FillipandClaude Opus 4.6
66afde80f3
AI Text-to-Speech & Voice Cloning for ComfyUI using OpenAudio S1-mini (Fish Audio). Features DualAR Transformer, DAC codec, voice cloning, emotion control tags, Whisper transcription, and per-token progress bars. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
"""Audio tensor conversion utilities for FL FishSpeech ComfyUI nodes."""
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from __future__ import annotations
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import torch
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def comfyui_audio_to_tensor(audio: dict) -> tuple[torch.Tensor, int]:
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"""Extract waveform tensor and sample rate from ComfyUI AUDIO format.
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Args:
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audio: ComfyUI AUDIO dict with "waveform" [B, C, T] and "sample_rate" int.
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Returns:
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(waveform, sample_rate) where waveform is [B, C, T] float tensor.
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"""
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waveform = audio["waveform"]
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sample_rate = audio["sample_rate"]
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return waveform, sample_rate
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def tensor_to_comfyui_audio(waveform: torch.Tensor, sample_rate: int) -> dict:
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"""Convert a waveform tensor to ComfyUI AUDIO format.
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Args:
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waveform: [B, C, T] or [C, T] or [T] tensor.
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sample_rate: Audio sample rate.
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Returns:
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ComfyUI AUDIO dict.
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"""
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if waveform.dim() == 1:
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waveform = waveform.unsqueeze(0).unsqueeze(0) # [1, 1, T]
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elif waveform.dim() == 2:
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waveform = waveform.unsqueeze(0) # [1, C, T]
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return {"waveform": waveform.cpu().float(), "sample_rate": sample_rate}
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def ensure_mono(waveform: torch.Tensor) -> torch.Tensor:
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"""Ensure audio is mono by averaging channels if stereo.
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Args:
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waveform: Audio tensor (1D, 2D [C, T], or 3D [B, C, T]).
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Returns:
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Mono audio tensor.
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"""
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if waveform.dim() == 1:
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return waveform
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elif waveform.dim() == 2:
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if waveform.shape[0] > 1:
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return waveform.mean(dim=0)
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return waveform[0]
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elif waveform.dim() == 3:
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if waveform.shape[1] > 1:
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return waveform.mean(dim=1, keepdim=True)
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return waveform
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return waveform
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def resample_audio(waveform: torch.Tensor, orig_sr: int, target_sr: int) -> torch.Tensor:
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"""Resample audio waveform if sample rates differ.
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Args:
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waveform: Audio tensor (any shape, last dim is time).
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orig_sr: Original sample rate.
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target_sr: Target sample rate.
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Returns:
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Resampled tensor (same shape except time dimension).
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"""
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if orig_sr == target_sr:
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return waveform
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import torchaudio
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return torchaudio.functional.resample(waveform, orig_sr, target_sr)
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