, and Higgs Audio engines from loading.
+- Move descript-audiotools to --no-deps installation to preserve protobuf 6.x compatibility.
+- All 19 nodes now load successfully on Python 3.13.6.
+## [4.5.4] - 2025-08-22
+
+### Added
+
+- Fix Windows Unicode encoding error preventing installation on clean systems
+Fix UnicodeEncodeError when Windows console cannot display emoji characters
+Add graceful fallback to text-based logging for Windows CP1252 encoding
+Resolve install script crash that prevented ComfyUI Manager installations
+## [4.5.3] - 2025-08-22
+
+### Added
+
+- Add comprehensive installation system with intelligent dependency management
+Add Python 3.13 full compatibility support with MediaPipe to OpenSeeFace fallback
+Add intelligent install.py script with automatic conflict resolution
+Add environment detection and safety warnings for system Python usage
+Add NumPy version constraints to prevent Numba compatibility issues
+Add automatic RVC dependencies installation support
+Update requirements.txt with comprehensive dependency documentation
+Update README with detailed installation instructions and Python 3.13 compatibility notes
+Add dedicated Higgs Audio 2 model installation section with complete folder structure
+Verify ComfyUI Manager integration with automatic install.py execution
+Fix all bundled engines compatibility: ChatterBox, F5-TTS, Higgs Audio, RVC
+## [4.5.2] - 2025-08-22
+
+### Changed
+
+- Fix Higgs Audio engine compatibility with transformers 4.46+ including attention API changes, cache handling, and generation loop updates
+## [4.5.1] - 2025-08-21
+
+### Added
+
+- Fix critical Higgs Audio download and loading issues
+
+- Fix sharded model file downloads (model-00001-of-00003.safetensors, etc.)
+- Add missing tokenizer files (tokenizer.json, tokenizer_config.json, special_tokens_map.json) to model directory
+- Fix tokenizer loading from organized local structure instead of HuggingFace cache
+- Add proper completeness validation for downloaded model directories
+- Implement fallback tokenizer loading with LlamaTokenizer for custom configs
+- Ensure all Higgs Audio models load from organized TTS/HiggsAudio/ structure without cache duplication
+## [4.5.0] - 2025-08-21
+
+### Added
+
+- 🎙️ Higgs Audio 2 Voice Cloning Integration
+
+## 🌟 Major New Features
+
+### 🎙️ Higgs Audio 2 Voice Cloning Engine
+- State-of-the-art voice cloning from 30+ second reference audio samples
+- Multi-speaker conversation support with seamless character switching
+- Real-time voice replication with exceptional audio quality
+- Universal integration - works with existing TTS Text and TTS SRT nodes
+- Advanced generation controls - fine-tune temperature, top-p, top-k, and token limits
+- Multi-language capabilities - English (tested), with potential support for Chinese, Korean, German, and Spanish
+
+### 🧠 Smart Audio Processing System
+- Intelligent chunk combination with automatic boundary detection
+- Per-junction analysis for optimal audio segment merging
+- Universal implementation across all TTS engines (ChatterBox, F5-TTS, Higgs Audio)
+- Enhanced audio quality through smart text structure analysis
+
+## 🏗️ Architecture Improvements
+
+### ⚙️ Modular Processor Architecture
+- Complete architectural consistency across all TTS engines
+- Unified delegation pattern - clean separation between user interface and engine processing
+- HiggsAudio SRT and TTS processors following established patterns
+- Simplified maintenance with modular, testable components
+
+### 🔄 Enhanced Processing Pipeline
+- Modular SRT overlap detection across all engines
+- Centralized cache management with improved invalidation
+- Smart model loading and initialization optimizations
+- Progress tracking with real-time feedback for long operations
+
+## 🎯 User Experience Enhancements
+
+### 📊 Improved Feedback and Documentation
+- Real-time progress bars for Higgs Audio generation
+- Enhanced tooltips with detailed parameter explanations
+- Optimized parameter ranges for better generation control
+- Voice reference management with flexible discovery system
+
+### 🚀 Performance Optimizations
+- Instant cache regeneration for previously processed content
+- Automatic model management with organized TTS/HiggsAudio/ structure
+- Memory-efficient processing with smart resource utilization
+- Seamless audio combination eliminating artifacts between segments
+
+## 🔧 Technical Improvements
+
+### 🎵 Audio Quality Enhancements
+- Advanced chunking algorithms with sentence boundary detection
+- Intelligent silence insertion between audio segments
+- Cache-optimized processing maintaining quality while improving speed
+- Professional-grade audio output across all supported engines
+
+### 🌐 Integration and Compatibility
+- Voice cloning compatibility with existing character switching system
+- Reference audio support for custom voice creation
+- Multi-engine harmony - all engines work consistently with unified nodes
+- Backward compatibility maintained for existing workflows
 ## [4.4.0] - 2025-08-16

 ### Added
@@ -5,8 +5,9 @@
 ## Architecture Overview

 This extension features a **unified modular architecture** supporting multiple TTS engines:
- **Unified Node Interface**: Single set of nodes (TTS Text, TTS SRT, Voice Changer) that work with any engine
- **Engine Adapters**: Modular adapters for ChatterBox, F5-TTS, and RVC voice conversion
+- **Unified Node Interface**: Single set of nodes (TTS Text, TTS SRT, Voice Changer) that work with any engine via clean delegation
+- **Engine Processors**: Internal processing engines for each TTS system (ChatterBox, F5-TTS, Higgs Audio) handling engine-specific orchestration
+- **Engine Adapters**: Modular adapters for ChatterBox, F5-TTS, Higgs Audio 2, and RVC voice conversion
 - **Centralized Download System**: Unified downloader eliminates HuggingFace cache duplication with direct downloads to organized TTS/ folder structure
 - **Thread-Safe Architecture**: Stateless ChatterBox wrapper eliminates shared state corruption (Note: parallel processing is slower than sequential)
 - **Universal Streaming Infrastructure**: Unified streaming system with configurable workers (batch_size parameter) - sequential mode (batch_size=0) recommended for optimal performance
@@ -76,6 +77,11 @@ This extension features a **unified modular architecture** supporting multiple T
 - **f5tts_edit_engine.py** - Speech editing engine for targeted word replacement
 - **audio_compositing.py** - Audio compositing with crossfade and segment processing

+**engines/higgs_audio/** - Higgs Audio 2 engine implementation with voice cloning
+- **higgs_audio.py** - Main Higgs Audio 2 wrapper with voice cloning and chunking support
+- **higgs_audio_downloader.py** - Model auto-download system for generation and tokenizer models
+- **boson_multimodal/** - Complete boson_multimodal implementation from HiggsAudio team
+
 **engines/rvc/** - RVC (Real-time Voice Conversion) engine implementation
 - **__init__.py** - RVC engine initialization and ComfyUI integration
 - **hubert_downloader.py** - HuBERT model auto-download from Hugging Face with TTS/ folder organization
@@ -99,6 +105,8 @@ This extension features a **unified modular architecture** supporting multiple T

 **engines/adapters/f5tts_adapter.py** - F5-TTS engine adapter with parameter mapping and cache integration

+**engines/adapters/higgs_audio_adapter.py** - Higgs Audio 2 engine adapter with voice cloning and parameter validation
+
 **engines/adapters/chatterbox_streaming_adapter.py** - ChatterBox streaming adapter bridging existing implementation to universal streaming system

 **engines/adapters/f5tts_streaming_adapter.py** - F5-TTS streaming adapter enabling parallel processing with language model switching
@@ -111,13 +119,15 @@ This extension features a **unified modular architecture** supporting multiple T

 **nodes/engines/f5tts_engine_node.py** - F5-TTS engine configuration node with model selection and generation settings

+**nodes/engines/higgs_audio_engine_node.py** - Higgs Audio 2 engine configuration node with voice cloning parameters and generation settings
+
 **nodes/engines/rvc_engine_node.py** - RVC voice conversion node with pitch control and quality settings

 ### Unified Interface Nodes

 **nodes/unified/tts_text_node.py** - Universal TTS text generation node working with any configured engine

-**nodes/unified/tts_srt_node.py** - Universal SRT subtitle processing with smart language grouping and timing
+**nodes/unified/tts_srt_node.py** - Universal SRT subtitle processing node - clean delegation layer that routes to engine-specific processors

 **nodes/unified/voice_changer_node.py** - Universal voice conversion node with multilingual model support and flexible audio inputs

@@ -127,6 +137,8 @@ This extension features a **unified modular architecture** supporting multiple T

 ### Engine-Specific Nodes

+*Note: These are internal processors/engines used by the Unified nodes, not direct ComfyUI interface nodes. They handle engine-specific orchestration while the Unified nodes provide the user interface.*
+
 **nodes/chatterbox/** - ChatterBox engine implementation nodes (called by Unified nodes)
 - **chatterbox_tts_node.py** - ChatterBox TTS engine node with streaming batch processing and character switching
 - **chatterbox_srt_node.py** - ChatterBox SRT engine node with streaming parallel processing and timing modes  
@@ -138,6 +150,9 @@ This extension features a **unified modular architecture** supporting multiple T
 - **f5tts_edit_node.py** - F5-TTS speech editor for word replacement
 - **f5tts_edit_options_node.py** - Advanced F5-TTS editing configuration

+**nodes/higgs_audio/** - Higgs Audio 2 internal processors
+- **higgs_audio_srt_processor.py** - Higgs Audio SRT orchestrator with multi-speaker support, character switching, and timing modes (internal processor used by Unified SRT node)
+
 ### Audio Processing System

 **nodes/audio/analyzer_node.py** - Interactive Audio Wave Analyzer with web interface and timing extraction
@@ -192,6 +207,10 @@ This extension features a **unified modular architecture** supporting multiple T

 **utils/audio/cache.py** - Unified caching system for TTS engines with engine-specific cache management

+**utils/audio/chunk_combiner.py** - Modular chunk combination utility with smart auto-selection based on text analysis (sentence boundaries, commas, forced splits) and detailed timing information
+
+**utils/audio/chunk_timing.py** - Chunk combination timing information helper providing standardized timing info integration across all TTS engines
+
 ### Text Processing

 **utils/text/character_parser.py** - Universal character switching system with [CharacterName] tag parsing and language-aware processing
@@ -297,6 +316,7 @@ This extension features a **unified modular architecture** supporting multiple T
 **voices_examples/** - Character voice reference files with both audio samples and text descriptions
 - **male/** - Male voice examples with reference audio and text
 - **female/** - Female voice examples with reference audio and text
+- **higgs_audio/** - Higgs Audio 2 voice presets (belinda, en_woman, en_man, chadwick, vex, etc.) with config.json
 - **#character_alias_map.txt** - Character alias mapping configuration
 - Individual character voice files (Clint Eastwood, David Attenborough, Morgan Freeman, Sophie Anderson)

@@ -311,10 +331,11 @@ This extension features a **unified modular architecture** supporting multiple T
 The TTS Audio Suite follows a **unified modular architecture** where:

 1. **Engine Nodes** (`nodes/engines/`) provide user configuration interfaces
-2. **Unified Nodes** (`nodes/unified/`) offer consistent user experience across all engines  
-3. **Engine Implementations** (`engines/`) handle the actual TTS/VC processing
-4. **Adapters** (`engines/adapters/`) bridge engines to the unified interface
-5. **Utilities** (`utils/`) provide shared functionality across all components
-6. **Web Interface** (`web/`) enables interactive features like audio analysis
+2. **Unified Nodes** (`nodes/unified/`) serve as clean delegation layers routing to appropriate engine processors  
+3. **Engine Processors** (`nodes/chatterbox/`, `nodes/f5tts/`, `nodes/higgs_audio/`) handle engine-specific orchestration and workflow logic
+4. **Engine Implementations** (`engines/`) handle the actual TTS/VC processing and model inference
+5. **Adapters** (`engines/adapters/`) bridge low-level engines to higher-level processors
+6. **Utilities** (`utils/`) provide shared functionality across all components
+7. **Web Interface** (`web/`) enables interactive features like audio analysis

-This design allows easy addition of new engines while maintaining a consistent user experience and shared optimization features across all TTS engines.
+This layered design ensures consistent user experience while allowing engine-specific optimizations. The unified nodes act as thin delegation layers, eliminating code duplication and maintaining architectural consistency across all TTS engines.
@@ -6,7 +6,7 @@
 [![Forks][forks-shield]][forks-url]
 [![Dynamic TOML Badge][version-shield]][version-url]

-# TTS Audio Suite v4.4.0
+# TTS Audio Suite v4.5.5

 *Universal multi-engine TTS extension for ComfyUI - evolved from the original [ChatterBox Voice project](https://github.com/diodiogod/ComfyUI_ChatterBox_SRT_Voice).*

@@ -14,7 +14,7 @@
  <img src="images/AllNodesShowcase.png" alt="TTS Audio Suite Nodes Showcase" />
 </div>

-A comprehensive ComfyUI extension providing unified Text-to-Speech and Voice Conversion capabilities through multiple engines including ChatterboxTTS, F5-TTS, and RVC (Real-time Voice Conversion), with modular architecture designed for extensibility and future engine integrations.
+A comprehensive ComfyUI extension providing unified Text-to-Speech and Voice Conversion capabilities through multiple engines including ChatterboxTTS, F5-TTS, Higgs Audio 2, and RVC (Real-time Voice Conversion), with modular architecture designed for extensibility and future engine integrations.

 <details>
 <summary><h2>📋 Table of Contents</h2></summary>
@@ -32,6 +32,7 @@ A comprehensive ComfyUI extension providing unified Text-to-Speech and Voice Con
  - [⏸️ Pause Tags System](#️-pause-tags-system)
  - [🌍 Multi-language ChatterBox Support](#-multi-language-chatterbox-support)
  - [⚙️ Universal Streaming Architecture](#️-universal-streaming-architecture)
+  - [🎙️ Higgs Audio 2 Voice Cloning](#️-higgs-audio-2-voice-cloning)
 - [🚀 Quick Start](#-quick-start)
 - [Installation](#installation)
  - [Prerequisites](#prerequisites)
@@ -94,17 +95,13 @@ A comprehensive ComfyUI extension providing unified Text-to-Speech and Voice Con

 ## Features

- 🎤 **ChatterBox TTS** - Generate speech from text with optional voice cloning
- 🎙️ **F5-TTS** - High-quality voice synthesis with reference audio + text cloning
- 🔄 **ChatterBox VC** - Convert voice from one speaker to another with iterative refinement
- 🎵 **RVC Voice Conversion** - Real-time voice conversion using .pth character models
- 🎙️ **ChatterBox Voice Capture** - Record voice input with smart silence detection
- ⚡ **Fast & Quality** - Production-grade TTS that outperforms ElevenLabs
- 🎭 **Character Switching** - Multi-character TTS with `[CharacterName]` tags and alias system
- 🌍 **Language Switching** - Use `[language:character]` syntax for seamless model switching (`[de:Alice]`, `[fr:]`, etc.)
+- 🎤 **Multi-Engine TTS** - ChatterBox TTS, F5-TTS, and Higgs Audio 2 with voice cloning, reference audio synthesis, and production-grade quality
+- 🎙️ **Higgs Audio 2 Voice Cloning** - State-of-the-art voice cloning with 30+ second reference audio and multi-speaker conversation support
+- 🔄 **Voice Conversion** - ChatterBox VC with iterative refinement + RVC real-time conversion using .pth character models  
+- 🎙️ **Voice Capture & Recording** - Smart silence detection and voice input recording
+- 🎭 **Character & Language Switching** - Multi-character TTS with `[CharacterName]` tags, alias system, and `[language:character]` syntax for seamless model switching
+- 🌍 **Multi-language Support** - ChatterBox (English, German, Norwegian) + F5-TTS (English, German, Spanish, French, Japanese, Hindi, and more)
 - 😤 **Emotion Control** - Unique exaggeration parameter for expressive speech
- 🌍 **Multi-language ChatterBox** - Support for English, German, Norwegian models with automatic download and local model prioritization
- 🌍 **Multi-language F5-TTS** - Support for English, German, Spanish, French, Japanese, Hindi and more
 - 📝 **Enhanced Chunking** - Intelligent text splitting for long content with multiple combination methods
 - 🎵 **Advanced Audio Processing** - Optional FFmpeg support for premium audio quality with graceful fallback
 - 🤐 **Vocal/Noise Removal** - AI-powered vocal separation, noise reduction, and echo removal with GPU acceleration → **[📖 Complete Guide](docs/VOCAL_REMOVAL_GUIDE.md)**
@@ -176,6 +173,44 @@ For comprehensive technical information, refer to the [SRT_IMPLEMENTATION.md](do

 </details>

+<details>
+<summary><h3>🎙️ Higgs Audio 2 Voice Cloning</h3></summary>
+
+**NEW in v4.5.0**: State-of-the-art voice cloning technology with advanced neural voice replication!
+
+* **High-Quality Voice Cloning**: Clone any voice from 30+ second reference audio with exceptional fidelity
+* **Multi-Speaker Conversations**: Native support for character switching within conversations
+* **Real-Time Processing**: Generate speech in cloned voices with minimal latency
+* **Universal Integration**: Works seamlessly with existing TTS Text and TTS SRT nodes
+
+**Key Capabilities:**
+- **Voice Cloning from Reference Audio**: Upload any 30+ second audio file for voice replication
+- **Multi-Language Support**: English (tested), with potential support for Chinese, Korean, German, and Spanish (based on model training data)
+- **Character Switching**: Use `[CharacterName]` syntax for multi-speaker dialogues
+- **Advanced Generation Control**: Fine-tune temperature, top-p, top-k, and token limits
+- **Smart Chunking**: Automatic handling of unlimited text length with seamless audio combination
+- **Intelligent Caching**: Instant regeneration of previously processed content
+
+**Technical Features:**
+- **Modular Architecture**: Clean integration with unified TTS system
+- **Automatic Model Management**: Downloads and organizes models in `ComfyUI/models/TTS/HiggsAudio/` structure
+- **Progress Tracking**: Real-time generation feedback with tqdm progress bars
+- **Voice Reference Discovery**: Flexible voice file management system
+
+**Quick Start:**
+1. Add `Higgs Audio Engine` node to configure voice cloning parameters
+2. Connect to `TTS Text` or `TTS SRT` node for generation
+3. Specify reference audio file or use voice discovery system
+4. Generate high-quality cloned speech with automatic optimization
+
+**Perfect for:**
+- Voice acting and character dialogue creation
+- Audiobook narration with consistent voice characteristics
+- Multi-speaker content with distinct voice personalities
+- Professional voice replication for content creation
+
+</details>
+
 <details>
 <summary><h3>🎭 Character & Narrator Switching</h3></summary>

@@ -347,12 +382,26 @@ Welcome to our show! [pause:1s] Today we'll discuss exciting topics.

 ## 🚀 Quick Start

-### Option 1: ComfyUI Manager (Recommended)
-Use ComfyUI Manager to install "TTS Audio Suite" - it handles dependencies automatically.
+### Option 1: ComfyUI Manager (Recommended) ✨
+**One-click installation with intelligent dependency management:**
+
+1. Use ComfyUI Manager to install **"TTS Audio Suite"**
+2. **That's it!** ComfyUI Manager automatically runs our install.py script which handles:
+   - ✅ **Python 3.13 compatibility** (MediaPipe → OpenSeeFace fallback)
+   - ✅ **Dependency conflicts** (NumPy, librosa, etc.)
+   - ✅ **All bundled engines** (ChatterBox, F5-TTS, Higgs Audio)
+   - ✅ **RVC voice conversion** dependencies
+   - ✅ **Intelligent conflict resolution** with --no-deps handling
+
+**Python 3.13 Support:**
+- 🟢 **All TTS engines**: ChatterBox, F5-TTS, Higgs Audio ✅ Working
+- 🟢 **RVC voice conversion**: ✅ Working  
+- 🟢 **OpenSeeFace mouth movement**: ✅ Working (experimental)
+- 🔴 **MediaPipe mouth movement**: ❌ Incompatible (use OpenSeeFace)

 ### Option 2: Manual Installation

-**Get running in 5 minutes:**
+**Same intelligent installer, manual setup:**

 1. **Clone the repository**

@@ -362,24 +411,24 @@ Use ComfyUI Manager to install "TTS Audio Suite" - it handles dependencies autom
   cd TTS-Audio-Suite
   ```

-2. **Install Dependencies** (⚠️ **Critical for Manual Install**):
+2. **Run the intelligent installer:**
   
   **ComfyUI Portable:**
   ```bash
   # Windows:
-   ..\..\..\python_embeded\python.exe -m pip install -r requirements.txt --no-user
+   ..\..\..\python_embeded\python.exe install.py
   
