From a45fe89fb6200d5f746d724dde84bafb18db0cb4 Mon Sep 17 00:00:00 2001
From: "Salvador E. Tropea" <stropea@inti.gob.ar>
Date: Tue, 3 Jun 2025 11:23:45 -0300
Subject: [PATCH] First release

- 5 nodes
- 'Batch Audios'
- 'Audio Channel Converter'
- 'Audio Channel Conv and Resampler'
- 'Audio Resampler'
- 'Select Audio from Batch'
---
 .flake8                                       |   8 +
 .gitignore                                    |   9 +-
 .pre-commit-config.yaml                       |  43 ++
 README.md                                     | 128 ++++-
 __init__.py                                   |  26 +
 .../audio_batch_select_example.jpg            | Bin 0 -> 9461 bytes
 .../audio_batch_select_example.json           |   1 +
 example_workflows/resample_force_stereo.jpg   | Bin 0 -> 14128 bytes
 example_workflows/resample_force_stereo.json  |   1 +
 nodes_audio.py                                | 450 ++++++++++++++++++
 pyproject.toml                                |  13 +
 requirements.txt                              |   6 +
 utils/ansi.py                                 | 113 +++++
 utils/logger.py                               |  84 ++++
 utils/misc.py                                 |   7 +
 15 files changed, 884 insertions(+), 5 deletions(-)
 create mode 100644 .flake8
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 __init__.py
 create mode 100644 example_workflows/audio_batch_select_example.jpg
 create mode 100644 example_workflows/audio_batch_select_example.json
 create mode 100644 example_workflows/resample_force_stereo.jpg
 create mode 100644 example_workflows/resample_force_stereo.json
 create mode 100644 nodes_audio.py
 create mode 100644 pyproject.toml
 create mode 100644 requirements.txt
 create mode 100644 utils/ansi.py
 create mode 100644 utils/logger.py
 create mode 100644 utils/misc.py

diff --git a/.flake8 b/.flake8
new file mode 100644
index 0000000..10dbcd5
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,8 @@
+# .flake8
+[flake8]
+max-line-length = 127
+# extend-ignore = E203, W503
+# Add plugins if you installed them via additional_dependencies:
+# select = C,E,F,W,B,B950  # Example: using flake8-bugbear (B, B950)
+# docstring-convention = google
+# ... other flake8 or plugin settings
diff --git a/.gitignore b/.gitignore
index 7b004e5..89d1f24 100644
--- a/.gitignore
+++ b/.gitignore
@@ -174,9 +174,9 @@ cython_debug/
 .abstra/
 
 # Visual Studio Code
-#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
 #  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
-#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  and can be added to the global gitignore or merged into this file. However, if you prefer,
 #  you could uncomment the following to ignore the enitre vscode folder
 # .vscode/
 
@@ -191,4 +191,7 @@ cython_debug/
 #  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
 #  refer to https://docs.cursor.com/context/ignore-files
 .cursorignore
-.cursorindexingignore
\ No newline at end of file
+.cursorindexingignore
+
+*.dst
+*.epr
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..dd05e9e
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,43 @@
+# .pre-commit-config.yaml
+
+# Optional: Set a minimum pre-commit version
+# min_pre_commit_version: '2.9.0'
+
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0 # Use the latest stable version
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+    # -   id: check-added-large-files # Optional: good for catching accidental large file commits
+
+-   repo: https://github.com/pycqa/flake8
+    rev: 7.0.0 # Use the latest stable version of flake8
+    hooks:
+    -   id: flake8
+        # Optional: specify arguments for flake8
+        # args: ['--max-line-length=88', '--extend-ignore=E203']
+        # It's often better to configure flake8 via .flake8, setup.cfg, or pyproject.toml
+        # so it's consistent whether run via pre-commit or manually.