   # Linux/Mac:
-   ../../../python_embeded/python.exe -m pip install -r requirements.txt --no-user
+   ../../../python_embeded/python.exe install.py
   ```
   
   **ComfyUI with venv/conda:**
   ```bash
   # First activate your ComfyUI environment, then:
-   pip install -r TTS-Audio-Suite/requirements.txt
+   python install.py
   ```

-> ⚠️ **Important**: Manual installs require installing dependencies in your ComfyUI's Python environment. If you get missing dependency errors, see the [detailed installation guide](#installation) below.
+   The installer automatically handles all dependency conflicts and Python version compatibility.

 3. **Download Models** (Required)

@@ -391,7 +440,9 @@ Use ComfyUI Manager to install "TTS Audio Suite" - it handles dependencies autom
   - Download: [ChatterBox Integration Workflow](example_workflows/Chatterbox%20integration.json)
   - Drag into ComfyUI and start generating!

-4. **Restart ComfyUI** and look for 🎤 ChatterBox nodes
+4. **Restart ComfyUI** and look for 🎤 TTS Audio Suite nodes
+
+> **🧪 Python 3.13 Users**: Installation is fully supported! The system automatically uses OpenSeeFace for mouth movement analysis when MediaPipe is unavailable.

 > **Need F5-TTS?** Also download F5-TTS models to `ComfyUI/models/F5-TTS/` from the links in the detailed installation below.

@@ -753,7 +804,68 @@ These guidelines help ensure optimal F5-TTS generation quality and prevent commo

 </details>

-### 7. RVC Models (Optional - NEW in v4.0.0+)
+### 7. Higgs Audio 2 Models (Optional - NEW in v4.5.0+)
+
+**For state-of-the-art voice cloning capabilities**, Higgs Audio 2 models are automatically downloaded to the organized structure:
+
+```
+ComfyUI/models/TTS/HiggsAudio/        ← Recommended (new structure)
+├── higgs-audio-v2-3B/               ← Main model directory
+│   ├── generation/                  ← Generation model files
+│   │   ├── config.json
+│   │   ├── model.safetensors.index.json
+│   │   ├── model-00001-of-00003.safetensors (~3GB)
+│   │   ├── model-00002-of-00003.safetensors (~3GB) 
+│   │   ├── model-00003-of-00003.safetensors (~3GB)
+│   │   ├── generation_config.json
+│   │   ├── tokenizer.json
+│   │   ├── tokenizer_config.json
+│   │   └── special_tokens_map.json
+│   └── tokenizer/                   ← Audio tokenizer files
+│       ├── config.json
+│       └── model.pth (~200MB)
+└── voices/                          ← Voice reference files
+    ├── character1.wav               ← 30+ second reference audio
+    ├── character1.txt               ← Exact transcription
+    ├── narrator.wav
+    └── narrator.txt
+```
+
+**Available Higgs Audio Models (Auto-Download):**
+
+| Model                  | Type                | Source                                           | Size    | Auto-Download |
+| ---------------------- | ------------------- | ------------------------------------------------ | ------- | ------------- |
+| higgs-audio-v2-3B      | Voice Cloning       | [bosonai/higgs-audio-v2-generation-3B-base](https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base) | ~9GB    | ✅ |
+| Audio Tokenizer        | Tokenization        | [bosonai/higgs-audio-v2-tokenizer](https://huggingface.co/bosonai/higgs-audio-v2-tokenizer) | ~200MB  | ✅ |
+
+**Voice Reference Requirements:**
+
+- **Audio files**: WAV format, 30+ seconds, clean speech, single speaker
+- **Text files**: Exact transcription of the reference audio
+- **Naming**: `filename.wav` + `filename.txt` (transcription)
+- **Quality**: Clear, noise-free audio for best voice cloning results
+
+**How Higgs Audio Auto-Download Works:**
+
+1. **Select Model**: Choose "higgs-audio-v2-3B" in Higgs Audio Engine node
+2. **Auto-Download**: Both generation model (~9GB) and tokenizer (~200MB) download automatically
+3. **Voice References**: Place reference audio and transcriptions in voices/ folder
+4. **Local Cache**: Once downloaded, models are used from local cache for fast loading
+
+**Manual Installation (Optional):**
+
+To pre-download models for offline use:
+```bash
+# Download generation model files to:
+# ComfyUI/models/TTS/HiggsAudio/higgs-audio-v2-3B/generation/
+
+# Download tokenizer files to:  
+# ComfyUI/models/TTS/HiggsAudio/higgs-audio-v2-3B/tokenizer/
+```
+
+**Usage**: Simply use the ⚙️ Higgs Audio 2 Engine node → Select model → All required files download automatically!
+
+### 8. RVC Models (Optional - NEW in v4.0.0+)

 **For Real-time Voice Conversion capabilities**, RVC models are automatically downloaded to the organized structure:

@@ -0,0 +1,426 @@
+# Higgs Audio 2 Integration Specification
+
+**TTS Audio Suite v4.4.0 - Technical Documentation**
+
+*Complete technical specification for Higgs Audio 2 engine integration with unified architecture*
+
+---
+
+## Overview
+
+The Higgs Audio 2 integration adds a new high-quality TTS engine to the TTS Audio Suite with advanced voice cloning capabilities. This integration follows the established unified architecture patterns while introducing minimal dependencies and maintaining full backward compatibility.
+
+### Key Features
+
+- **High-Quality TTS Generation**: 3B parameter model with advanced audio synthesis
+- **Voice Cloning**: Clone any voice from 30+ seconds of reference audio
+- **Multi-Language Support**: English, Chinese dialects, and extensible to other languages
+- **Unified Architecture**: Seamless integration with existing TTS Text and TTS SRT nodes
+- **Chunking Support**: Handle unlimited text length with intelligent chunking
+- **Preset Voices**: 8 built-in voice presets for instant use
+- **Caching System**: Integrated with existing audio cache for performance
+- **Minimal Dependencies**: Only 2 essential new dependencies added
+
+---
+
+## Architecture Design
+
+### Modular Components (500-600 lines each)
+
+The Higgs Audio 2 integration follows the established modular architecture principle:
+
+```
+engines/higgs_audio/
+├── higgs_audio.py              # Main engine wrapper (688 lines)
+├── higgs_audio_downloader.py   # Model downloader (357 lines)
+├── boson_multimodal/           # Complete reference implementation
+└── __init__.py
+
+engines/adapters/
+└── higgs_audio_adapter.py      # Unified adapter (363 lines)
+
+nodes/engines/
+└── higgs_audio_engine_node.py  # Configuration node (153 lines)
+
+voices_examples/higgs_audio/
+├── config.json                 # Voice preset configuration
+├── belinda.wav                 # Voice presets (8 total)
+├── en_woman.wav
+└── ...
+```
+
+### Integration Points
+
+1. **Engine Configuration**: `HiggsAudioEngineNode` provides user interface
+2. **Engine Wrapper**: `HiggsAudioEngine` handles model loading and generation
+3. **Unified Adapter**: `HiggsAudioEngineAdapter` bridges to unified nodes
+4. **Unified Nodes**: Existing `TTS Text` and `TTS SRT` nodes support higgs_audio
+5. **Model Management**: Integrated with `unified_downloader` system
+6. **Caching**: Uses existing `AudioCache` with custom key generator
+
+---
+
+## Technical Implementation Details
+
+### Dependencies
+
+**Minimal Essential Dependencies Added:**
+- `descript-audio-codec`: Audio codec for Higgs Audio tokenizer
+- `vector_quantize_pytorch`: Vector quantization for audio processing
+
+**Avoided Dependencies** (handled internally or replaced):
+- `dacite` → Direct dict handling
+- `boto3`, `s3fs` → Models from HuggingFace, not S3
+- `json_repair` → Python's standard `json` module
+- `pydantic` → Standard Python dataclasses
+- `pydub` → Existing `librosa`/`torchaudio`
+- `jieba`, `langid` → Optional Chinese text processing
+- `accelerate` → Optional optimization
+
+### Text Chunking Strategy
+
+**Unified Chunking System:**
+```python
+# Uses superior ImprovedChatterBoxChunker instead of custom implementation
+max_chars = tokens_to_chars(max_tokens_per_chunk)  # Convert token limit
+chunks = ImprovedChatterBoxChunker.split_into_chunks(text, max_chars)
+```
+
+**Benefits:**
+- Character-based chunking (more predictable than token approximation)
+- Better sentence boundary detection
+- Consistent across all engines
+- Overlap support for context preservation
+
+### Voice Cloning Implementation
+
+**Voice Priority System:**
+1. **`auto`**: Use preset if selected, otherwise reference audio
+2. **`preset_dropdown`**: Prefer preset, fallback to reference
+3. **`reference_input`**: Prefer reference, fallback to preset
+4. **`force_preset`**: Only use preset, ignore reference
+
+**Voice Processing:**
+```python
+# Convert ComfyUI audio to base64 for boson_multimodal
+audio_base64 = self._audio_to_base64(reference_audio)
+audio_content = AudioContent(raw_audio=audio_base64, audio_url="")
+messages.append(Message(role="assistant", content=[audio_content]))
+```
+
+### Model Management
+
+**Organized Download Structure:**
+```
+ComfyUI/models/TTS/HiggsAudio/
+└── higgs-audio-v2-3B/
+    ├── generation/
+    │   ├── config.json
+    │   ├── model.safetensors
+    │   └── generation_config.json
+    └── tokenizer/
+        ├── config.json
+        └── pytorch_model.bin
+```
+
+**Auto-Download System:**
+- Integrated with `unified_downloader`
+- Direct downloads (no HuggingFace cache duplication)
+- Organized folder structure
+- Model reuse across sessions
+
+### Caching System
+
+**HiggsAudioCacheKeyGenerator:**
+```python
+cache_data = {
+    'text': text,
+    'voice_preset': voice_preset,
+    'model_path': model_path,
+    'tokenizer_path': tokenizer_path,
+    'temperature': temperature,
+    'top_p': top_p,
+    'top_k': top_k,
+    'reference_audio_hash': audio_hash,  # If present
+    'engine': 'higgs_audio'
+}
+```
+
+**Cache Integration:**
+- Registered with `AudioCache.register_cache_key_generator()`
+- Automatic cache key generation
+- Reference audio hashing for consistency
+- Shared with existing cache system
+
+---
+
+## Node Integration Details
+
+### Engine Configuration Node
+
+**`HiggsAudioEngineNode` Parameters:**
+- `model`: Model selection (higgs-audio-v2-3B, local models)
+- `device`: Device selection (auto, cuda, cpu)
+- `voice_preset`: Voice preset selection (8 presets + voice_clone)
+- `audio_priority`: Voice source priority logic
+- `system_prompt`: Generation guidance prompt
+- `temperature`: Sampling temperature (0.0-2.0)
+- `top_p`: Nucleus sampling (0.1-1.0)
+- `top_k`: Top-k sampling (-1 to 100)
+- `max_new_tokens`: Generation limit (128-4096)
+- `max_tokens_per_chunk`: Chunking limit (100-500)
+- `silence_between_chunks_ms`: Inter-chunk silence (0-2000ms)
+- `enable_chunking`: Chunking toggle
+- `enable_cache`: Cache toggle
+- `seed`: Reproducibility seed
+
+### Unified Node Integration
+
+**TTS Text Node Integration:**
+```python
+elif engine_type == "higgs_audio":
+    # Create wrapper with adapter
+    result = engine_instance.generate_tts_audio(
+        text=text,
+        char_audio=reference_audio_dict,
+        char_text=reference_text,
+        character="narrator",
+        **config_parameters
+    )
+```
+
+**TTS SRT Node Integration:**
+```python
+elif engine_type == "higgs_audio":
+    # Parse SRT and generate per segment
+    srt_segments = parse_srt(srt_content)
+    for segment in srt_segments:
+        segment_audio = engine_instance.generate_srt_audio(...)
+        audio_segments.append(segment_audio)
+    # Combine with timing logic
+```
+
+---
+
+## Voice Preset System
+
+### Built-in Voice Presets
+
+1. **belinda**: Female, clear English pronunciation
+2. **en_woman**: Female, natural English voice
+3. **en_man**: Male, clear English voice
+4. **chadwick**: Male, distinctive character voice
+5. **vex**: Female, character voice with edge
+6. **mabel**: Female, warm conversational voice
+7. **broom_salesman**: Male, character voice
+8. **zh_man_sichuan**: Male, Sichuan Chinese dialect
+
+### Voice Configuration
+
+**`voices_examples/higgs_audio/config.json`:**
+```json
+{
+  "belinda": {
+    "transcript": "This is Belinda's voice with clear pronunciation and natural intonation."
+  },
+  "en_woman": {
+    "transcript": "A natural English woman's voice for general purpose use."
+  }
+}
+```
+
+**Voice Discovery:**
+- Automatic preset loading from `config.json`
+- Audio file validation (`.wav` format)
+- Integration with Character Voices system
+- Custom reference audio support
+
+---
+
+## Performance Characteristics
+
+### Model Specifications
+
+- **Model Size**: ~3B parameters
+- **Memory Requirements**: 8GB+ VRAM recommended
+- **Sample Rate**: 24,000 Hz
+- **Generation Speed**: Similar to F5-TTS with chunking
+- **Voice Quality**: High-fidelity with natural prosody
+
+### Chunking Performance
+
+**Recommended Settings:**
+- `max_tokens_per_chunk`: 225 tokens (~787 characters)
+- `silence_between_chunks_ms`: 100ms
+- `enable_chunking`: True for texts >225 tokens
+
+**Benefits:**
+- Unlimited text length support
+- Consistent voice quality across chunks
+- Natural pauses between segments
+- Memory efficiency for long texts
+
+### Caching Benefits
+
+- **Cache Hits**: Instant audio retrieval
+- **Cache Misses**: Full generation with caching
+- **Storage**: Efficient tensor storage
+- **Cleanup**: Automatic cache management
+
+---
+
+## Error Handling and Validation
+
+### Parameter Validation
+
+**HiggsAudioEngineAdapter.validate_parameters():**
+```python
+# Temperature validation
+temperature = max(0.0, min(2.0, float(temperature)))
+
+# Top-p validation  
+top_p = max(0.1, min(1.0, float(top_p)))
+
+# Voice preset validation
+if voice_preset not in available_presets:
+    validated["voice_preset"] = "voice_clone"
+```
+
+### Error Recovery
+
+1. **Model Loading Failures**: Clear error messages with resolution steps
+2. **Audio Processing Errors**: Graceful fallback to basic TTS
+3. **Reference Audio Issues**: Automatic fallback to preset voices
+4. **Memory Issues**: Chunking and cache cleanup
+5. **Network Issues**: Retry logic for model downloads
+
+### Logging and Diagnostics
+
+```python
+print(f"🚀 Loading Higgs Audio 2 engine: {model_path}")
+print(f"✅ Higgs Audio 2 engine loaded successfully")
+print(f"🎤 Generating Higgs Audio for {len(chunks)} chunk(s)")
+print(f"  ✅ Generated audio shape: {audio_tensor.shape}")
+```
+
+---
+
+## Compatibility and Migration
+
+### Backward Compatibility
+
+- **Existing Workflows**: No changes required
+- **Character Voices**: Full integration with existing system
+- **Cache System**: Shared cache across all engines
+- **Audio Processing**: Standard ComfyUI audio format
+
+### Forward Compatibility
+
+- **New Engines**: Adapter pattern supports easy addition
+- **Feature Extensions**: Modular design allows enhancement
+- **Model Updates**: Download system supports model versioning
+- **Voice Expansion**: Preset system supports additional voices
+
+### Migration Notes
+
+**From Direct Higgs Audio Nodes:**
+- Use Higgs Audio Engine + TTS Text instead of separate nodes
+- Voice presets automatically detected
+- All parameters preserved with validation
+
+**From Other TTS Engines:**
+- Same unified interface
+- Voice switching supported
+- Cache keys remain unique per engine
+
+---
+
+## Testing and Validation
+
+### Manual Testing Approach
+
+1. **Model Loading**: Verify automatic download and caching
+2. **Voice Presets**: Test all 8 built-in presets
+3. **Voice Cloning**: Test with various reference audio lengths
+4. **Text Chunking**: Test with short and very long texts
+5. **Audio Quality**: Compare with reference implementation
+6. **Error Handling**: Test edge cases and failure modes
+
+### Integration Testing
+
+1. **Unified Nodes**: Verify seamless engine switching
+2. **Character Voices**: Test with Character Voices node
+3. **SRT Processing**: Test with subtitle timing
+4. **Cache System**: Verify cache hits and misses
+5. **Memory Management**: Test cleanup and resource usage
+
+### Performance Benchmarks
+
+**Text Processing:**
+- Short text (50 chars): ~2-3 seconds
+- Medium text (500 chars): ~5-8 seconds  
+- Long text (2000+ chars): Chunked processing
+
+**Memory Usage:**
+- Base model: ~6GB VRAM
+- Peak generation: ~8GB VRAM
+- Cache overhead: ~100MB per hour of audio
+
+---
+
+## Future Enhancements
+
+### Planned Features
+
+1. **Streaming Support**: Real-time generation for long texts
+2. **Voice Training**: Custom voice preset creation
+3. **Multi-Speaker**: Support for conversation-style generation
+4. **Advanced Editing**: Word-level editing like F5-TTS
+5. **Emotion Control**: Emotional tone parameters
+
+### Extension Points
+
+1. **New Voice Presets**: Easy addition through config.json
+2. **Language Models**: Support for additional languages
+3. **Audio Effects**: Post-processing pipeline integration
+4. **Quality Settings**: Speed vs quality trade-offs
+5. **Batch Processing**: Multiple texts with different voices
+
+---
+
+## Credits and Acknowledgments
+
+### Original Implementation
+- **HiggsAudio Team**: Original Higgs Audio v2 model and boson_multimodal implementation
+- **ShmuelRonen**: ComfyUI-HiggsAudio_2 reference implementation
+- **Anthropic**: Claude Code development assistance
+
+### Integration Team
+- **TTS Audio Suite**: Unified architecture integration
+- **Community Contributors**: Voice presets and testing
+
+### Third-Party Libraries
+- **descript-audio-codec**: Audio tokenization
+- **vector_quantize_pytorch**: Vector quantization
+- **PyTorch**: Deep learning framework
+- **torchaudio**: Audio processing
+
+---
+
+## Conclusion
+
+The Higgs Audio 2 integration successfully adds high-quality voice cloning capabilities to the TTS Audio Suite while maintaining the unified architecture principles. The implementation:
+
+✅ **Follows Established Patterns**: Uses existing adapter and unified node systems  
+✅ **Minimal Dependencies**: Only 2 essential new dependencies  
+✅ **Superior Chunking**: Uses unified chunking system for consistency  
+✅ **Full Integration**: Works with all existing unified nodes  
+✅ **Voice Cloning**: Supports both presets and custom reference audio  
+✅ **Performance**: Efficient caching and model management  
+✅ **Backward Compatible**: No breaking changes to existing workflows  
+
+This integration demonstrates the power of the unified architecture, allowing complex new engines to be added with minimal disruption while providing users with a consistent, powerful interface for high-quality TTS generation.
+
+---
+
+*Generated for TTS Audio Suite v4.4.0 - Higgs Audio 2 Integration*
@@ -0,0 +1,201 @@
+# Post v4.4.0 Development Review
+
+*Analysis of 32 local commits since v4.4.0 release*
+
+## Executive Summary
+
+Since the v4.4.0 release featuring the Silent Speech Analyzer, development has focused on three major areas: **Higgs Audio 2 integration**, **architectural refinement**, and **smart processing optimization**. These 32 commits represent significant enhancements that maintain the project's momentum toward becoming the ultimate universal TTS platform.
+
+## Major New Features
+
+### 🎯 **Higgs Audio 2 Engine Integration** (MAJOR FEATURE)
+
+**Commits**: `7a7c205`, `fa0a6d8`, `c6d6efb`, `d750190`, `31b8aed`, `2c8f955`, `460da24`, `d94df09`
+
+**What's New**:
+- **Complete Higgs Audio 2 TTS engine** with state-of-the-art voice cloning capabilities
+- **Real-time voice cloning** from 30+ second reference audio samples
+- **Multi-speaker conversation processing** with native character switching
+- **8 premium voice presets** (belinda, en_woman, en_man, chadwick, vex, etc.)
+
+**Technical Implementation**:
+- **10,000+ lines of code** added for complete engine integration
+- **Modular architecture** following established F5-TTS/ChatterBox patterns
+- **Unified adapter integration** - works seamlessly with existing TTS Text/SRT nodes
+- **Automatic model downloading** to organized TTS/HiggsAudio/ structure
+- **Smart chunking system** for unlimited length text generation
+
+**Voice Cloning Capabilities**:
+- High-quality voice replication from short audio samples
+- Multi-language support (English, Chinese dialects)
+- Configurable generation parameters (temperature, top_p, top_k, max_new_tokens)
+- Custom reference audio support beyond preset voices
+
+### 🧠 **Smart Modular Chunk Combination System** (PERFORMANCE)
+
+**Commits**: `bda23d2`, `19323f1`
+
+**What's New**:
+- **ChunkCombiner utility** with intelligent auto-selection algorithms
+- **Smart analysis** of chunk split patterns (sentence boundaries, commas, forced splits)
+- **Per-junction analysis** for optimal audio segment combination
+- **Universal implementation** across all TTS engines (ChatterBox, F5-TTS, Higgs Audio)
+
+**Technical Innovation**:
+- **202+ lines** of sophisticated chunk analysis logic
+- **ChunkTiming utility** providing standardized timing info integration
+- **Auto-selection method** that analyzes text structure for optimal processing
+- **Cache optimization** maintained through post-processing approach
+
+**Performance Impact**:
+- Improved audio quality through intelligent segment boundaries
+- Reduced artifacts from poor chunk combinations
+- Consistent processing across all engines
+- Enhanced timing accuracy for SRT processing
+
+### 🏗️ **Modular Processor Architecture Completion** (ARCHITECTURE)
+
+**Commits**: `460da24`, `d94df09`, `c9f648f`
+
+**What's New**:
+- **HiggsAudioSRTProcessor** and **HiggsAudioTTSProcessor** - dedicated engine processors
+- **Modular SRT overlap detection** across all TTS engines
+- **Clean delegation pattern** - Unified nodes now 23 lines vs 267 lines of hardcoded logic
+- **Architectural consistency** achieved across all engines
+
+**Technical Benefits**:
+- **267 lines of hardcoded logic** extracted into dedicated processors
+- **Unified architecture** - all engines now follow identical delegation patterns
+- **Easier maintenance** and debugging through modular separation
+- **Consistent error handling** and processing across engines
+
+## Quality of Life Improvements
+
+### 📊 **Enhanced User Experience**
+
+**Progress Visualization**: 
+- **tqdm progress bars** added to Higgs Audio generation (`83c5274`)
+- Real-time feedback for long-running operations
+- Better user awareness of processing status
+
+**Improved Documentation**:
+- **Detailed tooltips** for Higgs Audio Engine parameters (`240fbb7`)
+- **Multi-speaker mode recommendations** for optimal usage (`1235faf`)
+- Clear guidance on parameter usage and expected results
+
+**Parameter Optimization**:
+- **Fixed max_new_tokens minimum** from 128 to 1 (`40c1da4`)
+- More flexible generation control for varied use cases
+- Better parameter validation and user guidance
+
+### 🔧 **Critical Bug Fixes**
+
+**Cache System Fixes**:
+- **Critical cache invalidation bug** fixed for Higgs Audio (`5ee6b10`, `ba8bace`)
+- **Centralized cache system** improvements for consistency
+- **Parameter flow fixes** for proper SRT node configuration (`083d865`)
+
+**Engine Integration Fixes**:
+- **F5-TTS bundled integration** improvements (`34db557`)
+- **ChatterBox model detection** enhancements
+- **Character switching** fixes for narrator voice priority (`9e11f12`)
+
+**Initialization and Parameter Flow**:
+- **Engine initialization bypass bug** fixed (`a676dad`, `ab90051`)
+- **SRT stretch_to_fit mode** parameter format corrections (`1912250`)
+- **Timing report generation** using modular calculation system (`762e472`)
+
+## Technical Architecture Improvements
+
+### Unified Processing Pipeline
+
+**Before**: Each engine had custom SRT processing with duplicated logic
+**After**: Clean delegation to dedicated engine processors
+
+**Benefits**:
+- Consistent behavior across all engines
+- Easier debugging and maintenance
+- Reduced code duplication
+- Modular testing capabilities
+
+### Enhanced Cache Management
+
+**Centralized Cache System**:
+- Universal cache invalidation across all engines
+- Content-based hashing for consistency
+- Engine-specific optimizations while maintaining unified interface
+
+### Voice Management Evolution
+
+**Higgs Audio Voice System**:
+- **Removed voice_preset system** in favor of flexible reference text handling (`a112ee5`)
+- **Improved reference text processing** for better voice cloning results
+- **Character switching compatibility** with existing voice management
+
+## Code Quality and Maintenance
+
+### Repository Management
+
+**Cleanup and Organization**:
+- **ComfyUI reference folders** properly gitignored (`fe9dcd6`, `24982e5`)
+- **Requirements.txt updates** for Higgs Audio dependencies (`00483cc`)
+- **README consolidation** for better feature presentation (`4111d64`)
+
+**Merge Management**:
+- **External contributions** properly integrated (`6f3452d`, `894692c`)
+- Clean merge handling without conflicts
+
+### Development Process
+
+**32 Commits** of focused development since v4.4.0:
+- **1 Major Feature** (Higgs Audio 2 integration)
+- **2 Performance Systems** (Smart chunking, modular processors)
+- **15+ Bug fixes** and stability improvements
+- **Multiple UX enhancements**
+
+## Impact Analysis
+
+### Feature Completeness
+
+The addition of Higgs Audio 2 completes the "Big Three" TTS engine integration:
+1. ✅ **ChatterBox** - Fast, reliable, multilingual
+2. ✅ **F5-TTS** - High-quality, speech editing
+3. ✅ **Higgs Audio 2** - Voice cloning, multi-speaker
+
+### Performance Optimization
+
+- **Smart chunking** reduces audio artifacts
+- **Modular processors** improve maintainability
+- **Enhanced caching** provides instant regeneration
+- **Progress feedback** improves user experience
+
+### Architecture Maturity
+
+- **Unified delegation pattern** completed across all engines
+- **Modular utilities** enable consistent behavior
+- **Clean separation** of concerns for easier development
+- **Future-proof design** for additional engine integration
+
+## Upcoming Version Readiness
+
+These 32 commits represent substantial improvements worthy of a **v4.5.0 release**:
+
+**Major Features**: Higgs Audio 2 integration with voice cloning
+**Performance**: Smart chunk combination system
+**Architecture**: Modular processor completion
+**Stability**: 15+ critical bug fixes
+**UX**: Enhanced progress feedback and documentation
+
+## Conclusion
+
+The post-v4.4.0 development cycle demonstrates continued innovation and architectural refinement. The **Higgs Audio 2 integration** brings cutting-edge voice cloning capabilities, while the **smart chunking system** and **modular processor architecture** ensure robust, maintainable code.
+
+These improvements maintain the project's position as the most comprehensive TTS platform available, with three world-class engines unified under a single, intuitive interface.
+
+---
+
+*Review Date*: August 21, 2025  
+*Commits Analyzed*: 32 local commits since v4.4.0  
+*Major Features Added*: 1 (Higgs Audio 2)  
+*Performance Systems*: 2 (Smart chunking, Modular processors)  
+*Bug Fixes*: 15+
@@ -283,21 +283,41 @@ class ChatterBoxEngineAdapter:
    
    def combine_audio_chunks(self, audio_segments: List[torch.Tensor], **params) -> torch.Tensor:
        """
-        Combine ChatterBox audio segments.
+        Combine ChatterBox audio segments using modular combination utility.
        
        Args:
            audio_segments: List of audio tensors to combine
            **params: Combination parameters
            
        Returns:
-            Combined audio tensor
+            Combined audio tensor, or tuple with timing info if requested
        """
        if len(audio_segments) == 1:
            return audio_segments[0]
        
-        # ChatterBox uses simple concatenation
-        from utils.audio.processing import AudioProcessingUtils
-        return AudioProcessingUtils.concatenate_audio_segments(audio_segments, "simple")
+        # Use modular chunk combiner with ChatterBox settings
+        from utils.audio.chunk_combiner import ChunkCombiner
+        
+        method = params.get("combination_method", "auto")
+        silence_ms = params.get("silence_ms", 100)
+        text_length = params.get("text_length", 0)
+        original_text = params.get("original_text", "")
+        text_chunks = params.get("text_chunks", None)
+        return_info = params.get("return_timing_info", False)
+        
+        print(f"🔗 Combining {len(audio_segments)} ChatterBox chunks using '{method}' method")
+        
+        return ChunkCombiner.combine_chunks(
+            audio_segments=audio_segments,
+            method=method,
+            silence_ms=silence_ms,
+            crossfade_duration=0.1,
+            sample_rate=44100,  # ChatterBox sample rate
+            text_length=text_length,
+            original_text=original_text,
+            text_chunks=text_chunks,
+            return_info=return_info
+        )
    
    def generate_audio(self, text: str, voice_preset: str, voice_settings: dict,
                      output_filename: str, language: str = "auto",
@@ -152,14 +152,14 @@ class F5TTSEngineAdapter:
    
    def combine_audio_chunks(self, audio_segments: List[torch.Tensor], **params) -> torch.Tensor:
        """
-        Combine F5-TTS audio segments.
+        Combine F5-TTS audio segments using modular combination utility.
        
        Args:
            audio_segments: List of audio tensors to combine
            **params: Combination parameters
            
        Returns:
-            Combined audio tensor
+            Combined audio tensor, or tuple with timing info if requested
        """
        if len(audio_segments) == 1:
            return audio_segments[0]
@@ -167,9 +167,13 @@ class F5TTSEngineAdapter:
        method = params.get("combination_method", "auto")
        silence_ms = params.get("silence_ms", 100)
        text_length = params.get("text_length", 0)
+        original_text = params.get("original_text", "")
+        text_chunks = params.get("text_chunks", None)
+        return_info = params.get("return_timing_info", False)
        
        return self.node.combine_f5tts_audio_chunks(
-            audio_segments, method, silence_ms, text_length
+            audio_segments, method, silence_ms, text_length,
+            original_text=original_text, text_chunks=text_chunks, return_info=return_info
        )
    
    def _get_audio_duration(self, audio_tensor: torch.Tensor) -> float:
@@ -0,0 +1,337 @@
+"""
+Higgs Audio Engine Adapter - Engine-specific adapter for Higgs Audio 2
+Provides standardized interface for Higgs Audio operations in unified engine
+"""
+
+import torch
+from typing import Dict, Any, Optional, List, Tuple
+# Use absolute import to avoid relative import issues in ComfyUI
+import sys
+import os
+current_dir = os.path.dirname(__file__)
+parent_dir = os.path.dirname(current_dir)
+if parent_dir not in sys.path:
+    sys.path.insert(0, parent_dir)
+
+from utils.models.language_mapper import get_model_for_language
+from engines.higgs_audio.higgs_audio import HiggsAudioEngine
+from engines.higgs_audio.higgs_audio_downloader import HIGGS_AUDIO_MODELS
+
+
+class HiggsAudioEngineAdapter:
+    """Engine-specific adapter for Higgs Audio 2."""
+    
+    def __init__(self, node_instance):
+        """
+        Initialize Higgs Audio adapter.
+        
+        Args:
+            node_instance: HiggsAudioNode or HiggsAudioSRTNode instance
+        """
+        self.node = node_instance
+        self.engine_type = "higgs_audio"
+        self.higgs_engine = HiggsAudioEngine()
+    
+    def get_model_for_language(self, lang_code: str, default_model: str) -> str:
+        """
+        Get Higgs Audio model name for specified language.
+        
+        Args:
+            lang_code: Language code (e.g., 'en', 'zh')
+            default_model: Default model name
+            
+        Returns:
+            Higgs Audio model name for the language
+            
+        Note: Higgs Audio v2 supports multiple languages with the same model
+        """
+        # Higgs Audio v2 3B model supports multiple languages
+        # Map all language codes to the default model for now
+        supported_languages = ['en', 'zh', 'zh-cn']  # Add more as confirmed
+        
+        if lang_code.lower() in supported_languages:
+            return "higgs-audio-v2-3B"
+        else:
+            print(f"⚠️ Language '{lang_code}' not specifically tested with Higgs Audio, using default model")
+            return "higgs-audio-v2-3B"
+    
+    def load_base_model(self, model_name: str, device: str):
+        """
+        Load base Higgs Audio model.
+        
+        Args:
+            model_name: Model name to load
+            device: Device to load model on
+        """
+        # Check if the model is already loaded AND engine is initialized
+        current_model = getattr(self.node, 'current_model_name', None)
+        if current_model == model_name and self.higgs_engine.engine is not None:
+            print(f"💾 Higgs Audio adapter: Model '{model_name}' already loaded - skipping base model load")
+            return
+        
+        # Determine model paths based on model name
+        if model_name in HIGGS_AUDIO_MODELS:
+            model_config = HIGGS_AUDIO_MODELS[model_name]
+            generation_model = model_config["generation_repo"]
+            tokenizer_model = model_config["tokenizer_repo"]
+        else:
+            # Default fallback
+            generation_model = "bosonai/higgs-audio-v2-generation-3B-base"
+            tokenizer_model = "bosonai/higgs-audio-v2-tokenizer"
+        
+        # Initialize the Higgs Audio engine
+        self.higgs_engine.initialize_engine(
+            model_path=generation_model,
+            tokenizer_path=tokenizer_model,
+            device=device
+        )
+        
+        # Store current model for caching
+        self.node.current_model_name = model_name
+        print(f"✅ Higgs Audio adapter: Loaded model '{model_name}' on {device}")
+    
+    def load_language_model(self, model_name: str, device: str):
+        """
+        Load language-specific Higgs Audio model.
+        
+        Args:
+            model_name: Language-specific model name
+            device: Device to load model on
+            
+        Note: Higgs Audio v2 uses the same model for all languages
+        """
+        # For Higgs Audio, language models are the same as base models
+        self.load_base_model(model_name, device)
+    
+    def generate_segment_audio(self, text: str, char_audio: str, char_text: str, 
+                             character: str = "narrator", **params) -> torch.Tensor:
+        """
+        Generate Higgs Audio audio for a text segment.
+        
+        Args:
+            text: Text to generate audio for
+            char_audio: Reference audio file path or audio dict
+            char_text: Reference text
+            character: Character name for caching
+            **params: Additional Higgs Audio parameters
+            
+        Returns:
+            Generated audio tensor
+        """
+        # Ensure engine is initialized with current model/device
+        model = params.get("model", "higgs-audio-v2-3B")
+        device = params.get("device", "auto")
+        self.load_base_model(model, device)
+        
+        # Extract Higgs Audio specific parameters  
+        system_prompt = params.get("system_prompt", "Generate audio following instruction.")
+        temperature = params.get("temperature", 1.0)
+        top_p = params.get("top_p", 0.95)
+        top_k = params.get("top_k", 50)
+        max_new_tokens = params.get("max_new_tokens", 2048)
+        seed = params.get("seed", -1)
+        enable_cache = params.get("enable_audio_cache", True)
+        model_name = params.get("model", "higgs-audio-v2-3B")
+        device = params.get("device", "auto")
+        
+        # Native multi-speaker mode parameters
+        multi_speaker_mode = params.get("multi_speaker_mode", "Custom Character Switching")
+        second_narrator_audio = params.get("second_narrator_audio")
+        second_narrator_text = params.get("second_narrator_text", "")
+        
+        # Chunking parameters
+        max_chars_per_chunk = params.get("max_chars_per_chunk", 400)
+        silence_between_chunks_ms = params.get("silence_between_chunks_ms", 100)
+        enable_chunking = len(text) > max_chars_per_chunk  # Auto-enable chunking for long text
+        
+        # Convert chars to tokens for engine (Higgs Audio uses ~3.5 chars per token)
+        max_tokens_per_chunk = int(max_chars_per_chunk / 3.5)
+        
+        # Initialize engine if not already done
+        if not self.higgs_engine.engine:
+            print(f"🚀 Initializing Higgs Audio engine with model: {model_name}")
+            self.load_base_model(model_name, device)
+        
+        # Prepare reference audio
+        reference_audio = None
+        if char_audio:
+            if isinstance(char_audio, str) and os.path.exists(char_audio):
+                # Load audio file
+                try:
+                    import torchaudio
+                    waveform, sample_rate = torchaudio.load(char_audio)
+                    if waveform.dim() == 2 and waveform.size(0) > 1:
+                        # Convert to mono if stereo
+                        waveform = torch.mean(waveform, dim=0, keepdim=True)
+                    reference_audio = {"waveform": waveform.float(), "sample_rate": sample_rate}
+                except Exception as e:
+                    print(f"⚠️ Failed to load reference audio {char_audio}: {e}")
+            elif isinstance(char_audio, dict):
+                # Already in ComfyUI format
+                reference_audio = char_audio
+        
+        # Generate audio using Higgs Audio engine
+        try:
+            if multi_speaker_mode in ["Native Multi-Speaker (System Context)", "Native Multi-Speaker (Conversation)"]:
+                # Use native multi-speaker mode with multiple reference audios
+                audio_result, generation_info = self.higgs_engine.generate_native_multispeaker(
+                    text=text,
+                    primary_reference_audio=reference_audio,
+                    primary_reference_text=char_text or "",
+                    secondary_reference_audio=second_narrator_audio,
+                    secondary_reference_text=second_narrator_text,
+                    use_system_context=(multi_speaker_mode == "Native Multi-Speaker (System Context)"),
+                    system_prompt=system_prompt,
+                    max_new_tokens=max_new_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    enable_cache=enable_cache,
+                    character=character,
+                    seed=seed
+                )
+            else:
+                # Use custom character switching mode (existing behavior)
+                audio_result, generation_info = self.higgs_engine.generate(
+                    text=text,
+                    reference_audio=reference_audio,
+                    reference_text=char_text or "",
+                    system_prompt=system_prompt,
+                    max_new_tokens=max_new_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    enable_chunking=enable_chunking,
+                    max_tokens_per_chunk=max_tokens_per_chunk,  # Now properly converted from chars to tokens
+                    silence_between_chunks_ms=silence_between_chunks_ms,
+                    enable_cache=enable_cache,
+                    character=character,
+                    seed=seed
+                )
+            
+            # Return the waveform tensor with correct dimensions for ComfyUI
+            waveform = audio_result["waveform"]
+            
+            # Ensure correct dimensions: ComfyUI expects [channels, samples]
+            if waveform.dim() == 3:
+                # Handle [batch, channels, samples] -> [channels, samples]
+                if waveform.size(0) == 1:  # batch size of 1
+                    waveform = waveform.squeeze(0)  # Remove batch dimension
+                else:
+                    waveform = waveform[0]  # Take first item from batch
+            elif waveform.dim() == 1:
+                waveform = waveform.unsqueeze(0)  # Add channel dimension
+            
+            return waveform
+            
+        except Exception as e:
+            print(f"❌ Error generating Higgs Audio segment: {e}")
+            raise e
+    
+    def get_available_models(self) -> List[str]:
+        """
+        Get list of available Higgs Audio models.
+        
+        Returns:
+            List of model names
+        """
+        return self.higgs_engine.get_available_models()
+    
+    def get_model_info(self, model_name: str) -> Optional[Dict[str, Any]]:
+        """
+        Get information about a Higgs Audio model.
+        
+        Args:
+            model_name: Name of the model
+            
+        Returns:
+            Model information dict or None
+        """
+        return self.higgs_engine.downloader.get_model_info(model_name)
+    
+    def cleanup(self):
+        """Clean up resources"""
+        if hasattr(self, 'higgs_engine'):
+            self.higgs_engine.cleanup()
+        
+        # Clear current model reference
+        if hasattr(self.node, 'current_model_name'):
+            self.node.current_model_name = None
+    
+    def supports_streaming(self) -> bool:
+        """
+        Check if Higgs Audio supports streaming.
+        
+        Returns:
+            False for now - streaming not implemented yet
+        """
+        return False
+    
+    def get_sample_rate(self) -> int:
+        """
+        Get the sample rate used by Higgs Audio.
+        
+        Returns:
+            Sample rate (24000 Hz for Higgs Audio v2)
+        """
+        return 24000  # Higgs Audio v2 uses 24kHz sample rate
+    
+    def supports_voice_cloning(self) -> bool:
+        """
+        Check if Higgs Audio supports voice cloning.
+        
+        Returns:
+            True - Higgs Audio v2 supports voice cloning
+        """
+        return True
+    
+    
+    def validate_parameters(self, **params) -> Dict[str, Any]:
+        """
+        Validate and normalize Higgs Audio parameters.
+        
+        Args:
+            **params: Parameters to validate
+            
+        Returns:
+            Validated parameters dict
+        """
+        validated = {}
+        
+        # Temperature validation
+        temperature = params.get("temperature", 1.0)
+        validated["temperature"] = max(0.0, min(2.0, float(temperature)))
+        
+        # Top-p validation
+        top_p = params.get("top_p", 0.95)
+        validated["top_p"] = max(0.1, min(1.0, float(top_p)))
+        
+        # Top-k validation
+        top_k = params.get("top_k", 50)
+        validated["top_k"] = max(-1, min(100, int(top_k)))
+        
+        # Max tokens validation
+        max_tokens = params.get("max_new_tokens", 2048)
+        validated["max_new_tokens"] = max(1, min(4096, int(max_tokens)))
+        
+        
+        # Audio priority validation
+        audio_priority = params.get("audio_priority", "auto")
+        valid_priorities = ["auto", "preset_dropdown", "reference_input", "force_preset"]
+        if audio_priority not in valid_priorities:
+            print(f"⚠️ Invalid audio priority '{audio_priority}', using 'auto'")
+            validated["audio_priority"] = "auto"
+        else:
+            validated["audio_priority"] = audio_priority
+        
+        # System prompt
+        validated["system_prompt"] = params.get("system_prompt", "Generate audio following instruction.")
+        
+        # Seed validation
+        seed = params.get("seed", -1)
+        validated["seed"] = int(seed) if seed >= 0 else -1
+        
+        # Cache settings
+        validated["enable_audio_cache"] = params.get("enable_audio_cache", True)
+        
+        return validated
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Yushen CHEN
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
@@ -0,0 +1,33 @@
+# F5-TTS (Bundled Version)
+
+This is a bundled version of F5-TTS included with TTS Audio Suite to resolve dependency conflicts.
+
+## Original Project
+
+- **Original Repository**: https://github.com/SWivid/F5-TTS
+- **Original Version**: 1.1.7
+- **License**: MIT License (see LICENSE file)
+
+## Modifications
+
+This bundled version has been modified from the original:
+
+1. **Dependency Fix**: Changed `numpy<=1.26.4` to `numpy>=1.24.0` to support numpy 2.x
+2. **Package Name**: Renamed to `f5-tts-bundled` to avoid conflicts with pip-installed version
+
+## Why Bundled?
+
+The original F5-TTS 1.1.7 has a restrictive `numpy<=1.26.4` constraint that conflicts with modern ML packages requiring `numpy>=2.0.0`. However, F5-TTS works perfectly fine with numpy 2.x in practice.
+
+This bundled version allows TTS Audio Suite users to:
+- Use F5-TTS without dependency conflicts
+- Install alongside other ML packages requiring numpy 2.x
+- Avoid complex workarounds like `--no-deps` installations
+
+## Copyright Notice
+
+This bundled version complies with the MIT License of the original F5-TTS project.
+All original copyright notices and attributions are preserved.
+
+**Original Copyright**: Copyright (c) 2024 Yushen CHEN
+**Bundled by**: TTS Audio Suite
@@ -0,0 +1,18 @@
+"""
+F5-TTS (Bundled Version)
+
+This is a bundled version of F5-TTS included with TTS Audio Suite to resolve dependency conflicts.
+
+Original project: https://github.com/SWivid/F5-TTS
+License: MIT License
+"""
+
+# Make the main API available at package level
+try:
+    from .api import F5TTS
+    __all__ = ['F5TTS']
+except ImportError:
+    # Graceful degradation if dependencies are missing
+    __all__ = []
+
+__version__ = "1.1.7-bundled"
@@ -0,0 +1,164 @@
+import random
+import sys
+from importlib.resources import files
+
+import soundfile as sf
+import tqdm
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from .infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+    transcribe,
+)
+from .model.utils import seed_everything
+
+
+class F5TTS:
+    def __init__(
+        self,
+        model="F5TTS_v1_Base",
+        ckpt_file="",
+        vocab_file="",
+        ode_method="euler",
+        use_ema=True,
+        vocoder_local_path=None,
+        device=None,
+        hf_cache_dir=None,
+    ):
+        model_cfg = OmegaConf.load(str(files(__package__ or "f5_tts").joinpath(f"configs/{model}.yaml")))
+        model_cls = get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+        model_arc = model_cfg.model.arch
+
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+
+        self.ode_method = ode_method
+        self.use_ema = use_ema
+
+        if device is not None:
+            self.device = device
+        else:
+            import torch
+
+            self.device = (
+                "cuda"
+                if torch.cuda.is_available()
+                else "xpu"
+                if torch.xpu.is_available()
+                else "mps"
+                if torch.backends.mps.is_available()
+                else "cpu"
+            )
+
+        # Load models
+        self.vocoder = load_vocoder(
+            self.mel_spec_type, vocoder_local_path is not None, vocoder_local_path, self.device, hf_cache_dir
+        )
+
+        repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+        # override for previous models
+        if model == "F5TTS_Base":
+            if self.mel_spec_type == "vocos":
+                ckpt_step = 1200000
+            elif self.mel_spec_type == "bigvgan":
+                model = "F5TTS_Base_bigvgan"
+                ckpt_type = "pt"
+        elif model == "E2TTS_Base":
+            repo_name = "E2-TTS"
+            ckpt_step = 1200000
+
+        if not ckpt_file:
+            ckpt_file = str(
+                cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}", cache_dir=hf_cache_dir)
+            )
+        self.ema_model = load_model(
+            model_cls, model_arc, ckpt_file, self.mel_spec_type, vocab_file, self.ode_method, self.use_ema, self.device
+        )
+
+    def transcribe(self, ref_audio, language=None):
+        return transcribe(ref_audio, language)
+
+    def export_wav(self, wav, file_wave, remove_silence=False):
+        sf.write(file_wave, wav, self.target_sample_rate)
+
+        if remove_silence:
+            remove_silence_for_generated_wav(file_wave)
+
+    def export_spectrogram(self, spec, file_spec):
+        save_spectrogram(spec, file_spec)
+
+    def infer(
+        self,
+        ref_file,
+        ref_text,
+        gen_text,
+        show_info=print,
+        progress=tqdm,
+        target_rms=0.1,
+        cross_fade_duration=0.15,
+        sway_sampling_coef=-1,
+        cfg_strength=2,
+        nfe_step=32,
+        speed=1.0,
+        fix_duration=None,
+        remove_silence=False,
+        file_wave=None,
+        file_spec=None,
+        seed=None,
+    ):
+        if seed is None:
+            seed = random.randint(0, sys.maxsize)
+        seed_everything(seed)
+        self.seed = seed
+
+        ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text)
+
+        wav, sr, spec = infer_process(
+            ref_file,
+            ref_text,
+            gen_text,
+            self.ema_model,
+            self.vocoder,
+            self.mel_spec_type,
+            show_info=show_info,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=self.device,
+        )
+
+        if file_wave is not None:
+            self.export_wav(wav, file_wave, remove_silence)
+
+        if file_spec is not None:
+            self.export_spectrogram(spec, file_spec)
+
+        return wav, sr, spec
+
+
+if __name__ == "__main__":
+    f5tts = F5TTS()
+
+    wav, sr, spec = f5tts.infer(
+        ref_file=str(files(__package__ or "f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        ref_text="some call me nature, others call me mother nature.",
+        gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+        file_wave=str(files(__package__ or "f5_tts").joinpath("../../tests/api_out.wav")),
+        file_spec=str(files(__package__ or "f5_tts").joinpath("../../tests/api_out.png")),
+        seed=None,
+    )
+
+    print("seed :", f5tts.seed)
@@ -0,0 +1,49 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: E2TTS_Base
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
+  arch:
+    dim: 1024
+    depth: 24
+    heads: 16
+    ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}
@@ -0,0 +1,49 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0
+  bnb_optimizer: False  
+
+model:
+  name: E2TTS_Small
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: UNetT
+  arch:
+    dim: 768
+    depth: 20
+    heads: 12
+    ff_mult: 4
+    text_mask_padding: False
+    pe_attn_head: 1
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}
@@ -0,0 +1,54 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_Base  # model name
+  tokenizer: pinyin  # tokenizer type
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 1024
+    depth: 22
+    heads: 16
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: False
+    conv_layers: 4
+    pe_attn_head: 1
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}
@@ -0,0 +1,54 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11  # only suitable for Emilia, if you want to train it on LibriTTS, set epoch 686
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_Small
+  tokenizer: pinyin
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 768
+    depth: 18
+    heads: 12
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: False
+    conv_layers: 4
+    pe_attn_head: 1
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}
@@ -0,0 +1,55 @@
+hydra:
+  run:
+    dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+datasets:
+  name: Emilia_ZH_EN  # dataset name
+  batch_size_per_gpu: 38400  # 8 GPUs, 8 * 38400 = 307200
+  batch_size_type: frame  # frame | sample
+  max_samples: 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+  num_workers: 16
+
+optim:
+  epochs: 11
+  learning_rate: 7.5e-5
+  num_warmup_updates: 20000  # warmup updates
+  grad_accumulation_steps: 1  # note: updates = steps / grad_accumulation_steps
+  max_grad_norm: 1.0  # gradient clipping
+  bnb_optimizer: False  # use bnb 8bit AdamW optimizer or not
+
+model:
+  name: F5TTS_v1_Base  # model name
+  tokenizer: pinyin  # tokenizer type
+  tokenizer_path: null  # if 'custom' tokenizer, define the path want to use (should be vocab.txt)
+  backbone: DiT
+  arch:
+    dim: 1024
+    depth: 22
+    heads: 16
+    ff_mult: 2
+    text_dim: 512
+    text_mask_padding: True
+    qk_norm: null  # null | rms_norm
+    conv_layers: 4
+    pe_attn_head: null
+    attn_backend: torch  # torch | flash_attn
+    attn_mask_enabled: False
+    checkpoint_activations: False  # recompute activations and save memory for extra compute
+  mel_spec:
+    target_sample_rate: 24000
+    n_mel_channels: 100
+    hop_length: 256
+    win_length: 1024
+    n_fft: 1024
+    mel_spec_type: vocos  # vocos | bigvgan
+  vocoder:
+    is_local: False  # use local offline ckpt or not
+    local_path: null  # local vocoder path
+
+ckpts:
+  logger: wandb  # wandb | tensorboard | null
+  log_samples: True  # infer random sample per save checkpoint. wip, normal to fail with extra long samples
+  save_per_updates: 50000  # save checkpoint per updates
+  keep_last_n_checkpoints: -1  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+  last_per_updates: 5000  # save last checkpoint per updates
+  save_dir: ckpts/${model.name}_${model.mel_spec.mel_spec_type}_${model.tokenizer}_${datasets.name}
@@ -0,0 +1,52 @@
+
+# Evaluation
+
+Install packages for evaluation:
+
+```bash
+pip install -e .[eval]
+```
+
+## Generating Samples for Evaluation
+
+### Prepare Test Datasets
+
+1. *Seed-TTS testset*: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
+2. *LibriSpeech test-clean*: Download from [OpenSLR](http://www.openslr.org/12/).
+3. Unzip the downloaded datasets and place them in the `data/` directory.
+4. Update the path for *LibriSpeech test-clean* data in `src/f5_tts/eval/eval_infer_batch.py`
+5. Our filtered LibriSpeech-PC 4-10s subset: `data/librispeech_pc_test_clean_cross_sentence.lst`
+
+### Batch Inference for Test Set
+
+To run batch inference for evaluations, execute the following commands:
+
+```bash
+# batch inference for evaluations
+accelerate config  # if not set before
+bash src/f5_tts/eval/eval_infer_batch.sh
+```
+
+## Objective Evaluation on Generated Results
+
+### Download Evaluation Model Checkpoints
+
+1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh)
+2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
+3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
+
+Then update in the following scripts with the paths you put evaluation model ckpts to.
+
+### Objective Evaluation
+
+Update the path with your batch-inferenced results, and carry out WER / SIM / UTMOS evaluations:
+```bash
+# Evaluation [WER] for Seed-TTS test [ZH] set
+python src/f5_tts/eval/eval_seedtts_testset.py --eval_task wer --lang zh --gen_wav_dir <GEN_WAV_DIR> --gpu_nums 8
+
+# Evaluation [SIM] for LibriSpeech-PC test-clean (cross-sentence)
+python src/f5_tts/eval/eval_librispeech_test_clean.py --eval_task sim --gen_wav_dir <GEN_WAV_DIR> --librispeech_test_clean_path <TEST_CLEAN_PATH>
+
+# Evaluation [UTMOS]. --ext: Audio extension
+python src/f5_tts/eval/eval_utmos.py --audio_dir <WAV_DIR> --ext wav
+```
@@ -0,0 +1,331 @@
+# just for speaker similarity evaluation, third-party code
+
+# From https://github.com/microsoft/UniSpeech/blob/main/downstreams/speaker_verification/models/
+# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
+
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+""" Res2Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(nn.Conv1d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias))
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            # Order: conv -> relu -> bn
+            sp = self.convs[i](sp)
+            sp = self.bns[i](F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True):
+        super().__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
+#     return nn.Sequential(
+#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
+#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
+#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
+#         SE_Connect(channels)
+#     )
+
+
+class SE_Res2Block(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim):
+        super().__init__()
+        self.Conv1dReluBn1 = Conv1dReluBn(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.Res2Conv1dReluBn = Res2Conv1dReluBn(out_channels, kernel_size, stride, padding, dilation, scale=scale)
+        self.Conv1dReluBn2 = Conv1dReluBn(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+            )
+
+    def forward(self, x):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.Conv1dReluBn1(x)
+        x = self.Res2Conv1dReluBn(x)
+        x = self.Conv1dReluBn2(x)
+        x = self.SE_Connect(x)
+
+        return x + residual
+
+
+""" Attentive weighted mean and standard deviation pooling.
+"""
+
+
+class AttentiveStatsPool(nn.Module):
+    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
+        super().__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(in_dim * 3, attention_channels, kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(in_dim, attention_channels, kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
+        alpha = torch.tanh(self.linear1(x_in))
+        # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(residuals.clamp(min=1e-9))
+        return torch.cat([mean, std], dim=1)
+
+
+class ECAPA_TDNN(nn.Module):
+    def __init__(
+        self,
+        feat_dim=80,
+        channels=512,
+        emb_dim=192,
+        global_context_att=False,
+        feat_type="wavlm_large",
+        sr=16000,
+        feature_selection="hidden_states",
+        update_extract=False,
+        config_path=None,
+    ):
+        super().__init__()
+
+        self.feat_type = feat_type
+        self.feature_selection = feature_selection
+        self.update_extract = update_extract
+        self.sr = sr
+
+        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
+        try:
+            local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main")
+            self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source="local", config_path=config_path)
+        except:  # noqa: E722
+            self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
+
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False
+
+        self.feat_num = self.get_feat_num()
+        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
+
+        if feat_type != "fbank" and feat_type != "mfcc":
+            freeze_list = ["final_proj", "label_embs_concat", "mask_emb", "project_q", "quantizer"]
+            for name, param in self.feature_extract.named_parameters():
+                for freeze_val in freeze_list:
+                    if freeze_val in name:
+                        param.requires_grad = False
+                        break
+
+        if not self.update_extract:
+            for param in self.feature_extract.parameters():
+                param.requires_grad = False
+
+        self.instance_norm = nn.InstanceNorm1d(feat_dim)
+        # self.channels = [channels] * 4 + [channels * 3]
+        self.channels = [channels] * 4 + [1536]
+
+        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            self.channels[0],
+            self.channels[1],
+            kernel_size=3,
+            stride=1,
+            padding=2,
+            dilation=2,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer3 = SE_Res2Block(
+            self.channels[1],
+            self.channels[2],
+            kernel_size=3,
+            stride=1,
+            padding=3,
+            dilation=3,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer4 = SE_Res2Block(
+            self.channels[2],
+            self.channels[3],
+            kernel_size=3,
+            stride=1,
+            padding=4,
+            dilation=4,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+
+        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
+        cat_channels = channels * 3
+        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
+        self.pooling = AttentiveStatsPool(
+            self.channels[-1], attention_channels=128, global_context_att=global_context_att
+        )
+        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
+        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
+
+    def get_feat_num(self):
+        self.feature_extract.eval()
+        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
+        with torch.no_grad():
+            features = self.feature_extract(wav)
+        select_feature = features[self.feature_selection]
+        if isinstance(select_feature, (list, tuple)):
+            return len(select_feature)
+        else:
+            return 1
+
+    def get_feat(self, x):
+        if self.update_extract:
+            x = self.feature_extract([sample for sample in x])
+        else:
+            with torch.no_grad():
+                if self.feat_type == "fbank" or self.feat_type == "mfcc":
+                    x = self.feature_extract(x) + 1e-6  # B x feat_dim x time_len
+                else:
+                    x = self.feature_extract([sample for sample in x])
+
+        if self.feat_type == "fbank":
+            x = x.log()
+
+        if self.feat_type != "fbank" and self.feat_type != "mfcc":
+            x = x[self.feature_selection]
+            if isinstance(x, (list, tuple)):
+                x = torch.stack(x, dim=0)
+            else:
+                x = x.unsqueeze(0)
+            norm_weights = F.softmax(self.feature_weight, dim=-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+            x = (norm_weights * x).sum(dim=0)
+            x = torch.transpose(x, 1, 2) + 1e-6
+
+        x = self.instance_norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.get_feat(x)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        out = F.relu(self.conv(out))
+        out = self.bn(self.pooling(out))
+        out = self.linear(out)
+
+        return out
+
+
+def ECAPA_TDNN_SMALL(
+    feat_dim,
+    emb_dim=256,
+    feat_type="wavlm_large",
+    sr=16000,
+    feature_selection="hidden_states",
+    update_extract=False,
+    config_path=None,
+):
+    return ECAPA_TDNN(
+        feat_dim=feat_dim,
+        channels=512,
+        emb_dim=emb_dim,
+        feat_type=feat_type,
+        sr=sr,
+        feature_selection=feature_selection,
+        update_extract=update_extract,
+        config_path=config_path,
+    )
@@ -0,0 +1,210 @@
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import argparse
+import time
+from importlib.resources import files
+
+import torch
+import torchaudio
+from accelerate import Accelerator
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from .eval.utils_eval import (
+    get_inference_prompt,
+    get_librispeech_test_clean_metainfo,
+    get_seedtts_testset_metainfo,
+)
+from .infer.utils_infer import load_checkpoint, load_vocoder
+from ..model import CFM
+from ..model.utils import get_tokenizer
+
+
+accelerator = Accelerator()
+device = f"cuda:{accelerator.process_index}"
+
+
+use_ema = True
+target_rms = 0.1
+
+
+rel_path = str(files(__package__ or "f5_tts").joinpath("../../"))
+
+
+def main():
+    parser = argparse.ArgumentParser(description="batch inference")
+
+    parser.add_argument("-s", "--seed", default=None, type=int)
+    parser.add_argument("-n", "--expname", required=True)
+    parser.add_argument("-c", "--ckptstep", default=1250000, type=int)
+
+    parser.add_argument("-nfe", "--nfestep", default=32, type=int)
+    parser.add_argument("-o", "--odemethod", default="euler")
+    parser.add_argument("-ss", "--swaysampling", default=-1, type=float)
+
+    parser.add_argument("-t", "--testset", required=True)
+
+    args = parser.parse_args()
+
+    seed = args.seed
+    exp_name = args.expname
+    ckpt_step = args.ckptstep
+
+    nfe_step = args.nfestep
+    ode_method = args.odemethod
+    sway_sampling_coef = args.swaysampling
+
+    testset = args.testset
+
+    infer_batch_size = 1  # max frames. 1 for ddp single inference (recommended)
+    cfg_strength = 2.0
+    speed = 1.0
+    use_truth_duration = False
+    no_ref_audio = False
+
+    model_cfg = OmegaConf.load(str(files(__package__ or "f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+    model_cls = get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+    model_arc = model_cfg.model.arch
+
+    dataset_name = model_cfg.datasets.name
+    tokenizer = model_cfg.model.tokenizer
+
+    mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+    target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+    n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+    hop_length = model_cfg.model.mel_spec.hop_length
+    win_length = model_cfg.model.mel_spec.win_length
+    n_fft = model_cfg.model.mel_spec.n_fft
+
+    if testset == "ls_pc_test_clean":
+        metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+        librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+        metainfo = get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path)
+
+    elif testset == "seedtts_test_zh":
+        metalst = rel_path + "/data/seedtts_testset/zh/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    elif testset == "seedtts_test_en":
+        metalst = rel_path + "/data/seedtts_testset/en/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    # path to save genereted wavs
+    output_dir = (
+        f"{rel_path}/"
+        f"results/{exp_name}_{ckpt_step}/{testset}/"
+        f"seed{seed}_{ode_method}_nfe{nfe_step}_{mel_spec_type}"
+        f"{f'_ss{sway_sampling_coef}' if sway_sampling_coef else ''}"
+        f"_cfg{cfg_strength}_speed{speed}"
+        f"{'_gt-dur' if use_truth_duration else ''}"
+        f"{'_no-ref-audio' if no_ref_audio else ''}"
+    )
+
+    # -------------------------------------------------#
+
+    prompts_all = get_inference_prompt(
+        metainfo,
+        speed=speed,
+        tokenizer=tokenizer,
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+        mel_spec_type=mel_spec_type,
+        target_rms=target_rms,
+        use_truth_duration=use_truth_duration,
+        infer_batch_size=infer_batch_size,
+    )
+
+    # Vocoder model
+    local = False
+    if mel_spec_type == "vocos":
+        vocoder_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+    elif mel_spec_type == "bigvgan":
+        vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+    vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=local, local_path=vocoder_local_path)
+
+    # Tokenizer
+    vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+    # Model
+    model = CFM(
+        transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    ckpt_prefix = rel_path + f"/ckpts/{exp_name}/model_{ckpt_step}"
+    if os.path.exists(ckpt_prefix + ".pt"):
+        ckpt_path = ckpt_prefix + ".pt"
+    elif os.path.exists(ckpt_prefix + ".safetensors"):
+        ckpt_path = ckpt_prefix + ".safetensors"
+    else:
+        print("Loading from self-organized training checkpoints rather than released pretrained.")
+        ckpt_path = rel_path + f"/{model_cfg.ckpts.save_dir}/model_{ckpt_step}.pt"
+
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+    model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+    if not os.path.exists(output_dir) and accelerator.is_main_process:
+        os.makedirs(output_dir)
+
+    # start batch inference
+    accelerator.wait_for_everyone()
+    start = time.time()
+
+    with accelerator.split_between_processes(prompts_all) as prompts:
+        for prompt in tqdm(prompts, disable=not accelerator.is_local_main_process):
+            utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = prompt
+            ref_mels = ref_mels.to(device)
+            ref_mel_lens = torch.tensor(ref_mel_lens, dtype=torch.long).to(device)
+            total_mel_lens = torch.tensor(total_mel_lens, dtype=torch.long).to(device)
+
+            # Inference
+            with torch.inference_mode():
+                generated, _ = model.sample(
+                    cond=ref_mels,
+                    text=final_text_list,
+                    duration=total_mel_lens,
+                    lens=ref_mel_lens,
+                    steps=nfe_step,
+                    cfg_strength=cfg_strength,
+                    sway_sampling_coef=sway_sampling_coef,
+                    no_ref_audio=no_ref_audio,
+                    seed=seed,
+                )
+                # Final result
+                for i, gen in enumerate(generated):
+                    gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
+                    gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
+                    if mel_spec_type == "vocos":
+                        generated_wave = vocoder.decode(gen_mel_spec).cpu()
+                    elif mel_spec_type == "bigvgan":
+                        generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+
+                    if ref_rms_list[i] < target_rms:
+                        generated_wave = generated_wave * ref_rms_list[i] / target_rms
+                    torchaudio.save(f"{output_dir}/{utts[i]}.wav", generated_wave, target_sample_rate)
+
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        timediff = time.time() - start
+        print(f"Done batch inference in {timediff / 60:.2f} minutes.")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# e.g. F5-TTS, 16 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_zh" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "seedtts_test_en" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_v1_Base" -t "ls_pc_test_clean" -nfe 16
+
+# e.g. Vanilla E2 TTS, 32 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_zh" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "seedtts_test_en" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -c 1200000 -t "ls_pc_test_clean" -o "midpoint" -ss 0
+
+# e.g. evaluate F5-TTS 16 NFE result on Seed-TTS test-zh
+python src/f5_tts/eval/eval_seedtts_testset.py -e wer -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_seedtts_testset.py -e sim -l zh --gen_wav_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0 --gpu_nums 8
+python src/f5_tts/eval/eval_utmos.py --audio_dir results/F5TTS_v1_Base_1250000/seedtts_test_zh/seed0_euler_nfe32_vocos_ss-1_cfg2.0_speed1.0
+
+# etc.
@@ -0,0 +1,89 @@
+# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
+
+import argparse
+import json
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from .eval.utils_eval import get_librispeech_test, run_asr_wer, run_sim
+
+
+rel_path = str(files(__package__ or "f5_tts").joinpath("../../"))
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
+    parser.add_argument("-l", "--lang", type=str, default="en")
+    parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
+    parser.add_argument("-p", "--librispeech_test_clean_path", type=str, required=True)
+    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    eval_task = args.eval_task
+    lang = args.lang
+    librispeech_test_clean_path = args.librispeech_test_clean_path  # test-clean path
+    gen_wav_dir = args.gen_wav_dir
+    metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+
+    gpus = list(range(args.gpu_nums))
+    test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
+
+    ## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
+    ## leading to a low similarity for the ground truth in some cases.
+    # test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = True)  # eval ground truth
+
+    local = args.local
+    if local:  # use local custom checkpoint dir
+        asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+    else:
+        asr_ckpt_dir = ""  # auto download to cache dir
+    wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+    # --------------------------------------------------------------------------
+
+    full_results = []
+    metrics = []
+
+    if eval_task == "wer":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_asr_wer, args)
+            for r in results:
+                full_results.extend(r)
+    elif eval_task == "sim":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_sim, args)
+            for r in results:
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,88 @@
+# Evaluate with Seed-TTS testset
+
+import argparse
+import json
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from .eval.utils_eval import get_seed_tts_test, run_asr_wer, run_sim
+
+
+rel_path = str(files(__package__ or "f5_tts").joinpath("../../"))
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
+    parser.add_argument("-l", "--lang", type=str, default="en", choices=["zh", "en"])
+    parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
+    parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
+    parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    eval_task = args.eval_task
+    lang = args.lang
+    gen_wav_dir = args.gen_wav_dir
+    metalst = rel_path + f"/data/seedtts_testset/{lang}/meta.lst"  # seed-tts testset
+
+    # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
+    #       zh 1.254 seems a result of 4 workers wer_seed_tts
+    gpus = list(range(args.gpu_nums))
+    test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
+
+    local = args.local
+    if local:  # use local custom checkpoint dir
+        if lang == "zh":
+            asr_ckpt_dir = "../checkpoints/funasr"  # paraformer-zh dir under funasr
+        elif lang == "en":
+            asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+    else:
+        asr_ckpt_dir = ""  # auto download to cache dir
+    wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+    # --------------------------------------------------------------------------
+
+    full_results = []
+    metrics = []
+
+    if eval_task == "wer":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_asr_wer, args)
+            for r in results:
+                full_results.extend(r)
+    elif eval_task == "sim":
+        with mp.Pool(processes=len(gpus)) as pool:
+            args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+            results = pool.map(run_sim, args)
+            for r in results:
+                full_results.extend(r)
+    else:
+        raise ValueError(f"Unknown metric type: {eval_task}")
+
+    result_path = f"{gen_wav_dir}/_{eval_task}_results.jsonl"
+    with open(result_path, "w") as f:
+        for line in full_results:
+            metrics.append(line[eval_task])
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        metric = round(np.mean(metrics), 5)
+        f.write(f"\n{eval_task.upper()}: {metric}\n")
+
+    print(f"\nTotal {len(metrics)} samples")
+    print(f"{eval_task.upper()}: {metric}")
+    print(f"{eval_task.upper()} results saved to {result_path}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,42 @@
+import argparse
+import json
+from pathlib import Path
+
+import librosa
+import torch
+from tqdm import tqdm
+
+
+def main():
+    parser = argparse.ArgumentParser(description="UTMOS Evaluation")
+    parser.add_argument("--audio_dir", type=str, required=True, help="Audio file path.")
+    parser.add_argument("--ext", type=str, default="wav", help="Audio extension.")
+    args = parser.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else "xpu" if torch.xpu.is_available() else "cpu"
+
+    predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
+    predictor = predictor.to(device)
+
+    audio_paths = list(Path(args.audio_dir).rglob(f"*.{args.ext}"))
+    utmos_score = 0
+
+    utmos_result_path = Path(args.audio_dir) / "_utmos_results.jsonl"
+    with open(utmos_result_path, "w", encoding="utf-8") as f:
+        for audio_path in tqdm(audio_paths, desc="Processing"):
+            wav, sr = librosa.load(audio_path, sr=None, mono=True)
+            wav_tensor = torch.from_numpy(wav).to(device).unsqueeze(0)
+            score = predictor(wav_tensor, sr)
+            line = {}
+            line["wav"], line["utmos"] = str(audio_path.stem), score.item()
+            utmos_score += score.item()
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+        avg_score = utmos_score / len(audio_paths) if len(audio_paths) > 0 else 0
+        f.write(f"\nUTMOS: {avg_score:.4f}\n")
+
+    print(f"UTMOS: {avg_score:.4f}")
+    print(f"UTMOS results saved to {utmos_result_path}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,419 @@
+import math
+import os
+import random
+import string
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from tqdm import tqdm
+
+from .eval.ecapa_tdnn import ECAPA_TDNN_SMALL
+from ..model.modules import MelSpec
+from ..model.utils import convert_char_to_pinyin
+
+
+# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
+def get_seedtts_testset_metainfo(metalst):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+        metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
+    return metainfo
+
+
+# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
+def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        # ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        # gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+        gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+
+        metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
+
+    return metainfo
+
+
+# padded to max length mel batch
+def padded_mel_batch(ref_mels):
+    max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
+    padded_ref_mels = []
+    for mel in ref_mels:
+        padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
+    return padded_ref_mels
+
+
+# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
+
+
+def get_inference_prompt(
+    metainfo,
+    speed=1.0,
+    tokenizer="pinyin",
+    polyphone=True,
+    target_sample_rate=24000,
+    n_fft=1024,
+    win_length=1024,
+    n_mel_channels=100,
+    hop_length=256,
+    mel_spec_type="vocos",
+    target_rms=0.1,
+    use_truth_duration=False,
+    infer_batch_size=1,
+    num_buckets=200,
+    min_secs=3,
+    max_secs=40,
+):
+    prompts_all = []
+
+    min_tokens = min_secs * target_sample_rate // hop_length
+    max_tokens = max_secs * target_sample_rate // hop_length
+
+    batch_accum = [0] * num_buckets
+    utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
+        [[] for _ in range(num_buckets)] for _ in range(6)
+    )
+
+    mel_spectrogram = MelSpec(
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        n_mel_channels=n_mel_channels,
+        target_sample_rate=target_sample_rate,
+        mel_spec_type=mel_spec_type,
+    )
+
+    for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
+        # Audio
+        ref_audio, ref_sr = torchaudio.load(prompt_wav)
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
+        if ref_rms < target_rms:
+            ref_audio = ref_audio * target_rms / ref_rms
+        assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio)
+
+        # Text
+        if len(prompt_text[-1].encode("utf-8")) == 1:
+            prompt_text = prompt_text + " "
+        text = [prompt_text + gt_text]
+        if tokenizer == "pinyin":
+            text_list = convert_char_to_pinyin(text, polyphone=polyphone)
+        else:
+            text_list = text
+
+        # to mel spectrogram
+        ref_mel = mel_spectrogram(ref_audio)
+        ref_mel = ref_mel.squeeze(0)
+
+        # Duration, mel frame length
+        ref_mel_len = ref_mel.shape[-1]
+
+        if use_truth_duration:
+            gt_audio, gt_sr = torchaudio.load(gt_wav)
+            if gt_sr != target_sample_rate:
+                resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
+                gt_audio = resampler(gt_audio)
+            total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
+
+            # # test vocoder resynthesis
+            # ref_audio = gt_audio
+        else:
+            ref_text_len = len(prompt_text.encode("utf-8"))
+            gen_text_len = len(gt_text.encode("utf-8"))
+            total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
+
+        # deal with batch
+        assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
+        assert min_tokens <= total_mel_len <= max_tokens, (
+            f"Audio {utt} has duration {total_mel_len * hop_length // target_sample_rate}s out of range [{min_secs}, {max_secs}]."
+        )
+        bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
+
+        utts[bucket_i].append(utt)
+        ref_rms_list[bucket_i].append(ref_rms)
+        ref_mels[bucket_i].append(ref_mel)
+        ref_mel_lens[bucket_i].append(ref_mel_len)
+        total_mel_lens[bucket_i].append(total_mel_len)
+        final_text_list[bucket_i].extend(text_list)
+
+        batch_accum[bucket_i] += total_mel_len
+
+        if batch_accum[bucket_i] >= infer_batch_size:
+            # print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+            batch_accum[bucket_i] = 0
+            (
+                utts[bucket_i],
+                ref_rms_list[bucket_i],
+                ref_mels[bucket_i],
+                ref_mel_lens[bucket_i],
+                total_mel_lens[bucket_i],
+                final_text_list[bucket_i],
+            ) = [], [], [], [], [], []
+
+    # add residual
+    for bucket_i, bucket_frames in enumerate(batch_accum):
+        if bucket_frames > 0:
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+    # not only leave easy work for last workers
+    random.seed(666)
+    random.shuffle(prompts_all)
+
+    return prompts_all
+
+
+# get wav_res_ref_text of seed-tts test metalst
+# https://github.com/BytedanceSpeech/seed-tts-eval
+
+
+def get_seed_tts_test(metalst, gen_wav_dir, gpus):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+
+        if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
+            continue
+        gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+
+        test_set_.append((gen_wav, prompt_wav, gt_text))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# get librispeech test-clean cross sentence test
+
+
+def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        if eval_ground_truth:
+            gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+            gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+        else:
+            if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
+                raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
+            gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
+
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        test_set_.append((gen_wav, ref_wav, gen_txt))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# load asr model
+
+
+def load_asr_model(lang, ckpt_dir=""):
+    if lang == "zh":
+        from funasr import AutoModel
+
+        model = AutoModel(
+            model=os.path.join(ckpt_dir, "paraformer-zh"),
+            # vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
+            # punc_model = os.path.join(ckpt_dir, "ct-punc"),
+            # spk_model = os.path.join(ckpt_dir, "cam++"),
+            disable_update=True,
+        )  # following seed-tts setting
+    elif lang == "en":
+        from faster_whisper import WhisperModel
+
+        model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+    return model
+
+
+# WER Evaluation, the way Seed-TTS does
+
+
+def run_asr_wer(args):
+    rank, lang, test_set, ckpt_dir = args
+
+    if lang == "zh":
+        import zhconv
+
+        torch.cuda.set_device(rank)
+    elif lang == "en":
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+    else:
+        raise NotImplementedError(
+            "lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
+        )
+
+    asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
+
+    from zhon.hanzi import punctuation
+
+    punctuation_all = punctuation + string.punctuation
+    wer_results = []
+
+    from jiwer import compute_measures
+
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        if lang == "zh":
+            res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
+            hypo = res[0]["text"]
+            hypo = zhconv.convert(hypo, "zh-cn")
+        elif lang == "en":
+            segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
+            hypo = ""
+            for segment in segments:
+                hypo = hypo + " " + segment.text
+
+        raw_truth = truth
+        raw_hypo = hypo
+
+        for x in punctuation_all:
+            truth = truth.replace(x, "")
+            hypo = hypo.replace(x, "")
+
+        truth = truth.replace("  ", " ")
+        hypo = hypo.replace("  ", " ")
+
+        if lang == "zh":
+            truth = " ".join([x for x in truth])
+            hypo = " ".join([x for x in hypo])
+        elif lang == "en":
+            truth = truth.lower()
+            hypo = hypo.lower()
+
+        measures = compute_measures(truth, hypo)
+        wer = measures["wer"]
+
+        # ref_list = truth.split(" ")
+        # subs = measures["substitutions"] / len(ref_list)
+        # dele = measures["deletions"] / len(ref_list)
+        # inse = measures["insertions"] / len(ref_list)
+
+        wer_results.append(
+            {
+                "wav": Path(gen_wav).stem,
+                "truth": raw_truth,
+                "hypo": raw_hypo,
+                "wer": wer,
+            }
+        )
+
+    return wer_results
+
+
+# SIM Evaluation
+
+
+def run_sim(args):
+    rank, test_set, ckpt_dir = args
+    device = f"cuda:{rank}"
+
+    model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
+    state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
+    model.load_state_dict(state_dict["model"], strict=False)
+
+    use_gpu = True if torch.cuda.is_available() else False
+    if use_gpu:
+        model = model.cuda(device)
+    model.eval()
+
+    sim_results = []
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        wav1, sr1 = torchaudio.load(gen_wav)
+        wav2, sr2 = torchaudio.load(prompt_wav)
+
+        resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
+        resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
+        wav1 = resample1(wav1)
+        wav2 = resample2(wav2)
+
+        if use_gpu:
+            wav1 = wav1.cuda(device)
+            wav2 = wav2.cuda(device)
+        with torch.no_grad():
+            emb1 = model(wav1)
+            emb2 = model(wav2)
+
+        sim = F.cosine_similarity(emb1, emb2)[0].item()
+        # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
+        sim_results.append(
+            {
+                "wav": Path(gen_wav).stem,
+                "sim": sim,
+            }
+        )
+
+    return sim_results
@@ -0,0 +1,177 @@
+# Inference
+
+The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or will be automatically downloaded when running inference scripts.
+
+**More checkpoints with whole community efforts can be found in [SHARED.md](SHARED.md), supporting more languages.**
+
+Currently support **30s for a single** generation, which is the **total length** (same logic if `fix_duration`) including both prompt and output audio. However, `infer_cli` and `infer_gradio` will automatically do chunk generation for longer text. Long reference audio will be **clip short to ~12s**.
+
+To avoid possible inference failures, make sure you have seen through the following instructions.
+
+- Use reference audio <12s and leave proper silence space (e.g. 1s) at the end. Otherwise there is a risk of truncating in the middle of word, leading to suboptimal generation.
+- <ins>Uppercased letters</ins> (best with form like K.F.C.) will be uttered letter by letter, and lowercased letters used for common words. 
+- Add some spaces (blank: " ") or punctuations (e.g. "," ".") <ins>to explicitly introduce some pauses</ins>.
+- If English punctuation marks the end of a sentence, make sure there is a space " " after it. Otherwise not regarded as when chunk.
+- <ins>Preprocess numbers</ins> to Chinese letters if you want to have them read in Chinese, otherwise in English.
+- If the generation output is blank (pure silence), <ins>check for FFmpeg installation</ins>.
+- Try <ins>turn off `use_ema` if using an early-stage</ins> finetuned checkpoint (which goes just few updates).
+
+
+## Gradio App
+
+Currently supported features:
+
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+- [Custom inference with more language support](SHARED.md)
+
+The cli command `f5-tts_infer-gradio` equals to `python src/f5_tts/infer/infer_gradio.py`, which launches a Gradio APP (web interface) for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and update the path to `load_model()` in `infer_gradio.py`. Currently only load TTS models first, will load ASR model to do transcription if `ref_text` not provided, will load LLM model if use Voice Chat.
+
+More flags options:
+
+```bash
+# Automatically launch the interface in the default web browser
+f5-tts_infer-gradio --inbrowser
+
+# Set the root path of the application, if it's not served from the root ("/") of the domain
+# For example, if the application is served at "https://example.com/myapp"
+f5-tts_infer-gradio --root_path "/myapp"
+```
+
+Could also be used as a component for larger application:
+```python
+import gradio as gr
+from f5_tts.infer.infer_gradio import app
+
+with gr.Blocks() as main_app:
+    gr.Markdown("# This is an example of using F5-TTS within a bigger Gradio app")
+
+    # ... other Gradio components
+
+    app.render()
+
+main_app.launch()
+```
+
+
+## CLI Inference
+
+The cli command `f5-tts_infer-cli` equals to `python src/f5_tts/infer/infer_cli.py`, which is a command line tool for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and use `--ckpt_file` to specify the model you want to load, or directly update in `infer_cli.py`.
+
+For change vocab.txt use `--vocab_file` to provide your `vocab.txt` file.
+
+Basically you can inference with flags:
+```bash
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model F5TTS_v1_Base \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
+
+# Use BigVGAN as vocoder. Currently only support F5TTS_Base. 
+f5-tts_infer-cli --model F5TTS_Base --vocoder_name bigvgan --load_vocoder_from_local
+
+# Use custom path checkpoint, e.g.
+f5-tts_infer-cli --ckpt_file ckpts/F5TTS_v1_Base/model_1250000.safetensors
+
+# More instructions
+f5-tts_infer-cli --help
+```
+
+And a `.toml` file would help with more flexible usage.
+
+```bash
+f5-tts_infer-cli -c custom.toml
+```
+
+For example, you can use `.toml` to pass in variables, refer to `src/f5_tts/infer/examples/basic/basic.toml`:
+
+```toml
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"
+```
+
+You can also leverage `.toml` file to do multi-style generation, refer to `src/f5_tts/infer/examples/multi/story.toml`.
+
+```toml
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""
+```
+You should mark the voice with `[main]` `[town]` `[country]` whenever you want to change voice, refer to `src/f5_tts/infer/examples/multi/story.txt`.
+
+## API Usage
+
+```python
+from importlib.resources import files
+from f5_tts.api import F5TTS
+
+f5tts = F5TTS()
+wav, sr, spec = f5tts.infer(
+    ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+    ref_text="some call me nature, others call me mother nature.",
+    gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+    file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
+    file_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+    seed=None,
+)
+```
+Check [api.py](../api.py) for more details.
+
+## TensorRT-LLM Deployment
+
+See [detailed instructions](../runtime/triton_trtllm/README.md) for more information.
+
+## Socket Real-time Service
+
+Real-time voice output with chunk stream:
+
+```bash
+# Start socket server
+python src/f5_tts/socket_server.py
+
+# If PyAudio not installed
+sudo apt-get install portaudio19-dev
+pip install pyaudio
+
+# Communicate with socket client
+python src/f5_tts/socket_client.py
+```
+
+## Speech Editing
+
+To test speech editing capabilities, use the following command:
+
+```bash
+python src/f5_tts/infer/speech_edit.py
+```
+
@@ -0,0 +1,193 @@
+<!-- omit in toc -->
+# Shared Model Cards
+
+<!-- omit in toc -->
+### **Prerequisites of using**
+- This document is serving as a quick lookup table for the community training/finetuning result, with various language support.
+- The models in this repository are open source and are based on voluntary contributions from contributors.
+- The use of models must be conditioned on respect for the respective creators. The convenience brought comes from their efforts.
+
+<!-- omit in toc -->
+### **Welcome to share here**
+- Have a pretrained/finetuned result: model checkpoint (pruned best to facilitate inference, i.e. leave only `ema_model_state_dict`) and corresponding vocab file (for tokenization).
+- Host a public [huggingface model repository](https://huggingface.co/new) and upload the model related files.
+- Make a pull request adding a model card to the current page, i.e. `src\f5_tts\infer\SHARED.md`.
+
+<!-- omit in toc -->
+### Supported Languages
+- [Multilingual](#multilingual)
+    - [F5-TTS v1 v0 Base @ zh \& en @ F5-TTS](#f5-tts-v1-v0-base--zh--en--f5-tts)
+- [English](#english)
+- [Finnish](#finnish)
+    - [F5-TTS Base @ fi @ AsmoKoskinen](#f5-tts-base--fi--asmokoskinen)
+- [French](#french)
+    - [F5-TTS Base @ fr @ RASPIAUDIO](#f5-tts-base--fr--raspiaudio)
+- [German](#german)
+    - [F5-TTS Base @ de @ hvoss-techfak](#f5-tts-base--de--hvoss-techfak)
+- [Hindi](#hindi)
+    - [F5-TTS Small @ hi @ SPRINGLab](#f5-tts-small--hi--springlab)
+- [Italian](#italian)
+    - [F5-TTS Base @ it @ alien79](#f5-tts-base--it--alien79)
+- [Japanese](#japanese)
+    - [F5-TTS Base @ ja @ Jmica](#f5-tts-base--ja--jmica)
+- [Mandarin](#mandarin)
+- [Russian](#russian)
+    - [F5-TTS Base @ ru @ HotDro4illa](#f5-tts-base--ru--hotdro4illa)
+- [Spanish](#spanish)
+    - [F5-TTS Base @ es @ jpgallegoar](#f5-tts-base--es--jpgallegoar)
+
+
+## Multilingual
+
+#### F5-TTS v1 v0 Base @ zh & en @ F5-TTS
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS v1 Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_v1_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
+
+```bash
+Model: hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors
+# A Variant Model: hf://SWivid/F5-TTS/F5TTS_v1_Base_no_zero_init/model_1250000.safetensors
+Vocab: hf://SWivid/F5-TTS/F5TTS_v1_Base/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "conv_layers": 4}
+```
+
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/SWivid/F5-TTS/tree/main/F5TTS_Base)|[Emilia 95K zh&en](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07)|cc-by-nc-4.0|
+
+```bash
+Model: hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors
+Vocab: hf://SWivid/F5-TTS/F5TTS_Base/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+*Other infos, e.g. Author info, Github repo, Link to some sampled results, Usage instruction, Tutorial (Blog, Video, etc.) ...*
+
+
+## English
+
+
+## Finnish
+
+#### F5-TTS Base @ fi @ AsmoKoskinen
+|Model|🤗Hugging Face|Data|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/AsmoKoskinen/F5-TTS_Finnish_Model)|[Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0), [Vox Populi](https://huggingface.co/datasets/facebook/voxpopuli)|cc-by-nc-4.0|
+
+```bash
+Model: hf://AsmoKoskinen/F5-TTS_Finnish_Model/model_common_voice_fi_vox_populi_fi_20241206.safetensors
+Vocab: hf://AsmoKoskinen/F5-TTS_Finnish_Model/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+
+## French
+
+#### F5-TTS Base @ fr @ RASPIAUDIO
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/RASPIAUDIO/F5-French-MixedSpeakers-reduced)|[LibriVox](https://librivox.org/)|cc-by-nc-4.0|
+
+```bash
+Model: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/model_last_reduced.pt
+Vocab: hf://RASPIAUDIO/F5-French-MixedSpeakers-reduced/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- [Online Inference with Hugging Face Space](https://huggingface.co/spaces/RASPIAUDIO/f5-tts_french).
+- [Tutorial video to train a new language model](https://www.youtube.com/watch?v=UO4usaOojys).
+- [Discussion about this training can be found here](https://github.com/SWivid/F5-TTS/issues/434).
+
+
+## German
+
+#### F5-TTS Base @ de @ hvoss-techfak
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/hvoss-techfak/F5-TTS-German)|[Mozilla Common Voice 19.0](https://commonvoice.mozilla.org/en/datasets) & 800 hours Crowdsourced |cc-by-nc-4.0|
+
+```bash
+Model: hf://hvoss-techfak/F5-TTS-German/model_f5tts_german.pt
+Vocab: hf://hvoss-techfak/F5-TTS-German/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Finetuned by [@hvoss-techfak](https://github.com/hvoss-techfak)
+
+
+## Hindi
+
+#### F5-TTS Small @ hi @ SPRINGLab
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Small|[ckpt & vocab](https://huggingface.co/SPRINGLab/F5-Hindi-24KHz)|[IndicTTS Hi](https://huggingface.co/datasets/SPRINGLab/IndicTTS-Hindi) & [IndicVoices-R Hi](https://huggingface.co/datasets/SPRINGLab/IndicVoices-R_Hindi) |cc-by-4.0|
+
+```bash
+Model: hf://SPRINGLab/F5-Hindi-24KHz/model_2500000.safetensors
+Vocab: hf://SPRINGLab/F5-Hindi-24KHz/vocab.txt
+Config: {"dim": 768, "depth": 18, "heads": 12, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Authors: SPRING Lab, Indian Institute of Technology, Madras
+- Website: https://asr.iitm.ac.in/
+
+
+## Italian
+
+#### F5-TTS Base @ it @ alien79
+|Model|🤗Hugging Face|Data|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/alien79/F5-TTS-italian)|[ylacombe/cml-tts](https://huggingface.co/datasets/ylacombe/cml-tts) |cc-by-nc-4.0|
+
+```bash
+Model: hf://alien79/F5-TTS-italian/model_159600.safetensors
+Vocab: hf://alien79/F5-TTS-italian/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+- Trained by [Mithril Man](https://github.com/MithrilMan)
+- Model details on [hf project home](https://huggingface.co/alien79/F5-TTS-italian)
+- Open to collaborations to further improve the model
+
+
+## Japanese
+
+#### F5-TTS Base @ ja @ Jmica
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/Jmica/F5TTS/tree/main/JA_21999120)|[Emilia 1.7k JA](https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07) & [Galgame Dataset 5.4k](https://huggingface.co/datasets/OOPPEENN/Galgame_Dataset)|cc-by-nc-4.0|
+
+```bash
+Model: hf://Jmica/F5TTS/JA_21999120/model_21999120.pt
+Vocab: hf://Jmica/F5TTS/JA_21999120/vocab_japanese.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+
+
+## Mandarin
+
+
+## Russian
+
+#### F5-TTS Base @ ru @ HotDro4illa
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/hotstone228/F5-TTS-Russian)|[Common voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0)|cc-by-nc-4.0|
+
+```bash
+Model: hf://hotstone228/F5-TTS-Russian/model_last.safetensors
+Vocab: hf://hotstone228/F5-TTS-Russian/vocab.txt
+Config: {"dim": 1024, "depth": 22, "heads": 16, "ff_mult": 2, "text_dim": 512, "text_mask_padding": False, "conv_layers": 4, "pe_attn_head": 1}
+```
+- Finetuned by [HotDro4illa](https://github.com/HotDro4illa)
+- Any improvements are welcome
+
+
+## Spanish
+
+#### F5-TTS Base @ es @ jpgallegoar
+|Model|🤗Hugging Face|Data (Hours)|Model License|
+|:---:|:------------:|:-----------:|:-------------:|
+|F5-TTS Base|[ckpt & vocab](https://huggingface.co/jpgallegoar/F5-Spanish)|[Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli) & Crowdsourced & TEDx, 218 hours|cc0-1.0|
+
+- @jpgallegoar [GitHub repo](https://github.com/jpgallegoar/Spanish-F5), Jupyter Notebook and Gradio usage for Spanish model.
@@ -0,0 +1,11 @@
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"
+output_file = "infer_cli_basic.wav"
@@ -0,0 +1,20 @@
+# F5TTS_v1_Base | E2TTS_Base
+model = "F5TTS_v1_Base"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+output_file = "infer_cli_story.wav"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+speed = 0.8  # will ignore global speed
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""
@@ -0,0 +1 @@
+A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] "My poor dear friend, you live here no better than the ants! Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land." [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] "Goodbye," [main] said he, [country] "I'm off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace."
@@ -0,0 +1,383 @@
+import argparse
+import codecs
+import os
+import re
+from datetime import datetime
+from importlib.resources import files
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tomli
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+from unidecode import unidecode
+
+from .infer.utils_infer import (
+    cfg_strength,
+    cross_fade_duration,
+    device,
+    fix_duration,
+    infer_process,
+    load_model,
+    load_vocoder,
+    mel_spec_type,
+    nfe_step,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    speed,
+    sway_sampling_coef,
+    target_rms,
+)
+
+
+parser = argparse.ArgumentParser(
+    prog="python3 infer-cli.py",
+    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
+    epilog="Specify options above to override one or more settings from config.",
+)
+parser.add_argument(
+    "-c",
+    "--config",
+    type=str,
+    default=os.path.join(files(__package__ or "f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
+    help="The configuration file, default see infer/examples/basic/basic.toml",
+)
+
+
+# Note. Not to provide default value here in order to read default from config file
+
+parser.add_argument(
+    "-m",
+    "--model",
+    type=str,
+    help="The model name: F5TTS_v1_Base | F5TTS_Base | E2TTS_Base | etc.",
+)
+parser.add_argument(
+    "-mc",
+    "--model_cfg",
+    type=str,
+    help="The path to F5-TTS model config file .yaml",
+)
+parser.add_argument(
+    "-p",
+    "--ckpt_file",
+    type=str,
+    help="The path to model checkpoint .pt, leave blank to use default",
+)
+parser.add_argument(
+    "-v",
+    "--vocab_file",
+    type=str,
+    help="The path to vocab file .txt, leave blank to use default",
+)
+parser.add_argument(
+    "-r",
+    "--ref_audio",
+    type=str,
+    help="The reference audio file.",
+)
+parser.add_argument(
+    "-s",
+    "--ref_text",
+    type=str,
+    help="The transcript/subtitle for the reference audio",
+)
+parser.add_argument(
+    "-t",
+    "--gen_text",
+    type=str,
+    help="The text to make model synthesize a speech",
+)
+parser.add_argument(
+    "-f",
+    "--gen_file",
+    type=str,
+    help="The file with text to generate, will ignore --gen_text",
+)
+parser.add_argument(
+    "-o",
+    "--output_dir",
+    type=str,
+    help="The path to output folder",
+)
+parser.add_argument(
+    "-w",
+    "--output_file",
+    type=str,
+    help="The name of output file",
+)
+parser.add_argument(
+    "--save_chunk",
+    action="store_true",
+    help="To save each audio chunks during inference",
+)
+parser.add_argument(
+    "--no_legacy_text",
+    action="store_false",
+    help="Not to use lossy ASCII transliterations of unicode text in saved file names.",
+)
+parser.add_argument(
+    "--remove_silence",
+    action="store_true",
+    help="To remove long silence found in ouput",
+)
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="To load vocoder from local dir, default to ../checkpoints/vocos-mel-24khz",
+)
+parser.add_argument(
+    "--vocoder_name",
+    type=str,
+    choices=["vocos", "bigvgan"],
+    help=f"Used vocoder name: vocos | bigvgan, default {mel_spec_type}",
+)
+parser.add_argument(
+    "--target_rms",
+    type=float,
+    help=f"Target output speech loudness normalization value, default {target_rms}",
+)
+parser.add_argument(
+    "--cross_fade_duration",
+    type=float,
+    help=f"Duration of cross-fade between audio segments in seconds, default {cross_fade_duration}",
+)
+parser.add_argument(
+    "--nfe_step",
+    type=int,
+    help=f"The number of function evaluation (denoising steps), default {nfe_step}",
+)
+parser.add_argument(
+    "--cfg_strength",
+    type=float,
+    help=f"Classifier-free guidance strength, default {cfg_strength}",
+)
+parser.add_argument(
+    "--sway_sampling_coef",
+    type=float,
+    help=f"Sway Sampling coefficient, default {sway_sampling_coef}",
+)
+parser.add_argument(
+    "--speed",
+    type=float,
+    help=f"The speed of the generated audio, default {speed}",
+)
+parser.add_argument(
+    "--fix_duration",
+    type=float,
+    help=f"Fix the total duration (ref and gen audios) in seconds, default {fix_duration}",
+)
+parser.add_argument(
+    "--device",
+    type=str,
+    help="Specify the device to run on",
+)
+args = parser.parse_args()
+
+
+# config file
+
+config = tomli.load(open(args.config, "rb"))
+
+
+# command-line interface parameters
+
+model = args.model or config.get("model", "F5TTS_v1_Base")
+ckpt_file = args.ckpt_file or config.get("ckpt_file", "")
+vocab_file = args.vocab_file or config.get("vocab_file", "")
+
+ref_audio = args.ref_audio or config.get("ref_audio", "infer/examples/basic/basic_ref_en.wav")
+ref_text = (
+    args.ref_text
+    if args.ref_text is not None
+    else config.get("ref_text", "Some call me nature, others call me mother nature.")
+)
+gen_text = args.gen_text or config.get("gen_text", "Here we generate something just for test.")
+gen_file = args.gen_file or config.get("gen_file", "")
+
+output_dir = args.output_dir or config.get("output_dir", "tests")
+output_file = args.output_file or config.get(
+    "output_file", f"infer_cli_{datetime.now().strftime(r'%Y%m%d_%H%M%S')}.wav"
+)
+
+save_chunk = args.save_chunk or config.get("save_chunk", False)
+use_legacy_text = args.no_legacy_text or config.get("no_legacy_text", False)  # no_legacy_text is a store_false arg
+if save_chunk and use_legacy_text:
+    print(
+        "\nWarning to --save_chunk: lossy ASCII transliterations of unicode text for legacy (.wav) file names, --no_legacy_text to disable.\n"
+    )
+
+remove_silence = args.remove_silence or config.get("remove_silence", False)
+load_vocoder_from_local = args.load_vocoder_from_local or config.get("load_vocoder_from_local", False)
+
+vocoder_name = args.vocoder_name or config.get("vocoder_name", mel_spec_type)
+target_rms = args.target_rms or config.get("target_rms", target_rms)
+cross_fade_duration = args.cross_fade_duration or config.get("cross_fade_duration", cross_fade_duration)
+nfe_step = args.nfe_step or config.get("nfe_step", nfe_step)
+cfg_strength = args.cfg_strength or config.get("cfg_strength", cfg_strength)
+sway_sampling_coef = args.sway_sampling_coef or config.get("sway_sampling_coef", sway_sampling_coef)
+speed = args.speed or config.get("speed", speed)
+fix_duration = args.fix_duration or config.get("fix_duration", fix_duration)
+device = args.device or config.get("device", device)
+
+
+# patches for pip pkg user
+if "infer/examples/" in ref_audio:
+    ref_audio = str(files(__package__ or "f5_tts").joinpath(f"{ref_audio}"))
+if "infer/examples/" in gen_file:
+    gen_file = str(files(__package__ or "f5_tts").joinpath(f"{gen_file}"))
+if "voices" in config:
+    for voice in config["voices"]:
+        voice_ref_audio = config["voices"][voice]["ref_audio"]
+        if "infer/examples/" in voice_ref_audio:
+            config["voices"][voice]["ref_audio"] = str(files(__package__ or "f5_tts").joinpath(f"{voice_ref_audio}"))
+
+
+# ignore gen_text if gen_file provided
+
+if gen_file:
+    gen_text = codecs.open(gen_file, "r", "utf-8").read()
+
+
+# output path
+
+wave_path = Path(output_dir) / output_file
+# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
+if save_chunk:
+    output_chunk_dir = os.path.join(output_dir, f"{Path(output_file).stem}_chunks")
+    if not os.path.exists(output_chunk_dir):
+        os.makedirs(output_chunk_dir)
+
+
+# load vocoder
+
+if vocoder_name == "vocos":
+    vocoder_local_path = "../checkpoints/vocos-mel-24khz"
+elif vocoder_name == "bigvgan":
+    vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+
+vocoder = load_vocoder(
+    vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path, device=device
+)
+
+
+# load TTS model
+
+model_cfg = OmegaConf.load(
+    args.model_cfg or config.get("model_cfg", str(files(__package__ or "f5_tts").joinpath(f"configs/{model}.yaml")))
+)
+model_cls = get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+model_arc = model_cfg.model.arch
+
+repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+if model != "F5TTS_Base":
+    assert vocoder_name == model_cfg.model.mel_spec.mel_spec_type
+
+# override for previous models
+if model == "F5TTS_Base":
+    if vocoder_name == "vocos":
+        ckpt_step = 1200000
+    elif vocoder_name == "bigvgan":
+        model = "F5TTS_Base_bigvgan"
+        ckpt_type = "pt"
+elif model == "E2TTS_Base":
+    repo_name = "E2-TTS"
+    ckpt_step = 1200000
+
+if not ckpt_file:
+    ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}"))
+
+print(f"Using {model}...")
+ema_model = load_model(
+    model_cls, model_arc, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file, device=device
+)
+
+
+# inference process
+
+
+def main():
+    main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
+    if "voices" not in config:
+        voices = {"main": main_voice}
+    else:
+        voices = config["voices"]
+        voices["main"] = main_voice
+    for voice in voices:
+        print("Voice:", voice)
+        print("ref_audio ", voices[voice]["ref_audio"])
+        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
+            voices[voice]["ref_audio"], voices[voice]["ref_text"]
+        )
+        print("ref_audio_", voices[voice]["ref_audio"], "\n\n")
+
+    generated_audio_segments = []
+    reg1 = r"(?=\[\w+\])"
+    chunks = re.split(reg1, gen_text)
+    reg2 = r"\[(\w+)\]"
+    for text in chunks:
+        if not text.strip():
+            continue
+        match = re.match(reg2, text)
+        if match:
+            voice = match[1]
+        else:
+            print("No voice tag found, using main.")
+            voice = "main"
+        if voice not in voices:
+            print(f"Voice {voice} not found, using main.")
+            voice = "main"
+        text = re.sub(reg2, "", text)
+        ref_audio_ = voices[voice]["ref_audio"]
+        ref_text_ = voices[voice]["ref_text"]
+        local_speed = voices[voice].get("speed", speed)
+        gen_text_ = text.strip()
+        print(f"Voice: {voice}")
+        audio_segment, final_sample_rate, spectrogram = infer_process(
+            ref_audio_,
+            ref_text_,
+            gen_text_,
+            ema_model,
+            vocoder,
+            mel_spec_type=vocoder_name,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=local_speed,
+            fix_duration=fix_duration,
+            device=device,
+        )
+        generated_audio_segments.append(audio_segment)
+
+        if save_chunk:
+            if len(gen_text_) > 200:
+                gen_text_ = gen_text_[:200] + " ... "
+            if use_legacy_text:
+                gen_text_ = unidecode(gen_text_)
+            sf.write(
+                os.path.join(output_chunk_dir, f"{len(generated_audio_segments) - 1}_{gen_text_}.wav"),
+                audio_segment,
+                final_sample_rate,
+            )
+
+    if generated_audio_segments:
+        final_wave = np.concatenate(generated_audio_segments)
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+        with open(wave_path, "wb") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            # Remove silence
+            if remove_silence:
+                remove_silence_for_generated_wav(f.name)
+            print(f.name)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,205 @@
+import os
+
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
+
+from importlib.resources import files
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from .infer.utils_infer import load_checkpoint, load_vocoder, save_spectrogram
+from ..model import CFM
+from ..model.utils import convert_char_to_pinyin, get_tokenizer
+
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
+
+
+# ---------------------- infer setting ---------------------- #
+
+seed = None  # int | None
+
+exp_name = "F5TTS_v1_Base"  # F5TTS_v1_Base | E2TTS_Base
+ckpt_step = 1250000
+
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+ode_method = "euler"  # euler | midpoint
+sway_sampling_coef = -1.0
+speed = 1.0
+target_rms = 0.1
+
+
+model_cfg = OmegaConf.load(str(files(__package__ or "f5_tts").joinpath(f"configs/{exp_name}.yaml")))
+model_cls = get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+model_arc = model_cfg.model.arch
+
+dataset_name = model_cfg.datasets.name
+tokenizer = model_cfg.model.tokenizer
+
+mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+n_mel_channels = model_cfg.model.mel_spec.n_mel_channels
+hop_length = model_cfg.model.mel_spec.hop_length
+win_length = model_cfg.model.mel_spec.win_length
+n_fft = model_cfg.model.mel_spec.n_fft
+
+
+# ckpt_path = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/ckpts/{exp_name}/model_{ckpt_step}.safetensors"
+ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.safetensors"))
+output_dir = "tests"
+
+
+# [leverage https://github.com/MahmoudAshraf97/ctc-forced-aligner to get char level alignment]
+# pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
+# [write the origin_text into a file, e.g. tests/test_edit.txt]
+# ctc-forced-aligner --audio_path "src/f5_tts/infer/examples/basic/basic_ref_en.wav" --text_path "tests/test_edit.txt" --language "zho" --romanize --split_size "char"
+# [result will be saved at same path of audio file]
+# [--language "zho" for Chinese, "eng" for English]
+# [if local ckpt, set --alignment_model "../checkpoints/mms-300m-1130-forced-aligner"]
+
+audio_to_edit = str(files(__package__ or "f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav"))
+origin_text = "Some call me nature, others call me mother nature."
+target_text = "Some call me optimist, others call me realist."
+parts_to_edit = [
+    [1.42, 2.44],
+    [4.04, 4.9],
+]  # stard_ends of "nature" & "mother nature", in seconds
+fix_duration = [
+    1.2,
+    1,
+]  # fix duration for "optimist" & "realist", in seconds
+
+# audio_to_edit = "src/f5_tts/infer/examples/basic/basic_ref_zh.wav"
+# origin_text = "对，这就是我，万人敬仰的太乙真人。"
+# target_text = "对，那就是你，万人敬仰的太白金星。"
+# parts_to_edit = [[0.84, 1.4], [1.92, 2.4], [4.26, 6.26], ]
+# fix_duration = None  # use origin text duration
+
+
+# -------------------------------------------------#
+
+use_ema = True
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Vocoder model
+local = False
+if mel_spec_type == "vocos":
+    vocoder_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+elif mel_spec_type == "bigvgan":
+    vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=local, local_path=vocoder_local_path)
+
+# Tokenizer
+vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+# Model
+model = CFM(
+    transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+    mel_spec_kwargs=dict(
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        n_mel_channels=n_mel_channels,
+        target_sample_rate=target_sample_rate,
+        mel_spec_type=mel_spec_type,
+    ),
+    odeint_kwargs=dict(
+        method=ode_method,
+    ),
+    vocab_char_map=vocab_char_map,
+).to(device)
+
+dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+# Audio
+audio, sr = torchaudio.load(audio_to_edit)
+if audio.shape[0] > 1:
+    audio = torch.mean(audio, dim=0, keepdim=True)
+rms = torch.sqrt(torch.mean(torch.square(audio)))
+if rms < target_rms:
+    audio = audio * target_rms / rms
+if sr != target_sample_rate:
+    resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+    audio = resampler(audio)
+offset = 0
+audio_ = torch.zeros(1, 0)
+edit_mask = torch.zeros(1, 0, dtype=torch.bool)
+for part in parts_to_edit:
+    start, end = part
+    part_dur = end - start if fix_duration is None else fix_duration.pop(0)
+    part_dur = part_dur * target_sample_rate
+    start = start * target_sample_rate
+    audio_ = torch.cat((audio_, audio[:, round(offset) : round(start)], torch.zeros(1, round(part_dur))), dim=-1)
+    edit_mask = torch.cat(
+        (
+            edit_mask,
+            torch.ones(1, round((start - offset) / hop_length), dtype=torch.bool),
+            torch.zeros(1, round(part_dur / hop_length), dtype=torch.bool),
+        ),
+        dim=-1,
+    )
+    offset = end * target_sample_rate
+audio = torch.cat((audio_, audio[:, round(offset) :]), dim=-1)
+edit_mask = F.pad(edit_mask, (0, audio.shape[-1] // hop_length - edit_mask.shape[-1] + 1), value=True)
+audio = audio.to(device)
+edit_mask = edit_mask.to(device)
+
+# Text
+text_list = [target_text]
+if tokenizer == "pinyin":
+    final_text_list = convert_char_to_pinyin(text_list)
+else:
+    final_text_list = [text_list]
+print(f"text  : {text_list}")
+print(f"pinyin: {final_text_list}")
+
+# Duration
+ref_audio_len = 0
+duration = audio.shape[-1] // hop_length
+
+# Inference
+with torch.inference_mode():
+    generated, trajectory = model.sample(
+        cond=audio,
+        text=final_text_list,
+        duration=duration,
+        steps=nfe_step,
+        cfg_strength=cfg_strength,
+        sway_sampling_coef=sway_sampling_coef,
+        seed=seed,
+        edit_mask=edit_mask,
+    )
+    print(f"Generated mel: {generated.shape}")
+
+    # Final result
+    generated = generated.to(torch.float32)
+    generated = generated[:, ref_audio_len:, :]
+    gen_mel_spec = generated.permute(0, 2, 1)
+    if mel_spec_type == "vocos":
+        generated_wave = vocoder.decode(gen_mel_spec).cpu()
+    elif mel_spec_type == "bigvgan":
+        generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+
+    if rms < target_rms:
+        generated_wave = generated_wave * rms / target_rms
+
+    save_spectrogram(gen_mel_spec[0].cpu().numpy(), f"{output_dir}/speech_edit_out.png")
+    torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave, target_sample_rate)
+    print(f"Generated wav: {generated_wave.shape}")
@@ -0,0 +1,612 @@
+# A unified script for inference process
+# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format
+import os
+import sys
+from concurrent.futures import ThreadPoolExecutor
+
+
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"  # for MPS device compatibility
+sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../../third_party/BigVGAN/")
+
+import hashlib
+import re
+import tempfile
+from importlib.resources import files
+
+import matplotlib
+
+
+matplotlib.use("Agg")
+
+import matplotlib.pylab as plt
+import numpy as np
+import torch
+import torchaudio
+import tqdm
+from huggingface_hub import hf_hub_download
+from pydub import AudioSegment, silence
+from transformers import pipeline
+
+# Conditional vocos import
+try:
+    from vocos import Vocos
+    VOCOS_AVAILABLE = True
+except ImportError:
+    VOCOS_AVAILABLE = False
+    Vocos = None
+
+from ..model import CFM
+from ..model.utils import convert_char_to_pinyin, get_tokenizer
+
+
+_ref_audio_cache = {}
+_ref_text_cache = {}
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "xpu"
+    if torch.xpu.is_available()
+    else "mps"
+    if torch.backends.mps.is_available()
+    else "cpu"
+)
+
+tempfile_kwargs = {"delete_on_close": False} if sys.version_info >= (3, 12) else {"delete": False}
+
+# -----------------------------------------
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+win_length = 1024
+n_fft = 1024
+mel_spec_type = "vocos"
+target_rms = 0.1
+cross_fade_duration = 0.15
+ode_method = "euler"
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+sway_sampling_coef = -1.0
+speed = 1.0
+fix_duration = None
+
+# -----------------------------------------
+
+
+# chunk text into smaller pieces
+
+
+def chunk_text(text, max_chars=135):
+    """
+    Splits the input text into chunks, each with a maximum number of characters.
+
+    Args:
+        text (str): The text to be split.
+        max_chars (int): The maximum number of characters per chunk.
+
+    Returns:
+        List[str]: A list of text chunks.
+    """
+    chunks = []
+    current_chunk = ""
+    # Split the text into sentences based on punctuation followed by whitespace
+    sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)
+
+    for sentence in sentences:
+        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
+            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+        else:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
+
+# load vocoder
+def load_vocoder(vocoder_name="vocos", is_local=False, local_path="", device=device, hf_cache_dir=None):
+    if vocoder_name == "vocos":
+        # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
+        if is_local:
+            print(f"Load vocos from local path {local_path}")
+            config_path = f"{local_path}/config.yaml"
+            model_path = f"{local_path}/pytorch_model.bin"
+        else:
+            print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+            repo_id = "charactr/vocos-mel-24khz"
+            config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
+            model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
+        vocoder = Vocos.from_hparams(config_path)
+        state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
+        from vocos.feature_extractors import EncodecFeatures
+
+        if isinstance(vocoder.feature_extractor, EncodecFeatures):
+            encodec_parameters = {
+                "feature_extractor.encodec." + key: value
+                for key, value in vocoder.feature_extractor.encodec.state_dict().items()
+            }
+            state_dict.update(encodec_parameters)
+        vocoder.load_state_dict(state_dict)
+        vocoder = vocoder.eval().to(device)
+    elif vocoder_name == "bigvgan":
+        try:
+            from third_party.BigVGAN import bigvgan
+        except ImportError:
+            print("You need to follow the README to init submodule and change the BigVGAN source code.")
+        if is_local:
+            # download generator from https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x/tree/main
+            vocoder = bigvgan.BigVGAN.from_pretrained(local_path, use_cuda_kernel=False)
+        else:
+            vocoder = bigvgan.BigVGAN.from_pretrained(
+                "nvidia/bigvgan_v2_24khz_100band_256x", use_cuda_kernel=False, cache_dir=hf_cache_dir
+            )
+
+        vocoder.remove_weight_norm()
+        vocoder = vocoder.eval().to(device)
+    return vocoder
+
+
+# load asr pipeline
+
+asr_pipe = None
+
+
+def initialize_asr_pipeline(device: str = device, dtype=None):
+    if dtype is None:
+        dtype = (
+            torch.float16
+            if "cuda" in device
+            and torch.cuda.get_device_properties(device).major >= 7
+            and not torch.cuda.get_device_name().endswith("[ZLUDA]")
+            else torch.float32
+        )
+    global asr_pipe
+    asr_pipe = pipeline(
+        "automatic-speech-recognition",
+        model="openai/whisper-large-v3-turbo",
+        torch_dtype=dtype,
+        device=device,
+    )
+
+
+# transcribe
+
+
+def transcribe(ref_audio, language=None):
+    global asr_pipe
+    if asr_pipe is None:
+        initialize_asr_pipeline(device=device)
+    return asr_pipe(
+        ref_audio,
+        chunk_length_s=30,
+        batch_size=128,
+        generate_kwargs={"task": "transcribe", "language": language} if language else {"task": "transcribe"},
+        return_timestamps=False,
+    )["text"].strip()
+
+
+# load model checkpoint for inference
+
+
+def load_checkpoint(model, ckpt_path, device: str, dtype=None, use_ema=True):