+        additional_dependencies: [
+            # Add any flake8 plugins you use here, e.g.:
+            # 'flake8-bugbear',
+            # 'flake8-comprehensions',
+            # 'flake8-docstrings',
+            # 'pep8-naming'
+        ]
+
+-   repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6 # Use the latest stable version
+    hooks:
+    -   id: codespell
+        args: [
+            # "--ignore-words-list=your,custom,words,here",
+            # Or point to a file:
+            # "--ignore-words=.codespellignore",
+            "--skip=*.json,*.lock,*.svg,*.css,*.html", # Skip file types less likely to need it
+            # "--check-filenames",
+            # "--check-hidden"
+        ]
+        # You can create a .codespellignore file with one word per line for words to ignore.
diff --git a/README.md b/README.md
index d3866fb..e98e87f 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,126 @@
-# ComfyUI-AudioBatch
-Audio batch, resampler and channel adjust nodes for ComfyUI
+# ComfyUI Audio Batch & Utility Nodes
+
+This repository provides a set of custom nodes for ComfyUI focused on audio batching and common audio processing tasks like
+channel conversion and resampling. These nodes are designed to help manage and prepare audio data within your ComfyUI
+workflows, especially when dealing with multiple audio inputs or outputs.
+
+## Nodes
+
+### 1. Batch Audios
+   - **Display Name:** `Batch Audios`
+   - **Internal Name:** `SET_AudioBatch`
+   - **Category:** `audio/batch`
+   - **Description:** Takes two audio inputs (which can themselves be batches) and combines them into a single, larger audio batch. The node handles differences in sample rate, channel count, and length between the two inputs to produce a unified batch.
+   - **Inputs:**
+     - `audio1` (AUDIO): The first audio input. Can be a single audio item or a batch.
+     - `audio2` (AUDIO): The second audio input. Can be a single audio item or a batch.
+   - **Output:**
+     - `audio_batch` (AUDIO): A single audio object where the waveforms from `audio1` and `audio2` are concatenated along the batch dimension.
+   - **Behavior Details:**
+     - **Sample Rate:** All audio in the output batch will be resampled to match the sample rate of `audio1`.
+     - **Channels:**
+       - If both inputs are mono, the output batch will be mono.
+       - If one input is mono and the other is stereo, the mono input will be converted to "fake stereo" (by duplicating its channel), and the output batch will be stereo.
+       - If both inputs are stereo, the output batch will be stereo.
+       - For more complex multi-channel inputs, it defaults to the maximum channel count of the two inputs with a warning, as advanced downmixing is not performed.
+     - **Length (Samples):** All audio clips in the output batch will be padded with silence at the end to match the length of the longest clip (after any resampling).
+     - **Input Batch Handling:** If `audio1` has B1 items and `audio2` has B2 items, the output `audio_batch` will contain B1 + B2 items.
+
+### 2. Select Audio from Batch
+   - **Display Name:** `Select Audio from Batch`
+   - **Internal Name:** `SET_SelectAudioFromBatch`
+   - **Category:** `audio/batch`
+   - **Description:** Selects a single audio stream from an input audio batch based on a specified index. Provides options for handling out-of-range indices.
+   - **Inputs:**
+     - `audio_batch` (AUDIO): An audio batch (e.g., from the "Batch Audios" node).
+     - `index` (INT): The 0-based index of the audio stream to select from the batch.
+     - `behavior_out_of_range` (COMBO): What to do if the `index` is out of range:
+       - `silence_original_length` (default): Output silent audio with the same channel count and duration as items in the original batch.
+       - `silence_fixed_length`: Output silent audio with a duration specified by `silence_duration_seconds`.
+       - `error`: Raise an error (which will halt the workflow and display an error in ComfyUI).
+     - `silence_duration_seconds` (FLOAT): The duration of the silent audio if `behavior_out_of_range` is set to `silence_fixed_length`.
+   - **Output:**
+     - `selected_audio` (AUDIO): The selected audio stream (as a batch of 1) or silent audio if the index was out of range (and behavior was not "error").
+
+### 3. Audio Channel Converter
+   - **Display Name:** `Audio Channel Converter`
+   - **Internal Name:** `SET_AudioChannelConverter`
+   - **Category:** `audio/conversion`
+   - **Description:** Converts the channel layout of an input audio (e.g., mono to stereo, stereo to mono). Handles batches.
+   - **Inputs:**
+     - `audio` (AUDIO): The input audio.
+     - `channel_conversion` (COMBO): The desired channel conversion strategy:
+       - `keep` (default): No changes are made to the channel count. Logs a warning if input has more than 2 channels.
+       - `stereo_to_mono`: Converts stereo (or multi-channel) audio to mono by averaging all input channels. If already mono, no change.
+       - `mono_to_stereo`: Converts mono audio to "fake stereo" by duplicating the mono channel. If already stereo, no change. For multi-channel (>2) inputs, it takes the first channel and duplicates it to create stereo.