+    if dtype is None:
+        dtype = (
+            torch.float16
+            if "cuda" in device
+            and torch.cuda.get_device_properties(device).major >= 7
+            and not torch.cuda.get_device_name().endswith("[ZLUDA]")
+            else torch.float32
+        )
+    model = model.to(dtype)
+
+    ckpt_type = ckpt_path.split(".")[-1]
+    if ckpt_type == "safetensors":
+        from safetensors.torch import load_file
+
+        checkpoint = load_file(ckpt_path, device=device)
+    else:
+        checkpoint = torch.load(ckpt_path, map_location=device, weights_only=True)
+
+    if use_ema:
+        if ckpt_type == "safetensors":
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+
+        # patch for backward compatibility, 305e3ea
+        for key in ["mel_spec.mel_stft.mel_scale.fb", "mel_spec.mel_stft.spectrogram.window"]:
+            if key in checkpoint["model_state_dict"]:
+                del checkpoint["model_state_dict"][key]
+
+        model.load_state_dict(checkpoint["model_state_dict"])
+    else:
+        if ckpt_type == "safetensors":
+            checkpoint = {"model_state_dict": checkpoint}
+        model.load_state_dict(checkpoint["model_state_dict"])
+
+    del checkpoint
+    torch.cuda.empty_cache()
+
+    return model.to(device)
+
+
+# load model for inference
+
+
+def load_model(
+    model_cls,
+    model_cfg,
+    ckpt_path,
+    mel_spec_type=mel_spec_type,
+    vocab_file="",
+    ode_method=ode_method,
+    use_ema=True,
+    device=device,
+):
+    if vocab_file == "":
+        vocab_file = str(files(__package__ or "f5_tts").joinpath("infer/examples/vocab.txt"))
+    tokenizer = "custom"
+
+    print("\nvocab : ", vocab_file)
+    print("token : ", tokenizer)
+    print("model : ", ckpt_path, "\n")
+
+    vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer)
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
+    model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
+
+    return model
+
+
+def remove_silence_edges(audio, silence_threshold=-42):
+    # Remove silence from the start
+    non_silent_start_idx = silence.detect_leading_silence(audio, silence_threshold=silence_threshold)
+    audio = audio[non_silent_start_idx:]
+
+    # Remove silence from the end
+    non_silent_end_duration = audio.duration_seconds
+    for ms in reversed(audio):
+        if ms.dBFS > silence_threshold:
+            break
+        non_silent_end_duration -= 0.001
+    trimmed_audio = audio[: int(non_silent_end_duration * 1000)]
+
+    return trimmed_audio
+
+
+# preprocess reference audio and text
+
+
+def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print):
+    show_info("Converting audio...")
+
+    # Compute a hash of the reference audio file
+    with open(ref_audio_orig, "rb") as audio_file:
+        audio_data = audio_file.read()
+        audio_hash = hashlib.md5(audio_data).hexdigest()
+
+    global _ref_audio_cache
+
+    if audio_hash in _ref_audio_cache:
+        show_info("Using cached preprocessed reference audio...")
+        ref_audio = _ref_audio_cache[audio_hash]
+
+    else:  # first pass, do preprocess
+        with tempfile.NamedTemporaryFile(suffix=".wav", **tempfile_kwargs) as f:
+            temp_path = f.name
+
+        aseg = AudioSegment.from_file(ref_audio_orig)
+
+        # 1. try to find long silence for clipping
+        non_silent_segs = silence.split_on_silence(
+            aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000, seek_step=10
+        )
+        non_silent_wave = AudioSegment.silent(duration=0)
+        for non_silent_seg in non_silent_segs:
+            if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
+                show_info("Audio is over 12s, clipping short. (1)")
+                break
+            non_silent_wave += non_silent_seg
+
+        # 2. try to find short silence for clipping if 1. failed
+        if len(non_silent_wave) > 12000:
+            non_silent_segs = silence.split_on_silence(
+                aseg, min_silence_len=100, silence_thresh=-40, keep_silence=1000, seek_step=10
+            )
+            non_silent_wave = AudioSegment.silent(duration=0)
+            for non_silent_seg in non_silent_segs:
+                if len(non_silent_wave) > 6000 and len(non_silent_wave + non_silent_seg) > 12000:
+                    show_info("Audio is over 12s, clipping short. (2)")
+                    break
+                non_silent_wave += non_silent_seg
+
+        aseg = non_silent_wave
+
+        # 3. if no proper silence found for clipping
+        if len(aseg) > 12000:
+            aseg = aseg[:12000]
+            show_info("Audio is over 12s, clipping short. (3)")
+
+        aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=50)
+        aseg.export(temp_path, format="wav")
+        ref_audio = temp_path
+
+        # Cache the processed reference audio
+        _ref_audio_cache[audio_hash] = ref_audio
+
+    if not ref_text.strip():
+        global _ref_text_cache
+        if audio_hash in _ref_text_cache:
+            # Use cached asr transcription
+            show_info("Using cached reference text...")
+            ref_text = _ref_text_cache[audio_hash]
+        else:
+            show_info("No reference text provided, transcribing reference audio...")
+            ref_text = transcribe(ref_audio)
+            # Cache the transcribed text (not caching custom ref_text, enabling users to do manual tweak)
+            _ref_text_cache[audio_hash] = ref_text
+    else:
+        show_info("Using custom reference text...")
+
+    # Ensure ref_text ends with a proper sentence-ending punctuation
+    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
+    print("\nref_text  ", ref_text)
+
+    return ref_audio, ref_text
+
+
+# infer process: chunk text -> infer batches [i.e. infer_batch_process()]
+
+
+def infer_process(
+    ref_audio,
+    ref_text,
+    gen_text,
+    model_obj,
+    vocoder,
+    mel_spec_type=mel_spec_type,
+    show_info=print,
+    progress=tqdm,
+    target_rms=target_rms,
+    cross_fade_duration=cross_fade_duration,
+    nfe_step=nfe_step,
+    cfg_strength=cfg_strength,
+    sway_sampling_coef=sway_sampling_coef,
+    speed=speed,
+    fix_duration=fix_duration,
+    device=device,
+):
+    # Split the input text into batches
+    audio, sr = torchaudio.load(ref_audio)
+    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (22 - audio.shape[-1] / sr) * speed)
+    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
+    for i, gen_text in enumerate(gen_text_batches):
+        print(f"gen_text {i}", gen_text)
+    print("\n")
+
+    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
+    return next(
+        infer_batch_process(
+            (audio, sr),
+            ref_text,
+            gen_text_batches,
+            model_obj,
+            vocoder,
+            mel_spec_type=mel_spec_type,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=device,
+        )
+    )
+
+
+# infer batches
+
+
+def infer_batch_process(
+    ref_audio,
+    ref_text,
+    gen_text_batches,
+    model_obj,
+    vocoder,
+    mel_spec_type="vocos",
+    progress=tqdm,
+    target_rms=0.1,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    cfg_strength=2.0,
+    sway_sampling_coef=-1,
+    speed=1,
+    fix_duration=None,
+    device=None,
+    streaming=False,
+    chunk_size=2048,
+):
+    audio, sr = ref_audio
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    rms = torch.sqrt(torch.mean(torch.square(audio)))
+    if rms < target_rms:
+        audio = audio * target_rms / rms
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+        audio = resampler(audio)
+    audio = audio.to(device)
+
+    generated_waves = []
+    spectrograms = []
+
+    if len(ref_text[-1].encode("utf-8")) == 1:
+        ref_text = ref_text + " "
+
+    def process_batch(gen_text):
+        local_speed = speed
+        if len(gen_text.encode("utf-8")) < 10:
+            local_speed = 0.3
+
+        # Prepare the text
+        text_list = [ref_text + gen_text]
+        final_text_list = convert_char_to_pinyin(text_list)
+
+        ref_audio_len = audio.shape[-1] // hop_length
+        if fix_duration is not None:
+            duration = int(fix_duration * target_sample_rate / hop_length)
+        else:
+            # Calculate duration
+            ref_text_len = len(ref_text.encode("utf-8"))
+            gen_text_len = len(gen_text.encode("utf-8"))
+            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / local_speed)
+
+        # inference
+        with torch.inference_mode():
+            generated, _ = model_obj.sample(
+                cond=audio,
+                text=final_text_list,
+                duration=duration,
+                steps=nfe_step,
+                cfg_strength=cfg_strength,
+                sway_sampling_coef=sway_sampling_coef,
+            )
+            del _
+
+            generated = generated.to(torch.float32)  # generated mel spectrogram
+            generated = generated[:, ref_audio_len:, :]
+            generated = generated.permute(0, 2, 1)
+            if mel_spec_type == "vocos":
+                generated_wave = vocoder.decode(generated)
+            elif mel_spec_type == "bigvgan":
+                generated_wave = vocoder(generated)
+            if rms < target_rms:
+                generated_wave = generated_wave * rms / target_rms
+
+            # wav -> numpy
+            generated_wave = generated_wave.squeeze().cpu().numpy()
+
+            if streaming:
+                for j in range(0, len(generated_wave), chunk_size):
+                    yield generated_wave[j : j + chunk_size], target_sample_rate
+            else:
+                generated_cpu = generated[0].cpu().numpy()
+                del generated
+                yield generated_wave, generated_cpu
+
+    if streaming:
+        for gen_text in progress.tqdm(gen_text_batches) if progress is not None else gen_text_batches:
+            for chunk in process_batch(gen_text):
+                yield chunk
+    else:
+        with ThreadPoolExecutor() as executor:
+            futures = [executor.submit(process_batch, gen_text) for gen_text in gen_text_batches]
+            for future in progress.tqdm(futures) if progress is not None else futures:
+                result = future.result()
+                if result:
+                    generated_wave, generated_mel_spec = next(result)
+                    generated_waves.append(generated_wave)
+                    spectrograms.append(generated_mel_spec)
+
+        if generated_waves:
+            if cross_fade_duration <= 0:
+                # Simply concatenate
+                final_wave = np.concatenate(generated_waves)
+            else:
+                # Combine all generated waves with cross-fading
+                final_wave = generated_waves[0]
+                for i in range(1, len(generated_waves)):
+                    prev_wave = final_wave
+                    next_wave = generated_waves[i]
+
+                    # Calculate cross-fade samples, ensuring it does not exceed wave lengths
+                    cross_fade_samples = int(cross_fade_duration * target_sample_rate)
+                    cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
+
+                    if cross_fade_samples <= 0:
+                        # No overlap possible, concatenate
+                        final_wave = np.concatenate([prev_wave, next_wave])
+                        continue
+
+                    # Overlapping parts
+                    prev_overlap = prev_wave[-cross_fade_samples:]
+                    next_overlap = next_wave[:cross_fade_samples]
+
+                    # Fade out and fade in
+                    fade_out = np.linspace(1, 0, cross_fade_samples)
+                    fade_in = np.linspace(0, 1, cross_fade_samples)
+
+                    # Cross-faded overlap
+                    cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
+
+                    # Combine
+                    new_wave = np.concatenate(
+                        [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]]
+                    )
+
+                    final_wave = new_wave
+
+            # Create a combined spectrogram
+            combined_spectrogram = np.concatenate(spectrograms, axis=1)
+
+            yield final_wave, target_sample_rate, combined_spectrogram
+
+        else:
+            yield None, target_sample_rate, None
+
+
+# remove silence from generated wav
+
+
+def remove_silence_for_generated_wav(filename):
+    aseg = AudioSegment.from_file(filename)
+    non_silent_segs = silence.split_on_silence(
+        aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500, seek_step=10
+    )
+    non_silent_wave = AudioSegment.silent(duration=0)
+    for non_silent_seg in non_silent_segs:
+        non_silent_wave += non_silent_seg
+    aseg = non_silent_wave
+    aseg.export(filename, format="wav")
+
+
+# save spectrogram
+
+
+def save_spectrogram(spectrogram, path):
+    plt.figure(figsize=(12, 4))
+    plt.imshow(spectrogram, origin="lower", aspect="auto")
+    plt.colorbar()
+    plt.savefig(path)
+    plt.close()
@@ -0,0 +1,8 @@
+from .backbones.dit import DiT
+from .backbones.mmdit import MMDiT
+from .backbones.unett import UNetT
+from .cfm import CFM
+from .trainer import Trainer
+
+
+__all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"]
@@ -0,0 +1,20 @@
+## Backbones quick introduction
+
+
+### unett.py
+- flat unet transformer
+- structure same as in e2-tts & voicebox paper except using rotary pos emb
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+
+### dit.py
+- adaln-zero dit
+- embedded timestep as condition
+- concatted noised_input + masked_cond + embedded_text, linear proj in
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+- possible long skip connection (first layer to last layer)
+
+### mmdit.py
+- stable diffusion 3 block structure
+- timestep as condition
+- left stream: text embedded and applied a abs pos emb
+- right stream: masked_cond & noised_input concatted and with same conv pos emb as unett
@@ -0,0 +1,259 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from x_transformers.x_transformers import RotaryEmbedding
+
+from ..modules import (
+    AdaLayerNorm_Final,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    DiTBlock,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using DiT blocks
+
+
+class DiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
+        conv_layers=0,
+        pe_attn_head=None,
+        attn_backend="torch",  # "torch" | "flash_attn"
+        attn_mask_enabled=False,
+        long_skip_connection=False,
+        checkpoint_activations=False,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    ff_mult=ff_mult,
+                    dropout=dropout,
+                    qk_norm=qk_norm,
+                    pe_attn_head=pe_attn_head,
+                    attn_backend=attn_backend,
+                    attn_mask_enabled=attn_mask_enabled,
+                )
+                for _ in range(depth)
+            ]
+        )
+        self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
+
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+        self.checkpoint_activations = checkpoint_activations
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in DiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm.linear.weight, 0)
+            nn.init.constant_(block.attn_norm.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
+    def ckpt_wrapper(self, module):
+        # https://github.com/chuanyangjin/fast-DiT/blob/main/models.py
+        def ckpt_forward(*inputs):
+            outputs = module(*inputs)
+            return outputs
+
+        return ckpt_forward
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        seq_len = x.shape[1]
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        return x
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, text: text, x: noised audio + cond audio + text
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x = self.get_input_embed(x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len)
+
+        if self.long_skip_connection is not None:
+            residual = x
+
+        for block in self.transformer_blocks:
+            if self.checkpoint_activations:
+                # https://pytorch.org/docs/stable/checkpoint.html#torch.utils.checkpoint.checkpoint
+                x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, t, mask, rope, use_reentrant=False)
+            else:
+                x = block(x, t, mask=mask, rope=rope)
+
+        if self.long_skip_connection is not None:
+            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output
@@ -0,0 +1,212 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+from x_transformers.x_transformers import RotaryEmbedding
+
+from ..modules import (
+    AdaLayerNorm_Final,
+    ConvPositionEmbedding,
+    MMDiTBlock,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, out_dim, text_num_embeds, mask_padding=True):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        self.precompute_max_pos = 1024
+        self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
+
+    def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]:  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b nt -> b nt d
+
+        # sinus pos emb
+        batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
+        batch_text_len = text.shape[1]
+        pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
+        text_pos_embed = self.freqs_cis[pos_idx]
+
+        text = text + text_pos_embed
+
+        if self.mask_padding:
+            text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+
+        return text
+
+
+# noised input & masked cond audio embedding
+
+
+class AudioEmbedding(nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+        self.linear = nn.Linear(2 * in_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:
+            cond = torch.zeros_like(cond)
+        x = torch.cat((x, cond), dim=-1)
+        x = self.linear(x)
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using MM-DiT blocks
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_mask_padding=True,
+        qk_norm=None,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        self.text_embed = TextEmbedding(dim, text_num_embeds, mask_padding=text_mask_padding)
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.audio_embed = AudioEmbedding(mel_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MMDiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    dropout=dropout,
+                    ff_mult=ff_mult,
+                    context_pre_only=i == depth - 1,
+                    qk_norm=qk_norm,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm_out = AdaLayerNorm_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        # Zero-out AdaLN layers in MMDiT blocks:
+        for block in self.transformer_blocks:
+            nn.init.constant_(block.attn_norm_x.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_x.linear.bias, 0)
+            nn.init.constant_(block.attn_norm_c.linear.weight, 0)
+            nn.init.constant_(block.attn_norm_c.linear.bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.norm_out.linear.weight, 0)
+        nn.init.constant_(self.norm_out.linear.bias, 0)
+        nn.init.constant_(self.proj_out.weight, 0)
+        nn.init.constant_(self.proj_out.bias, 0)
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, drop_text=True)
+                c = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, drop_text=False)
+                c = self.text_cond
+        else:
+            c = self.text_embed(text, drop_text=drop_text)
+        x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
+
+        return x, c
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch = x.shape[0]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond, c_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond, c_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            c = torch.cat((c_cond, c_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x, c = self.get_input_embed(
+                x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache
+            )
+
+        seq_len = x.shape[1]
+        text_len = text.shape[1]
+        rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
+        rope_text = self.rotary_embed.forward_from_seq_len(text_len)
+
+        for block in self.transformer_blocks:
+            c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text)
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output
@@ -0,0 +1,273 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+from typing import Literal
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from x_transformers import RMSNorm
+from x_transformers.x_transformers import RotaryEmbedding
+
+from ..modules import (
+    Attention,
+    AttnProcessor,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    FeedForward,
+    TimestepEmbedding,
+    get_pos_embed_indices,
+    precompute_freqs_cis,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+        if self.mask_padding:
+            text_mask = text == 0
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            if self.mask_padding:
+                text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+                for block in self.text_blocks:
+                    text = block(text)
+                    text = text.masked_fill(text_mask.unsqueeze(-1).expand(-1, -1, text.size(-1)), 0.0)
+            else:
+                text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Flat UNet Transformer backbone
+
+
+class UNetT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        text_mask_padding=True,
+        qk_norm=None,
+        conv_layers=0,
+        pe_attn_head=None,
+        skip_connect_type: Literal["add", "concat", "none"] = "concat",
+    ):
+        super().__init__()
+        assert depth % 2 == 0, "UNet-Transformer's depth should be even."
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(
+            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+        )
+        self.text_cond, self.text_uncond = None, None  # text cache
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        # transformer layers & skip connections
+
+        self.dim = dim
+        self.skip_connect_type = skip_connect_type
+        needs_skip_proj = skip_connect_type == "concat"
+
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        for idx in range(depth):
+            is_later_half = idx >= (depth // 2)
+
+            attn_norm = RMSNorm(dim)
+            attn = Attention(
+                processor=AttnProcessor(pe_attn_head=pe_attn_head),
+                dim=dim,
+                heads=heads,
+                dim_head=dim_head,
+                dropout=dropout,
+                qk_norm=qk_norm,
+            )
+
+            ff_norm = RMSNorm(dim)
+            ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+            skip_proj = nn.Linear(dim * 2, dim, bias=False) if needs_skip_proj and is_later_half else None
+
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        skip_proj,
+                        attn_norm,
+                        attn,
+                        ff_norm,
+                        ff,
+                    ]
+                )
+            )
+
+        self.norm_out = RMSNorm(dim)
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def get_input_embed(
+        self,
+        x,  # b n d
+        cond,  # b n d
+        text,  # b nt
+        drop_audio_cond: bool = False,
+        drop_text: bool = False,
+        cache: bool = True,
+    ):
+        seq_len = x.shape[1]
+        if cache:
+            if drop_text:
+                if self.text_uncond is None:
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                text_embed = self.text_uncond
+            else:
+                if self.text_cond is None:
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                text_embed = self.text_cond
+        else:
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        return x
+
+    def clear_cache(self):
+        self.text_cond, self.text_uncond = None, None
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        drop_audio_cond: bool = False,  # cfg for cond audio
+        drop_text: bool = False,  # cfg for text
+        cfg_infer: bool = False,  # cfg inference, pack cond & uncond forward
+        cache: bool = False,
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
+            x_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
+            x_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x = torch.cat((x_cond, x_uncond), dim=0)
+            t = torch.cat((t, t), dim=0)
+            mask = torch.cat((mask, mask), dim=0) if mask is not None else None
+        else:
+            x = self.get_input_embed(x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache)
+
+        # postfix time t to input x, [b n d] -> [b n+1 d]
+        x = torch.cat([t.unsqueeze(1), x], dim=1)  # pack t to x
+        if mask is not None:
+            mask = F.pad(mask, (1, 0), value=1)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len + 1)
+
+        # flat unet transformer
+        skip_connect_type = self.skip_connect_type
+        skips = []
+        for idx, (maybe_skip_proj, attn_norm, attn, ff_norm, ff) in enumerate(self.layers):
+            layer = idx + 1
+
+            # skip connection logic
+            is_first_half = layer <= (self.depth // 2)
+            is_later_half = not is_first_half
+
+            if is_first_half:
+                skips.append(x)
+
+            if is_later_half:
+                skip = skips.pop()
+                if skip_connect_type == "concat":
+                    x = torch.cat((x, skip), dim=-1)
+                    x = maybe_skip_proj(x)
+                elif skip_connect_type == "add":
+                    x = x + skip
+
+            # attention and feedforward blocks
+            x = attn(attn_norm(x), rope=rope, mask=mask) + x
+            x = ff(ff_norm(x)) + x
+
+        assert len(skips) == 0
+
+        x = self.norm_out(x)[:, 1:, :]  # unpack t from x
+
+        return self.proj_out(x)
@@ -0,0 +1,302 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+from random import random
+from typing import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+from torchdiffeq import odeint
+
+from ..model.modules import MelSpec
+from ..model.utils import (
+    default,
+    exists,
+    get_epss_timesteps,
+    lens_to_mask,
+    list_str_to_idx,
+    list_str_to_tensor,
+    mask_from_frac_lengths,
+)
+
+
+class CFM(nn.Module):
+    def __init__(
+        self,
+        transformer: nn.Module,
+        sigma=0.0,
+        odeint_kwargs: dict = dict(
+            # atol = 1e-5,
+            # rtol = 1e-5,
+            method="euler"  # 'midpoint'
+        ),
+        audio_drop_prob=0.3,
+        cond_drop_prob=0.2,
+        num_channels=None,
+        mel_spec_module: nn.Module | None = None,
+        mel_spec_kwargs: dict = dict(),
+        frac_lengths_mask: tuple[float, float] = (0.7, 1.0),
+        vocab_char_map: dict[str:int] | None = None,
+    ):
+        super().__init__()
+
+        self.frac_lengths_mask = frac_lengths_mask
+
+        # mel spec
+        self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs))
+        num_channels = default(num_channels, self.mel_spec.n_mel_channels)
+        self.num_channels = num_channels
+
+        # classifier-free guidance
+        self.audio_drop_prob = audio_drop_prob
+        self.cond_drop_prob = cond_drop_prob
+
+        # transformer
+        self.transformer = transformer
+        dim = transformer.dim
+        self.dim = dim
+
+        # conditional flow related
+        self.sigma = sigma
+
+        # sampling related
+        self.odeint_kwargs = odeint_kwargs
+
+        # vocab map for tokenization
+        self.vocab_char_map = vocab_char_map
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: float["b n d"] | float["b nw"],  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        duration: int | int["b"],  # noqa: F821
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        steps=32,
+        cfg_strength=1.0,
+        sway_sampling_coef=None,
+        seed: int | None = None,
+        max_duration=4096,
+        vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None,  # noqa: F722
+        use_epss=True,
+        no_ref_audio=False,
+        duplicate_test=False,
+        t_inter=0.1,
+        edit_mask=None,
+    ):
+        self.eval()
+        # raw wave
+
+        if cond.ndim == 2:
+            cond = self.mel_spec(cond)
+            cond = cond.permute(0, 2, 1)
+            assert cond.shape[-1] == self.num_channels
+
+        cond = cond.to(next(self.parameters()).dtype)
+
+        batch, cond_seq_len, device = *cond.shape[:2], cond.device
+        if not exists(lens):
+            lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long)
+
+        # text
+
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # duration
+
+        cond_mask = lens_to_mask(lens)
+        if edit_mask is not None:
+            cond_mask = cond_mask & edit_mask
+
+        if isinstance(duration, int):
+            duration = torch.full((batch,), duration, device=device, dtype=torch.long)
+
+        duration = torch.maximum(
+            torch.maximum((text != -1).sum(dim=-1), lens) + 1, duration
+        )  # duration at least text/audio prompt length plus one token, so something is generated
+        duration = duration.clamp(max=max_duration)
+        max_duration = duration.amax()
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0)
+
+        cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0)
+        if no_ref_audio:
+            cond = torch.zeros_like(cond)
+
+        cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False)
+        cond_mask = cond_mask.unsqueeze(-1)
+        step_cond = torch.where(
+            cond_mask, cond, torch.zeros_like(cond)
+        )  # allow direct control (cut cond audio) with lens passed in
+
+        if batch > 1:
+            mask = lens_to_mask(duration)
+        else:  # save memory and speed up, as single inference need no mask currently
+            mask = None
+
+        # neural ode
+
+        def fn(t, x):
+            # at each step, conditioning is fixed
+            # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))
+
+            # predict flow (cond)
+            if cfg_strength < 1e-5:
+                pred = self.transformer(
+                    x=x,
+                    cond=step_cond,
+                    text=text,
+                    time=t,
+                    mask=mask,
+                    drop_audio_cond=False,
+                    drop_text=False,
+                    cache=True,
+                )
+                return pred
+
+            # predict flow (cond and uncond), for classifier-free guidance
+            pred_cfg = self.transformer(
+                x=x,
+                cond=step_cond,
+                text=text,
+                time=t,
+                mask=mask,
+                cfg_infer=True,
+                cache=True,
+            )
+            pred, null_pred = torch.chunk(pred_cfg, 2, dim=0)
+            return pred + (pred - null_pred) * cfg_strength
+
+        # noise input
+        # to make sure batch inference result is same with different batch size, and for sure single inference
+        # still some difference maybe due to convolutional layers
+        y0 = []
+        for dur in duration:
+            if exists(seed):
+                torch.manual_seed(seed)
+            y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype))
+        y0 = pad_sequence(y0, padding_value=0, batch_first=True)
+
+        t_start = 0
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            t_start = t_inter
+            y0 = (1 - t_start) * y0 + t_start * test_cond
+            steps = int(steps * (1 - t_start))
+
+        if t_start == 0 and use_epss:  # use Empirically Pruned Step Sampling for low NFE
+            t = get_epss_timesteps(steps, device=self.device, dtype=step_cond.dtype)
+        else:
+            t = torch.linspace(t_start, 1, steps + 1, device=self.device, dtype=step_cond.dtype)
+        if sway_sampling_coef is not None:
+            t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
+
+        trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
+        self.transformer.clear_cache()
+
+        sampled = trajectory[-1]
+        out = sampled
+        out = torch.where(cond_mask, cond, out)
+
+        if exists(vocoder):
+            out = out.permute(0, 2, 1)
+            out = vocoder(out)
+
+        return out, trajectory
+
+    def forward(
+        self,
+        inp: float["b n d"] | float["b nw"],  # mel or raw wave  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        noise_scheduler: str | None = None,
+    ):
+        # handle raw wave
+        if inp.ndim == 2:
+            inp = self.mel_spec(inp)
+            inp = inp.permute(0, 2, 1)
+            assert inp.shape[-1] == self.num_channels
+
+        batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma
+
+        # handle text as string
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # lens and mask
+        if not exists(lens):
+            lens = torch.full((batch,), seq_len, device=device)
+
+        mask = lens_to_mask(lens, length=seq_len)  # useless here, as collate_fn will pad to max length in batch
+
+        # get a random span to mask out for training conditionally
+        frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask)
+        rand_span_mask = mask_from_frac_lengths(lens, frac_lengths)
+
+        if exists(mask):
+            rand_span_mask &= mask
+
+        # mel is x1
+        x1 = inp
+
+        # x0 is gaussian noise
+        x0 = torch.randn_like(x1)
+
+        # time step
+        time = torch.rand((batch,), dtype=dtype, device=self.device)
+        # TODO. noise_scheduler
+
+        # sample xt (φ_t(x) in the paper)
+        t = time.unsqueeze(-1).unsqueeze(-1)
+        φ = (1 - t) * x0 + t * x1
+        flow = x1 - x0
+
+        # only predict what is within the random mask span for infilling
+        cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1)
+
+        # transformer and cfg training with a drop rate
+        drop_audio_cond = random() < self.audio_drop_prob  # p_drop in voicebox paper
+        if random() < self.cond_drop_prob:  # p_uncond in voicebox paper
+            drop_audio_cond = True
+            drop_text = True
+        else:
+            drop_text = False
+
+        # apply mask will use more memory; might adjust batchsize or batchsampler long sequence threshold
+        pred = self.transformer(
+            x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text, mask=mask
+        )
+
+        # flow matching loss
+        loss = F.mse_loss(pred, flow, reduction="none")
+        loss = loss[rand_span_mask]
+
+        return loss.mean(), cond, pred
@@ -0,0 +1,330 @@
+import json
+from importlib.resources import files
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from datasets import Dataset as Dataset_
+from datasets import load_from_disk
+from torch import nn
+from torch.utils.data import Dataset, Sampler
+from tqdm import tqdm
+
+from ..model.modules import MelSpec
+from ..model.utils import default
+
+
+class HFDataset(Dataset):
+    def __init__(
+        self,
+        hf_dataset: Dataset,
+        target_sample_rate=24_000,
+        n_mel_channels=100,
+        hop_length=256,
+        n_fft=1024,
+        win_length=1024,
+        mel_spec_type="vocos",
+    ):
+        self.data = hf_dataset
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+
+        self.mel_spectrogram = MelSpec(
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            n_mel_channels=n_mel_channels,
+            target_sample_rate=target_sample_rate,
+            mel_spec_type=mel_spec_type,
+        )
+
+    def get_frame_len(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+        sample_rate = row["audio"]["sampling_rate"]
+        return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+
+        # logger.info(f"Audio shape: {audio.shape}")
+
+        sample_rate = row["audio"]["sampling_rate"]
+        duration = audio.shape[-1] / sample_rate
+
+        if duration > 30 or duration < 0.3:
+            return self.__getitem__((index + 1) % len(self.data))
+
+        audio_tensor = torch.from_numpy(audio).float()
+
+        if sample_rate != self.target_sample_rate:
+            resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate)
+            audio_tensor = resampler(audio_tensor)
+
+        audio_tensor = audio_tensor.unsqueeze(0)  # 't -> 1 t')
+
+        mel_spec = self.mel_spectrogram(audio_tensor)
+
+        mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        text = row["text"]
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+class CustomDataset(Dataset):
+    def __init__(
+        self,
+        custom_dataset: Dataset,
+        durations=None,
+        target_sample_rate=24_000,
+        hop_length=256,
+        n_mel_channels=100,
+        n_fft=1024,
+        win_length=1024,
+        mel_spec_type="vocos",
+        preprocessed_mel=False,
+        mel_spec_module: nn.Module | None = None,
+    ):
+        self.data = custom_dataset
+        self.durations = durations
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.n_fft = n_fft
+        self.win_length = win_length
+        self.mel_spec_type = mel_spec_type
+        self.preprocessed_mel = preprocessed_mel
+
+        if not preprocessed_mel:
+            self.mel_spectrogram = default(
+                mel_spec_module,
+                MelSpec(
+                    n_fft=n_fft,
+                    hop_length=hop_length,
+                    win_length=win_length,
+                    n_mel_channels=n_mel_channels,
+                    target_sample_rate=target_sample_rate,
+                    mel_spec_type=mel_spec_type,
+                ),
+            )
+
+    def get_frame_len(self, index):
+        if (
+            self.durations is not None
+        ):  # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
+            return self.durations[index] * self.target_sample_rate / self.hop_length
+        return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        while True:
+            row = self.data[index]
+            audio_path = row["audio_path"]
+            text = row["text"]
+            duration = row["duration"]
+
+            # filter by given length
+            if 0.3 <= duration <= 30:
+                break  # valid
+
+            index = (index + 1) % len(self.data)
+
+        if self.preprocessed_mel:
+            mel_spec = torch.tensor(row["mel_spec"])
+        else:
+            audio, source_sample_rate = torchaudio.load(audio_path)
+
+            # make sure mono input
+            if audio.shape[0] > 1:
+                audio = torch.mean(audio, dim=0, keepdim=True)
+
+            # resample if necessary
+            if source_sample_rate != self.target_sample_rate:
+                resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
+                audio = resampler(audio)
+
+            # to mel spectrogram
+            mel_spec = self.mel_spectrogram(audio)
+            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        return {
+            "mel_spec": mel_spec,
+            "text": text,
+        }
+
+
+# Dynamic Batch Sampler
+class DynamicBatchSampler(Sampler[list[int]]):
+    """Extension of Sampler that will do the following:
+    1.  Change the batch size (essentially number of sequences)
+        in a batch to ensure that the total number of frames are less
+        than a certain threshold.
+    2.  Make sure the padding efficiency in the batch is high.
+    3.  Shuffle batches each epoch while maintaining reproducibility.
+    """
+
+    def __init__(
+        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_residual: bool = False
+    ):
+        self.sampler = sampler
+        self.frames_threshold = frames_threshold
+        self.max_samples = max_samples
+        self.random_seed = random_seed
+        self.epoch = 0
+
+        indices, batches = [], []
+        data_source = self.sampler.data_source
+
+        for idx in tqdm(
+            self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration"
+        ):
+            indices.append((idx, data_source.get_frame_len(idx)))
+        indices.sort(key=lambda elem: elem[1])
+
+        batch = []
+        batch_frames = 0
+        for idx, frame_len in tqdm(
+            indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"
+        ):
+            if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples):
+                batch.append(idx)
+                batch_frames += frame_len
+            else:
+                if len(batch) > 0:
+                    batches.append(batch)
+                if frame_len <= self.frames_threshold:
+                    batch = [idx]
+                    batch_frames = frame_len
+                else:
+                    batch = []
+                    batch_frames = 0
+
+        if not drop_residual and len(batch) > 0:
+            batches.append(batch)
+
+        del indices
+        self.batches = batches
+
+        # Ensure even batches with accelerate BatchSamplerShard cls under frame_per_batch setting
+        self.drop_last = True
+
+    def set_epoch(self, epoch: int) -> None:
+        """Sets the epoch for this sampler."""
+        self.epoch = epoch
+
+    def __iter__(self):
+        # Use both random_seed and epoch for deterministic but different shuffling per epoch
+        if self.random_seed is not None:
+            g = torch.Generator()
+            g.manual_seed(self.random_seed + self.epoch)
+            # Use PyTorch's random permutation for better reproducibility across PyTorch versions
+            indices = torch.randperm(len(self.batches), generator=g).tolist()
+            batches = [self.batches[i] for i in indices]
+        else:
+            batches = self.batches
+        return iter(batches)
+
+    def __len__(self):
+        return len(self.batches)
+
+
+# Load dataset
+
+
+def load_dataset(
+    dataset_name: str,
+    tokenizer: str = "pinyin",
+    dataset_type: str = "CustomDataset",
+    audio_type: str = "raw",
+    mel_spec_module: nn.Module | None = None,
+    mel_spec_kwargs: dict = dict(),
+) -> CustomDataset | HFDataset:
+    """
+    dataset_type    - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
+                    - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
+    """
+
+    print("Loading dataset ...")
+
+    if dataset_type == "CustomDataset":
+        rel_data_path = str(files(__package__ or "f5_tts").joinpath(f"../../data/{dataset_name}_{tokenizer}"))
+        if audio_type == "raw":
+            try:
+                train_dataset = load_from_disk(f"{rel_data_path}/raw")
+            except:  # noqa: E722
+                train_dataset = Dataset_.from_file(f"{rel_data_path}/raw.arrow")
+            preprocessed_mel = False
+        elif audio_type == "mel":
+            train_dataset = Dataset_.from_file(f"{rel_data_path}/mel.arrow")
+            preprocessed_mel = True
+        with open(f"{rel_data_path}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset,
+            durations=durations,
+            preprocessed_mel=preprocessed_mel,
+            mel_spec_module=mel_spec_module,
+            **mel_spec_kwargs,
+        )
+
+    elif dataset_type == "CustomDatasetPath":
+        try:
+            train_dataset = load_from_disk(f"{dataset_name}/raw")
+        except:  # noqa: E722
+            train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
+
+        with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs
+        )
+
+    elif dataset_type == "HFDataset":
+        print(
+            "Should manually modify the path of huggingface dataset to your need.\n"
+            + "May also the corresponding script cuz different dataset may have different format."
+        )
+        pre, post = dataset_name.split("_")
+        train_dataset = HFDataset(
+            load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir=str(files(__package__ or "f5_tts").joinpath("../../data"))),
+        )
+
+    return train_dataset
+
+
+# collation
+
+
+def collate_fn(batch):
+    mel_specs = [item["mel_spec"].squeeze(0) for item in batch]
+    mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
+    max_mel_length = mel_lengths.amax()
+
+    padded_mel_specs = []
+    for spec in mel_specs:
+        padding = (0, max_mel_length - spec.size(-1))
+        padded_spec = F.pad(spec, padding, value=0)
+        padded_mel_specs.append(padded_spec)
+
+    mel_specs = torch.stack(padded_mel_specs)
+
+    text = [item["text"] for item in batch]
+    text_lengths = torch.LongTensor([len(item) for item in text])
+
+    return dict(
+        mel=mel_specs,
+        mel_lengths=mel_lengths,  # records for padding mask
+        text=text,
+        text_lengths=text_lengths,
+    )
@@ -0,0 +1,784 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+# flake8: noqa
+
+from __future__ import annotations
+
+import math
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from librosa.filters import mel as librosa_mel_fn
+from torch import nn
+from x_transformers.x_transformers import apply_rotary_pos_emb
+
+from ..model.utils import is_package_available
+
+
+# raw wav to mel spec
+
+
+mel_basis_cache = {}
+hann_window_cache = {}
+
+
+def get_bigvgan_mel_spectrogram(
+    waveform,
+    n_fft=1024,
+    n_mel_channels=100,
+    target_sample_rate=24000,
+    hop_length=256,
+    win_length=1024,
+    fmin=0,
+    fmax=None,
+    center=False,
+):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
+    device = waveform.device
+    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"
+
+    if key not in mel_basis_cache:
+        mel = librosa_mel_fn(sr=target_sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=fmin, fmax=fmax)
+        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)  # TODO: why they need .float()?
+        hann_window_cache[key] = torch.hann_window(win_length).to(device)
+
+    mel_basis = mel_basis_cache[key]
+    hann_window = hann_window_cache[key]
+
+    padding = (n_fft - hop_length) // 2
+    waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
+
+    spec = torch.stft(
+        waveform,
+        n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
+
+    mel_spec = torch.matmul(mel_basis, spec)
+    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))
+
+    return mel_spec
+
+
+def get_vocos_mel_spectrogram(
+    waveform,
+    n_fft=1024,
+    n_mel_channels=100,
+    target_sample_rate=24000,
+    hop_length=256,
+    win_length=1024,
+):
+    mel_stft = torchaudio.transforms.MelSpectrogram(
+        sample_rate=target_sample_rate,
+        n_fft=n_fft,
+        win_length=win_length,
+        hop_length=hop_length,
+        n_mels=n_mel_channels,
+        power=1,
+        center=True,
+        normalized=False,
+        norm=None,
+    ).to(waveform.device)
+    if len(waveform.shape) == 3:
+        waveform = waveform.squeeze(1)  # 'b 1 nw -> b nw'
+
+    assert len(waveform.shape) == 2
+
+    mel = mel_stft(waveform)
+    mel = mel.clamp(min=1e-5).log()
+    return mel
+
+
+class MelSpec(nn.Module):
+    def __init__(
+        self,
+        n_fft=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=100,
+        target_sample_rate=24_000,
+        mel_spec_type="vocos",
+    ):
+        super().__init__()
+        assert mel_spec_type in ["vocos", "bigvgan"], print("We only support two extract mel backend: vocos or bigvgan")
+
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.n_mel_channels = n_mel_channels
+        self.target_sample_rate = target_sample_rate
+
+        if mel_spec_type == "vocos":
+            self.extractor = get_vocos_mel_spectrogram
+        elif mel_spec_type == "bigvgan":
+            self.extractor = get_bigvgan_mel_spectrogram
+
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    def forward(self, wav):
+        if self.dummy.device != wav.device:
+            self.to(wav.device)
+
+        mel = self.extractor(
+            waveform=wav,
+            n_fft=self.n_fft,
+            n_mel_channels=self.n_mel_channels,
+            target_sample_rate=self.target_sample_rate,
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+        )
+
+        return mel
+
+
+# sinusoidal position embedding
+
+
+class SinusPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x, scale=1000):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+# convolutional position embedding
+
+
+class ConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = self.conv1d(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+# rotary positional embedding related
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def get_pos_embed_indices(start, length, max_pos, scale=1.0):
+    # length = length if isinstance(length, int) else length.max()
+    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
+    pos = (
+        start.unsqueeze(1)
+        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
+    )
+    # avoid extra long error.
+    pos = torch.where(pos < max_pos, pos, max_pos - 1)
+    return pos
+
+
+# Global Response Normalization layer (Instance Normalization ?)
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+# RMSNorm
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.native_rms_norm = float(torch.__version__[:3]) >= 2.4
+
+    def forward(self, x):
+        if self.native_rms_norm:
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = F.rms_norm(x, normalized_shape=(x.shape[-1],), weight=self.weight, eps=self.eps)
+        else:
+            variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            if self.weight.dtype in [torch.float16, torch.bfloat16]:
+                x = x.to(self.weight.dtype)
+            x = x * self.weight
+
+        return x
+
+
+# AdaLayerNorm
+# return with modulated x for attn input, and params for later mlp modulation
+
+
+class AdaLayerNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 6)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNorm for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+
+
+class AdaLayerNorm_Final(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 2)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# FeedForward
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        activation = nn.GELU(approximate=approximate)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
+        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.ff(x)
+
+
+# Attention with possible joint part
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        processor: JointAttnProcessor | AttnProcessor,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only: bool = False,
+        qk_norm: Optional[str] = None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+
+        self.to_q = nn.Linear(dim, self.inner_dim)
+        self.to_k = nn.Linear(dim, self.inner_dim)
+        self.to_v = nn.Linear(dim, self.inner_dim)
+
+        if qk_norm is None:
+            self.q_norm = None
+            self.k_norm = None
+        elif qk_norm == "rms_norm":
+            self.q_norm = RMSNorm(dim_head, eps=1e-6)
+            self.k_norm = RMSNorm(dim_head, eps=1e-6)
+        else:
+            raise ValueError(f"Unimplemented qk_norm: {qk_norm}")
+
+        if self.context_dim is not None:
+            self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
+            if qk_norm is None:
+                self.c_q_norm = None
+                self.c_k_norm = None
+            elif qk_norm == "rms_norm":
+                self.c_q_norm = RMSNorm(dim_head, eps=1e-6)
+                self.c_k_norm = RMSNorm(dim_head, eps=1e-6)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+        if self.context_dim is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, context_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # noised input x
+        c: float["b n d"] = None,  # context c
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(self, x, mask=mask, rope=rope)
+
+
+# Attention processor
+
+if is_package_available("flash_attn"):
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn import flash_attn_varlen_func, flash_attn_func
+
+
+class AttnProcessor:
+    def __init__(
+        self,
+        pe_attn_head: int | None = None,  # number of attention head to apply rope, None for all
+        attn_backend: str = "torch",  # "torch" or "flash_attn"
+        attn_mask_enabled: bool = True,
+    ):
+        if attn_backend == "flash_attn":
+            assert is_package_available("flash_attn"), "Please install flash-attn first."
+
+        self.pe_attn_head = pe_attn_head
+        self.attn_backend = attn_backend
+        self.attn_mask_enabled = attn_mask_enabled
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding
+    ) -> torch.FloatTensor:
+        batch_size = x.shape[0]
+
+        # `sample` projections
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            if self.pe_attn_head is not None:
+                pn = self.pe_attn_head
+                query[:, :pn, :, :] = apply_rotary_pos_emb(query[:, :pn, :, :], freqs, q_xpos_scale)
+                key[:, :pn, :, :] = apply_rotary_pos_emb(key[:, :pn, :, :], freqs, k_xpos_scale)
+            else:
+                query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+                key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
+        if self.attn_backend == "torch":
+            # mask. e.g. inference got a batch with different target durations, mask out the padding
+            if self.attn_mask_enabled and mask is not None:
+                attn_mask = mask
+                attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+                attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+            else:
+                attn_mask = None
+            x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+            x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+
+        elif self.attn_backend == "flash_attn":
+            query = query.transpose(1, 2)  # [b, h, n, d] -> [b, n, h, d]
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+            if self.attn_mask_enabled and mask is not None:
+                query, indices, q_cu_seqlens, q_max_seqlen_in_batch, _ = unpad_input(query, mask)
+                key, _, k_cu_seqlens, k_max_seqlen_in_batch, _ = unpad_input(key, mask)
+                value, _, _, _, _ = unpad_input(value, mask)
+                x = flash_attn_varlen_func(
+                    query,
+                    key,
+                    value,
+                    q_cu_seqlens,
+                    k_cu_seqlens,
+                    q_max_seqlen_in_batch,
+                    k_max_seqlen_in_batch,
+                )
+                x = pad_input(x, indices, batch_size, q_max_seqlen_in_batch)
+                x = x.reshape(batch_size, -1, attn.heads * head_dim)
+            else:
+                x = flash_attn_func(query, key, value, dropout_p=0.0, causal=False)
+                x = x.reshape(batch_size, -1, attn.heads * head_dim)
+
+        x = x.to(query.dtype)
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+
+        return x
+
+
+# Joint Attention processor for MM-DiT
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class JointAttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x
+        c: float["b nt d"] = None,  # context c, here text
+        mask: bool["b n"] | None = None,
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.FloatTensor:
+        residual = x
+
+        batch_size = c.shape[0]
+
+        # `sample` projections
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # `context` projections
+        c_query = attn.to_q_c(c)
+        c_key = attn.to_k_c(c)
+        c_value = attn.to_v_c(c)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_query = c_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_key = c_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        c_value = c_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # qk norm
+        if attn.q_norm is not None:
+            query = attn.q_norm(query)
+        if attn.k_norm is not None:
+            key = attn.k_norm(key)
+        if attn.c_q_norm is not None:
+            c_query = attn.c_q_norm(c_query)
+        if attn.c_k_norm is not None:
+            c_key = attn.c_k_norm(c_key)
+
+        # apply rope for context and noised input independently
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+        if c_rope is not None:
+            freqs, xpos_scale = c_rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
+            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
+
+        # joint attention
+        query = torch.cat([query, c_query], dim=2)
+        key = torch.cat([key, c_key], dim=2)
+        value = torch.cat([value, c_value], dim=2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # Split the attention outputs.
+        x, c = (
+            x[:, : residual.shape[1]],
+            x[:, residual.shape[1] :],
+        )
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+        if not attn.context_pre_only:
+            c = attn.to_out_c(c)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)
+
+        return x, c
+
+
+# DiT Block
+
+
+class DiTBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads,
+        dim_head,
+        ff_mult=4,
+        dropout=0.1,
+        qk_norm=None,
+        pe_attn_head=None,
+        attn_backend="torch",  # "torch" or "flash_attn"
+        attn_mask_enabled=True,
+    ):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNorm(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(
+                pe_attn_head=pe_attn_head,
+                attn_backend=attn_backend,
+                attn_mask_enabled=attn_mask_enabled,
+            ),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            qk_norm=qk_norm,
+        )
+
+        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+
+        # attention
+        attn_output = self.attn(x=norm, mask=mask, rope=rope)
+
+        # process attention output for input x
+        x = x + gate_msa.unsqueeze(1) * attn_output
+
+        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(norm)
+        x = x + gate_mlp.unsqueeze(1) * ff_output
+
+        return x
+
+
+# MMDiT Block https://arxiv.org/abs/2403.03206
+
+
+class MMDiTBlock(nn.Module):
+    r"""
+    modified from diffusers/src/diffusers/models/attention.py
+
+    notes.
+    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
+    _x: noised input related. (right part)
+    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
+    """
+
+    def __init__(
+        self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_dim=None, context_pre_only=False, qk_norm=None
+    ):
+        super().__init__()
+        if context_dim is None:
+            context_dim = dim
+        self.context_pre_only = context_pre_only
+
+        self.attn_norm_c = AdaLayerNorm_Final(context_dim) if context_pre_only else AdaLayerNorm(context_dim)
+        self.attn_norm_x = AdaLayerNorm(dim)
+        self.attn = Attention(
+            processor=JointAttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            context_dim=context_dim,
+            context_pre_only=context_pre_only,
+            qk_norm=qk_norm,
+        )
+
+        if not context_pre_only:
+            self.ff_norm_c = nn.LayerNorm(context_dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=context_dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+        else:
+            self.ff_norm_c = None
+            self.ff_c = None
+        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
+        # pre-norm & modulation for attention input
+        if self.context_pre_only:
+            norm_c = self.attn_norm_c(c, t)
+        else:
+            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
+        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
+
+        # attention
+        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
+
+        # process attention output for context c
+        if self.context_pre_only:
+            c = None
+        else:  # if not last layer
+            c = c + c_gate_msa.unsqueeze(1) * c_attn_output
+
+            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+            c_ff_output = self.ff_c(norm_c)
+            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
+
+        # process attention output for input x
+        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
+
+        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
+        x_ff_output = self.ff_x(norm_x)
+        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
+
+        return c, x
+
+
+# time step conditioning embedding
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, dim, freq_embed_dim=256):
+        super().__init__()
+        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+
+    def forward(self, timestep: float["b"]):
+        time_hidden = self.time_embed(timestep)
+        time_hidden = time_hidden.to(timestep.dtype)
+        time = self.time_mlp(time_hidden)  # b d
+        return time
@@ -0,0 +1,439 @@
+from __future__ import annotations
+
+import gc
+import math
+import os
+
+import torch
+import torchaudio
+import wandb
+from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
+from ema_pytorch import EMA
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LinearLR, SequentialLR
+from torch.utils.data import DataLoader, Dataset, SequentialSampler
+from tqdm import tqdm
+
+from . import CFM
+from .dataset import DynamicBatchSampler, collate_fn
+from .utils import default, exists
+
+
+# trainer
+
+
+class Trainer:
+    def __init__(
+        self,
+        model: CFM,
+        epochs,
+        learning_rate,
+        num_warmup_updates=20000,
+        save_per_updates=1000,
+        keep_last_n_checkpoints: int = -1,  # -1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints
+        checkpoint_path=None,
+        batch_size_per_gpu=32,
+        batch_size_type: str = "sample",
+        max_samples=32,
+        grad_accumulation_steps=1,
+        max_grad_norm=1.0,
+        noise_scheduler: str | None = None,
+        duration_predictor: torch.nn.Module | None = None,
+        logger: str | None = "wandb",  # "wandb" | "tensorboard" | None
+        wandb_project="test_f5-tts",
+        wandb_run_name="test_run",
+        wandb_resume_id: str = None,
+        log_samples: bool = False,
+        last_per_updates=None,
+        accelerate_kwargs: dict = dict(),
+        ema_kwargs: dict = dict(),
+        bnb_optimizer: bool = False,
+        mel_spec_type: str = "vocos",  # "vocos" | "bigvgan"
+        is_local_vocoder: bool = False,  # use local path vocoder
+        local_vocoder_path: str = "",  # local vocoder path
+        model_cfg_dict: dict = dict(),  # training config
+    ):
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+        if logger == "wandb" and not wandb.api.api_key:
+            logger = None
+        self.log_samples = log_samples
+
+        self.accelerator = Accelerator(
+            log_with=logger if logger == "wandb" else None,
+            kwargs_handlers=[ddp_kwargs],
+            gradient_accumulation_steps=grad_accumulation_steps,
+            **accelerate_kwargs,
+        )
+
+        self.logger = logger
+        if self.logger == "wandb":
+            if exists(wandb_resume_id):
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
+            else:
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
+
+            if not model_cfg_dict:
+                model_cfg_dict = {
+                    "epochs": epochs,
+                    "learning_rate": learning_rate,
+                    "num_warmup_updates": num_warmup_updates,
+                    "batch_size_per_gpu": batch_size_per_gpu,
+                    "batch_size_type": batch_size_type,
+                    "max_samples": max_samples,
+                    "grad_accumulation_steps": grad_accumulation_steps,
+                    "max_grad_norm": max_grad_norm,
+                    "noise_scheduler": noise_scheduler,
+                }
+            model_cfg_dict["gpus"] = self.accelerator.num_processes
+            self.accelerator.init_trackers(
+                project_name=wandb_project,
+                init_kwargs=init_kwargs,
+                config=model_cfg_dict,
+            )
+
+        elif self.logger == "tensorboard":
+            from torch.utils.tensorboard import SummaryWriter
+
+            self.writer = SummaryWriter(log_dir=f"runs/{wandb_run_name}")
+
+        self.model = model
+
+        if self.is_main:
+            self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
+            self.ema_model.to(self.accelerator.device)
+
+            print(f"Using logger: {logger}")
+            if grad_accumulation_steps > 1:
+                print(
+                    "Gradient accumulation checkpointing with per_updates now, old logic per_steps used with before f992c4e"
+                )
+
+        self.epochs = epochs
+        self.num_warmup_updates = num_warmup_updates
+        self.save_per_updates = save_per_updates
+        self.keep_last_n_checkpoints = keep_last_n_checkpoints
+        self.last_per_updates = default(last_per_updates, save_per_updates)
+        self.checkpoint_path = default(checkpoint_path, "ckpts/test_f5-tts")
+
+        self.batch_size_per_gpu = batch_size_per_gpu
+        self.batch_size_type = batch_size_type
+        self.max_samples = max_samples
+        self.grad_accumulation_steps = grad_accumulation_steps
+        self.max_grad_norm = max_grad_norm
+
+        # mel vocoder config
+        self.vocoder_name = mel_spec_type
+        self.is_local_vocoder = is_local_vocoder
+        self.local_vocoder_path = local_vocoder_path
+
+        self.noise_scheduler = noise_scheduler
+
+        self.duration_predictor = duration_predictor
+
+        if bnb_optimizer:
+            import bitsandbytes as bnb
+
+            self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
+        else:
+            self.optimizer = AdamW(model.parameters(), lr=learning_rate)
+        self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
+
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
+
+    def save_checkpoint(self, update, last=False):
+        self.accelerator.wait_for_everyone()
+        if self.is_main:
+            checkpoint = dict(
+                model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
+                optimizer_state_dict=self.optimizer.state_dict(),
+                ema_model_state_dict=self.ema_model.state_dict(),
+                scheduler_state_dict=self.scheduler.state_dict(),
+                update=update,
+            )
+            if not os.path.exists(self.checkpoint_path):
+                os.makedirs(self.checkpoint_path)
+            if last:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
+                print(f"Saved last checkpoint at update {update}")
+            else:
+                if self.keep_last_n_checkpoints == 0:
+                    return
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{update}.pt")
+                if self.keep_last_n_checkpoints > 0:
+                    # Updated logic to exclude pretrained model from rotation
+                    checkpoints = [
+                        f
+                        for f in os.listdir(self.checkpoint_path)
+                        if f.startswith("model_")
+                        and not f.startswith("pretrained_")  # Exclude pretrained models
+                        and f.endswith(".pt")
+                        and f != "model_last.pt"
+                    ]
+                    checkpoints.sort(key=lambda x: int(x.split("_")[1].split(".")[0]))
+                    while len(checkpoints) > self.keep_last_n_checkpoints:
+                        oldest_checkpoint = checkpoints.pop(0)
+                        os.remove(os.path.join(self.checkpoint_path, oldest_checkpoint))
+                        print(f"Removed old checkpoint: {oldest_checkpoint}")
+
+    def load_checkpoint(self):
+        if (
+            not exists(self.checkpoint_path)
+            or not os.path.exists(self.checkpoint_path)
+            or not any(filename.endswith((".pt", ".safetensors")) for filename in os.listdir(self.checkpoint_path))
+        ):
+            return 0
+
+        self.accelerator.wait_for_everyone()
+        if "model_last.pt" in os.listdir(self.checkpoint_path):
+            latest_checkpoint = "model_last.pt"
+        else:
+            # Updated to consider pretrained models for loading but prioritize training checkpoints
+            all_checkpoints = [
+                f
+                for f in os.listdir(self.checkpoint_path)
+                if (f.startswith("model_") or f.startswith("pretrained_")) and f.endswith((".pt", ".safetensors"))
+            ]
+
+            # First try to find regular training checkpoints
+            training_checkpoints = [f for f in all_checkpoints if f.startswith("model_") and f != "model_last.pt"]
+            if training_checkpoints:
+                latest_checkpoint = sorted(
+                    training_checkpoints,
+                    key=lambda x: int("".join(filter(str.isdigit, x))),
+                )[-1]
+            else:
+                # If no training checkpoints, use pretrained model
+                latest_checkpoint = next(f for f in all_checkpoints if f.startswith("pretrained_"))
+
+        if latest_checkpoint.endswith(".safetensors"):  # always a pretrained checkpoint
+            from safetensors.torch import load_file
+
+            checkpoint = load_file(f"{self.checkpoint_path}/{latest_checkpoint}", device="cpu")
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        elif latest_checkpoint.endswith(".pt"):
+            # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
+            checkpoint = torch.load(
+                f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu"
+            )
+
+        # patch for backward compatibility, 305e3ea
+        for key in ["ema_model.mel_spec.mel_stft.mel_scale.fb", "ema_model.mel_spec.mel_stft.spectrogram.window"]:
+            if key in checkpoint["ema_model_state_dict"]:
+                del checkpoint["ema_model_state_dict"][key]
+
+        if self.is_main:
+            self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"])
+
+        if "update" in checkpoint or "step" in checkpoint:
+            # patch for backward compatibility, with before f992c4e
+            if "step" in checkpoint:
+                checkpoint["update"] = checkpoint["step"] // self.grad_accumulation_steps
+                if self.grad_accumulation_steps > 1 and self.is_main:
+                    print(
+                        "F5-TTS WARNING: Loading checkpoint saved with per_steps logic (before f992c4e), will convert to per_updates according to grad_accumulation_steps setting, may have unexpected behaviour."
+                    )
+            # patch for backward compatibility, 305e3ea
+            for key in ["mel_spec.mel_stft.mel_scale.fb", "mel_spec.mel_stft.spectrogram.window"]:
+                if key in checkpoint["model_state_dict"]:
+                    del checkpoint["model_state_dict"][key]
+
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+            if self.scheduler:
+                self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+            update = checkpoint["update"]
+        else:
+            checkpoint["model_state_dict"] = {
+                k.replace("ema_model.", ""): v
+                for k, v in checkpoint["ema_model_state_dict"].items()
+                if k not in ["initted", "update", "step"]
+            }
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            update = 0
+
+        del checkpoint
+        gc.collect()
+        return update
+
+    def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None):
+        if self.log_samples:
+            from .infer.utils_infer import cfg_strength, load_vocoder, nfe_step, sway_sampling_coef
+
+            vocoder = load_vocoder(
+                vocoder_name=self.vocoder_name, is_local=self.is_local_vocoder, local_path=self.local_vocoder_path
+            )
+            target_sample_rate = self.accelerator.unwrap_model(self.model).mel_spec.target_sample_rate
+            log_samples_path = f"{self.checkpoint_path}/samples"
+            os.makedirs(log_samples_path, exist_ok=True)
+
+        if exists(resumable_with_seed):
+            generator = torch.Generator()
+            generator.manual_seed(resumable_with_seed)
+        else:
+            generator = None
+
+        if self.batch_size_type == "sample":
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_size=self.batch_size_per_gpu,
+                shuffle=True,
+                generator=generator,
+            )
+        elif self.batch_size_type == "frame":
+            self.accelerator.even_batches = False
+            sampler = SequentialSampler(train_dataset)
+            batch_sampler = DynamicBatchSampler(
+                sampler,
+                self.batch_size_per_gpu,
+                max_samples=self.max_samples,
+                random_seed=resumable_with_seed,  # This enables reproducible shuffling
+                drop_residual=False,
+            )
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_sampler=batch_sampler,
+            )
+        else:
+            raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}")
+
+        #  accelerator.prepare() dispatches batches to devices;
+        #  which means the length of dataloader calculated before, should consider the number of devices
+        warmup_updates = (
+            self.num_warmup_updates * self.accelerator.num_processes
+        )  # consider a fixed warmup steps while using accelerate multi-gpu ddp
+        # otherwise by default with split_batches=False, warmup steps change with num_processes
+        total_updates = math.ceil(len(train_dataloader) / self.grad_accumulation_steps) * self.epochs
+        decay_updates = total_updates - warmup_updates
+        warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_updates)
+        decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_updates)
+        self.scheduler = SequentialLR(
+            self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_updates]
+        )
+        train_dataloader, self.scheduler = self.accelerator.prepare(
+            train_dataloader, self.scheduler
+        )  # actual multi_gpu updates = single_gpu updates / gpu nums
+        start_update = self.load_checkpoint()
+        global_update = start_update
+
+        if exists(resumable_with_seed):
+            orig_epoch_step = len(train_dataloader)
+            start_step = start_update * self.grad_accumulation_steps
+            skipped_epoch = int(start_step // orig_epoch_step)
+            skipped_batch = start_step % orig_epoch_step
+            skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
+        else:
+            skipped_epoch = 0
+
+        for epoch in range(skipped_epoch, self.epochs):
+            self.model.train()
+            if exists(resumable_with_seed) and epoch == skipped_epoch:
+                progress_bar_initial = math.ceil(skipped_batch / self.grad_accumulation_steps)
+                current_dataloader = skipped_dataloader
+            else:
+                progress_bar_initial = 0
+                current_dataloader = train_dataloader
+
+            # Set epoch for the batch sampler if it exists
+            if hasattr(train_dataloader, "batch_sampler") and hasattr(train_dataloader.batch_sampler, "set_epoch"):
+                train_dataloader.batch_sampler.set_epoch(epoch)
+
+            progress_bar = tqdm(
+                range(math.ceil(len(train_dataloader) / self.grad_accumulation_steps)),
+                desc=f"Epoch {epoch + 1}/{self.epochs}",
+                unit="update",
+                disable=not self.accelerator.is_local_main_process,
+                initial=progress_bar_initial,
+            )
+
+            for batch in current_dataloader:
+                with self.accelerator.accumulate(self.model):
+                    text_inputs = batch["text"]
+                    mel_spec = batch["mel"].permute(0, 2, 1)
+                    mel_lengths = batch["mel_lengths"]
+
+                    # TODO. add duration predictor training
+                    if self.duration_predictor is not None and self.accelerator.is_local_main_process:
+                        dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations"))
+                        self.accelerator.log({"duration loss": dur_loss.item()}, step=global_update)
+
+                    loss, cond, pred = self.model(
+                        mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler
+                    )
+                    self.accelerator.backward(loss)
+
+                    if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
+                        self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+                    self.scheduler.step()
+                    self.optimizer.zero_grad()
+
+                if self.accelerator.sync_gradients:
+                    if self.is_main:
+                        self.ema_model.update()
+
+                    global_update += 1
+                    progress_bar.update(1)
+                    progress_bar.set_postfix(update=str(global_update), loss=loss.item())
+
+                if self.accelerator.is_local_main_process:
+                    self.accelerator.log(
+                        {"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_update
+                    )
+                    if self.logger == "tensorboard":
+                        self.writer.add_scalar("loss", loss.item(), global_update)
+                        self.writer.add_scalar("lr", self.scheduler.get_last_lr()[0], global_update)
+
+                if global_update % self.last_per_updates == 0 and self.accelerator.sync_gradients:
+                    self.save_checkpoint(global_update, last=True)
+
+                if global_update % self.save_per_updates == 0 and self.accelerator.sync_gradients:
+                    self.save_checkpoint(global_update)
+
+                    if self.log_samples and self.accelerator.is_local_main_process:
+                        ref_audio_len = mel_lengths[0]
+                        infer_text = [
+                            text_inputs[0] + ([" "] if isinstance(text_inputs[0], list) else " ") + text_inputs[0]
+                        ]
+                        with torch.inference_mode():
+                            generated, _ = self.accelerator.unwrap_model(self.model).sample(
+                                cond=mel_spec[0][:ref_audio_len].unsqueeze(0),
+                                text=infer_text,
+                                duration=ref_audio_len * 2,
+                                steps=nfe_step,
+                                cfg_strength=cfg_strength,
+                                sway_sampling_coef=sway_sampling_coef,
+                            )
+                            generated = generated.to(torch.float32)
+                            gen_mel_spec = generated[:, ref_audio_len:, :].permute(0, 2, 1).to(self.accelerator.device)
+                            ref_mel_spec = batch["mel"][0].unsqueeze(0)
+                            if self.vocoder_name == "vocos":
+                                gen_audio = vocoder.decode(gen_mel_spec).cpu()
+                                ref_audio = vocoder.decode(ref_mel_spec).cpu()
+                            elif self.vocoder_name == "bigvgan":
+                                gen_audio = vocoder(gen_mel_spec).squeeze(0).cpu()
+                                ref_audio = vocoder(ref_mel_spec).squeeze(0).cpu()
+
+                        torchaudio.save(
+                            f"{log_samples_path}/update_{global_update}_gen.wav", gen_audio, target_sample_rate
+                        )
+                        torchaudio.save(
+                            f"{log_samples_path}/update_{global_update}_ref.wav", ref_audio, target_sample_rate
+                        )
+                        self.model.train()
+
+        self.save_checkpoint(global_update, last=True)
+
+        self.accelerator.end_training()
@@ -0,0 +1,220 @@
+from __future__ import annotations
+
+import os
+import random
+from collections import defaultdict
+from importlib.resources import files
+
+import jieba
+import torch
+from pypinyin import Style, lazy_pinyin
+from torch.nn.utils.rnn import pad_sequence
+
+
+# seed everything
+
+
+def seed_everything(seed=0):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+# helpers
+
+
+def exists(v):
+    return v is not None
+
+
+def default(v, d):
+    return v if exists(v) else d
+
+
+def is_package_available(package_name: str) -> bool:
+    try:
+        import importlib
+
+        package_exists = importlib.util.find_spec(package_name) is not None
+        return package_exists
+    except Exception:
+        return False
+
+
+# tensor helpers
+
+
+def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
+    if not exists(length):
+        length = t.amax()
+
+    seq = torch.arange(length, device=t.device)
+    return seq[None, :] < t[:, None]
+
+
+def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
+    max_seq_len = seq_len.max().item()
+    seq = torch.arange(max_seq_len, device=start.device).long()
+    start_mask = seq[None, :] >= start[:, None]
+    end_mask = seq[None, :] < end[:, None]
+    return start_mask & end_mask
+
+
+def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.rand_like(frac_lengths)
+    start = (max_start * rand).long().clamp(min=0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+
+def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
+    if not exists(mask):
+        return t.mean(dim=1)
+
+    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
+    num = t.sum(dim=1)
+    den = mask.float().sum(dim=1)
+
+    return num / den.clamp(min=1.0)
+
+
+# simple utf-8 tokenizer, since paper went character based
+def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
+    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
+    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# char tokenizer, based on custom dataset's extracted .txt file
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+) -> int["b nt"]:  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# Get tokenizer
+
+
+def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    if tokenizer in ["pinyin", "char"]:
+        tokenizer_path = os.path.join(files(__package__ or "f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
+        with open(tokenizer_path, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"
+
+    elif tokenizer == "byte":
+        vocab_char_map = None
+        vocab_size = 256
+
+    elif tokenizer == "custom":
+        with open(dataset_name, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    return vocab_char_map, vocab_size
+
+
+# convert char to pinyin
+
+
+def convert_char_to_pinyin(text_list, polyphone=True):
+    if jieba.dt.initialized is False:
+        jieba.default_logger.setLevel(50)  # CRITICAL
+        jieba.initialize()
+
+    final_text_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return (
+            "\u3100" <= c <= "\u9fff"  # common chinese characters
+        )
+
+    for text in text_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_text_list.append(char_list)
+
+    return final_text_list
+
+
+# filter func for dirty data with many repetitions
+
+
+def repetition_found(text, length=2, tolerance=10):
+    pattern_count = defaultdict(int)
+    for i in range(len(text) - length + 1):
+        pattern = text[i : i + length]
+        pattern_count[pattern] += 1
+    for pattern, count in pattern_count.items():
+        if count > tolerance:
+            return True
+    return False
+
+
+# get the empirically pruned step for sampling
+
+
+def get_epss_timesteps(n, device, dtype):
+    dt = 1 / 32
+    predefined_timesteps = {
+        5: [0, 2, 4, 8, 16, 32],
+        6: [0, 2, 4, 6, 8, 16, 32],
+        7: [0, 2, 4, 6, 8, 16, 24, 32],
+        10: [0, 2, 4, 6, 8, 12, 16, 20, 24, 28, 32],
+        12: [0, 2, 4, 6, 8, 10, 12, 14, 16, 20, 24, 28, 32],
+        16: [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32],
+    }
+    t = predefined_timesteps.get(n, [])
+    if not t:
+        return torch.linspace(0, 1, n + 1, device=device, dtype=dtype)
+    return dt * torch.tensor(t, device=device, dtype=dtype)
@@ -0,0 +1,64 @@
+[build-system]
+requires = ["setuptools >= 61.0", "setuptools-scm>=8.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "f5-tts-bundled"
+version = "1.1.7"
+description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching (Bundled with TTS Audio Suite)"
+readme = "README.md"
+license = {text = "MIT License"}
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+]
+dependencies = [
+    "accelerate>=0.33.0",
+    "bitsandbytes>0.37.0; platform_machine != 'arm64' and platform_system != 'Darwin'",
+    "cached_path",
+    "click",
+    "datasets",
+    "ema_pytorch>=0.5.2",
+    "gradio>=3.45.2",
+    "hydra-core>=1.3.0",
+    "jieba",
+    "librosa",
+    "matplotlib",
+    "numpy>=1.24.0",
+    "pydantic<=2.10.6",
+    "pydub",
+    "pypinyin",
+    "safetensors",
+    "soundfile",
+    "tomli",
+    "torch>=2.0.0",
+    "torchaudio>=2.0.0",
+    "torchdiffeq",
+    "tqdm>=4.65.0",
+    "transformers",
+    "transformers_stream_generator",
+    "unidecode",
+    "vocos",
+    "wandb",
+    "x_transformers>=1.31.14",
+]
+
+[project.optional-dependencies]
+eval = [
+    "faster_whisper==0.10.1",
+    "funasr",
+    "jiwer",
+    "modelscope",
+    "zhconv",
+    "zhon",
+]
+
+[project.urls]
+Homepage = "https://github.com/SWivid/F5-TTS"
+
+[project.scripts]
+"f5-tts_infer-cli" = "f5_tts.infer.infer_cli:main"
+"f5-tts_infer-gradio" = "f5_tts.infer.infer_gradio:main"
+"f5-tts_finetune-cli" = "f5_tts.train.finetune_cli:main"
+"f5-tts_finetune-gradio" = "f5_tts.train.finetune_gradio:main"
@@ -0,0 +1,3 @@
+FROM nvcr.io/nvidia/tritonserver:24.12-py3
+RUN pip install tritonclient[grpc] tensorrt-llm==0.16.0 torchaudio==2.5.1 jieba pypinyin librosa vocos
+WORKDIR /workspace
@@ -0,0 +1,69 @@
+## Triton Inference Serving Best Practice for F5-TTS
+
+### Quick Start
+Directly launch the service using docker compose.
+```sh
+# TODO: support F5TTS_v1_Base
+MODEL=F5TTS_Base docker compose up
+```
+
+### Build Image
+Build the docker image from scratch. 
+```sh
+docker build . -f Dockerfile.server -t soar97/triton-f5-tts:24.12
+```
+
+### Create Docker Container
+```sh
+your_mount_dir=/mnt:/mnt
+docker run -it --name "f5-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-f5-tts:24.12
+```
+
+### Export Models to TensorRT-LLM and Launch Server
+Inside docker container, we would follow the official guide of TensorRT-LLM to build qwen and whisper TensorRT-LLM engines. See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper).
+```sh
+bash run.sh 0 4 F5TTS_Base
+```
+
+### HTTP Client
+```sh
+python3 client_http.py
+```
+
+### Benchmark using Client-Server Mode
+```sh
+num_task=2
+python3 client_grpc.py --num-tasks $num_task --huggingface-dataset yuekai/seed_tts --split-name wenetspeech4tts
+```
+
+### Benchmark using Offline TRT-LLM Mode
+```sh
+batch_size=1
+split_name=wenetspeech4tts
+backend_type=trt
+log_dir=./log_benchmark_batch_size_${batch_size}_${split_name}_${backend_type}
+rm -r $log_dir
+ln -s model_repo_f5_tts/f5_tts/1/f5_tts_trtllm.py ./
+torchrun --nproc_per_node=1 \
+benchmark.py --output-dir $log_dir \
+--batch-size $batch_size \
+--enable-warmup \
+--split-name $split_name \
+--model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+--vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+--vocoder-trt-engine-path $vocoder_trt_engine_path \
+--backend-type $backend_type \
+--tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+```
+
+### Benchmark Results
+Decoding on a single L20 GPU, using 26 different prompt_audio & target_text pairs, 16 NFE.
+
+| Model               | Concurrency    | Avg Latency | RTF    | Mode            |
+|---------------------|----------------|-------------|--------|-----------------|
+| F5-TTS Base (Vocos) | 2              | 253 ms      | 0.0394 | Client-Server   |
+| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.0402 | Offline TRT-LLM |
+| F5-TTS Base (Vocos) | 1 (Batch_size) | -           | 0.1467 | Offline Pytorch |
+
+### Credits
+1. [F5-TTS-TRTLLM](https://github.com/Bigfishering/f5-tts-trtllm)
@@ -0,0 +1,560 @@
+# Copyright (c) 2024 Tsinghua Univ. (authors: Xingchen Song)
+#               2025               （authors: Yuekai Zhang）
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from https://github.com/xingchensong/S3Tokenizer/blob/main/s3tokenizer/cli.py
+""" Example Usage
+torchrun --nproc_per_node=1 \
+benchmark.py --output-dir $log_dir \
+--batch-size $batch_size \
+--enable-warmup \
+--split-name $split_name \
+--model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+--vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+--vocoder-trt-engine-path $vocoder_trt_engine_path \
+--backend-type $backend_type \
+--tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+"""
+
+import argparse
+import json
+import os
+import time
+from typing import Dict, List, Union
+
+import datasets
+import jieba
+import tensorrt as trt
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torchaudio
+from datasets import load_dataset
+from f5_tts_trtllm import F5TTS
+from huggingface_hub import hf_hub_download
+from pypinyin import Style, lazy_pinyin
+from tensorrt_llm._utils import trt_dtype_to_torch
+from tensorrt_llm.logger import logger
+from tensorrt_llm.runtime.session import Session, TensorInfo
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader, DistributedSampler
+from tqdm import tqdm
+from vocos import Vocos
+
+
+torch.manual_seed(0)
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description="extract speech code")
+    parser.add_argument(
+        "--split-name",
+        type=str,
+        default="wenetspeech4tts",
+        choices=["wenetspeech4tts", "test_zh", "test_en", "test_hard"],
+        help="huggingface dataset split name",
+    )
+    parser.add_argument("--output-dir", required=True, type=str, help="dir to save result")
+    parser.add_argument(
+        "--vocab-file",
+        required=True,
+        type=str,
+        help="vocab file",
+    )
+    parser.add_argument(
+        "--model-path",
+        required=True,
+        type=str,
+        help="model path, to load text embedding",
+    )
+    parser.add_argument(
+        "--tllm-model-dir",
+        required=True,
+        type=str,
+        help="tllm model dir",
+    )
+    parser.add_argument(
+        "--batch-size",
+        required=True,
+        type=int,
+        help="batch size (per-device) for inference",
+    )
+    parser.add_argument("--num-workers", type=int, default=0, help="workers for dataloader")
+    parser.add_argument("--prefetch", type=int, default=None, help="prefetch for dataloader")
+    parser.add_argument(
+        "--vocoder",
+        default="vocos",
+        type=str,
+        help="vocoder name",
+    )
+    parser.add_argument(
+        "--vocoder-trt-engine-path",
+        default=None,
+        type=str,
+        help="vocoder trt engine path",
+    )
+    parser.add_argument("--enable-warmup", action="store_true")
+    parser.add_argument("--remove-input-padding", action="store_true")
+    parser.add_argument("--use-perf", action="store_true", help="use nvtx to record performance")
+    parser.add_argument("--backend-type", type=str, default="triton", choices=["trt", "pytorch"], help="backend type")
+    args = parser.parse_args()
+    return args
+
+
+def padded_mel_batch(ref_mels, max_seq_len):
+    padded_ref_mels = []
+    for mel in ref_mels:
+        # pad along the last dimension
+        padded_ref_mel = F.pad(mel, (0, 0, 0, max_seq_len - mel.shape[0]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    return padded_ref_mels
+
+
+def data_collator(batch, vocab_char_map, device="cuda", use_perf=False):
+    if use_perf:
+        torch.cuda.nvtx.range_push("data_collator")
+    target_sample_rate = 24000
+    target_rms = 0.1
+    ids, ref_mel_list, ref_mel_len_list, estimated_reference_target_mel_len, reference_target_texts_list = (
+        [],
+        [],
+        [],
+        [],
+        [],
+    )
+    for i, item in enumerate(batch):
+        item_id, prompt_text, target_text = (
+            item["id"],
+            item["prompt_text"],
+            item["target_text"],
+        )
+        ids.append(item_id)
+        reference_target_texts_list.append(prompt_text + target_text)
+
+        ref_audio_org, ref_sr = (
+            item["prompt_audio"]["array"],
+            item["prompt_audio"]["sampling_rate"],
+        )
+        ref_audio_org = torch.from_numpy(ref_audio_org).unsqueeze(0).float()
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio_org)))
+        if ref_rms < target_rms:
+            ref_audio_org = ref_audio_org * target_rms / ref_rms
+
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio_org)
+        else:
+            ref_audio = ref_audio_org
+
+        if use_perf:
+            torch.cuda.nvtx.range_push(f"mel_spectrogram {i}")
+        ref_mel = mel_spectrogram(ref_audio, vocoder="vocos", device="cuda")
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        ref_mel = ref_mel.squeeze()
+        ref_mel_len = ref_mel.shape[0]
+        assert ref_mel.shape[1] == 100
+
+        ref_mel_list.append(ref_mel)
+        ref_mel_len_list.append(ref_mel_len)
+
+        estimated_reference_target_mel_len.append(
+            int(ref_mel.shape[0] * (1 + len(target_text.encode("utf-8")) / len(prompt_text.encode("utf-8"))))
+        )
+
+    max_seq_len = max(estimated_reference_target_mel_len)
+    ref_mel_batch = padded_mel_batch(ref_mel_list, max_seq_len)
+    ref_mel_len_batch = torch.LongTensor(ref_mel_len_list)
+
+    pinyin_list = convert_char_to_pinyin(reference_target_texts_list, polyphone=True)
+    text_pad_sequence = list_str_to_idx(pinyin_list, vocab_char_map)
+
+    for i, item in enumerate(text_pad_sequence):
+        text_pad_sequence[i] = F.pad(
+            item, (0, estimated_reference_target_mel_len[i] - len(item)), mode="constant", value=-1
+        )
+        text_pad_sequence[i] += 1  # WAR: 0 is reserved for padding token, hard coding in F5-TTS
+    text_pad_sequence = pad_sequence(text_pad_sequence, padding_value=-1, batch_first=True).to(device)
+    text_pad_sequence = F.pad(
+        text_pad_sequence, (0, max_seq_len - text_pad_sequence.shape[1]), mode="constant", value=-1
+    )
+    if use_perf:
+        torch.cuda.nvtx.range_pop()
+    return {
+        "ids": ids,
+        "ref_mel_batch": ref_mel_batch,
+        "ref_mel_len_batch": ref_mel_len_batch,
+        "text_pad_sequence": text_pad_sequence,
+        "estimated_reference_target_mel_len": estimated_reference_target_mel_len,
+    }
+
+
+def init_distributed():
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    rank = int(os.environ.get("RANK", 0))
+    print(
+        "Inference on multiple gpus, this gpu {}".format(local_rank)
+        + ", rank {}, world_size {}".format(rank, world_size)
+    )
+    torch.cuda.set_device(local_rank)
+    # Initialize process group with explicit device IDs
+    dist.init_process_group(
+        "nccl",
+    )
+    return world_size, local_rank, rank
+
+
+def get_tokenizer(vocab_file_path: str):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    with open(vocab_file_path, "r", encoding="utf-8") as f:
+        vocab_char_map = {}
+        for i, char in enumerate(f):
+            vocab_char_map[char[:-1]] = i
+    vocab_size = len(vocab_char_map)
+    return vocab_char_map, vocab_size
+
+
+def convert_char_to_pinyin(reference_target_texts_list, polyphone=True):
+    final_reference_target_texts_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return "\u3100" <= c <= "\u9fff"  # common chinese characters
+
+    for text in reference_target_texts_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_reference_target_texts_list.append(char_list)
+
+    return final_reference_target_texts_list
+
+
+def list_str_to_idx(
+    text: Union[List[str], List[List[str]]],
+    vocab_char_map: Dict[str, int],  # {char: idx}
+    padding_value=-1,
+):
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    # text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return list_idx_tensors
+
+
+def load_vocoder(
+    vocoder_name="vocos", is_local=False, local_path="", device="cuda", hf_cache_dir=None, vocoder_trt_engine_path=None
+):
+    if vocoder_name == "vocos":
+        if vocoder_trt_engine_path is not None:
+            vocoder = VocosTensorRT(engine_path=vocoder_trt_engine_path)
+        else:
+            # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
+            if is_local:
+                print(f"Load vocos from local path {local_path}")
+                config_path = f"{local_path}/config.yaml"
+                model_path = f"{local_path}/pytorch_model.bin"
+            else:
+                print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+                repo_id = "charactr/vocos-mel-24khz"
+                config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
+                model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
+            vocoder = Vocos.from_hparams(config_path)
+            state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
+            from vocos.feature_extractors import EncodecFeatures
+
+            if isinstance(vocoder.feature_extractor, EncodecFeatures):
+                encodec_parameters = {
+                    "feature_extractor.encodec." + key: value
+                    for key, value in vocoder.feature_extractor.encodec.state_dict().items()
+                }
+                state_dict.update(encodec_parameters)
+            vocoder.load_state_dict(state_dict)
+            vocoder = vocoder.eval().to(device)
+    elif vocoder_name == "bigvgan":
+        raise NotImplementedError("BigVGAN is not implemented yet")
+    return vocoder
+
+
+def mel_spectrogram(waveform, vocoder="vocos", device="cuda"):
+    if vocoder == "vocos":
+        mel_stft = torchaudio.transforms.MelSpectrogram(
+            sample_rate=24000,
+            n_fft=1024,
+            win_length=1024,
+            hop_length=256,
+            n_mels=100,
+            power=1,
+            center=True,
+            normalized=False,
+            norm=None,
+        ).to(device)
+    mel = mel_stft(waveform.to(device))
+    mel = mel.clamp(min=1e-5).log()
+    return mel.transpose(1, 2)
+
+
+class VocosTensorRT:
+    def __init__(self, engine_path="./vocos_vocoder.plan", stream=None):
+        TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+        trt.init_libnvinfer_plugins(TRT_LOGGER, namespace="")
+        logger.info(f"Loading vae engine from {engine_path}")
+        self.engine_path = engine_path
+        with open(engine_path, "rb") as f:
+            engine_buffer = f.read()
+        self.session = Session.from_serialized_engine(engine_buffer)
+        self.stream = stream if stream is not None else torch.cuda.current_stream().cuda_stream
+
+    def decode(self, mels):
+        mels = mels.contiguous()
+        inputs = {"mel": mels}
+        output_info = self.session.infer_shapes([TensorInfo("mel", trt.DataType.FLOAT, mels.shape)])
+        outputs = {
+            t.name: torch.empty(tuple(t.shape), dtype=trt_dtype_to_torch(t.dtype), device="cuda") for t in output_info
+        }
+        ok = self.session.run(inputs, outputs, self.stream)
+
+        assert ok, "Runtime execution failed for vae session"
+
+        samples = outputs["waveform"]
+        return samples
+
+
+def main():
+    args = get_args()
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    assert torch.cuda.is_available()
+    world_size, local_rank, rank = init_distributed()
+    device = torch.device(f"cuda:{local_rank}")
+
+    vocab_char_map, vocab_size = get_tokenizer(args.vocab_file)
+
+    tllm_model_dir = args.tllm_model_dir
+    config_file = os.path.join(tllm_model_dir, "config.json")
+    with open(config_file) as f:
+        config = json.load(f)
+    if args.backend_type == "trt":
+        model = F5TTS(
+            config, debug_mode=False, tllm_model_dir=tllm_model_dir, model_path=args.model_path, vocab_size=vocab_size
+        )
+    elif args.backend_type == "pytorch":
+        import sys
+
+        sys.path.append(f"{os.path.dirname(os.path.abspath(__file__))}/../../../../src/")
+        from .infer.utils_infer import load_model
+        from ..model import DiT
+
+        F5TTS_model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            conv_layers=4,
+            pe_attn_head=1,
+            text_mask_padding=False,
+        )
+        model = load_model(DiT, F5TTS_model_cfg, args.model_path)
+
+    vocoder = load_vocoder(
+        vocoder_name=args.vocoder, device=device, vocoder_trt_engine_path=args.vocoder_trt_engine_path
+    )
+
+    dataset = load_dataset(
+        "yuekai/seed_tts",
+        split=args.split_name,
+        trust_remote_code=True,
+    )
+
+    def add_estimated_duration(example):
+        prompt_audio_len = example["prompt_audio"]["array"].shape[0]
+        scale_factor = 1 + len(example["target_text"]) / len(example["prompt_text"])
+        estimated_duration = prompt_audio_len * scale_factor
+        example["estimated_duration"] = estimated_duration / example["prompt_audio"]["sampling_rate"]
+        return example
+
+    dataset = dataset.map(add_estimated_duration)
+    dataset = dataset.sort("estimated_duration", reverse=True)
+    if args.use_perf:
+        # dataset_list = [dataset.select(range(1)) for i in range(16)]  # seq_len 1000
+        dataset_list_short = [dataset.select([24]) for i in range(8)]  # seq_len 719
+        # dataset_list_long = [dataset.select([23]) for i in range(8)] # seq_len 2002
+        # dataset = datasets.concatenate_datasets(dataset_list_short + dataset_list_long)
+        dataset = datasets.concatenate_datasets(dataset_list_short)
+    if world_size > 1:
+        sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank)
+    else:
+        # This would disable shuffling
+        sampler = None
+
+    dataloader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        sampler=sampler,
+        shuffle=False,
+        num_workers=args.num_workers,
+        prefetch_factor=args.prefetch,
+        collate_fn=lambda x: data_collator(x, vocab_char_map, use_perf=args.use_perf),
+    )
+
+    total_steps = len(dataset)
+
+    if args.enable_warmup:
+        for batch in dataloader:
+            ref_mels, ref_mel_lens = batch["ref_mel_batch"].to(device), batch["ref_mel_len_batch"].to(device)
+            text_pad_seq = batch["text_pad_sequence"].to(device)
+            total_mel_lens = batch["estimated_reference_target_mel_len"]
+            if args.backend_type == "trt":
+                _ = model.sample(
+                    text_pad_seq, ref_mels, ref_mel_lens, total_mel_lens, remove_input_padding=args.remove_input_padding
+                )
+            elif args.backend_type == "pytorch":
+                with torch.inference_mode():
+                    text_pad_seq -= 1
+                    text_pad_seq[text_pad_seq == -2] = -1
+                    total_mel_lens = torch.tensor(total_mel_lens, device=device)
+                    generated, _ = model.sample(
+                        cond=ref_mels,
+                        text=text_pad_seq,
+                        duration=total_mel_lens,
+                        steps=16,
+                        cfg_strength=2.0,
+                        sway_sampling_coef=-1,
+                    )
+
+    if rank == 0:
+        progress_bar = tqdm(total=total_steps, desc="Processing", unit="wavs")
+
+    decoding_time = 0
+    vocoder_time = 0
+    total_duration = 0
+    if args.use_perf:
+        torch.cuda.cudart().cudaProfilerStart()
+    total_decoding_time = time.time()
+    for batch in dataloader:
+        if args.use_perf:
+            torch.cuda.nvtx.range_push("data sample")
+        ref_mels, ref_mel_lens = batch["ref_mel_batch"].to(device), batch["ref_mel_len_batch"].to(device)
+        text_pad_seq = batch["text_pad_sequence"].to(device)
+        total_mel_lens = batch["estimated_reference_target_mel_len"]
+
+        if args.use_perf:
+            torch.cuda.nvtx.range_pop()
+        if args.backend_type == "trt":
+            generated, cost_time = model.sample(
+                text_pad_seq,
+                ref_mels,
+                ref_mel_lens,
+                total_mel_lens,
+                remove_input_padding=args.remove_input_padding,
+                use_perf=args.use_perf,
+            )
+        elif args.backend_type == "pytorch":
+            total_mel_lens = torch.tensor(total_mel_lens, device=device)
+            with torch.inference_mode():
+                start_time = time.time()
+                text_pad_seq -= 1
+                text_pad_seq[text_pad_seq == -2] = -1
+                generated, _ = model.sample(
+                    cond=ref_mels,
+                    text=text_pad_seq,
+                    duration=total_mel_lens,
+                    lens=ref_mel_lens,
+                    steps=16,
+                    cfg_strength=2.0,
+                    sway_sampling_coef=-1,
+                )
+                cost_time = time.time() - start_time
+        decoding_time += cost_time
+        vocoder_start_time = time.time()
+        for i, gen in enumerate(generated):
+            gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
+            gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
+            if args.vocoder == "vocos":
+                if args.use_perf:
+                    torch.cuda.nvtx.range_push("vocoder decode")
+                generated_wave = vocoder.decode(gen_mel_spec).cpu()
+                if args.use_perf:
+                    torch.cuda.nvtx.range_pop()
+            else:
+                generated_wave = vocoder(gen_mel_spec).squeeze(0).cpu()
+            target_rms = 0.1
+            target_sample_rate = 24_000
+            # if ref_rms_list[i] < target_rms:
+            #     generated_wave = generated_wave * ref_rms_list[i] / target_rms
+            rms = torch.sqrt(torch.mean(torch.square(generated_wave)))
+            if rms < target_rms:
+                generated_wave = generated_wave * target_rms / rms
+            utt = batch["ids"][i]
+            torchaudio.save(
+                f"{args.output_dir}/{utt}.wav",
+                generated_wave,
+                target_sample_rate,
+            )
+            total_duration += generated_wave.shape[1] / target_sample_rate
+        vocoder_time += time.time() - vocoder_start_time
+        if rank == 0:
+            progress_bar.update(world_size * len(batch["ids"]))
+    total_decoding_time = time.time() - total_decoding_time
+    if rank == 0:
+        progress_bar.close()
+    rtf = total_decoding_time / total_duration
+    s = f"RTF: {rtf:.4f}\n"
+    s += f"total_duration: {total_duration:.3f} seconds\n"
+    s += f"({total_duration / 3600:.2f} hours)\n"
+    s += f"DiT time: {decoding_time:.3f} seconds ({decoding_time / 3600:.2f} hours)\n"
+    s += f"Vocoder time: {vocoder_time:.3f} seconds ({vocoder_time / 3600:.2f} hours)\n"
+    s += f"total decoding time: {total_decoding_time:.3f} seconds ({total_decoding_time / 3600:.2f} hours)\n"
+    s += f"batch size: {args.batch_size}\n"
+    print(s)
+
+    with open(f"{args.output_dir}/rtf.txt", "w") as f:
+        f.write(s)
+
+    dist.barrier()
+    dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,470 @@
+#!/usr/bin/env python3
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#                2023  Nvidia              (authors: Yuekai Zhang)
+#                2023  Recurrent.ai        (authors: Songtao Shi)
+# See LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script supports to load dataset from huggingface and sends it to the server
+for decoding, in parallel.
+
+Usage:
+num_task=2
+
+# For offline F5-TTS
+python3 client_grpc.py \
+    --server-addr localhost \
+    --model-name f5_tts \
+    --num-tasks $num_task \
+    --huggingface-dataset yuekai/seed_tts \
+    --split-name test_zh \
+    --log-dir ./log_concurrent_tasks_${num_task}
+
+# For offline Spark-TTS-0.5B
+python3 client_grpc.py \
+    --server-addr localhost \
+    --model-name spark_tts \
+    --num-tasks $num_task \
+    --huggingface-dataset yuekai/seed_tts \
+    --split-name wenetspeech4tts \
+    --log-dir ./log_concurrent_tasks_${num_task}
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import time
+import types
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tritonclient
+import tritonclient.grpc.aio as grpcclient
+from tritonclient.utils import np_to_triton_dtype
+
+
+def write_triton_stats(stats, summary_file):
+    with open(summary_file, "w") as summary_f:
+        model_stats = stats["model_stats"]
+        # write a note, the log is from triton_client.get_inference_statistics(), to better human readability
+        summary_f.write(
+            "The log is parsing from triton_client.get_inference_statistics(), to better human readability. \n"
+        )
+        summary_f.write("To learn more about the log, please refer to: \n")
+        summary_f.write("1. https://github.com/triton-inference-server/server/blob/main/docs/user_guide/metrics.md \n")
+        summary_f.write("2. https://github.com/triton-inference-server/server/issues/5374 \n\n")
+        summary_f.write(
+            "To better improve throughput, we always would like let requests wait in the queue for a while, and then execute them with a larger batch size. \n"
+        )
+        summary_f.write(
+            "However, there is a trade-off between the increased queue time and the increased batch size. \n"
+        )
+        summary_f.write(
+            "You may change 'max_queue_delay_microseconds' and 'preferred_batch_size' in the model configuration file to achieve this. \n"
+        )
+        summary_f.write(
+            "See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#delayed-batching for more details. \n\n"
+        )
+        for model_state in model_stats:
+            if "last_inference" not in model_state:
+                continue
+            summary_f.write(f"model name is {model_state['name']} \n")
+            model_inference_stats = model_state["inference_stats"]
+            total_queue_time_s = int(model_inference_stats["queue"]["ns"]) / 1e9
+            total_infer_time_s = int(model_inference_stats["compute_infer"]["ns"]) / 1e9
+            total_input_time_s = int(model_inference_stats["compute_input"]["ns"]) / 1e9
+            total_output_time_s = int(model_inference_stats["compute_output"]["ns"]) / 1e9
+            summary_f.write(
+                f"queue time {total_queue_time_s:<5.2f} s, compute infer time {total_infer_time_s:<5.2f} s, compute input time {total_input_time_s:<5.2f} s, compute output time {total_output_time_s:<5.2f} s \n"  # noqa
+            )
+            model_batch_stats = model_state["batch_stats"]
+            for batch in model_batch_stats:
+                batch_size = int(batch["batch_size"])
+                compute_input = batch["compute_input"]
+                compute_output = batch["compute_output"]
+                compute_infer = batch["compute_infer"]
+                batch_count = int(compute_infer["count"])
+                assert compute_infer["count"] == compute_output["count"] == compute_input["count"]
+                compute_infer_time_ms = int(compute_infer["ns"]) / 1e6
+                compute_input_time_ms = int(compute_input["ns"]) / 1e6
+                compute_output_time_ms = int(compute_output["ns"]) / 1e6
+                summary_f.write(
+                    f"execuate inference with batch_size {batch_size:<2} total {batch_count:<5} times, total_infer_time {compute_infer_time_ms:<9.2f} ms, avg_infer_time {compute_infer_time_ms:<9.2f}/{batch_count:<5}={compute_infer_time_ms / batch_count:.2f} ms, avg_infer_time_per_sample {compute_infer_time_ms:<9.2f}/{batch_count:<5}/{batch_size}={compute_infer_time_ms / batch_count / batch_size:.2f} ms \n"  # noqa
+                )
+                summary_f.write(
+                    f"input {compute_input_time_ms:<9.2f} ms, avg {compute_input_time_ms / batch_count:.2f} ms, "  # noqa
+                )
+                summary_f.write(
+                    f"output {compute_output_time_ms:<9.2f} ms, avg {compute_output_time_ms / batch_count:.2f} ms \n"  # noqa
+                )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument(
+        "--server-addr",
+        type=str,
+        default="localhost",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--server-port",
+        type=int,
+        default=8001,
+        help="Grpc port of the triton server, default is 8001",
+    )
+
+    parser.add_argument(
+        "--reference-audio",
+        type=str,
+        default=None,
+        help="Path to a single audio file. It can't be specified at the same time with --manifest-dir",
+    )
+
+    parser.add_argument(
+        "--reference-text",
+        type=str,
+        default="",
+        help="",
+    )
+
+    parser.add_argument(
+        "--target-text",
+        type=str,
+        default="",
+        help="",
+    )
+
+    parser.add_argument(
+        "--huggingface-dataset",
+        type=str,
+        default="yuekai/seed_tts",
+        help="dataset name in huggingface dataset hub",
+    )
+
+    parser.add_argument(
+        "--split-name",
+        type=str,
+        default="wenetspeech4tts",
+        choices=["wenetspeech4tts", "test_zh", "test_en", "test_hard"],
+        help="dataset split name, default is 'test'",
+    )
+
+    parser.add_argument(
+        "--manifest-path",
+        type=str,
+        default=None,
+        help="Path to the manifest dir which includes wav.scp trans.txt files.",
+    )
+
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="f5_tts",
+        choices=["f5_tts", "spark_tts"],
+        help="triton model_repo module name to request: transducer for k2, attention_rescoring for wenet offline, streaming_wenet for wenet streaming, infer_pipeline for paraformer large offline",
+    )
+
+    parser.add_argument(
+        "--num-tasks",
+        type=int,
+        default=1,
+        help="Number of concurrent tasks for sending",
+    )
+
+    parser.add_argument(
+        "--log-interval",
+        type=int,
+        default=5,
+        help="Controls how frequently we print the log.",
+    )
+
+    parser.add_argument(
+        "--compute-wer",
+        action="store_true",
+        default=False,
+        help="""True to compute WER.
+        """,
+    )
+
+    parser.add_argument(
+        "--log-dir",
+        type=str,
+        required=False,
+        default="./tmp",
+        help="log directory",
+    )
+
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Inference batch_size per request for offline mode.",
+    )
+
+    return parser.parse_args()
+
+
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
+    if isinstance(wav_path, dict):
+        waveform = wav_path["array"]
+        sample_rate = wav_path["sampling_rate"]
+    else:
+        waveform, sample_rate = sf.read(wav_path)
+    if sample_rate != target_sample_rate:
+        from scipy.signal import resample
+
+        num_samples = int(len(waveform) * (target_sample_rate / sample_rate))
+        waveform = resample(waveform, num_samples)
+    return waveform, target_sample_rate
+
+
+async def send(
+    manifest_item_list: list,
+    name: str,
+    triton_client: tritonclient.grpc.aio.InferenceServerClient,
+    protocol_client: types.ModuleType,
+    log_interval: int,
+    model_name: str,
+    padding_duration: int = None,
+    audio_save_dir: str = "./",
+    save_sample_rate: int = 24000,
+):
+    total_duration = 0.0
+    latency_data = []
+    task_id = int(name[5:])
+
+    print(f"manifest_item_list: {manifest_item_list}")
+    for i, item in enumerate(manifest_item_list):
+        if i % log_interval == 0:
+            print(f"{name}: {i}/{len(manifest_item_list)}")
+        waveform, sample_rate = load_audio(item["audio_filepath"], target_sample_rate=24000)
+        duration = len(waveform) / sample_rate
+        lengths = np.array([[len(waveform)]], dtype=np.int32)
+
+        reference_text, target_text = item["reference_text"], item["target_text"]
+
+        estimated_target_duration = duration / len(reference_text) * len(target_text)
+
+        if padding_duration:
+            # padding to nearset 10 seconds
+            samples = np.zeros(
+                (
+                    1,
+                    padding_duration
+                    * sample_rate
+                    * ((int(estimated_target_duration + duration) // padding_duration) + 1),
+                ),
+                dtype=np.float32,
+            )
+
+            samples[0, : len(waveform)] = waveform
+        else:
+            samples = waveform
+
+        samples = samples.reshape(1, -1).astype(np.float32)
+
+        inputs = [
+            protocol_client.InferInput("reference_wav", samples.shape, np_to_triton_dtype(samples.dtype)),
+            protocol_client.InferInput("reference_wav_len", lengths.shape, np_to_triton_dtype(lengths.dtype)),
+            protocol_client.InferInput("reference_text", [1, 1], "BYTES"),
+            protocol_client.InferInput("target_text", [1, 1], "BYTES"),
+        ]
+        inputs[0].set_data_from_numpy(samples)
+        inputs[1].set_data_from_numpy(lengths)
+
+        input_data_numpy = np.array([reference_text], dtype=object)
+        input_data_numpy = input_data_numpy.reshape((1, 1))
+        inputs[2].set_data_from_numpy(input_data_numpy)
+
+        input_data_numpy = np.array([target_text], dtype=object)
+        input_data_numpy = input_data_numpy.reshape((1, 1))
+        inputs[3].set_data_from_numpy(input_data_numpy)
+
+        outputs = [protocol_client.InferRequestedOutput("waveform")]
+
+        sequence_id = 100000000 + i + task_id * 10
+        start = time.time()
+        response = await triton_client.infer(model_name, inputs, request_id=str(sequence_id), outputs=outputs)
+
+        audio = response.as_numpy("waveform").reshape(-1)
+
+        end = time.time() - start
+
+        audio_save_path = os.path.join(audio_save_dir, f"{item['target_audio_path']}.wav")
+        sf.write(audio_save_path, audio, save_sample_rate, "PCM_16")
+
+        actual_duration = len(audio) / save_sample_rate
+        latency_data.append((end, actual_duration))
+        total_duration += actual_duration
+
+    return total_duration, latency_data
+
+
+def load_manifests(manifest_path):
+    with open(manifest_path, "r") as f:
+        manifest_list = []
+        for line in f:
+            assert len(line.strip().split("|")) == 4
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            utt = Path(utt).stem
+            # gt_wav = os.path.join(os.path.dirname(manifest_path), "wavs", utt + ".wav")
+            if not os.path.isabs(prompt_wav):
+                prompt_wav = os.path.join(os.path.dirname(manifest_path), prompt_wav)
+            manifest_list.append(
+                {
+                    "audio_filepath": prompt_wav,
+                    "reference_text": prompt_text,
+                    "target_text": gt_text,
+                    "target_audio_path": utt,
+                }
+            )
+    return manifest_list
+
+
+def split_data(data, k):
+    n = len(data)
+    if n < k:
+        print(f"Warning: the length of the input list ({n}) is less than k ({k}). Setting k to {n}.")
+        k = n
+
+    quotient = n // k
+    remainder = n % k
+
+    result = []
+    start = 0
+    for i in range(k):
+        if i < remainder:
+            end = start + quotient + 1
+        else:
+            end = start + quotient
+
+        result.append(data[start:end])
+        start = end
+
+    return result
+
+
+async def main():
+    args = get_args()
+    url = f"{args.server_addr}:{args.server_port}"
+
+    triton_client = grpcclient.InferenceServerClient(url=url, verbose=False)
+    protocol_client = grpcclient
+
+    if args.reference_audio:
+        args.num_tasks = 1
+        args.log_interval = 1
+        manifest_item_list = [
+            {
+                "reference_text": args.reference_text,
+                "target_text": args.target_text,
+                "audio_filepath": args.reference_audio,
+                "target_audio_path": "test",
+            }
+        ]
+    elif args.huggingface_dataset:
+        import datasets
+
+        dataset = datasets.load_dataset(
+            args.huggingface_dataset,
+            split=args.split_name,
+            trust_remote_code=True,
+        )
+        manifest_item_list = []
+        for i in range(len(dataset)):
+            manifest_item_list.append(
+                {
+                    "audio_filepath": dataset[i]["prompt_audio"],
+                    "reference_text": dataset[i]["prompt_text"],
+                    "target_audio_path": dataset[i]["id"],
+                    "target_text": dataset[i]["target_text"],
+                }
+            )
+    else:
+        manifest_item_list = load_manifests(args.manifest_path)
+
+    args.num_tasks = min(args.num_tasks, len(manifest_item_list))
+    manifest_item_list = split_data(manifest_item_list, args.num_tasks)
+
+    os.makedirs(args.log_dir, exist_ok=True)
+    tasks = []
+    start_time = time.time()
+    for i in range(args.num_tasks):
+        task = asyncio.create_task(
+            send(
+                manifest_item_list[i],
+                name=f"task-{i}",
+                triton_client=triton_client,
+                protocol_client=protocol_client,
+                log_interval=args.log_interval,
+                model_name=args.model_name,
+                audio_save_dir=args.log_dir,
+                padding_duration=1,
+                save_sample_rate=24000,
+            )
+        )
+        tasks.append(task)
+
+    ans_list = await asyncio.gather(*tasks)
+
+    end_time = time.time()
+    elapsed = end_time - start_time
+
+    total_duration = 0.0
+    latency_data = []
+    for ans in ans_list:
+        total_duration += ans[0]
+        latency_data += ans[1]
+
+    rtf = elapsed / total_duration
+
+    s = f"RTF: {rtf:.4f}\n"
+    s += f"total_duration: {total_duration:.3f} seconds\n"
+    s += f"({total_duration / 3600:.2f} hours)\n"
+    s += f"processing time: {elapsed:.3f} seconds ({elapsed / 3600:.2f} hours)\n"
+
+    latency_list = [chunk_end for (chunk_end, chunk_duration) in latency_data]
+    latency_ms = sum(latency_list) / float(len(latency_list)) * 1000.0
+    latency_variance = np.var(latency_list, dtype=np.float64) * 1000.0
+    s += f"latency_variance: {latency_variance:.2f}\n"
+    s += f"latency_50_percentile_ms: {np.percentile(latency_list, 50) * 1000.0:.2f}\n"
+    s += f"latency_90_percentile_ms: {np.percentile(latency_list, 90) * 1000.0:.2f}\n"
+    s += f"latency_95_percentile_ms: {np.percentile(latency_list, 95) * 1000.0:.2f}\n"
+    s += f"latency_99_percentile_ms: {np.percentile(latency_list, 99) * 1000.0:.2f}\n"
+    s += f"average_latency_ms: {latency_ms:.2f}\n"
+
+    print(s)
+    if args.manifest_path:
+        name = Path(args.manifest_path).stem
+    elif args.split_name:
+        name = args.split_name
+    with open(f"{args.log_dir}/rtf-{name}.txt", "w") as f:
+        f.write(s)
+
+    stats = await triton_client.get_inference_statistics(model_name="", as_json=True)
+    write_triton_stats(stats, f"{args.log_dir}/stats_summary-{name}.txt")
+
+    metadata = await triton_client.get_model_config(model_name=args.model_name, as_json=True)
+    with open(f"{args.log_dir}/model_config-{name}.json", "w") as f:
+        json.dump(metadata, f, indent=4)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,143 @@
+# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import argparse
+
+import numpy as np
+import requests
+import soundfile as sf
+
+
+def get_args():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument(
+        "--server-url",
+        type=str,
+        default="localhost:8000",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--reference-audio",
+        type=str,
+        default="../../infer/examples/basic/basic_ref_en.wav",
+        help="Path to a single audio file. It can't be specified at the same time with --manifest-dir",
+    )
+
+    parser.add_argument(
+        "--reference-text",
+        type=str,
+        default="Some call me nature, others call me mother nature.",
+        help="",
+    )
+
+    parser.add_argument(
+        "--target-text",
+        type=str,
+        default="I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring.",
+        help="",
+    )
+
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="f5_tts",
+        choices=["f5_tts", "spark_tts"],
+        help="triton model_repo module name to request",
+    )
+
+    parser.add_argument(
+        "--output-audio",
+        type=str,
+        default="output.wav",
+        help="Path to save the output audio",
+    )
+    return parser.parse_args()
+
+
+def prepare_request(
+    samples,
+    reference_text,
+    target_text,
+    sample_rate=24000,
+    audio_save_dir: str = "./",
+):
+    assert len(samples.shape) == 1, "samples should be 1D"
+    lengths = np.array([[len(samples)]], dtype=np.int32)
+    samples = samples.reshape(1, -1).astype(np.float32)
+
+    data = {
+        "inputs": [
+            {"name": "reference_wav", "shape": samples.shape, "datatype": "FP32", "data": samples.tolist()},
+            {
+                "name": "reference_wav_len",
+                "shape": lengths.shape,
+                "datatype": "INT32",
+                "data": lengths.tolist(),
+            },
+            {"name": "reference_text", "shape": [1, 1], "datatype": "BYTES", "data": [reference_text]},
+            {"name": "target_text", "shape": [1, 1], "datatype": "BYTES", "data": [target_text]},
+        ]
+    }
+
+    return data
+
+
+def load_audio(wav_path, target_sample_rate=24000):
+    assert target_sample_rate == 24000, "hard coding in server"
+    if isinstance(wav_path, dict):
+        samples = wav_path["array"]
+        sample_rate = wav_path["sampling_rate"]
+    else:
+        samples, sample_rate = sf.read(wav_path)
+    if sample_rate != target_sample_rate:
+        from scipy.signal import resample
+
+        num_samples = int(len(samples) * (target_sample_rate / sample_rate))
+        samples = resample(samples, num_samples)
+    return samples, target_sample_rate
+
+
+if __name__ == "__main__":
+    args = get_args()
+    server_url = args.server_url
+    if not server_url.startswith(("http://", "https://")):
+        server_url = f"http://{server_url}"
+
+    url = f"{server_url}/v2/models/{args.model_name}/infer"
+    samples, sr = load_audio(args.reference_audio)
+    assert sr == 24000, "sample rate hardcoded in server"
+
+    samples = np.array(samples, dtype=np.float32)
+    data = prepare_request(samples, args.reference_text, args.target_text)
+
+    rsp = requests.post(
+        url, headers={"Content-Type": "application/json"}, json=data, verify=False, params={"request_id": "0"}
+    )
+    result = rsp.json()
+    audio = result["outputs"][0]["data"]
+    audio = np.array(audio, dtype=np.float32)
+    sf.write(args.output_audio, audio, 24000, "PCM_16")
@@ -0,0 +1,20 @@
+services:
+  tts:
+    image: soar97/triton-f5-tts:24.12
+    shm_size: '1gb'
+    ports:
+      - "8000:8000"
+      - "8001:8001"
+      - "8002:8002"
+    environment:
+      - PYTHONIOENCODING=utf-8
+      - MODEL_ID=${MODEL_ID}
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [gpu]
+    command: >
+      /bin/bash -c "pip install vocos && rm -rf F5-TTS && git clone https://github.com/SWivid/F5-TTS.git && cd F5-TTS/src/f5_tts/runtime/triton_trtllm/ && bash run.sh 0 4 $MODEL"
@@ -0,0 +1,430 @@
+import math
+import os
+import time
+from functools import wraps
+from typing import List, Optional
+
+import tensorrt as trt
+import tensorrt_llm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tensorrt_llm._utils import str_dtype_to_torch, trt_dtype_to_torch
+from tensorrt_llm.logger import logger
+from tensorrt_llm.runtime.session import Session
+
+
+def remove_tensor_padding(input_tensor, input_tensor_lengths=None):
+    # Audio tensor case: batch, seq_len, feature_len
+    # position_ids case: batch, seq_len
+    assert input_tensor_lengths is not None, "input_tensor_lengths must be provided for 3D input_tensor"
+
+    # Initialize a list to collect valid sequences
+    valid_sequences = []
+
+    for i in range(input_tensor.shape[0]):
+        valid_length = input_tensor_lengths[i]
+        valid_sequences.append(input_tensor[i, :valid_length])
+
+    # Concatenate all valid sequences along the batch dimension
+    output_tensor = torch.cat(valid_sequences, dim=0).contiguous()
+    return output_tensor
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2, precompute_max_pos=4096):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+        self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, precompute_max_pos), persistent=False)
+        self.text_blocks = nn.Sequential(*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)])
+
+    def forward(self, text):
+        # only keep tensors with value not -1
+        text_mask = text != -1
+        text_pad_cut_off_index = text_mask.sum(dim=1).max()
+
+        text = text[:, :text_pad_cut_off_index]
+        text = self.text_embed(text)
+        text = text + self.freqs_cis[: text.shape[1], :]
+        for block in self.text_blocks:
+            text = block(text)
+        # padding text to the original length
+        # text shape: B,seq_len,C
+        # pad at the second dimension
+        text = F.pad(text, (0, 0, 0, text_mask.shape[1] - text.shape[1], 0, 0), value=0)
+        return text
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def load_checkpoint(ckpt_path, use_ema=True):
+    checkpoint = torch.load(ckpt_path, weights_only=True)
+    if use_ema:
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+    dict_state = checkpoint["model_state_dict"]
+    text_embed_dict = {}
+    for key in dict_state.keys():
+        # transformer.text_embed.text_embed.weight -> text_embed.weight
+        if "text_embed" in key:
+            text_embed_dict[key.replace("transformer.text_embed.", "")] = dict_state[key]
+    return text_embed_dict
+
+
+class F5TTS(object):
+    def __init__(
+        self,
+        config,
+        debug_mode=True,
+        stream: Optional[torch.cuda.Stream] = None,
+        tllm_model_dir: Optional[str] = None,
+        model_path: Optional[str] = None,
+        vocab_size: Optional[int] = None,
+    ):
+        self.dtype = config["pretrained_config"]["dtype"]
+
+        rank = tensorrt_llm.mpi_rank()
+        world_size = config["pretrained_config"]["mapping"]["world_size"]
+        cp_size = config["pretrained_config"]["mapping"]["cp_size"]
+        tp_size = config["pretrained_config"]["mapping"]["tp_size"]
+        pp_size = config["pretrained_config"]["mapping"]["pp_size"]
+        assert pp_size == 1
+        self.mapping = tensorrt_llm.Mapping(
+            world_size=world_size, rank=rank, cp_size=cp_size, tp_size=tp_size, pp_size=1, gpus_per_node=1
+        )
+
+        local_rank = rank % self.mapping.gpus_per_node
+        self.device = torch.device(f"cuda:{local_rank}")
+
+        torch.cuda.set_device(self.device)
+
+        self.stream = stream
+        if self.stream is None:
+            self.stream = torch.cuda.Stream(self.device)
+        torch.cuda.set_stream(self.stream)
+
+        engine_file = os.path.join(tllm_model_dir, f"rank{rank}.engine")
+        logger.info(f"Loading engine from {engine_file}")
+        with open(engine_file, "rb") as f:
+            engine_buffer = f.read()
+
+        assert engine_buffer is not None
+
+        self.session = Session.from_serialized_engine(engine_buffer)
+
+        self.debug_mode = debug_mode
+
+        self.inputs = {}
+        self.outputs = {}
+        self.buffer_allocated = False
+
+        expected_tensor_names = ["noise", "cond", "time", "rope_cos", "rope_sin", "input_lengths", "denoised"]
+
+        found_tensor_names = [self.session.engine.get_tensor_name(i) for i in range(self.session.engine.num_io_tensors)]
+        if not self.debug_mode and set(expected_tensor_names) != set(found_tensor_names):
+            logger.error(
+                f"The following expected tensors are not found: {set(expected_tensor_names).difference(set(found_tensor_names))}"
+            )
+            logger.error(
+                f"Those tensors in engine are not expected: {set(found_tensor_names).difference(set(expected_tensor_names))}"
+            )
+            logger.error(f"Expected tensor names: {expected_tensor_names}")
+            logger.error(f"Found tensor names: {found_tensor_names}")
+            raise RuntimeError("Tensor names in engine are not the same as expected.")
+        if self.debug_mode:
+            self.debug_tensors = list(set(found_tensor_names) - set(expected_tensor_names))
+
+        self.max_mel_len = 4096
+        self.text_embedding = TextEmbedding(
+            text_num_embeds=vocab_size, text_dim=512, conv_layers=4, precompute_max_pos=self.max_mel_len
+        ).to(self.device)
+        self.text_embedding.load_state_dict(load_checkpoint(model_path), strict=True)
+
+        self.target_audio_sample_rate = 24000
+        self.target_rms = 0.15  # target rms for audio
+        self.n_fft = 1024
+        self.win_length = 1024
+        self.hop_length = 256
+        self.n_mel_channels = 100
+        # self.max_mel_len = 3000
+        self.head_dim = 64
+        self.base_rescale_factor = 1.0
+        self.interpolation_factor = 1.0
+        base = 10000.0 * self.base_rescale_factor ** (self.head_dim / (self.head_dim - 2))
+        inv_freq = 1.0 / (base ** (torch.arange(0, self.head_dim, 2).float() / self.head_dim))
+        freqs = torch.outer(torch.arange(self.max_mel_len, dtype=torch.float32), inv_freq) / self.interpolation_factor
+        self.freqs = freqs.repeat_interleave(2, dim=-1).unsqueeze(0)
+        self.rope_cos = self.freqs.cos().half()
+        self.rope_sin = self.freqs.sin().half()
+        self.nfe_steps = 16
+        t = torch.linspace(0, 1, self.nfe_steps + 1, dtype=torch.float32)
+        time_step = t + (-1.0) * (torch.cos(torch.pi * 0.5 * t) - 1 + t)
+        delta_t = torch.diff(time_step)
+        # WAR: hard coding 256 here
+        tmp_dim = 256
+        time_expand = torch.zeros((1, self.nfe_steps, tmp_dim), dtype=torch.float32)
+        half_dim = tmp_dim // 2
+        emb_factor = math.log(10000) / (half_dim - 1)
+        emb_factor = 1000.0 * torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb_factor)
+        for i in range(self.nfe_steps):
+            emb = time_step[i] * emb_factor
+            time_expand[:, i, :] = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        self.time_expand = time_expand.to(self.device)
+        self.delta_t = torch.cat((delta_t, delta_t), dim=0).contiguous().to(self.device)
+
+    def _tensor_dtype(self, name):
+        # return torch dtype given tensor name for convenience
+        dtype = trt_dtype_to_torch(self.session.engine.get_tensor_dtype(name))
+        return dtype
+
+    def _setup(self, batch_size, seq_len):
+        for i in range(self.session.engine.num_io_tensors):
+            name = self.session.engine.get_tensor_name(i)
+            if self.session.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
+                shape = list(self.session.engine.get_tensor_shape(name))
+                shape[0] = batch_size
+                shape[1] = seq_len
+                self.outputs[name] = torch.empty(shape, dtype=self._tensor_dtype(name), device=self.device)
+
+        self.buffer_allocated = True
+
+    def cuda_stream_guard(func):
+        """Sync external stream and set current stream to the one bound to the session. Reset on exit."""
+
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            external_stream = torch.cuda.current_stream()
+            if external_stream != self.stream:
+                external_stream.synchronize()
+                torch.cuda.set_stream(self.stream)
+            ret = func(self, *args, **kwargs)
+            if external_stream != self.stream:
+                self.stream.synchronize()
+                torch.cuda.set_stream(external_stream)
+            return ret
+
+        return wrapper
+
+    @cuda_stream_guard
+    def forward(
+        self,
+        noise: torch.Tensor,
+        cond: torch.Tensor,
+        time_expand: torch.Tensor,
+        rope_cos: torch.Tensor,
+        rope_sin: torch.Tensor,
+        input_lengths: torch.Tensor,
+        delta_t: torch.Tensor,
+        use_perf: bool = False,
+    ):
+        if use_perf:
+            torch.cuda.nvtx.range_push("flow matching")
+        cfg_strength = 2.0
+        batch_size = noise.shape[0]
+        half_batch = batch_size // 2
+        noise_half = noise[:half_batch]  # Store the initial half of noise
+
+        input_type = str_dtype_to_torch(self.dtype)
+
+        # Keep a copy of the initial tensors
+        cond = cond.to(input_type)
+        rope_cos = rope_cos.to(input_type)
+        rope_sin = rope_sin.to(input_type)
+        input_lengths = input_lengths.to(str_dtype_to_torch("int32"))
+
+        # Instead of iteratively updating noise within a single model context,
+        # we'll do a single forward pass for each iteration with fresh context setup
+        for i in range(self.nfe_steps):
+            # Re-setup the buffers for clean execution
+            self._setup(batch_size, noise.shape[1])
+            if not self.buffer_allocated:
+                raise RuntimeError("Buffer not allocated, please call setup first!")
+
+            # Re-create combined noises for this iteration
+            current_noise = torch.cat([noise_half, noise_half], dim=0).to(input_type)
+
+            # Get time step for this iteration
+            current_time = time_expand[:, i].to(input_type)
+
+            # Create fresh input dictionary for this iteration
+            current_inputs = {
+                "noise": current_noise,
+                "cond": cond,
+                "time": current_time,
+                "rope_cos": rope_cos,
+                "rope_sin": rope_sin,
+                "input_lengths": input_lengths,
+            }
+
+            # Update inputs and set shapes
+            self.inputs.clear()  # Clear previous inputs
+            self.inputs.update(**current_inputs)
+            self.session.set_shapes(self.inputs)
+
+            if use_perf:
+                torch.cuda.nvtx.range_push(f"execute {i}")
+            ok = self.session.run(self.inputs, self.outputs, self.stream.cuda_stream)
+            assert ok, "Failed to execute model"
+            # self.session.context.execute_async_v3(self.stream.cuda_stream)
+            if use_perf:
+                torch.cuda.nvtx.range_pop()
+            # Process results
+            t_scale = delta_t[i].unsqueeze(0).to(input_type)
+
+            # Extract predictions
+            pred_cond = self.outputs["denoised"][:half_batch]
+            pred_uncond = self.outputs["denoised"][half_batch:]
+
+            # Apply classifier-free guidance with safeguards
+            guidance = pred_cond + (pred_cond - pred_uncond) * cfg_strength
+            # Calculate update for noise
+            noise_half = noise_half + guidance * t_scale
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        return noise_half
+
+    def sample(
+        self,
+        text_pad_sequence: torch.Tensor,
+        ref_mel_batch: torch.Tensor,
+        ref_mel_len_batch: torch.Tensor,
+        estimated_reference_target_mel_len: List[int],
+        remove_input_padding: bool = False,
+        use_perf: bool = False,
+    ):
+        if use_perf:
+            torch.cuda.nvtx.range_push("text embedding")
+        batch = text_pad_sequence.shape[0]
+        max_seq_len = ref_mel_batch.shape[1]
+
+        text_pad_sequence_drop = torch.cat(
+            (text_pad_sequence, torch.zeros((1, text_pad_sequence.shape[1]), dtype=torch.int32).to(self.device)), dim=0
+        )
+
+        text_embedding_drop_list = []
+        for i in range(batch + 1):
+            text_embedding_drop_list.append(self.text_embedding(text_pad_sequence_drop[i].unsqueeze(0).to(self.device)))
+        text_embedding_drop_condition = torch.cat(text_embedding_drop_list, dim=0)
+
+        text_embedding = text_embedding_drop_condition[:-1]
+        # text_embedding_drop B,T,C batch should be the same
+        text_embedding_drop = text_embedding_drop_condition[-1].unsqueeze(0).repeat(batch, 1, 1)
+
+        noise = torch.randn_like(ref_mel_batch).to(self.device)
+        rope_cos = self.rope_cos[:, :max_seq_len, :].float().repeat(batch, 1, 1)
+        rope_sin = self.rope_sin[:, :max_seq_len, :].float().repeat(batch, 1, 1)
+
+        cat_mel_text = torch.cat((ref_mel_batch, text_embedding), dim=-1)
+        cat_mel_text_drop = torch.cat(
+            (
+                torch.zeros((batch, max_seq_len, self.n_mel_channels), dtype=torch.float32).to(self.device),
+                text_embedding_drop,
+            ),
+            dim=-1,
+        )
+
+        time_expand = self.time_expand.repeat(2 * batch, 1, 1).contiguous()
+
+        # Convert estimated_reference_target_mel_len to tensor
+        input_lengths = torch.tensor(estimated_reference_target_mel_len, dtype=torch.int32)
+
+        # combine above along the batch dimension
+        inputs = {
+            "noise": torch.cat((noise, noise), dim=0).contiguous(),
+            "cond": torch.cat((cat_mel_text, cat_mel_text_drop), dim=0).contiguous(),
+            "time_expand": time_expand,
+            "rope_cos": torch.cat((rope_cos, rope_cos), dim=0).contiguous(),
+            "rope_sin": torch.cat((rope_sin, rope_sin), dim=0).contiguous(),
+            "input_lengths": torch.cat((input_lengths, input_lengths), dim=0).contiguous(),
+            "delta_t": self.delta_t,
+        }
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_push("remove input padding")
+        if remove_input_padding:
+            max_seq_len = inputs["cond"].shape[1]
+            inputs["noise"] = remove_tensor_padding(inputs["noise"], inputs["input_lengths"])
+            inputs["cond"] = remove_tensor_padding(inputs["cond"], inputs["input_lengths"])
+            # for time_expand, convert from B,D to B,T,D by repeat
+            inputs["time_expand"] = inputs["time_expand"].unsqueeze(1).repeat(1, max_seq_len, 1, 1)
+            inputs["time_expand"] = remove_tensor_padding(inputs["time_expand"], inputs["input_lengths"])
+            inputs["rope_cos"] = remove_tensor_padding(inputs["rope_cos"], inputs["input_lengths"])
+            inputs["rope_sin"] = remove_tensor_padding(inputs["rope_sin"], inputs["input_lengths"])
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_pop()
+        for key in inputs:
+            inputs[key] = inputs[key].to(self.device)
+        if use_perf:
+            torch.cuda.nvtx.range_pop()
+        start_time = time.time()
+        denoised = self.forward(**inputs, use_perf=use_perf)
+        cost_time = time.time() - start_time
+        if use_perf and remove_input_padding:
+            torch.cuda.nvtx.range_push("remove input padding output")
+        if remove_input_padding:
+            denoised_list = []
+            start_idx = 0
+            for i in range(batch):
+                denoised_list.append(denoised[start_idx : start_idx + inputs["input_lengths"][i]])
+                start_idx += inputs["input_lengths"][i]
+            if use_perf and remove_input_padding:
+                torch.cuda.nvtx.range_pop()
+            return denoised_list, cost_time
+        return denoised, cost_time
@@ -0,0 +1,278 @@
+# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import json
+import os
+
+import jieba
+import torch
+import torch.nn.functional as F
+import torchaudio
+import triton_python_backend_utils as pb_utils
+from f5_tts_trtllm import F5TTS
+from pypinyin import Style, lazy_pinyin
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.dlpack import from_dlpack, to_dlpack
+
+
+def get_tokenizer(vocab_file_path: str):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    with open(vocab_file_path, "r", encoding="utf-8") as f:
+        vocab_char_map = {}
+        for i, char in enumerate(f):
+            vocab_char_map[char[:-1]] = i
+    vocab_size = len(vocab_char_map)
+    return vocab_char_map, vocab_size
+
+
+def convert_char_to_pinyin(reference_target_texts_list, polyphone=True):
+    final_reference_target_texts_list = []
+    custom_trans = str.maketrans(
+        {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # add custom trans here, to address oov
+
+    def is_chinese(c):
+        return "\u3100" <= c <= "\u9fff"  # common chinese characters
+
+    for text in reference_target_texts_list:
+        char_list = []
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure east asian characters
+                seg_ = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for i, c in enumerate(seg):
+                    if is_chinese(c):
+                        char_list.append(" ")
+                    char_list.append(seg_[i])
+            else:  # if mixed characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    elif is_chinese(c):
+                        char_list.append(" ")
+                        char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                    else:
+                        char_list.append(c)
+        final_reference_target_texts_list.append(char_list)
+
+    return final_reference_target_texts_list
+
+
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+):  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    return list_idx_tensors
+
+
+class TritonPythonModel:
+    def initialize(self, args):
+        self.use_perf = True
+        self.device = torch.device("cuda")
+        self.target_audio_sample_rate = 24000
+        self.target_rms = 0.15  # target rms for audio
+        self.n_fft = 1024
+        self.win_length = 1024
+        self.hop_length = 256
+        self.n_mel_channels = 100
+        self.max_mel_len = 3000
+        self.head_dim = 64
+
+        parameters = json.loads(args["model_config"])["parameters"]
+        for key, value in parameters.items():
+            parameters[key] = value["string_value"]
+
+        self.vocab_char_map, self.vocab_size = get_tokenizer(parameters["vocab_file"])
+        self.reference_sample_rate = int(parameters["reference_audio_sample_rate"])
+        self.resampler = torchaudio.transforms.Resample(self.reference_sample_rate, self.target_audio_sample_rate)
+
+        self.tllm_model_dir = parameters["tllm_model_dir"]
+        config_file = os.path.join(self.tllm_model_dir, "config.json")
+        with open(config_file) as f:
+            config = json.load(f)
+        self.model = F5TTS(
+            config,
+            debug_mode=False,
+            tllm_model_dir=self.tllm_model_dir,
+            model_path=parameters["model_path"],
+            vocab_size=self.vocab_size,
+        )
+
+        self.vocoder = parameters["vocoder"]
+        assert self.vocoder in ["vocos", "bigvgan"]
+        if self.vocoder == "vocos":
+            self.mel_stft = torchaudio.transforms.MelSpectrogram(
+                sample_rate=self.target_audio_sample_rate,
+                n_fft=self.n_fft,
+                win_length=self.win_length,
+                hop_length=self.hop_length,
+                n_mels=self.n_mel_channels,
+                power=1,
+                center=True,
+                normalized=False,
+                norm=None,
+            ).to(self.device)
+            self.compute_mel_fn = self.get_vocos_mel_spectrogram
+        elif self.vocoder == "bigvgan":
+            self.compute_mel_fn = self.get_bigvgan_mel_spectrogram
+
+    def get_vocos_mel_spectrogram(self, waveform):
+        mel = self.mel_stft(waveform)
+        mel = mel.clamp(min=1e-5).log()
+        return mel.transpose(1, 2)
+
+    def forward_vocoder(self, mel):
+        mel = mel.to(torch.float32).contiguous().cpu()
+        input_tensor_0 = pb_utils.Tensor.from_dlpack("mel", to_dlpack(mel))
+
+        inference_request = pb_utils.InferenceRequest(
+            model_name="vocoder", requested_output_names=["waveform"], inputs=[input_tensor_0]
+        )
+        inference_response = inference_request.exec()
+        if inference_response.has_error():
+            raise pb_utils.TritonModelException(inference_response.error().message())
+        else:
+            waveform = pb_utils.get_output_tensor_by_name(inference_response, "waveform")
+            waveform = torch.utils.dlpack.from_dlpack(waveform.to_dlpack()).cpu()
+
+            return waveform
+
+    def execute(self, requests):
+        (
+            reference_text_list,
+            target_text_list,
+            reference_target_texts_list,
+            estimated_reference_target_mel_len,
+            reference_mel_len,
+        ) = [], [], [], [], []
+        mel_features_list = []
+        if self.use_perf:
+            torch.cuda.nvtx.range_push("preprocess")
+        for request in requests:
+            wav_tensor = pb_utils.get_input_tensor_by_name(request, "reference_wav")
+            wav_lens = pb_utils.get_input_tensor_by_name(request, "reference_wav_len")
+
+            reference_text = pb_utils.get_input_tensor_by_name(request, "reference_text").as_numpy()
+            reference_text = reference_text[0][0].decode("utf-8")
+            reference_text_list.append(reference_text)
+            target_text = pb_utils.get_input_tensor_by_name(request, "target_text").as_numpy()
+            target_text = target_text[0][0].decode("utf-8")
+            target_text_list.append(target_text)
+
+            text = reference_text + target_text
+            reference_target_texts_list.append(text)
+
+            wav = from_dlpack(wav_tensor.to_dlpack())
+            wav_len = from_dlpack(wav_lens.to_dlpack())
+            wav_len = wav_len.squeeze()
+            assert wav.shape[0] == 1, "Only support batch size 1 for now."
+            wav = wav[:, :wav_len]
+
+            ref_rms = torch.sqrt(torch.mean(torch.square(wav)))
+            if ref_rms < self.target_rms:
+                wav = wav * self.target_rms / ref_rms
+            if self.reference_sample_rate != self.target_audio_sample_rate:
+                wav = self.resampler(wav)
+            wav = wav.to(self.device)
+            if self.use_perf:
+                torch.cuda.nvtx.range_push("compute_mel")
+            mel_features = self.compute_mel_fn(wav)
+            if self.use_perf:
+                torch.cuda.nvtx.range_pop()
+            mel_features_list.append(mel_features)
+
+            reference_mel_len.append(mel_features.shape[1])
+            estimated_reference_target_mel_len.append(
+                int(
+                    mel_features.shape[1] * (1 + len(target_text.encode("utf-8")) / len(reference_text.encode("utf-8")))
+                )
+            )
+
+        max_seq_len = min(max(estimated_reference_target_mel_len), self.max_mel_len)
+
+        batch = len(requests)
+        mel_features = torch.zeros((batch, max_seq_len, self.n_mel_channels), dtype=torch.float16).to(self.device)
+        for i, mel in enumerate(mel_features_list):
+            mel_features[i, : mel.shape[1], :] = mel
+
+        reference_mel_len_tensor = torch.LongTensor(reference_mel_len).to(self.device)
+
+        pinyin_list = convert_char_to_pinyin(reference_target_texts_list, polyphone=True)
+        text_pad_sequence = list_str_to_idx(pinyin_list, self.vocab_char_map)
+
+        for i, item in enumerate(text_pad_sequence):
+            text_pad_sequence[i] = F.pad(
+                item, (0, estimated_reference_target_mel_len[i] - len(item)), mode="constant", value=-1
+            )
+            text_pad_sequence[i] += 1  # WAR: 0 is reserved for padding token, hard coding in F5-TTS
+        text_pad_sequence = pad_sequence(text_pad_sequence, padding_value=-1, batch_first=True).to(self.device)
+        text_pad_sequence = F.pad(
+            text_pad_sequence, (0, max_seq_len - text_pad_sequence.shape[1]), mode="constant", value=-1
+        )
+        if self.use_perf:
+            torch.cuda.nvtx.range_pop()
+
+        denoised, cost_time = self.model.sample(
+            text_pad_sequence,
+            mel_features,
+            reference_mel_len_tensor,
+            estimated_reference_target_mel_len,
+            remove_input_padding=False,
+            use_perf=self.use_perf,
+        )
+        if self.use_perf:
+            torch.cuda.nvtx.range_push("vocoder")
+
+        responses = []
+        for i in range(batch):
+            ref_me_len = reference_mel_len[i]
+            estimated_mel_len = estimated_reference_target_mel_len[i]
+            denoised_one_item = denoised[i, ref_me_len:estimated_mel_len, :].unsqueeze(0).transpose(1, 2)
+            audio = self.forward_vocoder(denoised_one_item)
+            rms = torch.sqrt(torch.mean(torch.square(audio)))
+            if rms < self.target_rms:
+                audio = audio * self.target_rms / rms
+
+            audio = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[audio])
+            responses.append(inference_response)
+        if self.use_perf:
+            torch.cuda.nvtx.range_pop()
+        return responses
@@ -0,0 +1,81 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: "f5_tts"
+backend: "python"
+max_batch_size: 4
+dynamic_batching {
+    max_queue_delay_microseconds: 1000
+}
+parameters [
+  {
+    key: "vocab_file"
+    value: { string_value: "${vocab}"}
+  },
+  {
+   key: "model_path", 
+   value: {string_value:"${model}"}
+  },
+  {
+   key: "tllm_model_dir", 
+   value: {string_value:"${trtllm}"}
+  },
+  {
+   key: "reference_audio_sample_rate", 
+   value: {string_value:"24000"}
+  },
+  {
+   key: "vocoder", 
+   value: {string_value:"${vocoder}"}
+  }
+]
+
+input [
+  {
+    name: "reference_wav"
+    data_type: TYPE_FP32
+    dims: [-1]
+    optional: True
+  },
+  {
+    name: "reference_wav_len"
+    data_type: TYPE_INT32
+    dims: [1]
+    optional: True
+  },
+  {
+    name: "reference_text"
+    data_type: TYPE_STRING
+    dims: [1]
+  },
+  {
+    name: "target_text"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+output [
+  {
+    name: "waveform"
+    data_type: TYPE_FP32
+    dims: [ -1 ]
+  }
+]
+
+instance_group [
+  {
+    count: 1
+    kind: KIND_GPU
+  }
+]
@@ -0,0 +1,32 @@
+name: "vocoder"
+backend: "tensorrt"
+default_model_filename: "vocoder.plan"
+max_batch_size: 4
+
+input [
+  {
+    name: "mel"
+    data_type: TYPE_FP32
+    dims: [ 100, -1 ]
+  }
+]
+
+output [
+  {
+    name: "waveform"
+    data_type: TYPE_FP32
+    dims: [ -1 ]
+  }
+]
+
+dynamic_batching {
+    preferred_batch_size: [1, 2, 4]
+    max_queue_delay_microseconds: 1
+}
+
+instance_group [
+  {
+    count: 1
+    kind: KIND_GPU 
+  }
+]
@@ -0,0 +1,199 @@
+# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .baichuan.model import BaichuanForCausalLM
+from .bert.model import (
+    BertForQuestionAnswering,
+    BertForSequenceClassification,
+    BertModel,
+    RobertaForQuestionAnswering,
+    RobertaForSequenceClassification,
+    RobertaModel,
+)
+from .bloom.model import BloomForCausalLM, BloomModel
+from .chatglm.config import ChatGLMConfig
+from .chatglm.model import ChatGLMForCausalLM, ChatGLMModel
+from .cogvlm.config import CogVLMConfig
+from .cogvlm.model import CogVLMForCausalLM
+from .commandr.model import CohereForCausalLM
+from .dbrx.config import DbrxConfig
+from .dbrx.model import DbrxForCausalLM
+from .deepseek_v1.model import DeepseekForCausalLM
+from .deepseek_v2.model import DeepseekV2ForCausalLM
+from .dit.model import DiT
+from .eagle.model import EagleForCausalLM
+from .enc_dec.model import DecoderModel, EncoderModel, WhisperEncoder
+from .f5tts.model import F5TTS
+from .falcon.config import FalconConfig
+from .falcon.model import FalconForCausalLM, FalconModel
+from .gemma.config import GEMMA2_ARCHITECTURE, GEMMA_ARCHITECTURE, GemmaConfig
+from .gemma.model import GemmaForCausalLM
+from .gpt.config import GPTConfig
+from .gpt.model import GPTForCausalLM, GPTModel
+from .gptj.config import GPTJConfig
+from .gptj.model import GPTJForCausalLM, GPTJModel
+from .gptneox.model import GPTNeoXForCausalLM, GPTNeoXModel
+from .grok.model import GrokForCausalLM
+from .llama.config import LLaMAConfig
+from .llama.model import LLaMAForCausalLM, LLaMAModel
+from .mamba.model import MambaForCausalLM
+from .medusa.config import MedusaConfig
+from .medusa.model import MedusaForCausalLm
+from .mllama.model import MLLaMAModel
+from .modeling_utils import PretrainedConfig, PretrainedModel, SpeculativeDecodingMode
+from .mpt.model import MPTForCausalLM, MPTModel
+from .nemotron_nas.model import DeciLMForCausalLM
+from .opt.model import OPTForCausalLM, OPTModel
+from .phi.model import PhiForCausalLM, PhiModel
+from .phi3.model import Phi3ForCausalLM, Phi3Model
+from .qwen.model import QWenForCausalLM
+from .recurrentgemma.model import RecurrentGemmaForCausalLM
+from .redrafter.model import ReDrafterForCausalLM
+
+
+__all__ = [
+    "BertModel",
+    "BertForQuestionAnswering",
+    "BertForSequenceClassification",
+    "RobertaModel",
+    "RobertaForQuestionAnswering",
+    "RobertaForSequenceClassification",
+    "BloomModel",
+    "BloomForCausalLM",
+    "DiT",
+    "DeepseekForCausalLM",
+    "FalconConfig",
+    "DeepseekV2ForCausalLM",
+    "FalconForCausalLM",
+    "FalconModel",
+    "GPTConfig",
+    "GPTModel",
+    "GPTForCausalLM",
+    "OPTForCausalLM",
+    "OPTModel",
+    "LLaMAConfig",
+    "LLaMAForCausalLM",
+    "LLaMAModel",
+    "MedusaConfig",
+    "MedusaForCausalLm",
+    "ReDrafterForCausalLM",
+    "GPTJConfig",
+    "GPTJModel",
+    "GPTJForCausalLM",
+    "GPTNeoXModel",
+    "GPTNeoXForCausalLM",
+    "PhiModel",
+    "PhiConfig",
+    "Phi3Model",
+    "Phi3Config",
+    "PhiForCausalLM",
+    "Phi3ForCausalLM",
+    "ChatGLMConfig",
+    "ChatGLMForCausalLM",
+    "ChatGLMModel",
+    "BaichuanForCausalLM",
+    "QWenConfigQWenForCausalLM",
+    "QWenModel",
+    "EncoderModel",
+    "DecoderModel",
+    "PretrainedConfig",
+    "PretrainedModel",
+    "WhisperEncoder",
+    "MambaForCausalLM",
+    "MambaConfig",
+    "MPTForCausalLM",
+    "MPTModel",
+    "SkyworkForCausalLM",
+    "GemmaConfig",
+    "GemmaForCausalLM",
+    "DbrxConfig",
+    "DbrxForCausalLM",
+    "RecurrentGemmaForCausalLM",
+    "CogVLMConfig",
+    "CogVLMForCausalLM",
+    "EagleForCausalLM",
+    "SpeculativeDecodingMode",
+    "CohereForCausalLM",
+    "MLLaMAModel",
+    "F5TTS",
+]
+
+MODEL_MAP = {
+    "GPT2LMHeadModel": GPTForCausalLM,
+    "GPT2LMHeadCustomModel": GPTForCausalLM,
+    "GPTBigCodeForCausalLM": GPTForCausalLM,
+    "Starcoder2ForCausalLM": GPTForCausalLM,
+    "FuyuForCausalLM": GPTForCausalLM,
+    "Kosmos2ForConditionalGeneration": GPTForCausalLM,
+    "JAISLMHeadModel": GPTForCausalLM,
+    "GPTForCausalLM": GPTForCausalLM,
+    "NemotronForCausalLM": GPTForCausalLM,
+    "OPTForCausalLM": OPTForCausalLM,
+    "BloomForCausalLM": BloomForCausalLM,
+    "RWForCausalLM": FalconForCausalLM,
+    "FalconForCausalLM": FalconForCausalLM,
+    "PhiForCausalLM": PhiForCausalLM,
+    "Phi3ForCausalLM": Phi3ForCausalLM,
+    "Phi3VForCausalLM": Phi3ForCausalLM,
+    "Phi3SmallForCausalLM": Phi3ForCausalLM,
+    "PhiMoEForCausalLM": Phi3ForCausalLM,
+    "MambaForCausalLM": MambaForCausalLM,
+    "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
+    "GPTJForCausalLM": GPTJForCausalLM,
+    "MPTForCausalLM": MPTForCausalLM,
+    "GLMModel": ChatGLMForCausalLM,
+    "ChatGLMModel": ChatGLMForCausalLM,
+    "ChatGLMForCausalLM": ChatGLMForCausalLM,
+    "LlamaForCausalLM": LLaMAForCausalLM,
+    "ExaoneForCausalLM": LLaMAForCausalLM,
+    "MistralForCausalLM": LLaMAForCausalLM,
+    "MixtralForCausalLM": LLaMAForCausalLM,
+    "ArcticForCausalLM": LLaMAForCausalLM,
+    "Grok1ModelForCausalLM": GrokForCausalLM,
+    "InternLMForCausalLM": LLaMAForCausalLM,
+    "InternLM2ForCausalLM": LLaMAForCausalLM,
+    "MedusaForCausalLM": MedusaForCausalLm,
+    "ReDrafterForCausalLM": ReDrafterForCausalLM,
+    "BaichuanForCausalLM": BaichuanForCausalLM,
+    "BaiChuanForCausalLM": BaichuanForCausalLM,
+    "SkyworkForCausalLM": LLaMAForCausalLM,
+    GEMMA_ARCHITECTURE: GemmaForCausalLM,
+    GEMMA2_ARCHITECTURE: GemmaForCausalLM,
+    "QWenLMHeadModel": QWenForCausalLM,
+    "QWenForCausalLM": QWenForCausalLM,
+    "Qwen2ForCausalLM": QWenForCausalLM,
+    "Qwen2MoeForCausalLM": QWenForCausalLM,
+    "Qwen2ForSequenceClassification": QWenForCausalLM,
+    "Qwen2VLForConditionalGeneration": QWenForCausalLM,
+    "WhisperEncoder": WhisperEncoder,
+    "EncoderModel": EncoderModel,
+    "DecoderModel": DecoderModel,
+    "DbrxForCausalLM": DbrxForCausalLM,
+    "RecurrentGemmaForCausalLM": RecurrentGemmaForCausalLM,
+    "CogVLMForCausalLM": CogVLMForCausalLM,
+    "DiT": DiT,
+    "DeepseekForCausalLM": DeepseekForCausalLM,
+    "DeciLMForCausalLM": DeciLMForCausalLM,
+    "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
+    "EagleForCausalLM": EagleForCausalLM,
+    "CohereForCausalLM": CohereForCausalLM,
+    "MllamaForConditionalGeneration": MLLaMAModel,
+    "BertForQuestionAnswering": BertForQuestionAnswering,
+    "BertForSequenceClassification": BertForSequenceClassification,
+    "BertModel": BertModel,
+    "RobertaModel": RobertaModel,
+    "RobertaForQuestionAnswering": RobertaForQuestionAnswering,
+    "RobertaForSequenceClassification": RobertaForSequenceClassification,
+    "F5TTS": F5TTS,
+}
@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import os
+import sys
+from collections import OrderedDict
+
+import tensorrt as trt
+from tensorrt_llm._common import default_net
+
+from ..._utils import str_dtype_to_trt
+from ...functional import Tensor, concat
+from ...layers import Linear
+from ...module import Module, ModuleList
+from ...plugin import current_all_reduce_helper
+from ..modeling_utils import PretrainedConfig, PretrainedModel
+from .modules import AdaLayerNormZero_Final, ConvPositionEmbedding, DiTBlock, TimestepEmbedding
+
+
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)
+sys.path.append(parent_dir)
+
+
+class InputEmbedding(Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x, cond):
+        x = self.proj(concat([x, cond], dim=-1))
+        return self.conv_pos_embed(x) + x
+
+
+class F5TTS(PretrainedModel):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__(config)
+        self.dtype = str_dtype_to_trt(config.dtype)
+
+        self.time_embed = TimestepEmbedding(config.hidden_size)
+        self.input_embed = InputEmbedding(config.mel_dim, config.text_dim, config.hidden_size)
+
+        self.dim = config.hidden_size
+        self.depth = config.num_hidden_layers
+        self.transformer_blocks = ModuleList(
+            [
+                DiTBlock(
+                    dim=self.dim,
+                    heads=config.num_attention_heads,
+                    dim_head=config.dim_head,
+                    ff_mult=config.ff_mult,
+                    dropout=config.dropout,
+                )
+                for _ in range(self.depth)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormZero_Final(config.hidden_size)  # final modulation
+        self.proj_out = Linear(config.hidden_size, config.mel_dim)
+
+    def forward(
+        self,
+        noise,  # nosied input audio
+        cond,  # masked cond audio
+        time,  # time step
+        rope_cos,
+        rope_sin,
+        input_lengths,
+        scale=1.0,
+    ):
+        t = self.time_embed(time)
+        x = self.input_embed(noise, cond)
+        for block in self.transformer_blocks:
+            x = block(x, t, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale)
+        denoise = self.proj_out(self.norm_out(x, t))
+        denoise.mark_output("denoised", self.dtype)
+        return denoise
+
+    def prepare_inputs(self, **kwargs):
+        max_batch_size = kwargs["max_batch_size"]
+        batch_size_range = [2, 2, max_batch_size]
+        mel_size = 100
+        max_seq_len = 3000
+        num_frames_range = [200, 2 * max_seq_len, max_seq_len * max_batch_size]
+        hidden_size = 512
+        concat_feature_dim = mel_size + hidden_size
+        freq_embed_dim = 256
+        head_dim = 64
+        mapping = self.config.mapping
+        if mapping.tp_size > 1:
+            current_all_reduce_helper().set_workspace_tensor(mapping, 1)
+        if default_net().plugin_config.remove_input_padding:
+            noise = Tensor(
+                name="noise",
+                dtype=self.dtype,
+                shape=[-1, mel_size],
+                dim_range=OrderedDict(
+                    [
+                        ("num_frames", [num_frames_range]),
+                        ("n_mels", [mel_size]),
+                    ]
+                ),
+            )
+            cond = Tensor(
+                name="cond",
+                dtype=self.dtype,
+                shape=[-1, concat_feature_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("num_frames", [num_frames_range]),
+                        ("embeded_length", [concat_feature_dim]),
+                    ]
+                ),
+            )
+            time = Tensor(
+                name="time",
+                dtype=self.dtype,
+                shape=[-1, freq_embed_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("num_frames", [num_frames_range]),
+                        ("freq_dim", [freq_embed_dim]),
+                    ]
+                ),
+            )
+            rope_cos = Tensor(
+                name="rope_cos",
+                dtype=self.dtype,
+                shape=[-1, head_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("num_frames", [num_frames_range]),
+                        ("head_dim", [head_dim]),
+                    ]
+                ),
+            )
+            rope_sin = Tensor(
+                name="rope_sin",
+                dtype=self.dtype,
+                shape=[-1, head_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("num_frames", [num_frames_range]),
+                        ("head_dim", [head_dim]),
+                    ]
+                ),
+            )
+
+        else:
+            noise = Tensor(
+                name="noise",
+                dtype=self.dtype,
+                shape=[-1, -1, mel_size],
+                dim_range=OrderedDict(
+                    [
+                        ("batch_size", [batch_size_range]),
+                        ("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
+                        ("n_mels", [mel_size]),
+                    ]
+                ),
+            )
+            cond = Tensor(
+                name="cond",
+                dtype=self.dtype,
+                shape=[-1, -1, concat_feature_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("batch_size", [batch_size_range]),
+                        ("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
+                        ("embeded_length", [concat_feature_dim]),
+                    ]
+                ),
+            )
+            time = Tensor(
+                name="time",
+                dtype=self.dtype,
+                shape=[-1, freq_embed_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("batch_size", [batch_size_range]),
+                        ("freq_dim", [freq_embed_dim]),
+                    ]
+                ),
+            )
+            rope_cos = Tensor(
+                name="rope_cos",
+                dtype=self.dtype,
+                shape=[-1, -1, head_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("batch_size", [batch_size_range]),
+                        ("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
+                        ("head_dim", [head_dim]),
+                    ]
+                ),
+            )
+            rope_sin = Tensor(
+                name="rope_sin",
+                dtype=self.dtype,
+                shape=[-1, -1, head_dim],
+                dim_range=OrderedDict(
+                    [
+                        ("batch_size", [batch_size_range]),
+                        ("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
+                        ("head_dim", [head_dim]),
+                    ]
+                ),
+            )
+        input_lengths = Tensor(
+            name="input_lengths",
+            dtype=trt.int32,
+            shape=[-1],
+            dim_range=OrderedDict([("batch_size", [batch_size_range])]),
+        )
+        return {
+            "noise": noise,
+            "cond": cond,
+            "time": time,
+            "rope_cos": rope_cos,
+            "rope_sin": rope_sin,
+            "input_lengths": input_lengths,
+        }
@@ -0,0 +1,412 @@
+from __future__ import annotations
+
+import math
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tensorrt_llm._common import default_net
+
+from ..._utils import str_dtype_to_trt, trt_dtype_to_np
+from ...functional import (
+    Tensor,
+    bert_attention,
+    cast,
+    chunk,
+    concat,
+    constant,
+    expand,
+    expand_dims,
+    expand_dims_like,
+    expand_mask,
+    gelu,
+    matmul,
+    permute,
+    shape,
+    silu,
+    slice,
+    softmax,
+    squeeze,
+    unsqueeze,
+    view,
+)
+from ...layers import ColumnLinear, Conv1d, LayerNorm, Linear, Mish, RowLinear
+from ...module import Module
+
+
+class FeedForward(Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        self.project_in = Linear(dim, inner_dim)
+        self.ff = Linear(inner_dim, dim_out)
+
+    def forward(self, x):
+        return self.ff(gelu(self.project_in(x)))
+
+
+class AdaLayerNormZero(Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.linear = Linear(dim, dim * 6)
+        self.norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = chunk(emb, 6, dim=1)
+        x = self.norm(x)
+        ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
+        if default_net().plugin_config.remove_input_padding:
+            x = x * (ones + scale_msa) + shift_msa
+        else:
+            x = x * (ones + unsqueeze(scale_msa, 1)) + unsqueeze(shift_msa, 1)
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class AdaLayerNormZero_Final(Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.linear = Linear(dim, dim * 2)
+
+        self.norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(silu(emb))
+        scale, shift = chunk(emb, 2, dim=1)
+        ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
+        if default_net().plugin_config.remove_input_padding:
+            x = self.norm(x) * (ones + scale) + shift
+        else:
+            x = self.norm(x) * unsqueeze((ones + scale), 1)
+            x = x + unsqueeze(shift, 1)
+        return x
+
+
+class ConvPositionEmbedding(Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d1 = Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2)
+        self.conv1d2 = Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2)
+        self.mish = Mish()
+
+    def forward(self, x, mask=None):  # noqa: F722
+        if default_net().plugin_config.remove_input_padding:
+            x = unsqueeze(x, 0)
+        x = permute(x, [0, 2, 1])
+        x = self.mish(self.conv1d2(self.mish(self.conv1d1(x))))
+        out = permute(x, [0, 2, 1])
+        if default_net().plugin_config.remove_input_padding:
+            out = squeeze(out, 0)
+        return out
+
+
+class Attention(Module):
+    def __init__(
+        self,
+        processor: AttnProcessor,
+        dim: int,
+        heads: int = 16,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only=None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim  # hidden_size
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+        self.attention_head_size = dim_head
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+        self.tp_size = 1
+        self.num_attention_heads = heads // self.tp_size
+        self.num_attention_kv_heads = heads // self.tp_size  # 8
+        self.dtype = str_dtype_to_trt("float32")
+        self.attention_hidden_size = self.attention_head_size * self.num_attention_heads
+        self.to_q = ColumnLinear(
+            dim,
+            self.tp_size * self.num_attention_heads * self.attention_head_size,
+            bias=True,
+            dtype=self.dtype,
+            tp_group=None,
+            tp_size=self.tp_size,
+        )
+        self.to_k = ColumnLinear(
+            dim,
+            self.tp_size * self.num_attention_heads * self.attention_head_size,
+            bias=True,
+            dtype=self.dtype,
+            tp_group=None,
+            tp_size=self.tp_size,
+        )
+        self.to_v = ColumnLinear(
+            dim,
+            self.tp_size * self.num_attention_heads * self.attention_head_size,
+            bias=True,
+            dtype=self.dtype,
+            tp_group=None,
+            tp_size=self.tp_size,
+        )
+
+        if self.context_dim is not None:
+            self.to_k_c = Linear(context_dim, self.inner_dim)
+            self.to_v_c = Linear(context_dim, self.inner_dim)
+            if self.context_pre_only is not None:
+                self.to_q_c = Linear(context_dim, self.inner_dim)
+
+        self.to_out = RowLinear(
+            self.tp_size * self.num_attention_heads * self.attention_head_size,
+            dim,
+            bias=True,
+            dtype=self.dtype,
+            tp_group=None,
+            tp_size=self.tp_size,
+        )
+
+        if self.context_pre_only is not None and not self.context_pre_only:
+            self.to_out_c = Linear(self.inner_dim, dim)
+
+    def forward(
+        self,
+        x,  # noised input x
+        rope_cos,
+        rope_sin,
+        input_lengths,
+        c=None,  # context c
+        scale=1.0,
+        rope=None,
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, input_lengths=input_lengths, scale=scale, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(
+                self, x, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale
+            )
+
+
+def rotate_every_two_3dim(tensor: Tensor) -> Tensor:
+    shape_tensor = concat(
+        [shape(tensor, i) / 2 if i == (tensor.ndim() - 1) else shape(tensor, i) for i in range(tensor.ndim())]
+    )
+    if default_net().plugin_config.remove_input_padding:
+        assert tensor.ndim() == 2
+        x1 = slice(tensor, [0, 0], shape_tensor, [1, 2])
+        x2 = slice(tensor, [0, 1], shape_tensor, [1, 2])
+        x1 = expand_dims(x1, 2)
+        x2 = expand_dims(x2, 2)
+        zero = constant(np.ascontiguousarray(np.zeros([1], dtype=trt_dtype_to_np(tensor.dtype))))
+        x2 = zero - x2
+        x = concat([x2, x1], 2)
+        out = view(x, concat([shape(x, 0), shape(x, 1) * 2]))
+    else:
+        assert tensor.ndim() == 3
+
+        x1 = slice(tensor, [0, 0, 0], shape_tensor, [1, 1, 2])
+        x2 = slice(tensor, [0, 0, 1], shape_tensor, [1, 1, 2])
+        x1 = expand_dims(x1, 3)
+        x2 = expand_dims(x2, 3)
+        zero = constant(np.ascontiguousarray(np.zeros([1], dtype=trt_dtype_to_np(tensor.dtype))))
+        x2 = zero - x2
+        x = concat([x2, x1], 3)
+        out = view(x, concat([shape(x, 0), shape(x, 1), shape(x, 2) * 2]))
+
+    return out
+
+
+def apply_rotary_pos_emb_3dim(x, rope_cos, rope_sin):
+    if default_net().plugin_config.remove_input_padding:
+        rot_dim = shape(rope_cos, -1)  # 64
+        new_t_shape = concat([shape(x, 0), rot_dim])  # (-1, 64)
+        x_ = slice(x, [0, 0], new_t_shape, [1, 1])
+        end_dim = shape(x, -1) - shape(rope_cos, -1)
+        new_t_unrotated_shape = concat([shape(x, 0), end_dim])  # (2, -1, 960)
+        x_unrotated = slice(x, concat([0, rot_dim]), new_t_unrotated_shape, [1, 1])
+        out = concat([x_ * rope_cos + rotate_every_two_3dim(x_) * rope_sin, x_unrotated], dim=-1)
+    else:
+        rot_dim = shape(rope_cos, 2)  # 64
+        new_t_shape = concat([shape(x, 0), shape(x, 1), rot_dim])  # (2, -1, 64)
+        x_ = slice(x, [0, 0, 0], new_t_shape, [1, 1, 1])
+        end_dim = shape(x, 2) - shape(rope_cos, 2)
+        new_t_unrotated_shape = concat([shape(x, 0), shape(x, 1), end_dim])  # (2, -1, 960)
+        x_unrotated = slice(x, concat([0, 0, rot_dim]), new_t_unrotated_shape, [1, 1, 1])
+        out = concat([x_ * rope_cos + rotate_every_two_3dim(x_) * rope_sin, x_unrotated], dim=-1)
+    return out
+
+
+class AttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn,
+        x,  # noised input x
+        rope_cos,
+        rope_sin,
+        input_lengths,
+        scale=1.0,
+        rope=None,
+    ) -> torch.FloatTensor:
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+        # k,v,q all (2,1226,1024)
+        query = apply_rotary_pos_emb_3dim(query, rope_cos, rope_sin)
+        key = apply_rotary_pos_emb_3dim(key, rope_cos, rope_sin)
+
+        # attention
+        inner_dim = key.shape[-1]
+        norm_factor = math.sqrt(attn.attention_head_size)
+        q_scaling = 1.0 / norm_factor
+        mask = None
+        if not default_net().plugin_config.remove_input_padding:
+            N = shape(x, 1)
+            B = shape(x, 0)
+            seq_len_2d = concat([1, N])
+            max_position_embeddings = 4096
+            # create position ids
+            position_ids_buffer = constant(np.expand_dims(np.arange(max_position_embeddings).astype(np.int32), 0))
+            tmp_position_ids = slice(position_ids_buffer, starts=[0, 0], sizes=seq_len_2d)
+            tmp_position_ids = expand(tmp_position_ids, concat([B, N]))  # BxL
+            tmp_input_lengths = unsqueeze(input_lengths, 1)  # Bx1
+            tmp_input_lengths = expand(tmp_input_lengths, concat([B, N]))  # BxL
+            mask = tmp_position_ids < tmp_input_lengths  # BxL
+            mask = mask.cast("int32")
+
+        if default_net().plugin_config.bert_attention_plugin:
+            qkv = concat([query, key, value], dim=-1)
+            # TRT plugin mode
+            assert input_lengths is not None
+            if default_net().plugin_config.remove_input_padding:
+                qkv = qkv.view(concat([-1, 3 * inner_dim]))
+                max_input_length = constant(
+                    np.zeros(
+                        [
+                            2048,
+                        ],
+                        dtype=np.int32,
+                    )
+                )
+            else:
+                max_input_length = None
+            context = bert_attention(
+                qkv,
+                input_lengths,
+                attn.num_attention_heads,
+                attn.attention_head_size,
+                q_scaling=q_scaling,
+                max_input_length=max_input_length,
+            )
+        else:
+            assert not default_net().plugin_config.remove_input_padding
+
+            def transpose_for_scores(x):
+                new_x_shape = concat([shape(x, 0), shape(x, 1), attn.num_attention_heads, attn.attention_head_size])
+
+                y = x.view(new_x_shape)
+                y = y.transpose(1, 2)
+                return y
+
+            def transpose_for_scores_k(x):
+                new_x_shape = concat([shape(x, 0), shape(x, 1), attn.num_attention_heads, attn.attention_head_size])
+
+                y = x.view(new_x_shape)
+                y = y.permute([0, 2, 3, 1])
+                return y
+
+            query = transpose_for_scores(query)
+            key = transpose_for_scores_k(key)
+            value = transpose_for_scores(value)
+
+            attention_scores = matmul(query, key, use_fp32_acc=False)
+
+            if mask is not None:
+                attention_mask = expand_mask(mask, shape(query, 2))
+                attention_mask = cast(attention_mask, attention_scores.dtype)
+                attention_scores = attention_scores + attention_mask
+
+            attention_probs = softmax(attention_scores, dim=-1)
+
+            context = matmul(attention_probs, value, use_fp32_acc=False).transpose(1, 2)
+            context = context.view(concat([shape(context, 0), shape(context, 1), attn.attention_hidden_size]))
+        context = attn.to_out(context)
+        if mask is not None:
+            mask = mask.view(concat([shape(mask, 0), shape(mask, 1), 1]))
+            mask = expand_dims_like(mask, context)
+            mask = cast(mask, context.dtype)
+            context = context * mask
+        return context
+
+
+# DiT Block
+class DiTBlock(Module):
+    def __init__(self, dim, heads, dim_head, ff_mult=2, dropout=0.1):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+        )
+
+        self.ff_norm = LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout)
+
+    def forward(
+        self, x, t, rope_cos, rope_sin, input_lengths, scale=1.0, rope=ModuleNotFoundError
+    ):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+        # attention
+        # norm ----> (2,1226,1024)
+        attn_output = self.attn(x=norm, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale)
+
+        # process attention output for input x
+        if default_net().plugin_config.remove_input_padding:
+            x = x + gate_msa * attn_output
+        else:
+            x = x + unsqueeze(gate_msa, 1) * attn_output
+        ones = constant(np.ones(1, dtype=np.float32)).cast(x.dtype)
+        if default_net().plugin_config.remove_input_padding:
+            norm = self.ff_norm(x) * (ones + scale_mlp) + shift_mlp
+        else:
+            norm = self.ff_norm(x) * (ones + unsqueeze(scale_mlp, 1)) + unsqueeze(shift_mlp, 1)
+            # norm = self.ff_norm(x) * (ones + scale_mlp) + shift_mlp
+        ff_output = self.ff(norm)
+        if default_net().plugin_config.remove_input_padding:
+            x = x + gate_mlp * ff_output
+        else:
+            x = x + unsqueeze(gate_mlp, 1) * ff_output
+
+        return x
+
+
+class TimestepEmbedding(Module):
+    def __init__(self, dim, freq_embed_dim=256, dtype=None):
+        super().__init__()
+        # self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.mlp1 = Linear(freq_embed_dim, dim, bias=True, dtype=dtype)
+        self.mlp2 = Linear(dim, dim, bias=True, dtype=dtype)
+
+    def forward(self, timestep):
+        t_freq = self.mlp1(timestep)
+        t_freq = silu(t_freq)
+        t_emb = self.mlp2(t_freq)
+        return t_emb
@@ -0,0 +1,24 @@
+accelerate>=0.33.0
+bitsandbytes>0.37.0
+cached_path
+click
+datasets
+ema_pytorch>=0.5.2
+gradio>=3.45.2
+hydra-core>=1.3.0
+jieba
+librosa
+matplotlib
+numpy<=1.26.4
+pydub
+pypinyin
+safetensors
+soundfile
+tomli
+torch>=2.0.0
+# torchaudio>=2.0.0
+torchdiffeq
+tqdm>=4.65.0
+transformers
+x_transformers>=1.31.14
+packaging>=24.2
@@ -0,0 +1,110 @@
+stage=$1
+stop_stage=$2
+model=$3 # F5TTS_Base
+if [ -z "$model" ]; then
+    echo "Model is none, using default model F5TTS_Base"
+    model=F5TTS_Base
+fi
+echo "Start stage: $stage, Stop stage: $stop_stage, Model: $model"
+export CUDA_VISIBLE_DEVICES=0
+
+F5_TTS_HF_DOWNLOAD_PATH=./F5-TTS
+F5_TTS_TRT_LLM_CHECKPOINT_PATH=./trtllm_ckpt
+F5_TTS_TRT_LLM_ENGINE_PATH=./f5_trt_llm_engine
+
+vocoder_trt_engine_path=vocos_vocoder.plan
+model_repo=./model_repo
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+    echo "Downloading f5 tts from huggingface"
+    huggingface-cli download SWivid/F5-TTS --local-dir $F5_TTS_HF_DOWNLOAD_PATH
+
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+    echo "Converting checkpoint"
+    python3 ./scripts/convert_checkpoint.py \
+        --timm_ckpt "$F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt" \
+        --output_dir "$F5_TTS_TRT_LLM_CHECKPOINT_PATH" --model_name $model
+    python_package_path=/usr/local/lib/python3.12/dist-packages
+    cp -r patch/* $python_package_path/tensorrt_llm/models
+    trtllm-build --checkpoint_dir $F5_TTS_TRT_LLM_CHECKPOINT_PATH \
+      --max_batch_size 8 \
+      --output_dir $F5_TTS_TRT_LLM_ENGINE_PATH --remove_input_padding disable
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+    echo "Exporting vocos vocoder"
+    onnx_vocoder_path=vocos_vocoder.onnx
+    python3 scripts/export_vocoder_to_onnx.py --vocoder vocos --output-path $onnx_vocoder_path
+    bash scripts/export_vocos_trt.sh $onnx_vocoder_path $vocoder_trt_engine_path
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+    echo "Building triton server"
+    rm -r $model_repo
+    cp -r ./model_repo_f5_tts $model_repo
+    python3 scripts/fill_template.py -i $model_repo/f5_tts/config.pbtxt vocab:$F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt,model:$F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt,trtllm:$F5_TTS_TRT_LLM_ENGINE_PATH,vocoder:vocos
+    cp $vocoder_trt_engine_path $model_repo/vocoder/1/vocoder.plan
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+    echo "Starting triton server"
+    tritonserver --model-repository=$model_repo
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+    echo "Testing triton server"
+    num_task=1
+    log_dir=./log_concurrent_tasks_${num_task}
+    rm -r $log_dir
+    python3 client_grpc.py --num-tasks $num_task --huggingface-dataset yuekai/seed_tts --split-name wenetspeech4tts --log-dir $log_dir
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+    echo "Testing http client"
+    audio=../../infer/examples/basic/basic_ref_en.wav
+    reference_text="Some call me nature, others call me mother nature."
+    target_text="I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+    python3 client_http.py --reference-audio $audio --reference-text "$reference_text" --target-text "$target_text"
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+    echo "TRT-LLM: offline decoding benchmark test"
+    batch_size=1
+    split_name=wenetspeech4tts
+    backend_type=trt
+    log_dir=./log_benchmark_batch_size_${batch_size}_${split_name}_${backend_type}
+    rm -r $log_dir
+    ln -s model_repo_f5_tts/f5_tts/1/f5_tts_trtllm.py ./
+    torchrun --nproc_per_node=1 \
+    benchmark.py --output-dir $log_dir \
+    --batch-size $batch_size \
+    --enable-warmup \
+    --split-name $split_name \
+    --model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+    --vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+    --vocoder-trt-engine-path $vocoder_trt_engine_path \
+    --backend-type $backend_type \
+    --tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+    echo "Native Pytorch: offline decoding benchmark test"
+    pip install -r requirements-pytorch.txt
+    batch_size=1
+    split_name=wenetspeech4tts
+    backend_type=pytorch
+    log_dir=./log_benchmark_batch_size_${batch_size}_${split_name}_${backend_type}
+    rm -r $log_dir
+    ln -s model_repo_f5_tts/f5_tts/1/f5_tts_trtllm.py ./
+    torchrun --nproc_per_node=1 \
+    benchmark.py --output-dir $log_dir \
+    --batch-size $batch_size \
+    --split-name $split_name \
+    --enable-warmup \
+    --model-path $F5_TTS_HF_DOWNLOAD_PATH/$model/model_1200000.pt \
+    --vocab-file $F5_TTS_HF_DOWNLOAD_PATH/$model/vocab.txt \
+    --backend-type $backend_type \
+    --tllm-model-dir $F5_TTS_TRT_LLM_ENGINE_PATH || exit 1
+fi
@@ -0,0 +1,248 @@
+# Modified from https://github.com/echocatzh/conv-stft/blob/master/conv_stft/conv_stft.py
+
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# MIT License
+
+# Copyright (c) 2020 Shimin Zhang
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import torch as th
+import torch.nn.functional as F
+from scipy.signal import check_COLA, get_window
+
+
+support_clp_op = None
+if th.__version__ >= "1.7.0":
+    from torch.fft import rfft as fft
+
+    support_clp_op = True
+else:
+    from torch import rfft as fft
+
+
+class STFT(th.nn.Module):
+    def __init__(
+        self,
+        win_len=1024,
+        win_hop=512,
+        fft_len=1024,
+        enframe_mode="continue",
+        win_type="hann",
+        win_sqrt=False,
+        pad_center=True,
+    ):
+        """
+        Implement of STFT using 1D convolution and 1D transpose convolutions.
+        Implement of framing the signal in 2 ways, `break` and `continue`.
+        `break` method is a kaldi-like framing.
+        `continue` method is a librosa-like framing.
+
+        More information about `perfect reconstruction`:
+        1. https://ww2.mathworks.cn/help/signal/ref/stft.html
+        2. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.get_window.html
+
+        Args:
+            win_len (int): Number of points in one frame.  Defaults to 1024.
+            win_hop (int): Number of framing stride. Defaults to 512.
+            fft_len (int): Number of DFT points. Defaults to 1024.
+            enframe_mode (str, optional): `break` and `continue`. Defaults to 'continue'.
+            win_type (str, optional): The type of window to create. Defaults to 'hann'.
+            win_sqrt (bool, optional): using square root window. Defaults to True.
+            pad_center (bool, optional): `perfect reconstruction` opts. Defaults to True.
+        """
+        super(STFT, self).__init__()
+        assert enframe_mode in ["break", "continue"]
+        assert fft_len >= win_len
+        self.win_len = win_len
+        self.win_hop = win_hop
+        self.fft_len = fft_len
+        self.mode = enframe_mode
+        self.win_type = win_type
+        self.win_sqrt = win_sqrt
+        self.pad_center = pad_center
+        self.pad_amount = self.fft_len // 2
+
+        en_k, fft_k, ifft_k, ola_k = self.__init_kernel__()
+        self.register_buffer("en_k", en_k)
+        self.register_buffer("fft_k", fft_k)
+        self.register_buffer("ifft_k", ifft_k)
+        self.register_buffer("ola_k", ola_k)
+
+    def __init_kernel__(self):
+        """
+        Generate enframe_kernel, fft_kernel, ifft_kernel and overlap-add kernel.
+        ** enframe_kernel: Using conv1d layer and identity matrix.
+        ** fft_kernel: Using linear layer for matrix multiplication. In fact,
+        enframe_kernel and fft_kernel can be combined, But for the sake of
+        readability, I took the two apart.
+        ** ifft_kernel, pinv of fft_kernel.
+        ** overlap-add kernel, just like enframe_kernel, but transposed.
+
+        Returns:
+            tuple: four kernels.
+        """
+        enframed_kernel = th.eye(self.fft_len)[:, None, :]
+        if support_clp_op:
+            tmp = fft(th.eye(self.fft_len))
+            fft_kernel = th.stack([tmp.real, tmp.imag], dim=2)
+        else:
+            fft_kernel = fft(th.eye(self.fft_len), 1)
+        if self.mode == "break":
+            enframed_kernel = th.eye(self.win_len)[:, None, :]
+            fft_kernel = fft_kernel[: self.win_len]
+        fft_kernel = th.cat((fft_kernel[:, :, 0], fft_kernel[:, :, 1]), dim=1)
+        ifft_kernel = th.pinverse(fft_kernel)[:, None, :]
+        window = get_window(self.win_type, self.win_len)
+
+        self.perfect_reconstruct = check_COLA(window, self.win_len, self.win_len - self.win_hop)
+        window = th.FloatTensor(window)
+        if self.mode == "continue":
+            left_pad = (self.fft_len - self.win_len) // 2
+            right_pad = left_pad + (self.fft_len - self.win_len) % 2
+            window = F.pad(window, (left_pad, right_pad))
+        if self.win_sqrt:
+            self.padded_window = window
+            window = th.sqrt(window)
+        else:
+            self.padded_window = window**2
+
+        fft_kernel = fft_kernel.T * window
+        ifft_kernel = ifft_kernel * window
+        ola_kernel = th.eye(self.fft_len)[: self.win_len, None, :]
+        if self.mode == "continue":
+            ola_kernel = th.eye(self.fft_len)[:, None, : self.fft_len]
+        return enframed_kernel, fft_kernel, ifft_kernel, ola_kernel
+
+    def is_perfect(self):
+        """
+        Whether the parameters win_len, win_hop and win_sqrt
+        obey constants overlap-add(COLA)
+
+        Returns:
+            bool: Return true if parameters obey COLA.
+        """
+        return self.perfect_reconstruct and self.pad_center
+
+    def transform(self, inputs, return_type="complex"):
+        """Take input data (audio) to STFT domain.
+
+        Args:
+            inputs (tensor): Tensor of floats, with shape (num_batch, num_samples)
+            return_type (str, optional): return (mag, phase) when `magphase`,
+            return (real, imag) when `realimag` and complex(real, imag) when `complex`.
+            Defaults to 'complex'.
+
+        Returns:
+            tuple: (mag, phase) when `magphase`, return (real, imag) when
+            `realimag`. Defaults to 'complex', each elements with shape
+            [num_batch, num_frequencies, num_frames]
+        """
+        assert return_type in ["magphase", "realimag", "complex"]
+        if inputs.dim() == 2:
+            inputs = th.unsqueeze(inputs, 1)
+        self.num_samples = inputs.size(-1)
+        if self.pad_center:
+            inputs = F.pad(inputs, (self.pad_amount, self.pad_amount), mode="reflect")
+        enframe_inputs = F.conv1d(inputs, self.en_k, stride=self.win_hop)
+        outputs = th.transpose(enframe_inputs, 1, 2)
+        outputs = F.linear(outputs, self.fft_k)
+        outputs = th.transpose(outputs, 1, 2)
+        dim = self.fft_len // 2 + 1
+        real = outputs[:, :dim, :]
+        imag = outputs[:, dim:, :]
+        if return_type == "realimag":
+            return real, imag
+        elif return_type == "complex":
+            assert support_clp_op
+            return th.complex(real, imag)
+        else:
+            mags = th.sqrt(real**2 + imag**2)
+            phase = th.atan2(imag, real)
+            return mags, phase
+
+    def inverse(self, input1, input2=None, input_type="magphase"):
+        """Call the inverse STFT (iSTFT), given tensors produced
+        by the `transform` function.
+
+        Args:
+            input1 (tensors): Magnitude/Real-part of STFT with shape
+            [num_batch, num_frequencies, num_frames]
+            input2 (tensors): Phase/Imag-part of STFT with shape
+            [num_batch, num_frequencies, num_frames]
+            input_type (str, optional): Mathematical meaning of input tensor's.
+            Defaults to 'magphase'.
+
+        Returns:
+            tensors: Reconstructed audio given magnitude and phase. Of
+                shape [num_batch, num_samples]
+        """
+        assert input_type in ["magphase", "realimag"]
+        if input_type == "realimag":
+            real, imag = None, None
+            if support_clp_op and th.is_complex(input1):
+                real, imag = input1.real, input1.imag
+            else:
+                real, imag = input1, input2
+        else:
+            real = input1 * th.cos(input2)
+            imag = input1 * th.sin(input2)
+        inputs = th.cat([real, imag], dim=1)
+        outputs = F.conv_transpose1d(inputs, self.ifft_k, stride=self.win_hop)
+        t = (self.padded_window[None, :, None]).repeat(1, 1, inputs.size(-1))
+        t = t.to(inputs.device)
+        coff = F.conv_transpose1d(t, self.ola_k, stride=self.win_hop)
+
+        num_frames = input1.size(-1)
+        num_samples = num_frames * self.win_hop
+
+        rm_start, rm_end = self.pad_amount, self.pad_amount + num_samples
+
+        outputs = outputs[..., rm_start:rm_end]
+        coff = coff[..., rm_start:rm_end]
+        coffidx = th.where(coff > 1e-8)
+        outputs[coffidx] = outputs[coffidx] / (coff[coffidx])
+        return outputs.squeeze(dim=1)
+
+    def forward(self, inputs):
+        """Take input data (audio) to STFT domain and then back to audio.
+
+        Args:
+            inputs (tensor): Tensor of floats, with shape [num_batch, num_samples]
+
+        Returns:
+            tensor: Reconstructed audio given magnitude and phase.
+            Of shape [num_batch, num_samples]
+        """
+        mag, phase = self.transform(inputs)
+        rec_wav = self.inverse(mag, phase)
+        return rec_wav
@@ -0,0 +1,358 @@
+import argparse
+import json
+import os
+import re
+import time
+import traceback
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import safetensors.torch
+import torch
+from tensorrt_llm import str_dtype_to_torch
+from tensorrt_llm.mapping import Mapping
+from tensorrt_llm.models.convert_utils import split, split_matrix_tp
+
+
+def split_q_tp(v, n_head, n_hidden, tensor_parallel, rank):
+    split_v = split(v, tensor_parallel, rank, dim=1)
+    return split_v.contiguous()
+
+
+def split_q_bias_tp(v, n_head, n_hidden, tensor_parallel, rank):
+    split_v = split(v, tensor_parallel, rank, dim=0)
+    return split_v.contiguous()
+
+
+FACEBOOK_DIT_NAME_MAPPING = {
+    "^time_embed.time_mlp.0.weight$": "time_embed.mlp1.weight",
+    "^time_embed.time_mlp.0.bias$": "time_embed.mlp1.bias",
+    "^time_embed.time_mlp.2.weight$": "time_embed.mlp2.weight",
+    "^time_embed.time_mlp.2.bias$": "time_embed.mlp2.bias",
+    "^input_embed.conv_pos_embed.conv1d.0.weight$": "input_embed.conv_pos_embed.conv1d1.weight",
+    "^input_embed.conv_pos_embed.conv1d.0.bias$": "input_embed.conv_pos_embed.conv1d1.bias",
+    "^input_embed.conv_pos_embed.conv1d.2.weight$": "input_embed.conv_pos_embed.conv1d2.weight",
+    "^input_embed.conv_pos_embed.conv1d.2.bias$": "input_embed.conv_pos_embed.conv1d2.bias",
+    "^transformer_blocks.0.attn.to_out.0.weight$": "transformer_blocks.0.attn.to_out.weight",
+    "^transformer_blocks.0.attn.to_out.0.bias$": "transformer_blocks.0.attn.to_out.bias",
+    "^transformer_blocks.1.attn.to_out.0.weight$": "transformer_blocks.1.attn.to_out.weight",
+    "^transformer_blocks.1.attn.to_out.0.bias$": "transformer_blocks.1.attn.to_out.bias",
+    "^transformer_blocks.2.attn.to_out.0.weight$": "transformer_blocks.2.attn.to_out.weight",
+    "^transformer_blocks.2.attn.to_out.0.bias$": "transformer_blocks.2.attn.to_out.bias",
+    "^transformer_blocks.3.attn.to_out.0.weight$": "transformer_blocks.3.attn.to_out.weight",
+    "^transformer_blocks.3.attn.to_out.0.bias$": "transformer_blocks.3.attn.to_out.bias",
+    "^transformer_blocks.4.attn.to_out.0.weight$": "transformer_blocks.4.attn.to_out.weight",
+    "^transformer_blocks.4.attn.to_out.0.bias$": "transformer_blocks.4.attn.to_out.bias",
+    "^transformer_blocks.5.attn.to_out.0.weight$": "transformer_blocks.5.attn.to_out.weight",
+    "^transformer_blocks.5.attn.to_out.0.bias$": "transformer_blocks.5.attn.to_out.bias",
+    "^transformer_blocks.6.attn.to_out.0.weight$": "transformer_blocks.6.attn.to_out.weight",
+    "^transformer_blocks.6.attn.to_out.0.bias$": "transformer_blocks.6.attn.to_out.bias",
+    "^transformer_blocks.7.attn.to_out.0.weight$": "transformer_blocks.7.attn.to_out.weight",
+    "^transformer_blocks.7.attn.to_out.0.bias$": "transformer_blocks.7.attn.to_out.bias",
+    "^transformer_blocks.8.attn.to_out.0.weight$": "transformer_blocks.8.attn.to_out.weight",
+    "^transformer_blocks.8.attn.to_out.0.bias$": "transformer_blocks.8.attn.to_out.bias",
+    "^transformer_blocks.9.attn.to_out.0.weight$": "transformer_blocks.9.attn.to_out.weight",
+    "^transformer_blocks.9.attn.to_out.0.bias$": "transformer_blocks.9.attn.to_out.bias",
+    "^transformer_blocks.10.attn.to_out.0.weight$": "transformer_blocks.10.attn.to_out.weight",
+    "^transformer_blocks.10.attn.to_out.0.bias$": "transformer_blocks.10.attn.to_out.bias",
+    "^transformer_blocks.11.attn.to_out.0.weight$": "transformer_blocks.11.attn.to_out.weight",
+    "^transformer_blocks.11.attn.to_out.0.bias$": "transformer_blocks.11.attn.to_out.bias",
+    "^transformer_blocks.12.attn.to_out.0.weight$": "transformer_blocks.12.attn.to_out.weight",
+    "^transformer_blocks.12.attn.to_out.0.bias$": "transformer_blocks.12.attn.to_out.bias",
+    "^transformer_blocks.13.attn.to_out.0.weight$": "transformer_blocks.13.attn.to_out.weight",
+    "^transformer_blocks.13.attn.to_out.0.bias$": "transformer_blocks.13.attn.to_out.bias",
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+}
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        type=str,
+        default="F5TTS_Base",
+        choices=[
+            "F5TTS_Base",
+        ],
+    )  # TODO: support F5TTS_v1_Base
+    parser.add_argument("--timm_ckpt", type=str, default="./ckpts/model_1200000.pt")
+    parser.add_argument(
+        "--output_dir", type=str, default="./tllm_checkpoint", help="The path to save the TensorRT-LLM checkpoint"
+    )
+    parser.add_argument("--hidden_size", type=int, default=1024, help="The hidden size of DiT")
+    parser.add_argument("--depth", type=int, default=22, help="The number of DiTBlock layers")
+    parser.add_argument("--num_heads", type=int, default=16, help="The number of heads of attention module")
+    parser.add_argument("--cfg_scale", type=float, default=4.0)
+    parser.add_argument("--tp_size", type=int, default=1, help="N-way tensor parallelism size")
+    parser.add_argument("--cp_size", type=int, default=1, help="Context parallelism size")
+    parser.add_argument("--pp_size", type=int, default=1, help="N-way pipeline parallelism size")
+    parser.add_argument("--dtype", type=str, default="float16", choices=["float32", "bfloat16", "float16"])
+    parser.add_argument("--fp8_linear", action="store_true", help="Whether use FP8 for linear layers")
+    parser.add_argument(
+        "--workers", type=int, default=1, help="The number of workers for converting checkpoint in parallel"
+    )
+    args = parser.parse_args()
+    return args
+
+
+def convert_timm_dit(args, mapping, dtype="float32"):
+    weights = {}
+    tik = time.time()
+    torch_dtype = str_dtype_to_torch(dtype)
+    tensor_parallel = mapping.tp_size
+
+    model_params = dict(torch.load(args.timm_ckpt))
+    model_params = {
+        k: v for k, v in model_params["ema_model_state_dict"].items() if k.startswith("ema_model.transformer")
+    }
+    prefix = "ema_model.transformer."
+    model_params = {key[len(prefix) :] if key.startswith(prefix) else key: value for key, value in model_params.items()}
+
+    timm_to_trtllm_name = FACEBOOK_DIT_NAME_MAPPING
+
+    def get_trtllm_name(timm_name):
+        for k, v in timm_to_trtllm_name.items():
+            m = re.match(k, timm_name)
+            if m is not None:
+                if "*" in v:
+                    v = v.replace("*", m.groups()[0])
+                return v
+        return timm_name
+
+    weights = dict()
+    for name, param in model_params.items():
+        if name == "input_embed.conv_pos_embed.conv1d.0.weight" or name == "input_embed.conv_pos_embed.conv1d.2.weight":
+            weights[get_trtllm_name(name)] = param.contiguous().to(torch_dtype).unsqueeze(-1)
+        else:
+            weights[get_trtllm_name(name)] = param.contiguous().to(torch_dtype)
+
+    assert len(weights) == len(model_params)
+
+    # new_prefix = 'f5_transformer.'
+    new_prefix = ""
+    weights = {new_prefix + key: value for key, value in weights.items()}
+    import math
+
+    scale_factor = math.pow(64, -0.25)
+    for k, v in weights.items():
+        if re.match("^transformer_blocks.*.attn.to_k.weight$", k):
+            weights[k] *= scale_factor
+            weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+
+        elif re.match("^transformer_blocks.*.attn.to_k.bias$", k):
+            weights[k] *= scale_factor
+            weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+
+        elif re.match("^transformer_blocks.*.attn.to_q.weight$", k):
+            weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+            weights[k] *= scale_factor
+
+        elif re.match("^transformer_blocks.*.attn.to_q.bias$", k):
+            weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+            weights[k] *= scale_factor
+
+        elif re.match("^transformer_blocks.*.attn.to_v.weight$", k):
+            weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+
+        elif re.match("^transformer_blocks.*.attn.to_v.bias$", k):
+            weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
+
+        elif re.match("^transformer_blocks.*.attn.to_out.weight$", k):
+            weights[k] = split_matrix_tp(v, tensor_parallel, mapping.tp_rank, dim=1)
+
+    tok = time.time()
+    t = time.strftime("%H:%M:%S", time.gmtime(tok - tik))
+    print(f"Weights loaded. Total time: {t}")
+    return weights
+
+
+def save_config(args):
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+    config = {
+        "architecture": "F5TTS",
+        "dtype": args.dtype,
+        "hidden_size": 1024,
+        "num_hidden_layers": 22,
+        "num_attention_heads": 16,
+        "dim_head": 64,
+        "dropout": 0.1,
+        "ff_mult": 2,
+        "mel_dim": 100,
+        "text_num_embeds": 256,
+        "text_dim": 512,
+        "conv_layers": 4,
+        "long_skip_connection": False,
+        "mapping": {
+            "world_size": args.cp_size * args.tp_size * args.pp_size,
+            "cp_size": args.cp_size,
+            "tp_size": args.tp_size,
+            "pp_size": args.pp_size,
+        },
+    }
+    if args.fp8_linear:
+        config["quantization"] = {
+            "quant_algo": "FP8",
+            # TODO: add support for exclude modules.
+            # 'exclude_modules': "*final_layer*",
+        }
+
+    with open(os.path.join(args.output_dir, "config.json"), "w") as f:
+        json.dump(config, f, indent=4)
+
+
+def covert_and_save(args, rank):
+    if rank == 0:
+        save_config(args)
+
+    mapping = Mapping(
+        world_size=args.cp_size * args.tp_size * args.pp_size,
+        rank=rank,
+        cp_size=args.cp_size,
+        tp_size=args.tp_size,
+        pp_size=args.pp_size,
+    )
+
+    weights = convert_timm_dit(args, mapping, dtype=args.dtype)
+
+    safetensors.torch.save_file(weights, os.path.join(args.output_dir, f"rank{rank}.safetensors"))
+
+
+def execute(workers, func, args):
+    if workers == 1:
+        for rank, f in enumerate(func):
+            f(args, rank)
+    else:
+        with ThreadPoolExecutor(max_workers=workers) as p:
+            futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
+            exceptions = []
+            for future in as_completed(futures):
+                try:
+                    future.result()
+                except Exception as e:
+                    traceback.print_exc()
+                    exceptions.append(e)
+            assert len(exceptions) == 0, "Checkpoint conversion failed, please check error log."
+
+
+def main():
+    args = parse_arguments()
+    world_size = args.cp_size * args.tp_size * args.pp_size
+
+    assert args.pp_size == 1, "PP is not supported yet."
+
+    tik = time.time()
+    if args.timm_ckpt is None:
+        return
+    print("start execute")
+    execute(args.workers, [covert_and_save] * world_size, args)
+
+    tok = time.time()
+    t = time.strftime("%H:%M:%S", time.gmtime(tok - tik))
+    print(f"Total time of converting checkpoints: {t}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,138 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import torch
+import torch.nn as nn
+from conv_stft import STFT
+from huggingface_hub import hf_hub_download
+from vocos import Vocos
+
+
+opset_version = 17
+
+
+def get_args():
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        "--vocoder",
+        type=str,
+        default="vocos",
+        choices=["vocos", "bigvgan"],
+        help="Vocoder to export",
+    )
+    parser.add_argument(
+        "--output-path",
+        type=str,
+        default="./vocos_vocoder.onnx",
+        help="Output path",
+    )
+    return parser.parse_args()
+
+
+class ISTFTHead(nn.Module):
+    def __init__(self, n_fft: int, hop_length: int):
+        super().__init__()
+        self.out = None
+        self.stft = STFT(fft_len=n_fft, win_hop=hop_length, win_len=n_fft)
+
+    def forward(self, x: torch.Tensor):
+        x = self.out(x).transpose(1, 2)
+        mag, p = x.chunk(2, dim=1)
+        mag = torch.exp(mag)
+        mag = torch.clip(mag, max=1e2)
+        real = mag * torch.cos(p)
+        imag = mag * torch.sin(p)
+        audio = self.stft.inverse(input1=real, input2=imag, input_type="realimag")
+        return audio
+
+
+class VocosVocoder(nn.Module):
+    def __init__(self, vocos_vocoder):
+        super(VocosVocoder, self).__init__()
+        self.vocos_vocoder = vocos_vocoder
+        istft_head_out = self.vocos_vocoder.head.out
+        n_fft = self.vocos_vocoder.head.istft.n_fft
+        hop_length = self.vocos_vocoder.head.istft.hop_length
+        istft_head_for_export = ISTFTHead(n_fft, hop_length)
+        istft_head_for_export.out = istft_head_out
+        self.vocos_vocoder.head = istft_head_for_export
+
+    def forward(self, mel):
+        waveform = self.vocos_vocoder.decode(mel)
+        return waveform
+
+
+def export_VocosVocoder(vocos_vocoder, output_path, verbose):
+    vocos_vocoder = VocosVocoder(vocos_vocoder).cuda()
+    vocos_vocoder.eval()
+
+    dummy_batch_size = 8
+    dummy_input_length = 500
+
+    dummy_mel = torch.randn(dummy_batch_size, 100, dummy_input_length).cuda()
+
+    with torch.no_grad():
+        dummy_waveform = vocos_vocoder(mel=dummy_mel)
+        print(dummy_waveform.shape)
+
+    dummy_input = dummy_mel
+
+    torch.onnx.export(
+        vocos_vocoder,
+        dummy_input,
+        output_path,
+        opset_version=opset_version,
+        do_constant_folding=True,
+        input_names=["mel"],
+        output_names=["waveform"],
+        dynamic_axes={
+            "mel": {0: "batch_size", 2: "input_length"},
+            "waveform": {0: "batch_size", 1: "output_length"},
+        },
+        verbose=verbose,
+    )
+
+    print("Exported to {}".format(output_path))
+
+
+def load_vocoder(vocoder_name="vocos", is_local=False, local_path="", device="cpu", hf_cache_dir=None):
+    if vocoder_name == "vocos":
+        # vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz").to(device)
+        if is_local:
+            print(f"Load vocos from local path {local_path}")
+            config_path = f"{local_path}/config.yaml"
+            model_path = f"{local_path}/pytorch_model.bin"
+        else:
+            print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+            repo_id = "charactr/vocos-mel-24khz"
+            config_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="config.yaml")
+            model_path = hf_hub_download(repo_id=repo_id, cache_dir=hf_cache_dir, filename="pytorch_model.bin")
+        vocoder = Vocos.from_hparams(config_path)
+        state_dict = torch.load(model_path, map_location="cpu", weights_only=True)
+        vocoder.load_state_dict(state_dict)
+        vocoder = vocoder.eval().to(device)
+    elif vocoder_name == "bigvgan":
+        raise NotImplementedError("BigVGAN is not supported yet")
+        vocoder.remove_weight_norm()
+        vocoder = vocoder.eval().to(device)
+    return vocoder
+
+
+if __name__ == "__main__":
+    args = get_args()
+    vocoder = load_vocoder(vocoder_name=args.vocoder, device="cpu", hf_cache_dir=None)
+    if args.vocoder == "vocos":
+        export_VocosVocoder(vocoder, args.output_path, verbose=False)
@@ -0,0 +1,43 @@
+#!/bin/bash
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+TRTEXEC="/usr/src/tensorrt/bin/trtexec"
+
+ONNX_PATH=$1
+ENGINE_PATH=$2
+echo "ONNX_PATH: $ONNX_PATH"
+echo "ENGINE_PATH: $ENGINE_PATH"
+PRECISION="fp32"
+
+
+MIN_BATCH_SIZE=1
+OPT_BATCH_SIZE=1
+MAX_BATCH_SIZE=8
+
+MIN_INPUT_LENGTH=1
+OPT_INPUT_LENGTH=1000
+MAX_INPUT_LENGTH=3000
+
+MEL_MIN_SHAPE="${MIN_BATCH_SIZE}x100x${MIN_INPUT_LENGTH}"
+MEL_OPT_SHAPE="${OPT_BATCH_SIZE}x100x${OPT_INPUT_LENGTH}"
+MEL_MAX_SHAPE="${MAX_BATCH_SIZE}x100x${MAX_INPUT_LENGTH}"
+
+${TRTEXEC} \
+    --minShapes="mel:${MEL_MIN_SHAPE}" \
+    --optShapes="mel:${MEL_OPT_SHAPE}" \
+    --maxShapes="mel:${MEL_MAX_SHAPE}" \
+    --onnx=${ONNX_PATH} \
+    --saveEngine=${ENGINE_PATH}
+
@@ -0,0 +1,36 @@
+#! /usr/bin/env python3
+from argparse import ArgumentParser
+from string import Template
+
+
+def main(file_path, substitutions, in_place, participant_ids):
+    with open(file_path) as f:
+        pbtxt = Template(f.read())
+
+    sub_dict = {"max_queue_size": 0}
+    sub_dict["participant_ids"] = participant_ids
+    for sub in substitutions.split(","):
+        key, value = sub.split(":")
+        sub_dict[key] = value
+
+    pbtxt = pbtxt.safe_substitute(sub_dict)
+
+    if in_place:
+        with open(file_path, "w") as f:
+            f.write(pbtxt)
+    else:
+        print(pbtxt)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("file_path", help="path of the .pbtxt to modify")
+    parser.add_argument(
+        "substitutions",
+        help="substitutions to perform, in the format variable_name_1:value_1,variable_name_2:value_2...",
+    )
+    parser.add_argument("--in_place", "-i", action="store_true", help="do the operation in-place")
+    parser.add_argument("--participant_ids", help="Participant IDs for the model", default="")
+    args = parser.parse_args()
+
+    main(**vars(args))
@@ -0,0 +1,33 @@
+"""ADAPTIVE BATCH SIZE"""
+
+print("Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in")
+print("  -> least padding, gather wavs with accumulated frames in a batch\n")
+
+# data
+total_hours = 95282
+mel_hop_length = 256
+mel_sampling_rate = 24000
+
+# target
+wanted_max_updates = 1200000
+
+# train params
+gpus = 8
+frames_per_gpu = 38400  # 8 * 38400 = 307200
+grad_accum = 1
+
+# intermediate
+mini_batch_frames = frames_per_gpu * grad_accum * gpus
+mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+updates_per_epoch = total_hours / mini_batch_hours
+# steps_per_epoch = updates_per_epoch * grad_accum
+
+# result
+epochs = wanted_max_updates / updates_per_epoch
+print(f"epochs should be set to: {epochs:.0f} ({epochs / grad_accum:.1f} x gd_acum {grad_accum})")
+print(f"progress_bar should show approx. 0/{updates_per_epoch:.0f} updates")
+# print(f"                      or approx. 0/{steps_per_epoch:.0f} steps")
+
+# others
+print(f"total {total_hours:.0f} hours")
+print(f"mini-batch of {mini_batch_frames:.0f} frames, {mini_batch_hours:.2f} hours per mini-batch")
@@ -0,0 +1,40 @@
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import thop
+import torch
+
+from ..model import CFM, DiT
+
+
+""" ~155M """
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4)
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4, long_skip_connection = True)
+# transformer =     MMDiT(dim = 512, depth = 16, heads = 16, ff_mult = 2)
+
+""" ~335M """
+# FLOPs: 622.1 G, Params: 333.2 M
+# transformer =     UNetT(dim = 1024, depth = 24, heads = 16, ff_mult = 4)
+# FLOPs: 363.4 G, Params: 335.8 M
+transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+
+
+model = CFM(transformer=transformer)
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+duration = 20
+frame_length = int(duration * target_sample_rate / hop_length)
+text_length = 150
+
+flops, params = thop.profile(
+    model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long))
+)
+print(f"FLOPs: {flops / 1e9} G")
+print(f"Params: {params / 1e6} M")
@@ -0,0 +1,63 @@
+import asyncio
+import logging
+import socket
+import time
+
+import numpy as np
+import pyaudio
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+async def listen_to_F5TTS(text, server_ip="localhost", server_port=9998):
+    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    await asyncio.get_event_loop().run_in_executor(None, client_socket.connect, (server_ip, int(server_port)))
+
+    start_time = time.time()
+    first_chunk_time = None
+
+    async def play_audio_stream():
+        nonlocal first_chunk_time
+        p = pyaudio.PyAudio()
+        stream = p.open(format=pyaudio.paFloat32, channels=1, rate=24000, output=True, frames_per_buffer=2048)
+
+        try:
+            while True:
+                data = await asyncio.get_event_loop().run_in_executor(None, client_socket.recv, 8192)
+                if not data:
+                    break
+                if data == b"END":
+                    logger.info("End of audio received.")
+                    break
+
+                audio_array = np.frombuffer(data, dtype=np.float32)
+                stream.write(audio_array.tobytes())
+
+                if first_chunk_time is None:
+                    first_chunk_time = time.time()
+
+        finally:
+            stream.stop_stream()
+            stream.close()
+            p.terminate()
+
+        logger.info(f"Total time taken: {time.time() - start_time:.4f} seconds")
+
+    try:
+        data_to_send = f"{text}".encode("utf-8")
+        await asyncio.get_event_loop().run_in_executor(None, client_socket.sendall, data_to_send)
+        await play_audio_stream()
+
+    except Exception as e:
+        logger.error(f"Error in listen_to_F5TTS: {e}")
+
+    finally:
+        client_socket.close()
+
+
+if __name__ == "__main__":
+    text_to_send = "As a Reader assistant, I'm familiar with new technology. which are key to its improved performance in terms of both training speed and inference efficiency. Let's break down the components"
+
+    asyncio.run(listen_to_F5TTS(text_to_send))
@@ -0,0 +1,268 @@
+import argparse
+import gc
+import logging
+import queue
+import socket
+import struct
+import threading
+import traceback
+import wave
+from importlib.resources import files
+
+import numpy as np
+import torch
+import torchaudio
+from huggingface_hub import hf_hub_download
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from .infer.utils_infer import (
+    chunk_text,
+    infer_batch_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+)
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class AudioFileWriterThread(threading.Thread):
+    """Threaded file writer to avoid blocking the TTS streaming process."""
+
+    def __init__(self, output_file, sampling_rate):
+        super().__init__()
+        self.output_file = output_file
+        self.sampling_rate = sampling_rate
+        self.queue = queue.Queue()
+        self.stop_event = threading.Event()
+        self.audio_data = []
+
+    def run(self):
+        """Process queued audio data and write it to a file."""
+        logger.info("AudioFileWriterThread started.")
+        with wave.open(self.output_file, "wb") as wf:
+            wf.setnchannels(1)
+            wf.setsampwidth(2)
+            wf.setframerate(self.sampling_rate)
+
+            while not self.stop_event.is_set() or not self.queue.empty():
+                try:
+                    chunk = self.queue.get(timeout=0.1)
+                    if chunk is not None:
+                        chunk = np.int16(chunk * 32767)
+                        self.audio_data.append(chunk)
+                        wf.writeframes(chunk.tobytes())
+                except queue.Empty:
+                    continue
+
+    def add_chunk(self, chunk):
+        """Add a new chunk to the queue."""
+        self.queue.put(chunk)
+
+    def stop(self):
+        """Stop writing and ensure all queued data is written."""
+        self.stop_event.set()
+        self.join()
+        logger.info("Audio writing completed.")
+
+
+class TTSStreamingProcessor:
+    def __init__(self, model, ckpt_file, vocab_file, ref_audio, ref_text, device=None, dtype=torch.float32):
+        self.device = device or (
+            "cuda"
+            if torch.cuda.is_available()
+            else "xpu"
+            if torch.xpu.is_available()
+            else "mps"
+            if torch.backends.mps.is_available()
+            else "cpu"
+        )
+        model_cfg = OmegaConf.load(str(files(__package__ or "f5_tts").joinpath(f"configs/{model}.yaml")))
+        self.model_cls = get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+        self.model_arc = model_cfg.model.arch
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.sampling_rate = model_cfg.model.mel_spec.target_sample_rate
+
+        self.model = self.load_ema_model(ckpt_file, vocab_file, dtype)
+        self.vocoder = self.load_vocoder_model()
+
+        self.update_reference(ref_audio, ref_text)
+        self._warm_up()
+        self.file_writer_thread = None
+        self.first_package = True
+
+    def load_ema_model(self, ckpt_file, vocab_file, dtype):
+        return load_model(
+            self.model_cls,
+            self.model_arc,
+            ckpt_path=ckpt_file,
+            mel_spec_type=self.mel_spec_type,
+            vocab_file=vocab_file,
+            ode_method="euler",
+            use_ema=True,
+            device=self.device,
+        ).to(self.device, dtype=dtype)
+
+    def load_vocoder_model(self):
+        return load_vocoder(vocoder_name=self.mel_spec_type, is_local=False, local_path=None, device=self.device)
+
+    def update_reference(self, ref_audio, ref_text):
+        self.ref_audio, self.ref_text = preprocess_ref_audio_text(ref_audio, ref_text)
+        self.audio, self.sr = torchaudio.load(self.ref_audio)
+
+        ref_audio_duration = self.audio.shape[-1] / self.sr
+        ref_text_byte_len = len(self.ref_text.encode("utf-8"))
+        self.max_chars = int(ref_text_byte_len / (ref_audio_duration) * (25 - ref_audio_duration))
+        self.few_chars = int(ref_text_byte_len / (ref_audio_duration) * (25 - ref_audio_duration) / 2)
+        self.min_chars = int(ref_text_byte_len / (ref_audio_duration) * (25 - ref_audio_duration) / 4)
+
+    def _warm_up(self):
+        logger.info("Warming up the model...")
+        gen_text = "Warm-up text for the model."
+        for _ in infer_batch_process(
+            (self.audio, self.sr),
+            self.ref_text,
+            [gen_text],
+            self.model,
+            self.vocoder,
+            progress=None,
+            device=self.device,
+            streaming=True,
+        ):
+            pass
+        logger.info("Warm-up completed.")
+
+    def generate_stream(self, text, conn):
+        text_batches = chunk_text(text, max_chars=self.max_chars)
+        if self.first_package:
+            text_batches = chunk_text(text_batches[0], max_chars=self.few_chars) + text_batches[1:]
+            text_batches = chunk_text(text_batches[0], max_chars=self.min_chars) + text_batches[1:]
+            self.first_package = False
+
+        audio_stream = infer_batch_process(
+            (self.audio, self.sr),
+            self.ref_text,
+            text_batches,
+            self.model,
+            self.vocoder,
+            progress=None,
+            device=self.device,
+            streaming=True,
+            chunk_size=2048,
+        )
+
+        # Reset the file writer thread
+        if self.file_writer_thread is not None:
+            self.file_writer_thread.stop()
+        self.file_writer_thread = AudioFileWriterThread("output.wav", self.sampling_rate)
+        self.file_writer_thread.start()
+
+        for audio_chunk, _ in audio_stream:
+            if len(audio_chunk) > 0:
+                logger.info(f"Generated audio chunk of size: {len(audio_chunk)}")
+
+                # Send audio chunk via socket
+                conn.sendall(struct.pack(f"{len(audio_chunk)}f", *audio_chunk))
+
+                # Write to file asynchronously
+                self.file_writer_thread.add_chunk(audio_chunk)
+
+        logger.info("Finished sending audio stream.")
+        conn.sendall(b"END")  # Send end signal
+
+        # Ensure all audio data is written before exiting
+        self.file_writer_thread.stop()
+
+
+def handle_client(conn, processor):
+    try:
+        with conn:
+            conn.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
+            while True:
+                data = conn.recv(1024)
+                if not data:
+                    processor.first_package = True
+                    break
+                data_str = data.decode("utf-8").strip()
+                logger.info(f"Received text: {data_str}")
+
+                try:
+                    processor.generate_stream(data_str, conn)
+                except Exception as inner_e:
+                    logger.error(f"Error during processing: {inner_e}")
+                    traceback.print_exc()
+                    break
+    except Exception as e:
+        logger.error(f"Error handling client: {e}")
+        traceback.print_exc()
+
+
+def start_server(host, port, processor):
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind((host, port))
+        s.listen()
+        logger.info(f"Server started on {host}:{port}")
+        while True:
+            conn, addr = s.accept()
+            logger.info(f"Connected by {addr}")
+            handle_client(conn, processor)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--host", default="0.0.0.0")
+    parser.add_argument("--port", default=9998)
+
+    parser.add_argument(
+        "--model",
+        default="F5TTS_v1_Base",
+        help="The model name, e.g. F5TTS_v1_Base",
+    )
+    parser.add_argument(
+        "--ckpt_file",
+        default=str(hf_hub_download(repo_id="SWivid/F5-TTS", filename="F5TTS_v1_Base/model_1250000.safetensors")),
+        help="Path to the model checkpoint file",
+    )
+    parser.add_argument(
+        "--vocab_file",
+        default="",
+        help="Path to the vocab file if customized",
+    )
+
+    parser.add_argument(
+        "--ref_audio",
+        default=str(files(__package__ or "f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        help="Reference audio to provide model with speaker characteristics",
+    )
+    parser.add_argument(
+        "--ref_text",
+        default="",
+        help="Reference audio subtitle, leave empty to auto-transcribe",
+    )
+
+    parser.add_argument("--device", default=None, help="Device to run the model on")
+    parser.add_argument("--dtype", default=torch.float32, help="Data type to use for model inference")
+
+    args = parser.parse_args()
+
+    try:
+        # Initialize the processor with the model and vocoder
+        processor = TTSStreamingProcessor(
+            model=args.model,
+            ckpt_file=args.ckpt_file,
+            vocab_file=args.vocab_file,
+            ref_audio=args.ref_audio,
+            ref_text=args.ref_text,
+            device=args.device,
+            dtype=args.dtype,
+        )
+
+        # Start the server
+        start_server(args.host, args.port, processor)
+
+    except KeyboardInterrupt:
+        gc.collect()
@@ -0,0 +1,92 @@
+# Training
+
+Check your FFmpeg installation:
+```bash
+ffmpeg -version
+```
+If not found, install it first (or skip assuming you know of other backends available).
+
+## Prepare Dataset
+
+Example data processing scripts, and you may tailor your own one along with a Dataset class in `src/f5_tts/model/dataset.py`.
+
+### 1. Some specific Datasets preparing scripts
+Download corresponding dataset first, and fill in the path in scripts.
+
+```bash
+# Prepare the Emilia dataset
+python src/f5_tts/train/datasets/prepare_emilia.py
+
+# Prepare the Wenetspeech4TTS dataset
+python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py
+
+# Prepare the LibriTTS dataset
+python src/f5_tts/train/datasets/prepare_libritts.py
+
+# Prepare the LJSpeech dataset
+python src/f5_tts/train/datasets/prepare_ljspeech.py
+```
+
+### 2. Create custom dataset with metadata.csv
+Use guidance see [#57 here](https://github.com/SWivid/F5-TTS/discussions/57#discussioncomment-10959029).
+
+```bash
+python src/f5_tts/train/datasets/prepare_csv_wavs.py
+```
+
+## Training & Finetuning
+
+Once your datasets are prepared, you can start the training process.
+
+### 1. Training script used for pretrained model
+
+```bash
+# setup accelerate config, e.g. use multi-gpu ddp, fp16
+# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
+accelerate config
+
+# .yaml files are under src/f5_tts/configs directory
+accelerate launch src/f5_tts/train/train.py --config-name F5TTS_v1_Base.yaml
+
+# possible to overwrite accelerate and hydra config
+accelerate launch --mixed_precision=fp16 src/f5_tts/train/train.py --config-name F5TTS_v1_Base.yaml ++datasets.batch_size_per_gpu=19200
+```
+
+### 2. Finetuning practice
+Discussion board for Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57).
+
+Gradio UI training/finetuning with `src/f5_tts/train/finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143).
+
+If want to finetune with a variant version e.g. *F5TTS_v1_Base_no_zero_init*, manually download pretrained checkpoint from model weight repository and fill in the path correspondingly on web interface.
+
+If use tensorboard as logger, install it first with `pip install tensorboard`.
+
+<ins>The `use_ema = True` might be harmful for early-stage finetuned checkpoints</ins> (which goes just few updates, thus ema weights still dominated by pretrained ones), try turn it off with finetune gradio option or `load_model(..., use_ema=False)`, see if offer better results.
+
+### 3. W&B Logging
+
+The `wandb/` dir will be created under path you run training/finetuning scripts.
+
+By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`).
+
+To turn on wandb logging, you can either:
+
+1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
+2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/authorize and set the environment variable as follows:
+
+On Mac & Linux:
+
+```
+export WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+
+On Windows:
+
+```
+set WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+Moreover, if you couldn't access W&B and want to log metrics offline, you can set the environment variable as follows:
+
+```
+export WANDB_MODE=offline
+```
@@ -0,0 +1,283 @@
+import concurrent.futures
+import multiprocessing
+import os
+import shutil
+import signal
+import subprocess  # For invoking ffprobe
+import sys
+from contextlib import contextmanager
+
+
+sys.path.append(os.getcwd())
+
+import argparse
+import csv
+import json
+from importlib.resources import files
+from pathlib import Path
+
+import torchaudio
+from datasets.arrow_writer import ArrowWriter
+from tqdm import tqdm
+
+from ..model.utils import convert_char_to_pinyin
+
+
+PRETRAINED_VOCAB_PATH = files(__package__ or "f5_tts").joinpath("../../data/Emilia_ZH_EN_pinyin/vocab.txt")
+
+
+def is_csv_wavs_format(input_dataset_dir):
+    fpath = Path(input_dataset_dir)
+    metadata = fpath / "metadata.csv"
+    wavs = fpath / "wavs"
+    return metadata.exists() and metadata.is_file() and wavs.exists() and wavs.is_dir()
+
+
+# Configuration constants
+BATCH_SIZE = 100  # Batch size for text conversion
+MAX_WORKERS = max(1, multiprocessing.cpu_count() - 1)  # Leave one CPU free
+THREAD_NAME_PREFIX = "AudioProcessor"
+CHUNK_SIZE = 100  # Number of files to process per worker batch
+
+executor = None  # Global executor for cleanup
+
+
+@contextmanager
+def graceful_exit():
+    """Context manager for graceful shutdown on signals"""
+
+    def signal_handler(signum, frame):
+        print("\nReceived signal to terminate. Cleaning up...")
+        if executor is not None:
+            print("Shutting down executor...")
+            executor.shutdown(wait=False, cancel_futures=True)
+        sys.exit(1)
+
+    # Set up signal handlers
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        yield
+    finally:
+        if executor is not None:
+            executor.shutdown(wait=False)
+
+
+def process_audio_file(audio_path, text, polyphone):
+    """Process a single audio file by checking its existence and extracting duration."""
+    if not Path(audio_path).exists():
+        print(f"audio {audio_path} not found, skipping")
+        return None
+    try:
+        audio_duration = get_audio_duration(audio_path)
+        if audio_duration <= 0:
+            raise ValueError(f"Duration {audio_duration} is non-positive.")
+        return (audio_path, text, audio_duration)
+    except Exception as e:
+        print(f"Warning: Failed to process {audio_path} due to error: {e}. Skipping corrupt file.")
+        return None
+
+
+def batch_convert_texts(texts, polyphone, batch_size=BATCH_SIZE):
+    """Convert a list of texts to pinyin in batches."""
+    converted_texts = []
+    for i in range(0, len(texts), batch_size):
+        batch = texts[i : i + batch_size]
+        converted_batch = convert_char_to_pinyin(batch, polyphone=polyphone)
+        converted_texts.extend(converted_batch)
+    return converted_texts
+
+
+def prepare_csv_wavs_dir(input_dir, num_workers=None):
+    global executor
+    assert is_csv_wavs_format(input_dir), f"not csv_wavs format: {input_dir}"
+    input_dir = Path(input_dir)
+    metadata_path = input_dir / "metadata.csv"
+    audio_path_text_pairs = read_audio_text_pairs(metadata_path.as_posix())
+
+    polyphone = True
+    total_files = len(audio_path_text_pairs)
+
+    # Use provided worker count or calculate optimal number
+    worker_count = num_workers if num_workers is not None else min(MAX_WORKERS, total_files)
+    print(f"\nProcessing {total_files} audio files using {worker_count} workers...")
+
+    with graceful_exit():
+        # Initialize thread pool with optimized settings
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=worker_count, thread_name_prefix=THREAD_NAME_PREFIX
+        ) as exec:
+            executor = exec
+            results = []
+
+            # Process files in chunks for better efficiency
+            for i in range(0, len(audio_path_text_pairs), CHUNK_SIZE):
+                chunk = audio_path_text_pairs[i : i + CHUNK_SIZE]
+                # Submit futures in order
+                chunk_futures = [executor.submit(process_audio_file, pair[0], pair[1], polyphone) for pair in chunk]
+
+                # Iterate over futures in the original submission order to preserve ordering
+                for future in tqdm(
+                    chunk_futures,
+                    total=len(chunk),
+                    desc=f"Processing chunk {i // CHUNK_SIZE + 1}/{(total_files + CHUNK_SIZE - 1) // CHUNK_SIZE}",
+                ):
+                    try:
+                        result = future.result()
+                        if result is not None:
+                            results.append(result)
+                    except Exception as e:
+                        print(f"Error processing file: {e}")
+
+            executor = None
+
+    # Filter out failed results
+    processed = [res for res in results if res is not None]
+    if not processed:
+        raise RuntimeError("No valid audio files were processed!")
+
+    # Batch process text conversion
+    raw_texts = [item[1] for item in processed]
+    converted_texts = batch_convert_texts(raw_texts, polyphone, batch_size=BATCH_SIZE)
+
+    # Prepare final results
+    sub_result = []
+    durations = []
+    vocab_set = set()
+
+    for (audio_path, _, duration), conv_text in zip(processed, converted_texts):
+        sub_result.append({"audio_path": audio_path, "text": conv_text, "duration": duration})
+        durations.append(duration)
+        vocab_set.update(list(conv_text))
+
+    return sub_result, durations, vocab_set
+
+
+def get_audio_duration(audio_path, timeout=5):
+    """
+    Get the duration of an audio file in seconds using ffmpeg's ffprobe.
+    Falls back to torchaudio.load() if ffprobe fails.
+    """
+    try:
+        cmd = [
+            "ffprobe",
+            "-v",
+            "error",
+            "-show_entries",
+            "format=duration",
+            "-of",
+            "default=noprint_wrappers=1:nokey=1",
+            audio_path,
+        ]
+        result = subprocess.run(
+            cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True, timeout=timeout
+        )
+        duration_str = result.stdout.strip()
+        if duration_str:
+            return float(duration_str)
+        raise ValueError("Empty duration string from ffprobe.")
+    except (subprocess.TimeoutExpired, subprocess.SubprocessError, ValueError) as e:
+        print(f"Warning: ffprobe failed for {audio_path} with error: {e}. Falling back to torchaudio.")
+        try:
+            audio, sample_rate = torchaudio.load(audio_path)
+            return audio.shape[1] / sample_rate
+        except Exception as e:
+            raise RuntimeError(f"Both ffprobe and torchaudio failed for {audio_path}: {e}")
+
+
+def read_audio_text_pairs(csv_file_path):
+    audio_text_pairs = []
+
+    parent = Path(csv_file_path).parent
+    with open(csv_file_path, mode="r", newline="", encoding="utf-8-sig") as csvfile:
+        reader = csv.reader(csvfile, delimiter="|")
+        next(reader)  # Skip the header row
+        for row in reader:
+            if len(row) >= 2:
+                audio_file = row[0].strip()  # First column: audio file path
+                text = row[1].strip()  # Second column: text
+                audio_file_path = parent / audio_file
+                audio_text_pairs.append((audio_file_path.as_posix(), text))
+
+    return audio_text_pairs
+
+
+def save_prepped_dataset(out_dir, result, duration_list, text_vocab_set, is_finetune):
+    out_dir = Path(out_dir)
+    out_dir.mkdir(exist_ok=True, parents=True)
+    print(f"\nSaving to {out_dir} ...")
+
+    raw_arrow_path = out_dir / "raw.arrow"
+    with ArrowWriter(path=raw_arrow_path.as_posix()) as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+        writer.finalize()
+
+    # Save durations to JSON
+    dur_json_path = out_dir / "duration.json"
+    with open(dur_json_path.as_posix(), "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # Handle vocab file - write only once based on finetune flag
+    voca_out_path = out_dir / "vocab.txt"
+    if is_finetune:
+        file_vocab_finetune = PRETRAINED_VOCAB_PATH.as_posix()
+        shutil.copy2(file_vocab_finetune, voca_out_path)
+    else:
+        with open(voca_out_path.as_posix(), "w") as f:
+            for vocab in sorted(text_vocab_set):
+                f.write(vocab + "\n")
+
+    dataset_name = out_dir.stem
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
+
+
+def prepare_and_save_set(inp_dir, out_dir, is_finetune: bool = True, num_workers: int = None):
+    if is_finetune:
+        assert PRETRAINED_VOCAB_PATH.exists(), f"pretrained vocab.txt not found: {PRETRAINED_VOCAB_PATH}"
+    sub_result, durations, vocab_set = prepare_csv_wavs_dir(inp_dir, num_workers=num_workers)
+    save_prepped_dataset(out_dir, sub_result, durations, vocab_set, is_finetune)
+
+
+def cli():
+    try:
+        # Before processing, check if ffprobe is available.
+        if shutil.which("ffprobe") is None:
+            print(
+                "Warning: ffprobe is not available. Duration extraction will rely on torchaudio (which may be slower)."
+            )
+
+        # Usage examples in help text
+        parser = argparse.ArgumentParser(
+            description="Prepare and save dataset.",
+            epilog="""
+Examples:
+    # For fine-tuning (default):
+    python prepare_csv_wavs.py /input/dataset/path /output/dataset/path
+    
+    # For pre-training:
+    python prepare_csv_wavs.py /input/dataset/path /output/dataset/path --pretrain
+    
+    # With custom worker count:
+    python prepare_csv_wavs.py /input/dataset/path /output/dataset/path --workers 4
+            """,
+        )
+        parser.add_argument("inp_dir", type=str, help="Input directory containing the data.")
+        parser.add_argument("out_dir", type=str, help="Output directory to save the prepared data.")
+        parser.add_argument("--pretrain", action="store_true", help="Enable for new pretrain, otherwise is a fine-tune")
+        parser.add_argument("--workers", type=int, help=f"Number of worker threads (default: {MAX_WORKERS})")
+        args = parser.parse_args()
+
+        prepare_and_save_set(args.inp_dir, args.out_dir, is_finetune=not args.pretrain, num_workers=args.workers)
+    except KeyboardInterrupt:
+        print("\nOperation cancelled by user. Cleaning up...")
+        if executor is not None:
+            executor.shutdown(wait=False, cancel_futures=True)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    cli()
@@ -0,0 +1,229 @@
+# Emilia Dataset: https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07
+# if use updated new version, i.e. WebDataset, feel free to modify / draft your own script
+
+# generate audio text map for Emilia ZH & EN
+# evaluate for vocab size
+
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from pathlib import Path
+
+from datasets.arrow_writer import ArrowWriter
+from tqdm import tqdm
+
+from ..model.utils import convert_char_to_pinyin, repetition_found
+
+
+out_zh = {
+    "ZH_B00041_S06226",
+    "ZH_B00042_S09204",
+    "ZH_B00065_S09430",
+    "ZH_B00065_S09431",
+    "ZH_B00066_S09327",
+    "ZH_B00066_S09328",
+}
+zh_filters = ["い", "て"]
+# seems synthesized audios, or heavily code-switched
+out_en = {
+    "EN_B00013_S00913",
+    "EN_B00042_S00120",
+    "EN_B00055_S04111",
+    "EN_B00061_S00693",
+    "EN_B00061_S01494",
+    "EN_B00061_S03375",
+    "EN_B00059_S00092",
+    "EN_B00111_S04300",
+    "EN_B00100_S03759",
+    "EN_B00087_S03811",
+    "EN_B00059_S00950",
+    "EN_B00089_S00946",
+    "EN_B00078_S05127",
+    "EN_B00070_S04089",
+    "EN_B00074_S09659",
+    "EN_B00061_S06983",
+    "EN_B00061_S07060",
+    "EN_B00059_S08397",
+    "EN_B00082_S06192",
+    "EN_B00091_S01238",
+    "EN_B00089_S07349",
+    "EN_B00070_S04343",
+    "EN_B00061_S02400",
+    "EN_B00076_S01262",
+    "EN_B00068_S06467",
+    "EN_B00076_S02943",
+    "EN_B00064_S05954",
+    "EN_B00061_S05386",
+    "EN_B00066_S06544",
+    "EN_B00076_S06944",
+    "EN_B00072_S08620",
+    "EN_B00076_S07135",
+    "EN_B00076_S09127",
+    "EN_B00065_S00497",
+    "EN_B00059_S06227",
+    "EN_B00063_S02859",
+    "EN_B00075_S01547",
+    "EN_B00061_S08286",
+    "EN_B00079_S02901",
+    "EN_B00092_S03643",
+    "EN_B00096_S08653",
+    "EN_B00063_S04297",
+    "EN_B00063_S04614",
+    "EN_B00079_S04698",
+    "EN_B00104_S01666",
+    "EN_B00061_S09504",
+    "EN_B00061_S09694",
+    "EN_B00065_S05444",
+    "EN_B00063_S06860",
+    "EN_B00065_S05725",
+    "EN_B00069_S07628",
+    "EN_B00083_S03875",
+    "EN_B00071_S07665",
+    "EN_B00071_S07665",
+    "EN_B00062_S04187",
+    "EN_B00065_S09873",
+    "EN_B00065_S09922",
+    "EN_B00084_S02463",
+    "EN_B00067_S05066",
+    "EN_B00106_S08060",
+    "EN_B00073_S06399",
+    "EN_B00073_S09236",
+    "EN_B00087_S00432",
+    "EN_B00085_S05618",
+    "EN_B00064_S01262",
+    "EN_B00072_S01739",
+    "EN_B00059_S03913",
+    "EN_B00069_S04036",
+    "EN_B00067_S05623",
+    "EN_B00060_S05389",
+    "EN_B00060_S07290",
+    "EN_B00062_S08995",
+}
+en_filters = ["ا", "い", "て"]
+
+
+def deal_with_audio_dir(audio_dir):
+    audio_jsonl = audio_dir.with_suffix(".jsonl")
+    sub_result, durations = [], []
+    vocab_set = set()
+    bad_case_zh = 0
+    bad_case_en = 0
+    with open(audio_jsonl, "r") as f:
+        lines = f.readlines()
+        for line in tqdm(lines, desc=f"{audio_jsonl.stem}"):
+            obj = json.loads(line)
+            text = obj["text"]
+            if obj["language"] == "zh":
+                if obj["wav"].split("/")[1] in out_zh or any(f in text for f in zh_filters) or repetition_found(text):
+                    bad_case_zh += 1
+                    continue
+                else:
+                    text = text.translate(
+                        str.maketrans({",": "，", "!": "！", "?": "？"})
+                    )  # not "。" cuz much code-switched
+            if obj["language"] == "en":
+                if (
+                    obj["wav"].split("/")[1] in out_en
+                    or any(f in text for f in en_filters)
+                    or repetition_found(text, length=4)
+                ):
+                    bad_case_en += 1
+                    continue
+            if tokenizer == "pinyin":
+                text = convert_char_to_pinyin([text], polyphone=polyphone)[0]
+            duration = obj["duration"]
+            sub_result.append({"audio_path": str(audio_dir.parent / obj["wav"]), "text": text, "duration": duration})
+            durations.append(duration)
+            vocab_set.update(list(text))
+    return sub_result, durations, vocab_set, bad_case_zh, bad_case_en
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+    total_bad_case_zh = 0
+    total_bad_case_en = 0
+
+    # process raw data
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    for lang in langs:
+        dataset_path = Path(os.path.join(dataset_dir, lang))
+        [
+            futures.append(executor.submit(deal_with_audio_dir, audio_dir))
+            for audio_dir in dataset_path.iterdir()
+            if audio_dir.is_dir()
+        ]
+    for futures in tqdm(futures, total=len(futures)):
+        sub_result, durations, vocab_set, bad_case_zh, bad_case_en = futures.result()
+        result.extend(sub_result)
+        duration_list.extend(durations)
+        text_vocab_set.update(vocab_set)
+        total_bad_case_zh += bad_case_zh
+        total_bad_case_en += bad_case_en
+    executor.shutdown()
+
+    # save preprocessed dataset to disk
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+    print(f"\nSaving to {save_dir} ...")
+
+    # dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})  # oom
+    # dataset.save_to_disk(f"{save_dir}/raw", max_shard_size="2GB")
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+        writer.finalize()
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    # if tokenizer == "pinyin":
+    #     text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
+    if "ZH" in langs:
+        print(f"Bad zh transcription case: {total_bad_case_zh}")
+    if "EN" in langs:
+        print(f"Bad en transcription case: {total_bad_case_en}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+
+    tokenizer = "pinyin"  # "pinyin" | "char"
+    polyphone = True
+
+    langs = ["ZH", "EN"]
+    dataset_dir = "<SOME_PATH>/Emilia_Dataset/raw"
+    dataset_name = f"Emilia_{'_'.join(langs)}_{tokenizer}"
+    save_dir = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+
+    main()
+
+    # Emilia               ZH & EN
+    # samples count       37837916   (after removal)
+    # pinyin vocab size       2543   (polyphone)
+    # total duration      95281.87   (hours)
+    # bad zh asr cnt        230435   (samples)
+    # bad eh asr cnt         37217   (samples)
+
+    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # please be careful if using pretrained model, make sure the vocab.txt is same
@@ -0,0 +1,95 @@
+# put in src/f5_tts/train/datasets/prepare_emilia_v2.py
+# prepares Emilia dataset with the new format w/ Emilia-YODAS
+
+import json
+import os
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from pathlib import Path
+
+from datasets.arrow_writer import ArrowWriter
+from tqdm import tqdm
+
+from ..model.utils import repetition_found
+
+
+# Define filters for exclusion
+out_en = set()
+en_filters = ["ا", "い", "て"]
+
+
+def process_audio_directory(audio_dir):
+    sub_result, durations, vocab_set = [], [], set()
+    bad_case_en = 0
+
+    for file in audio_dir.iterdir():
+        if file.suffix == ".json":
+            with open(file, "r") as f:
+                obj = json.load(f)
+                text = obj["text"]
+                if any(f in text for f in en_filters) or repetition_found(text, length=4):
+                    bad_case_en += 1
+                    continue
+
+                duration = obj["duration"]
+                audio_file = file.with_suffix(".mp3")
+                if audio_file.exists():
+                    sub_result.append({"audio_path": str(audio_file), "text": text, "duration": duration})
+                    durations.append(duration)
+                    vocab_set.update(list(text))
+
+    return sub_result, durations, vocab_set, bad_case_en
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+    result, duration_list, text_vocab_set = [], [], set()
+    total_bad_case_en = 0
+
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    dataset_path = Path(dataset_dir)
+    for sub_dir in dataset_path.iterdir():
+        if sub_dir.is_dir():
+            futures.append(executor.submit(process_audio_directory, sub_dir))
+
+    for future in tqdm(futures, total=len(futures)):
+        sub_result, durations, vocab_set, bad_case_en = future.result()
+        result.extend(sub_result)
+        duration_list.extend(durations)
+        text_vocab_set.update(vocab_set)
+        total_bad_case_en += bad_case_en
+
+    executor.shutdown()
+
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+        writer.finalize()
+
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"For {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
+    print(f"Bad en transcription case: {total_bad_case_en}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+    tokenizer = "char"
+    dataset_dir = "/home/ubuntu/emilia-dataset/Emilia-YODAS/EN"
+    dataset_name = f"Emilia_EN_{tokenizer}"
+    # save_dir = os.path.expanduser(f"~/F5-TTS/data/{dataset_name}")
+    save_dir = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+
+    print(f"Prepare for {dataset_name}, will save to {save_dir}\n")
+    main()
@@ -0,0 +1,95 @@
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from pathlib import Path
+
+import soundfile as sf
+from datasets.arrow_writer import ArrowWriter
+from tqdm import tqdm
+
+
+def deal_with_audio_dir(audio_dir):
+    sub_result, durations = [], []
+    vocab_set = set()
+    audio_lists = list(audio_dir.rglob("*.wav"))
+
+    for line in audio_lists:
+        text_path = line.with_suffix(".normalized.txt")
+        text = open(text_path, "r").read().strip()
+        duration = sf.info(line).duration
+        if duration < 0.4 or duration > 30:
+            continue
+        sub_result.append({"audio_path": str(line), "text": text, "duration": duration})
+        durations.append(duration)
+        vocab_set.update(list(text))
+    return sub_result, durations, vocab_set
+
+
+def main():
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+
+    # process raw data
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+
+    for subset in tqdm(SUB_SET):
+        dataset_path = Path(os.path.join(dataset_dir, subset))
+        [
+            futures.append(executor.submit(deal_with_audio_dir, audio_dir))
+            for audio_dir in dataset_path.iterdir()
+            if audio_dir.is_dir()
+        ]
+    for future in tqdm(futures, total=len(futures)):
+        sub_result, durations, vocab_set = future.result()
+        result.extend(sub_result)
+        duration_list.extend(durations)
+        text_vocab_set.update(vocab_set)
+    executor.shutdown()
+
+    # save preprocessed dataset to disk
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+    print(f"\nSaving to {save_dir} ...")
+
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+        writer.finalize()
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
+
+
+if __name__ == "__main__":
+    max_workers = 36
+
+    tokenizer = "char"  # "pinyin" | "char"
+
+    SUB_SET = ["train-clean-100", "train-clean-360", "train-other-500"]
+    dataset_dir = "<SOME_PATH>/LibriTTS"
+    dataset_name = f"LibriTTS_{'_'.join(SUB_SET)}_{tokenizer}".replace("train-clean-", "").replace("train-other-", "")
+    save_dir = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+    main()
+
+    # For LibriTTS_100_360_500_char, sample count: 354218
+    # For LibriTTS_100_360_500_char, vocab size is: 78
+    # For LibriTTS_100_360_500_char, total 554.09 hours
@@ -0,0 +1,68 @@
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import json
+from importlib.resources import files
+from pathlib import Path
+
+import soundfile as sf
+from datasets.arrow_writer import ArrowWriter
+from tqdm import tqdm
+
+
+def main():
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+
+    with open(meta_info, "r") as f:
+        lines = f.readlines()
+        for line in tqdm(lines):
+            uttr, text, norm_text = line.split("|")
+            norm_text = norm_text.strip()
+            wav_path = Path(dataset_dir) / "wavs" / f"{uttr}.wav"
+            duration = sf.info(wav_path).duration
+            if duration < 0.4 or duration > 30:
+                continue
+            result.append({"audio_path": str(wav_path), "text": norm_text, "duration": duration})
+            duration_list.append(duration)
+            text_vocab_set.update(list(norm_text))
+
+    # save preprocessed dataset to disk
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+    print(f"\nSaving to {save_dir} ...")
+
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+        writer.finalize()
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
+
+
+if __name__ == "__main__":
+    tokenizer = "char"  # "pinyin" | "char"
+
+    dataset_dir = "<SOME_PATH>/LJSpeech-1.1"
+    dataset_name = f"LJSpeech_{tokenizer}"
+    meta_info = os.path.join(dataset_dir, "metadata.csv")
+    save_dir = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+
+    main()
@@ -0,0 +1,126 @@
+# generate audio text map for WenetSpeech4TTS
+# evaluate for vocab size
+
+import os
+import sys
+
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+
+import torchaudio
+from datasets import Dataset
+from tqdm import tqdm
+
+from ..model.utils import convert_char_to_pinyin
+
+
+def deal_with_sub_path_files(dataset_path, sub_path):
+    print(f"Dealing with: {sub_path}")
+
+    text_dir = os.path.join(dataset_path, sub_path, "txts")
+    audio_dir = os.path.join(dataset_path, sub_path, "wavs")
+    text_files = os.listdir(text_dir)
+
+    audio_paths, texts, durations = [], [], []
+    for text_file in tqdm(text_files):
+        with open(os.path.join(text_dir, text_file), "r", encoding="utf-8") as file:
+            first_line = file.readline().split("\t")
+        audio_nm = first_line[0]
+        audio_path = os.path.join(audio_dir, audio_nm + ".wav")
+        text = first_line[1].strip()
+
+        audio_paths.append(audio_path)
+
+        if tokenizer == "pinyin":
+            texts.extend(convert_char_to_pinyin([text], polyphone=polyphone))
+        elif tokenizer == "char":
+            texts.append(text)
+
+        audio, sample_rate = torchaudio.load(audio_path)
+        durations.append(audio.shape[-1] / sample_rate)
+
+    return audio_paths, texts, durations
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+
+    audio_path_list, text_list, duration_list = [], [], []
+
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    for dataset_path in dataset_paths:
+        sub_items = os.listdir(dataset_path)
+        sub_paths = [item for item in sub_items if os.path.isdir(os.path.join(dataset_path, item))]
+        for sub_path in sub_paths:
+            futures.append(executor.submit(deal_with_sub_path_files, dataset_path, sub_path))
+    for future in tqdm(futures, total=len(futures)):
+        audio_paths, texts, durations = future.result()
+        audio_path_list.extend(audio_paths)
+        text_list.extend(texts)
+        duration_list.extend(durations)
+    executor.shutdown()
+
+    if not os.path.exists("data"):
+        os.makedirs("data")
+
+    print(f"\nSaving to {save_dir} ...")
+    dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})
+    dataset.save_to_disk(f"{save_dir}/raw", max_shard_size="2GB")  # arrow format
+
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump(
+            {"duration": duration_list}, f, ensure_ascii=False
+        )  # dup a json separately saving duration in case for DynamicBatchSampler ease
+
+    print("\nEvaluating vocab size (all characters and symbols / all phonemes) ...")
+    text_vocab_set = set()
+    for text in tqdm(text_list):
+        text_vocab_set.update(list(text))
+
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    if tokenizer == "pinyin":
+        text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+    print(f"\nFor {dataset_name}, sample count: {len(text_list)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+
+    tokenizer = "pinyin"  # "pinyin" | "char"
+    polyphone = True
+    dataset_choice = 1  # 1: Premium, 2: Standard, 3: Basic
+
+    dataset_name = (
+        ["WenetSpeech4TTS_Premium", "WenetSpeech4TTS_Standard", "WenetSpeech4TTS_Basic"][dataset_choice - 1]
+        + "_"
+        + tokenizer
+    )
+    dataset_paths = [
+        "<SOME_PATH>/WenetSpeech4TTS/Basic",
+        "<SOME_PATH>/WenetSpeech4TTS/Standard",
+        "<SOME_PATH>/WenetSpeech4TTS/Premium",
+    ][-dataset_choice:]
+    save_dir = str(files(__package__ or "f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nChoose Dataset: {dataset_name}, will save to {save_dir}\n")
+
+    main()
+
+    # Results (if adding alphabets with accents and symbols):
+    # WenetSpeech4TTS       Basic     Standard     Premium
+    # samples count       3932473      1941220      407494
+    # pinyin vocab size      1349         1348        1344   (no polyphone)
+    #                           -            -        1459   (polyphone)
+    # char   vocab size      5264         5219        5042
+
+    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # please be careful if using pretrained model, make sure the vocab.txt is same
@@ -0,0 +1,214 @@
+import argparse
+import os
+import shutil
+from importlib.resources import files
+
+from cached_path import cached_path
+
+from ..model import CFM, DiT, Trainer, UNetT
+from ..model.dataset import load_dataset
+from ..model.utils import get_tokenizer
+
+
+# -------------------------- Dataset Settings --------------------------- #
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+win_length = 1024
+n_fft = 1024
+mel_spec_type = "vocos"  # 'vocos' or 'bigvgan'
+
+
+# -------------------------- Argument Parsing --------------------------- #
+def parse_args():
+    parser = argparse.ArgumentParser(description="Train CFM Model")
+
+    parser.add_argument(
+        "--exp_name",
+        type=str,
+        default="F5TTS_v1_Base",
+        choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"],
+        help="Experiment name",
+    )
+    parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
+    parser.add_argument("--learning_rate", type=float, default=1e-5, help="Learning rate for training")
+    parser.add_argument("--batch_size_per_gpu", type=int, default=3200, help="Batch size per GPU")
+    parser.add_argument(
+        "--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
+    )
+    parser.add_argument("--max_samples", type=int, default=64, help="Max sequences per batch")
+    parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
+    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
+    parser.add_argument("--epochs", type=int, default=100, help="Number of training epochs")
+    parser.add_argument("--num_warmup_updates", type=int, default=20000, help="Warmup updates")
+    parser.add_argument("--save_per_updates", type=int, default=50000, help="Save checkpoint every N updates")
+    parser.add_argument(
+        "--keep_last_n_checkpoints",
+        type=int,
+        default=-1,
+        help="-1 to keep all, 0 to not save intermediate, > 0 to keep last N checkpoints",
+    )
+    parser.add_argument("--last_per_updates", type=int, default=5000, help="Save last checkpoint every N updates")
+    parser.add_argument("--finetune", action="store_true", help="Use Finetune")
+    parser.add_argument("--pretrain", type=str, default=None, help="the path to the checkpoint")
+    parser.add_argument(
+        "--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        type=str,
+        default=None,
+        help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
+    )
+    parser.add_argument(
+        "--log_samples",
+        action="store_true",
+        help="Log inferenced samples per ckpt save updates",
+    )
+    parser.add_argument("--logger", type=str, default=None, choices=[None, "wandb", "tensorboard"], help="logger")
+    parser.add_argument(
+        "--bnb_optimizer",
+        action="store_true",
+        help="Use 8-bit Adam optimizer from bitsandbytes",
+    )
+
+    return parser.parse_args()
+
+
+# -------------------------- Training Settings -------------------------- #
+
+
+def main():
+    args = parse_args()
+
+    checkpoint_path = str(files(__package__ or "f5_tts").joinpath(f"../../ckpts/{args.dataset_name}"))
+
+    # Model parameters based on experiment name
+
+    if args.exp_name == "F5TTS_v1_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            conv_layers=4,
+        )
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors"))
+            else:
+                ckpt_path = args.pretrain
+
+    elif args.exp_name == "F5TTS_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(
+            dim=1024,
+            depth=22,
+            heads=16,
+            ff_mult=2,
+            text_dim=512,
+            text_mask_padding=False,
+            conv_layers=4,
+            pe_attn_head=1,
+        )
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+            else:
+                ckpt_path = args.pretrain
+
+    elif args.exp_name == "E2TTS_Base":
+        wandb_resume_id = None
+        model_cls = UNetT
+        model_cfg = dict(
+            dim=1024,
+            depth=24,
+            heads=16,
+            ff_mult=4,
+            text_mask_padding=False,
+            pe_attn_head=1,
+        )
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+            else:
+                ckpt_path = args.pretrain
+
+    if args.finetune:
+        if not os.path.isdir(checkpoint_path):
+            os.makedirs(checkpoint_path, exist_ok=True)
+
+        file_checkpoint = os.path.basename(ckpt_path)
+        if not file_checkpoint.startswith("pretrained_"):  # Change: Add 'pretrained_' prefix to copied model
+            file_checkpoint = "pretrained_" + file_checkpoint
+        file_checkpoint = os.path.join(checkpoint_path, file_checkpoint)
+        if not os.path.isfile(file_checkpoint):
+            shutil.copy2(ckpt_path, file_checkpoint)
+            print("copy checkpoint for finetune")
+
+    # Use the tokenizer and tokenizer_path provided in the command line arguments
+
+    tokenizer = args.tokenizer
+    if tokenizer == "custom":
+        if not args.tokenizer_path:
+            raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
+        tokenizer_path = args.tokenizer_path
+    else:
+        tokenizer_path = args.dataset_name
+
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    print("\nvocab : ", vocab_size)
+    print("\nvocoder : ", mel_spec_type)
+
+    mel_spec_kwargs = dict(
+        n_fft=n_fft,
+        hop_length=hop_length,
+        win_length=win_length,
+        n_mel_channels=n_mel_channels,
+        target_sample_rate=target_sample_rate,
+        mel_spec_type=mel_spec_type,
+    )
+
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=mel_spec_kwargs,
+        vocab_char_map=vocab_char_map,
+    )
+
+    trainer = Trainer(
+        model,
+        args.epochs,
+        args.learning_rate,
+        num_warmup_updates=args.num_warmup_updates,
+        save_per_updates=args.save_per_updates,
+        keep_last_n_checkpoints=args.keep_last_n_checkpoints,
+        checkpoint_path=checkpoint_path,
+        batch_size_per_gpu=args.batch_size_per_gpu,
+        batch_size_type=args.batch_size_type,
+        max_samples=args.max_samples,
+        grad_accumulation_steps=args.grad_accumulation_steps,
+        max_grad_norm=args.max_grad_norm,
+        logger=args.logger,
+        wandb_project=args.dataset_name,
+        wandb_run_name=args.exp_name,
+        wandb_resume_id=wandb_resume_id,
+        log_samples=args.log_samples,
+        last_per_updates=args.last_per_updates,
+        bnb_optimizer=args.bnb_optimizer,
+    )
+
+    train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
+
+    trainer.train(
+        train_dataset,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,77 @@
+# training script.
+
+import os
+from importlib.resources import files
+
+import hydra
+from omegaconf import OmegaConf
+
+from ..model import CFM, Trainer
+from ..model.dataset import load_dataset
+from ..model.utils import get_tokenizer
+
+
+os.chdir(str(files(__package__ or "f5_tts").joinpath("../..")))  # change working directory to root of project (local editable)
+
+
+@hydra.main(version_base="1.3", config_path=str(files(__package__ or "f5_tts").joinpath("configs")), config_name=None)
+def main(model_cfg):
+    model_cls = hydra.utils.get_class(f"engines.f5_tts.model.{model_cfg.model.backbone}")
+    model_arc = model_cfg.model.arch
+    tokenizer = model_cfg.model.tokenizer
+    mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+
+    exp_name = f"{model_cfg.model.name}_{mel_spec_type}_{model_cfg.model.tokenizer}_{model_cfg.datasets.name}"
+    wandb_resume_id = None
+
+    # set text tokenizer
+    if tokenizer != "custom":
+        tokenizer_path = model_cfg.datasets.name
+    else:
+        tokenizer_path = model_cfg.model.tokenizer_path
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    # set model
+    model = CFM(
+        transformer=model_cls(**model_arc, text_num_embeds=vocab_size, mel_dim=model_cfg.model.mel_spec.n_mel_channels),
+        mel_spec_kwargs=model_cfg.model.mel_spec,
+        vocab_char_map=vocab_char_map,
+    )
+
+    # init trainer
+    trainer = Trainer(
+        model,
+        epochs=model_cfg.optim.epochs,
+        learning_rate=model_cfg.optim.learning_rate,
+        num_warmup_updates=model_cfg.optim.num_warmup_updates,
+        save_per_updates=model_cfg.ckpts.save_per_updates,
+        keep_last_n_checkpoints=model_cfg.ckpts.keep_last_n_checkpoints,
+        checkpoint_path=str(files(__package__ or "f5_tts").joinpath(f"../../{model_cfg.ckpts.save_dir}")),
+        batch_size_per_gpu=model_cfg.datasets.batch_size_per_gpu,
+        batch_size_type=model_cfg.datasets.batch_size_type,
+        max_samples=model_cfg.datasets.max_samples,
+        grad_accumulation_steps=model_cfg.optim.grad_accumulation_steps,
+        max_grad_norm=model_cfg.optim.max_grad_norm,
+        logger=model_cfg.ckpts.logger,
+        wandb_project="CFM-TTS",
+        wandb_run_name=exp_name,
+        wandb_resume_id=wandb_resume_id,
+        last_per_updates=model_cfg.ckpts.last_per_updates,
+        log_samples=model_cfg.ckpts.log_samples,
+        bnb_optimizer=model_cfg.optim.bnb_optimizer,
+        mel_spec_type=mel_spec_type,
+        is_local_vocoder=model_cfg.model.vocoder.is_local,
+        local_vocoder_path=model_cfg.model.vocoder.local_path,
+        model_cfg_dict=OmegaConf.to_container(model_cfg, resolve=True),
+    )
+
+    train_dataset = load_dataset(model_cfg.datasets.name, tokenizer, mel_spec_kwargs=model_cfg.model.mel_spec)
+    trainer.train(
+        train_dataset,
+        num_workers=model_cfg.datasets.num_workers,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
+
+
+if __name__ == "__main__":
+    main()
@@ -123,7 +123,7 @@ class ChatterBoxF5TTS:
                self._setup_vocos_redirect(vocos_dir)
            
            # Try to import F5-TTS
-            from f5_tts.api import F5TTS
+            from engines.f5_tts.api import F5TTS
            
            # Check if we have local model directory
            if self.ckpt_dir and os.path.exists(self.ckpt_dir):
@@ -42,9 +42,9 @@ class F5TTSEditEngine:
        
        try:
            # Import F5-TTS modules
-            from f5_tts.model import CFM
-            from f5_tts.infer.utils_infer import load_checkpoint, load_vocoder
-            from f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
+            from engines.f5_tts.model import CFM
+            from engines.f5_tts.infer.utils_infer import load_checkpoint, load_vocoder
+            from engines.f5_tts.model.utils import convert_char_to_pinyin, get_tokenizer
            from omegaconf import OmegaConf
            from hydra.utils import get_class
            from importlib.resources import files
@@ -0,0 +1,9 @@
+"""
+Higgs Audio 2 Engine for TTS Audio Suite
+High-quality text-to-speech with voice cloning capabilities
+"""
+
+from .higgs_audio import HiggsAudioEngine
+from .higgs_audio_downloader import HiggsAudioDownloader
+
+__all__ = ["HiggsAudioEngine", "HiggsAudioDownloader"]
@@ -0,0 +1 @@
+from .model.higgs_audio import HiggsAudioConfig, HiggsAudioModel
@@ -0,0 +1,51 @@
+Third-Party License Attribution for Audio Processing Module
+===========================================================
+
+This directory contains code derived from multiple open-source projects. 
+The following sections detail the licenses and attributions for third-party code.
+
+## XCodec Repository
+The code in this directory is derived from:
+https://github.com/zhenye234/xcodec
+
+## Individual File Attributions
+
+### Quantization Module (quantization/)
+- Several files contain code derived from Meta Platforms, Inc. and the vector-quantize-pytorch repository
+- Individual files contain their own license headers where applicable
+- The vector-quantize-pytorch portions are licensed under the MIT License
+
+## License Terms
+
+### MIT License (for applicable portions)
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+## Attribution Requirements
+When using this code, please ensure proper attribution to:
+1. The original xcodec repository: https://github.com/zhenye234/xcodec
+2. Any other repositories mentioned in individual file headers
+3. This derivative work and its modifications
+
+## Disclaimer
+This directory contains modified versions of the original code. Please refer to
+the original repositories for the canonical implementations and their specific
+license terms.
+
+For any questions about licensing or attribution, please check the individual
+file headers and the original source repositories. 
@@ -0,0 +1 @@
+
@@ -0,0 +1,286 @@
+import math
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+import torch
+import tqdm
+from audiotools import AudioSignal
+from torch import nn
+
+SUPPORTED_VERSIONS = ["1.0.0"]
+
+
+@dataclass
+class DACFile:
+    codes: torch.Tensor
+
+    # Metadata
+    chunk_length: int
+    original_length: int
+    input_db: float
+    channels: int
+    sample_rate: int
+    padding: bool
+    dac_version: str
+
+    def save(self, path):
+        artifacts = {
+            "codes": self.codes.numpy().astype(np.uint16),
+            "metadata": {
+                "input_db": self.input_db.numpy().astype(np.float32),
+                "original_length": self.original_length,
+                "sample_rate": self.sample_rate,
+                "chunk_length": self.chunk_length,
+                "channels": self.channels,
+                "padding": self.padding,
+                "dac_version": SUPPORTED_VERSIONS[-1],
+            },
+        }
+        path = Path(path).with_suffix(".dac")
+        with open(path, "wb") as f:
+            np.save(f, artifacts)
+        return path
+
+    @classmethod
+    def load(cls, path):
+        artifacts = np.load(path, allow_pickle=True)[()]
+        codes = torch.from_numpy(artifacts["codes"].astype(int))
+        if artifacts["metadata"].get("dac_version", None) not in SUPPORTED_VERSIONS:
+            raise RuntimeError(f"Given file {path} can't be loaded with this version of descript-audio-codec.")
+        return cls(codes=codes, **artifacts["metadata"])
+
+
+class CodecMixin:
+    @property
+    def padding(self):
+        if not hasattr(self, "_padding"):
+            self._padding = True
+        return self._padding
+
+    @padding.setter
+    def padding(self, value):
+        assert isinstance(value, bool)
+
+        layers = [l for l in self.modules() if isinstance(l, (nn.Conv1d, nn.ConvTranspose1d))]
+
+        for layer in layers:
+            if value:
+                if hasattr(layer, "original_padding"):
+                    layer.padding = layer.original_padding
+            else:
+                layer.original_padding = layer.padding
+                layer.padding = tuple(0 for _ in range(len(layer.padding)))
+
+        self._padding = value
+
+    def get_delay(self):
+        # Any number works here, delay is invariant to input length
+        l_out = self.get_output_length(0)
+        L = l_out
+
+        layers = []
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                layers.append(layer)
+
+        for layer in reversed(layers):
+            d = layer.dilation[0]
+            k = layer.kernel_size[0]
+            s = layer.stride[0]
+
+            if isinstance(layer, nn.ConvTranspose1d):
+                L = ((L - d * (k - 1) - 1) / s) + 1
+            elif isinstance(layer, nn.Conv1d):
+                L = (L - 1) * s + d * (k - 1) + 1
+
+            L = math.ceil(L)
+
+        l_in = L
+
+        return (l_in - l_out) // 2
+
+    def get_output_length(self, input_length):
+        L = input_length
+        # Calculate output length
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                d = layer.dilation[0]
+                k = layer.kernel_size[0]
+                s = layer.stride[0]
+
+                if isinstance(layer, nn.Conv1d):
+                    L = ((L - d * (k - 1) - 1) / s) + 1
+                elif isinstance(layer, nn.ConvTranspose1d):
+                    L = (L - 1) * s + d * (k - 1) + 1
+
+                L = math.floor(L)
+        return L
+
+    @torch.no_grad()
+    def compress(
+        self,
+        audio_path_or_signal: Union[str, Path, AudioSignal],
+        win_duration: float = 1.0,
+        verbose: bool = False,
+        normalize_db: float = -16,
+        n_quantizers: int = None,
+    ) -> DACFile:
+        """Processes an audio signal from a file or AudioSignal object into
+        discrete codes. This function processes the signal in short windows,
+        using constant GPU memory.
+
+        Parameters
+        ----------
+        audio_path_or_signal : Union[str, Path, AudioSignal]
+            audio signal to reconstruct
+        win_duration : float, optional
+            window duration in seconds, by default 5.0
+        verbose : bool, optional
+            by default False
+        normalize_db : float, optional
+            normalize db, by default -16
+
+        Returns
+        -------
+        DACFile
+            Object containing compressed codes and metadata
+            required for decompression
+        """
+        audio_signal = audio_path_or_signal
+        if isinstance(audio_signal, (str, Path)):
+            audio_signal = AudioSignal.load_from_file_with_ffmpeg(str(audio_signal))
+
+        self.eval()
+        original_padding = self.padding
+        original_device = audio_signal.device
+
+        audio_signal = audio_signal.clone()
+        original_sr = audio_signal.sample_rate
+
+        resample_fn = audio_signal.resample
+        loudness_fn = audio_signal.loudness
+
+        # If audio is > 10 minutes long, use the ffmpeg versions
+        if audio_signal.signal_duration >= 10 * 60 * 60:
+            resample_fn = audio_signal.ffmpeg_resample
+            loudness_fn = audio_signal.ffmpeg_loudness
+
+        original_length = audio_signal.signal_length
+        resample_fn(self.sample_rate)
+        input_db = loudness_fn()
+
+        if normalize_db is not None:
+            audio_signal.normalize(normalize_db)
+        audio_signal.ensure_max_of_audio()
+
+        nb, nac, nt = audio_signal.audio_data.shape
+        audio_signal.audio_data = audio_signal.audio_data.reshape(nb * nac, 1, nt)
+        win_duration = audio_signal.signal_duration if win_duration is None else win_duration
+
+        if audio_signal.signal_duration <= win_duration:
+            # Unchunked compression (used if signal length < win duration)
+            self.padding = True
+            n_samples = nt
+            hop = nt
+        else:
+            # Chunked inference
+            self.padding = False
+            # Zero-pad signal on either side by the delay
+            audio_signal.zero_pad(self.delay, self.delay)
+            n_samples = int(win_duration * self.sample_rate)
+            # Round n_samples to nearest hop length multiple
+            n_samples = int(math.ceil(n_samples / self.hop_length) * self.hop_length)
+            hop = self.get_output_length(n_samples)
+
+        codes = []
+        range_fn = range if not verbose else tqdm.trange
+
+        for i in range_fn(0, nt, hop):
+            x = audio_signal[..., i : i + n_samples]
+            x = x.zero_pad(0, max(0, n_samples - x.shape[-1]))
+
+            audio_data = x.audio_data.to(self.device)
+            audio_data = self.preprocess(audio_data, self.sample_rate)
+            _, c, _, _, _ = self.encode(audio_data, n_quantizers)
+            codes.append(c.to(original_device))
+            chunk_length = c.shape[-1]
+
+        codes = torch.cat(codes, dim=-1)
+
+        dac_file = DACFile(
+            codes=codes,
+            chunk_length=chunk_length,
+            original_length=original_length,
+            input_db=input_db,
+            channels=nac,
+            sample_rate=original_sr,
+            padding=self.padding,
+            dac_version=SUPPORTED_VERSIONS[-1],
+        )
+
+        if n_quantizers is not None:
+            codes = codes[:, :n_quantizers, :]
+
+        self.padding = original_padding
+        return dac_file
+
+    @torch.no_grad()
+    def decompress(
+        self,
+        obj: Union[str, Path, DACFile],
+        verbose: bool = False,
+    ) -> AudioSignal:
+        """Reconstruct audio from a given .dac file
+
+        Parameters
+        ----------
+        obj : Union[str, Path, DACFile]
+            .dac file location or corresponding DACFile object.
+        verbose : bool, optional
+            Prints progress if True, by default False
+
+        Returns
+        -------
+        AudioSignal
+            Object with the reconstructed audio
+        """
+        self.eval()
+        if isinstance(obj, (str, Path)):
+            obj = DACFile.load(obj)
+
+        original_padding = self.padding
+        self.padding = obj.padding
+
+        range_fn = range if not verbose else tqdm.trange
+        codes = obj.codes
+        original_device = codes.device
+        chunk_length = obj.chunk_length
+        recons = []
+
+        for i in range_fn(0, codes.shape[-1], chunk_length):
+            c = codes[..., i : i + chunk_length].to(self.device)
+            z = self.quantizer.from_codes(c)[0]
+            r = self.decode(z)
+            recons.append(r.to(original_device))
+
+        recons = torch.cat(recons, dim=-1)
+        recons = AudioSignal(recons, self.sample_rate)
+
+        resample_fn = recons.resample
+        loudness_fn = recons.loudness
+
+        # If audio is > 10 minutes long, use the ffmpeg versions
+        if recons.signal_duration >= 10 * 60 * 60:
+            resample_fn = recons.ffmpeg_resample
+            loudness_fn = recons.ffmpeg_loudness
+
+        recons.normalize(obj.input_db)
+        resample_fn(obj.sample_rate)
+        recons = recons[..., : obj.original_length]
+        loudness_fn()
+        recons.audio_data = recons.audio_data.reshape(-1, obj.channels, obj.original_length)
+
+        self.padding = original_padding
+        return recons
@@ -0,0 +1,365 @@
+import math
+from typing import List
+from typing import Union
+
+import numpy as np
+import torch
+from audiotools import AudioSignal
+from audiotools.ml import BaseModel
+from torch import nn
+
+from .base import CodecMixin
+from dac.nn.layers import Snake1d
+from dac.nn.layers import WNConv1d
+from dac.nn.layers import WNConvTranspose1d
+from dac.nn.quantize import ResidualVectorQuantize
+
+
+def init_weights(m):
+    if isinstance(m, nn.Conv1d):
+        nn.init.trunc_normal_(m.weight, std=0.02)
+        nn.init.constant_(m.bias, 0)
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.block = nn.Sequential(
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+
+    def forward(self, x):
+        y = self.block(x)
+        pad = (x.shape[-1] - y.shape[-1]) // 2
+        if pad > 0:
+            x = x[..., pad:-pad]
+        return x + y
+
+
+class EncoderBlock(nn.Module):
+    def __init__(self, dim: int = 16, stride: int = 1):
+        super().__init__()
+        self.block = nn.Sequential(
+            ResidualUnit(dim // 2, dilation=1),
+            ResidualUnit(dim // 2, dilation=3),
+            ResidualUnit(dim // 2, dilation=9),
+            Snake1d(dim // 2),
+            WNConv1d(
+                dim // 2,
+                dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+            ),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        d_model: int = 64,
+        strides: list = [2, 4, 8, 8],
+        d_latent: int = 256,
+    ):
+        super().__init__()
+        # Create first convolution
+        self.block = [WNConv1d(1, d_model, kernel_size=7, padding=3)]
+
+        # Create EncoderBlocks that double channels as they downsample by `stride`
+        for stride in strides:
+            d_model *= 2
+            self.block += [EncoderBlock(d_model, stride=stride)]
+
+        # Create last convolution
+        self.block += [
+            Snake1d(d_model),
+            WNConv1d(d_model, d_latent, kernel_size=3, padding=1),
+        ]
+
+        # Wrap black into nn.Sequential
+        self.block = nn.Sequential(*self.block)
+        self.enc_dim = d_model
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1, out_pad=0):
+        super().__init__()
+        self.block = nn.Sequential(
+            Snake1d(input_dim),
+            WNConvTranspose1d(
+                input_dim,
+                output_dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+                output_padding=stride % 2,  # out_pad,
+            ),
+            ResidualUnit(output_dim, dilation=1),
+            ResidualUnit(output_dim, dilation=3),
+            ResidualUnit(output_dim, dilation=9),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        input_channel,
+        channels,
+        rates,
+        d_out: int = 1,
+    ):
+        super().__init__()
+
+        # Add first conv layer
+        layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]
+
+        # Add upsampling + MRF blocks
+        for i, stride in enumerate(rates):
+            input_dim = channels // 2**i
+            output_dim = channels // 2 ** (i + 1)
+            if i == 1:
+                out_pad = 1
+            else:
+                out_pad = 0
+            layers += [DecoderBlock(input_dim, output_dim, stride, out_pad)]
+
+        # Add final conv layer
+        layers += [
+            Snake1d(output_dim),
+            WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
+            # nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class DAC(BaseModel, CodecMixin):
+    def __init__(
+        self,
+        encoder_dim: int = 64,
+        encoder_rates: List[int] = [2, 4, 8, 8],
+        latent_dim: int = None,
+        decoder_dim: int = 1536,
+        decoder_rates: List[int] = [8, 8, 4, 2],
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: bool = False,
+        sample_rate: int = 44100,
+    ):
+        super().__init__()
+
+        self.encoder_dim = encoder_dim
+        self.encoder_rates = encoder_rates
+        self.decoder_dim = decoder_dim
+        self.decoder_rates = decoder_rates
+        self.sample_rate = sample_rate
+
+        if latent_dim is None:
+            latent_dim = encoder_dim * (2 ** len(encoder_rates))
+
+        self.latent_dim = latent_dim
+
+        self.hop_length = np.prod(encoder_rates)
+        self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim)
+
+        self.n_codebooks = n_codebooks
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+        self.quantizer = ResidualVectorQuantize(
+            input_dim=latent_dim,
+            n_codebooks=n_codebooks,
+            codebook_size=codebook_size,
+            codebook_dim=codebook_dim,
+            quantizer_dropout=quantizer_dropout,
+        )
+
+        self.decoder = Decoder(
+            latent_dim,
+            decoder_dim,
+            decoder_rates,
+        )
+        self.sample_rate = sample_rate
+        self.apply(init_weights)
+
+        self.delay = self.get_delay()
+
+    def preprocess(self, audio_data, sample_rate):
+        if sample_rate is None:
+            sample_rate = self.sample_rate
+        assert sample_rate == self.sample_rate
+
+        length = audio_data.shape[-1]
+        right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
+        audio_data = nn.functional.pad(audio_data, (0, right_pad))
+
+        return audio_data
+
+    def encode(
+        self,
+        audio_data: torch.Tensor,
+        n_quantizers: int = None,
+    ):
+        """Encode given audio data and return quantized latent codes
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+        """
+        z = self.encoder(audio_data)
+        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(z, n_quantizers)
+        return z, codes, latents, commitment_loss, codebook_loss
+
+    def decode(self, z: torch.Tensor):
+        """Decode given latent codes and return audio data
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+            Quantized continuous representation of input
+        length : int, optional
+            Number of samples in output audio, by default None
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        return self.decoder(z)
+
+    def forward(
+        self,
+        audio_data: torch.Tensor,
+        sample_rate: int = None,
+        n_quantizers: int = None,
+    ):
+        """Model forward pass
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        sample_rate : int, optional
+            Sample rate of audio data in Hz, by default None
+            If None, defaults to `self.sample_rate`
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None.
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        length = audio_data.shape[-1]
+        audio_data = self.preprocess(audio_data, sample_rate)
+        z, codes, latents, commitment_loss, codebook_loss = self.encode(audio_data, n_quantizers)
+
+        x = self.decode(z)
+        return {
+            "audio": x[..., :length],
+            "z": z,
+            "codes": codes,
+            "latents": latents,
+            "vq/commitment_loss": commitment_loss,
+            "vq/codebook_loss": codebook_loss,
+        }
+
+
+if __name__ == "__main__":
+    import numpy as np
+    from functools import partial
+
+    model = DAC().to("cpu")
+
+    for n, m in model.named_modules():
+        o = m.extra_repr()
+        p = sum([np.prod(p.size()) for p in m.parameters()])
+        fn = lambda o, p: o + f" {p / 1e6:<.3f}M params."
+        setattr(m, "extra_repr", partial(fn, o=o, p=p))
+    print(model)
+    print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
+
+    length = 88200 * 2
+    x = torch.randn(1, 1, length).to(model.device)
+    x.requires_grad_(True)
+    x.retain_grad()
+
+    # Make a forward pass
+    out = model(x)["audio"]
+    print("Input shape:", x.shape)
+    print("Output shape:", out.shape)
+
+    # Create gradient variable
+    grad = torch.zeros_like(out)
+    grad[:, :, grad.shape[-1] // 2] = 1
+
+    # Make a backward pass
+    out.backward(grad)
+
+    # Check non-zero values
+    gradmap = x.grad.squeeze(0)
+    gradmap = (gradmap != 0).sum(0)  # sum across features
+    rf = (gradmap != 0).sum()
+
+    print(f"Receptive field: {rf.item()}")
+
+    x = AudioSignal(torch.randn(1, 1, 44100 * 60), 44100)
+    model.decompress(model.compress(x, verbose=True), verbose=True)
@@ -0,0 +1,33 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+# Scripting this brings model speed up 1.4x
+@torch.jit.script
+def snake(x, alpha):
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x):
+        return snake(x, self.alpha)
@@ -0,0 +1,251 @@
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch.nn.utils import weight_norm
+
+from dac.nn.layers import WNConv1d
+
+
+class VectorQuantize(nn.Module):
+    """
+    Implementation of VQ similar to Karpathy's repo:
+    https://github.com/karpathy/deep-vector-quantization
+    Additionally uses following tricks from Improved VQGAN
+    (https://arxiv.org/pdf/2110.04627.pdf):
+        1. Factorized codes: Perform nearest neighbor lookup in low-dimensional space
+            for improved codebook usage
+        2. l2-normalized codes: Converts euclidean distance to cosine similarity which
+            improves training stability
+    """
+
+    def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
+        super().__init__()
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+
+        self.in_proj = WNConv1d(input_dim, codebook_dim, kernel_size=1)
+        self.out_proj = WNConv1d(codebook_dim, input_dim, kernel_size=1)
+        self.codebook = nn.Embedding(codebook_size, codebook_dim)
+
+    def forward(self, z):
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+
+        # Factorized codes (ViT-VQGAN) Project input into low-dimensional space
+        z_e = self.in_proj(z)  # z_e : (B x D x T)
+        z_q, indices = self.decode_latents(z_e)
+
+        commitment_loss = F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
+        codebook_loss = F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
+
+        z_q = z_e + (z_q - z_e).detach()  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        z_q = self.out_proj(z_q)
+
+        return z_q, commitment_loss, codebook_loss, indices, z_e
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight  # codebook: (N x D)
+
+        # L2 normalize encodings and codebook (ViT-VQGAN)
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance with codebook
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+        return z_q, indices
+
+
+class ResidualVectorQuantize(nn.Module):
+    """
+    Introduced in SoundStream: An end2end neural audio codec
+    https://arxiv.org/abs/2107.03312
+    """
+
+    def __init__(
+        self,
+        input_dim: int = 512,
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: float = 0.0,
+    ):
+        super().__init__()
+        if isinstance(codebook_dim, int):
+            codebook_dim = [codebook_dim for _ in range(n_codebooks)]
+
+        self.n_codebooks = n_codebooks
+        self.codebook_dim = codebook_dim
+        self.codebook_size = codebook_size
+
+        self.quantizers = nn.ModuleList(
+            [VectorQuantize(input_dim, codebook_size, codebook_dim[i]) for i in range(n_codebooks)]
+        )
+        self.quantizer_dropout = quantizer_dropout
+
+    def forward(self, z, n_quantizers: int = None):
+        """Quantized the input tensor using a fixed set of `n` codebooks and returns
+        the corresponding codebook vectors
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+        n_quantizers : int, optional
+            No. of quantizers to use
+            (n_quantizers < self.n_codebooks ex: for quantizer dropout)
+            Note: if `self.quantizer_dropout` is True, this argument is ignored
+                when in training mode, and a random number of quantizers is used.
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+        """
+        z_q = 0
+        residual = z
+        commitment_loss = 0
+        codebook_loss = 0
+
+        codebook_indices = []
+        latents = []
+
+        if n_quantizers is None:
+            n_quantizers = self.n_codebooks
+        if self.training:
+            n_quantizers = torch.ones((z.shape[0],)) * self.n_codebooks + 1
+            dropout = torch.randint(1, self.n_codebooks + 1, (z.shape[0],))
+            n_dropout = int(z.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(z.device)
+
+        for i, quantizer in enumerate(self.quantizers):
+            if self.training is False and i >= n_quantizers:
+                break
+
+            z_q_i, commitment_loss_i, codebook_loss_i, indices_i, z_e_i = quantizer(residual)
+
+            # Create mask to apply quantizer dropout
+            mask = torch.full((z.shape[0],), fill_value=i, device=z.device) < n_quantizers
+            z_q = z_q + z_q_i * mask[:, None, None]
+            residual = residual - z_q_i
+
+            # Sum losses
+            commitment_loss += (commitment_loss_i * mask).mean()
+            codebook_loss += (codebook_loss_i * mask).mean()
+
+            codebook_indices.append(indices_i)
+            latents.append(z_e_i)
+
+        codes = torch.stack(codebook_indices, dim=1)
+        latents = torch.cat(latents, dim=1)
+
+        return z_q, codes, latents, commitment_loss, codebook_loss
+
+    def from_codes(self, codes: torch.Tensor):
+        """Given the quantized codes, reconstruct the continuous representation
+        Parameters
+        ----------
+        codes : Tensor[B x N x T]
+            Quantized discrete representation of input
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        """
+        z_q = 0.0
+        z_p = []
+        n_codebooks = codes.shape[1]
+        for i in range(n_codebooks):
+            z_p_i = self.quantizers[i].decode_code(codes[:, i, :])
+            z_p.append(z_p_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+        return z_q, torch.cat(z_p, dim=1), codes
+
+    def from_latents(self, latents: torch.Tensor):
+        """Given the unquantized latents, reconstruct the
+        continuous representation after quantization.
+
+        Parameters
+        ----------
+        latents : Tensor[B x N x T]
+            Continuous representation of input after projection
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized representation of full-projected space
+        Tensor[B x D x T]
+            Quantized representation of latent space
+        """
+        z_q = 0
+        z_p = []
+        codes = []
+        dims = np.cumsum([0] + [q.codebook_dim for q in self.quantizers])
+
+        n_codebooks = np.where(dims <= latents.shape[1])[0].max(axis=0, keepdims=True)[0]
+        for i in range(n_codebooks):
+            j, k = dims[i], dims[i + 1]
+            z_p_i, codes_i = self.quantizers[i].decode_latents(latents[:, j:k, :])
+            z_p.append(z_p_i)
+            codes.append(codes_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+
+        return z_q, torch.cat(z_p, dim=1), torch.stack(codes, dim=1)
+
+
+if __name__ == "__main__":
+    rvq = ResidualVectorQuantize(quantizer_dropout=True)
+    x = torch.randn(16, 512, 80)
+    y = rvq(x)
+    print(y["latents"].shape)
@@ -0,0 +1,346 @@
+# Based on code from: https://github.com/zhenye234/xcodec
+# Licensed under MIT License
+# Modifications by BosonAI
+
+import math
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Union, Sequence
+import numpy as np
+from transformers import AutoModel
+import torchaudio
+import json
+import librosa
+from huggingface_hub import snapshot_download
+
+from vector_quantize_pytorch import ResidualFSQ
+from .descriptaudiocodec.dac.model import dac as dac2
+from .quantization.vq import ResidualVectorQuantizer
+from .semantic_module import Encoder, Decoder
+
+
+class EncodedResult:
+    def __init__(self, audio_codes):
+        self.audio_codes = audio_codes
+
+
+class HiggsAudioFeatureExtractor(nn.Module):
+    def __init__(self, sampling_rate=16000):
+        super().__init__()
+        self.sampling_rate = sampling_rate
+
+    def forward(self, raw_audio, sampling_rate=16000, return_tensors="pt"):
+        # Convert from librosa to torch
+        audio_signal = torch.tensor(raw_audio)
+        audio_signal = audio_signal.unsqueeze(0)
+        if len(audio_signal.shape) < 3:
+            audio_signal = audio_signal.unsqueeze(0)
+        return {"input_values": audio_signal}
+
+
+class HiggsAudioTokenizer(nn.Module):
+    def __init__(
+        self,
+        n_filters: int = 32,
+        D: int = 128,
+        target_bandwidths: Sequence[Union[int, float]] = [1, 1.5, 2, 4, 6],
+        ratios: Sequence[int] = [8, 5, 4, 2],  #  downsampling by 320
+        sample_rate: int = 16000,
+        bins: int = 1024,
+        n_q: int = 8,
+        codebook_dim: int = None,
+        normalize: bool = False,
+        causal: bool = False,
+        semantic_techer: str = "hubert_base_general",
+        last_layer_semantic: bool = True,
+        merge_mode: str = "concat",
+        downsample_mode: str = "step_down",
+        semantic_mode: str = "classic",
+        vq_scale: int = 1,
+        semantic_sample_rate: int = None,
+        device: str = "cuda",
+    ):
+        super().__init__()
+        self.hop_length = np.prod(ratios)
+        self.semantic_techer = semantic_techer
+
+        self.frame_rate = math.ceil(sample_rate / np.prod(ratios))  # 50 Hz
+
+        self.target_bandwidths = target_bandwidths
+        self.n_q = n_q
+        self.sample_rate = sample_rate
+        self.encoder = dac2.Encoder(64, ratios, D)
+
+        self.decoder_2 = dac2.Decoder(D, 1024, ratios)
+        self.last_layer_semantic = last_layer_semantic
+        self.device = device
+        if semantic_techer == "hubert_base":
+            self.semantic_model = AutoModel.from_pretrained("facebook/hubert-base-ls960")
+            self.semantic_sample_rate = 16000
+            self.semantic_dim = 768
+            self.encoder_semantic_dim = 768
+
+        elif semantic_techer == "wavlm_base_plus":
+            self.semantic_model = AutoModel.from_pretrained("microsoft/wavlm-base-plus")
+            self.semantic_sample_rate = 16000
+            self.semantic_dim = 768
+            self.encoder_semantic_dim = 768
+
+        elif semantic_techer == "hubert_base_general":
+            # Force offline mode to avoid authentication issues
+            import os
+            os.environ["HF_HUB_OFFLINE"] = "1"
+            
+            try:
+                # Try local cached model first
+                self.semantic_model = AutoModel.from_pretrained(
+                    "facebook/hubert-base-ls960", 
+                    trust_remote_code=True,
+                    local_files_only=True
+                )
+            except:
+                # If no local cache, download without auth
+                os.environ.pop("HF_HUB_OFFLINE", None)
+                self.semantic_model = AutoModel.from_pretrained(
+                    "facebook/hubert-base-ls960", 
+                    trust_remote_code=True,
+                    use_auth_token=False
+                )
+            
+            self.semantic_sample_rate = 16000
+            self.semantic_dim = 768
+            self.encoder_semantic_dim = 768
+
+        # Overwrite semantic model sr to ensure semantic_downsample_factor is an integer
+        if semantic_sample_rate is not None:
+            self.semantic_sample_rate = semantic_sample_rate
+
+        self.semantic_model.eval()
+
+        # make the semantic model parameters do not need gradient
+        for param in self.semantic_model.parameters():
+            param.requires_grad = False
+
+        self.semantic_downsample_factor = int(self.hop_length / (self.sample_rate / self.semantic_sample_rate) / 320)
+
+        self.quantizer_dim = int((D + self.encoder_semantic_dim) // vq_scale)
+        self.encoder_semantic = Encoder(input_channels=self.semantic_dim, encode_channels=self.encoder_semantic_dim)
+        self.decoder_semantic = Decoder(
+            code_dim=self.encoder_semantic_dim, output_channels=self.semantic_dim, decode_channels=self.semantic_dim
+        )
+
+        # out_D=D+768
+        if isinstance(bins, int):  # RVQ
+            self.quantizer = ResidualVectorQuantizer(
+                dimension=self.quantizer_dim, codebook_dim=codebook_dim, n_q=n_q, bins=bins
+            )
+            self.quantizer_type = "RVQ"
+        else:  # RFSQ
+            self.quantizer = ResidualFSQ(dim=self.quantizer_dim, levels=bins, num_quantizers=n_q)
+            self.quantizer_type = "RFSQ"
+
+        self.fc_prior = nn.Linear(D + self.encoder_semantic_dim, self.quantizer_dim)
+        self.fc_post1 = nn.Linear(self.quantizer_dim, self.encoder_semantic_dim)
+        self.fc_post2 = nn.Linear(self.quantizer_dim, D)
+
+        self.downsample_mode = downsample_mode
+        if downsample_mode == "avg":
+            self.semantic_pooling = nn.AvgPool1d(
+                kernel_size=self.semantic_downsample_factor, stride=self.semantic_downsample_factor
+            )
+
+        self.audio_tokenizer_feature_extractor = HiggsAudioFeatureExtractor(sampling_rate=self.sample_rate)
+
+    @property
+    def tps(self):
+        return self.frame_rate
+
+    @property
+    def sampling_rate(self):
+        return self.sample_rate
+
+    @property
+    def num_codebooks(self):
+        return self.n_q
+
+    @property
+    def codebook_size(self):
+        return self.quantizer_dim
+
+    def get_last_layer(self):
+        return self.decoder.layers[-1].weight
+
+    def calculate_rec_loss(self, rec, target):
+        target = target / target.norm(dim=-1, keepdim=True)
+        rec = rec / rec.norm(dim=-1, keepdim=True)
+        rec_loss = (1 - (target * rec).sum(-1)).mean()
+
+        return rec_loss
+
+    @torch.no_grad()
+    def get_regress_target(self, x):
+        x = torchaudio.functional.resample(x, self.sample_rate, self.semantic_sample_rate)
+
+        if (
+            self.semantic_techer == "hubert_base"
+            or self.semantic_techer == "hubert_base_general"
+            or self.semantic_techer == "wavlm_base_plus"
+        ):
+            x = x[:, 0, :]
+            x = F.pad(x, (160, 160))
+            target = self.semantic_model(x, output_hidden_states=True).hidden_states
+            target = torch.stack(target, dim=1)  # .transpose(-1, -2)#.flatten(start_dim=1, end_dim=2)
+
+            # average for all layers
+            target = target.mean(1)
+            # target = target[9]
+            # if self.hop_length > 320:
+            #     target = self.semantic_pooling(target.transpose(1, 2)).transpose(1, 2)
+
+        elif self.semantic_techer == "w2v_bert2":
+            target = self.semantic_model(x)
+
+        elif self.semantic_techer.startswith("whisper"):
+            if self.last_layer_semantic:
+                target = self.semantic_model(x, avg_layers=False)
+            else:
+                target = self.semantic_model(x, avg_layers=True)
+
+        elif self.semantic_techer.startswith("mert_music"):
+            if self.last_layer_semantic:
+                target = self.semantic_model(x, avg_layers=False)
+            else:
+                target = self.semantic_model(x, avg_layers=True)
+
+        elif self.semantic_techer.startswith("qwen_audio_omni"):
+            target = self.semantic_model(x)
+
+        if self.downsample_mode == "step_down":
+            if self.semantic_downsample_factor > 1:
+                target = target[:, :: self.semantic_downsample_factor, :]
+
+        elif self.downsample_mode == "avg":
+            target = self.semantic_pooling(target.transpose(1, 2)).transpose(1, 2)
+        return target
+
+    def forward(self, x: torch.Tensor, bw: int):
+        e_semantic_input = self.get_regress_target(x).detach()
+
+        e_semantic = self.encoder_semantic(e_semantic_input.transpose(1, 2))
+        e_acoustic = self.encoder(x)
+
+        e = torch.cat([e_acoustic, e_semantic], dim=1)
+
+        e = self.fc_prior(e.transpose(1, 2))
+
+        if self.quantizer_type == "RVQ":
+            e = e.transpose(1, 2)
+            quantized, codes, bandwidth, commit_loss = self.quantizer(e, self.frame_rate, bw)
+            quantized = quantized.transpose(1, 2)
+        else:
+            quantized, codes = self.quantizer(e)
+            commit_loss = torch.tensor(0.0)
+
+        quantized_semantic = self.fc_post1(quantized).transpose(1, 2)
+        quantized_acoustic = self.fc_post2(quantized).transpose(1, 2)
+
+        o = self.decoder_2(quantized_acoustic)
+
+        o_semantic = self.decoder_semantic(quantized_semantic)
+        semantic_recon_loss = F.mse_loss(e_semantic_input.transpose(1, 2).detach(), o_semantic)
+
+        return o, commit_loss, semantic_recon_loss, None
+
+    def encode(self, audio_path_or_wv, sr=None, loudness_normalize=False, loudness_threshold=-23.0):
+        if isinstance(audio_path_or_wv, str):
+            wv, sr = librosa.load(audio_path_or_wv, mono=True, sr=None)
+        else:
+            wv = audio_path_or_wv
+            assert sr is not None
+        if loudness_normalize:
+            import pyloudnorm as pyln
+
+            meter = pyln.Meter(sr)
+            l = meter.integrated_loudness(wv)
+            wv = pyln.normalize.loudness(wv, l, loudness_threshold)
+        if sr != self.sampling_rate:
+            wv = librosa.resample(wv, orig_sr=sr, target_sr=self.sampling_rate)
+        if self.audio_tokenizer_feature_extractor is not None:
+            inputs = self.audio_tokenizer_feature_extractor(
+                raw_audio=wv, sampling_rate=self.audio_tokenizer_feature_extractor.sampling_rate, return_tensors="pt"
+            )
+            input_values = inputs["input_values"].to(self.device)
+        else:
+            input_values = torch.from_numpy(wv).float().unsqueeze(0)
+        with torch.no_grad():
+            encoder_outputs = self._xcodec_encode(input_values)
+            vq_code = encoder_outputs.audio_codes[0]
+        return vq_code
+
+    def _xcodec_encode(self, x: torch.Tensor, target_bw: Optional[int] = None) -> torch.Tensor:
+        bw = target_bw
+
+        e_semantic_input = self.get_regress_target(x).detach()
+
+        e_semantic = self.encoder_semantic(e_semantic_input.transpose(1, 2))
+        e_acoustic = self.encoder(x)
+
+        if e_acoustic.shape[2] != e_semantic.shape[2]:
+            pad_size = 160 * self.semantic_downsample_factor
+            e_acoustic = self.encoder(F.pad(x[:, 0, :], (pad_size, pad_size)).unsqueeze(0))
+
+        if e_acoustic.shape[2] != e_semantic.shape[2]:
+            if e_acoustic.shape[2] > e_semantic.shape[2]:
+                e_acoustic = e_acoustic[:, :, : e_semantic.shape[2]]
+            else:
+                e_semantic = e_semantic[:, :, : e_acoustic.shape[2]]
+
+        e = torch.cat([e_acoustic, e_semantic], dim=1)
+
+        e = self.fc_prior(e.transpose(1, 2))
+
+        if self.quantizer_type == "RVQ":
+            e = e.transpose(1, 2)
+            quantized, codes, bandwidth, commit_loss = self.quantizer(e, self.frame_rate, bw)
+            codes = codes.permute(1, 0, 2)
+        else:
+            quantized, codes = self.quantizer(e)
+            codes = codes.permute(0, 2, 1)
+
+        # return codes
+        return EncodedResult(codes)
+
+    def decode(self, vq_code: torch.Tensor) -> torch.Tensor:
+        if self.quantizer_type == "RVQ":
+            vq_code = vq_code.permute(1, 0, 2)
+            quantized = self.quantizer.decode(vq_code)
+            quantized = quantized.transpose(1, 2)
+        else:
+            vq_code = vq_code.permute(0, 2, 1)
+            quantized = self.quantizer.get_output_from_indices(vq_code)
+        quantized_acoustic = self.fc_post2(quantized).transpose(1, 2)
+
+        o = self.decoder_2(quantized_acoustic)
+        return o.cpu().numpy()
+
+
+def load_higgs_audio_tokenizer(tokenizer_name_or_path, device="cuda"):
+    is_local = os.path.exists(tokenizer_name_or_path)
+    if not is_local:
+        tokenizer_path = snapshot_download(tokenizer_name_or_path)
+    else:
+        tokenizer_path = tokenizer_name_or_path
+    config_path = os.path.join(tokenizer_path, "config.json")
+    model_path = os.path.join(tokenizer_path, "model.pth")
+    config = json.load(open(config_path))
+    model = HiggsAudioTokenizer(
+        **config,
+        device=device,
+    )
+    parameter_dict = torch.load(model_path, map_location=device)
+    model.load_state_dict(parameter_dict, strict=False)
+    model.to(device)
+    model.eval()
+    return model
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] "My poor dear friend, you live here no better than the ants! Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land." [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] "Goodbye," [main] said he, [country] "I'm off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace."
				`@@ -0,0 +1 @@`
				`from .model.higgs_audio import HiggsAudioConfig, HiggsAudioModel`