+       - `force_mono`: Always converts the input to mono by averaging all channels, regardless of the original channel count.
+       - `force_stereo`:
+         - If input is mono, converts to "fake stereo".
+         - If input is stereo, no change.
+         - If input has more than 2 channels, it takes the first channel and duplicates it to create stereo.
+   - **Output:**
+     - `audio_out` (AUDIO): The audio with the converted channel layout. The batch size and sample rate are preserved.
+
+### 4. Audio Resampler
+   - **Display Name:** `Audio Resampler`
+   - **Internal Name:** `SET_AudioResampler`
+   - **Category:** `audio/conversion`
+   - **Description:** Resamples the input audio to a specified target sample rate using `torchaudio.transforms.Resample`. Handles batches.
+   - **Inputs:**
+     - `audio` (AUDIO): The input audio.
+     - `target_sample_rate` (INT): The desired sample rate in Hz (e.g., 44100, 48000, 16000). If set to 0 or if it matches the original sample rate, resampling is skipped.
+   - **Output:**
+     - `audio_out` (AUDIO): The resampled audio. The batch size and channel count are preserved.
+
+### 5. Audio Channel Conv and Resampler
+   - **Display Name:** `Audio Channel Conv and Resampler`
+   - **Internal Name:** `SET_AudioChannelConvResampler`
+   - **Category:** `audio/conversion`
+   - **Description:** A convenience node that combines channel conversion and resampling into a single step.
+   - **Inputs:**
+     - `audio` (AUDIO): The input audio.
+     - `channel_conversion` (COMBO): Same options as the "Audio Channel Converter" node.
+     - `target_sample_rate` (INT): Same options as the "Audio Resampler" node.
+   - **Output:**
+     - `audio_out` (AUDIO): The audio after both channel conversion and resampling have been applied.
+
+## Installation
+
+1.  Clone this repository into your `ComfyUI/custom_nodes/` directory:
+    ```bash
+    cd ComfyUI/custom_nodes/
+    git clone https://github.com/set-soft/ComfyUI-AudioBatch ComfyUI-AudioBatch
+    ```
+2.  Restart ComfyUI.
+
+The nodes should then appear under the "audio/batch" and "audio/conversion" categories in the "Add Node" menu.
+
+## Dependencies
+
+- PyTorch
+- Torchaudio (for resampling and potentially other audio operations)
+- NumPy (often used with audio data)
+
+These are typically already present in a standard ComfyUI environment.
+
+## Usage Notes
+
+- **AUDIO Type:** These nodes work with ComfyUI's standard "AUDIO" data type, which is a Python dictionary containing:
+  - `'waveform'`: A `torch.Tensor` of shape `(batch_size, num_channels, num_samples)`.
+  - `'sample_rate'`: An `int` representing the sample rate in Hz.
+- **Logging:** The nodes use Python's `logging` module. Debug messages can be helpful for understanding the transformations being applied.
+  You can control log verbosity through ComfyUI's startup arguments (e.g., `--preview-method auto --verbose` for more detailed ComfyUI logs
+  which might also affect custom node loggers if they are configured to inherit levels). The logger name used is "AudioBatch".
+  You can force debugging level for these nodes defining the `AUDIOBATCH_NODES_DEBUG` environment variable to `1`.
+
+## Future Improvements / TODO
+
+- Add more sophisticated downmixing options for multi-channel audio (e.g., 5.1 to stereo).
+- Allow user to choose padding value (e.g., silence, edge, reflect) for length matching in "Batch Audios".
+- Option in "Batch Audios" to truncate to shortest instead of padding to longest.
+- More options for stereo-to-mono conversion (e.g., take left channel, take right channel).
+- If you are interested on them, please open an issue.
+
+## License
+
+[GPL-3.0](LICENSE)
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..19f2aa7
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) 2025 Salvador E. Tropea
+# Copyright (c) 2025 Instituto Nacional de Tecnologïa Industrial
+# License: GPL-3.0
+# Project: ComfyUI-AudioBatch
+from . import nodes_audio
+import inspect
+import logging
+from .utils.misc import NODES_NAME
+
+init_logger = logging.getLogger(NODES_NAME + ".__init__")
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+for name, obj in inspect.getmembers(nodes_audio):
+    if not inspect.isclass(obj) or not hasattr(obj, "INPUT_TYPES"):
+        continue
+    assert hasattr(obj, "UNIQUE_NAME"), f"No name for {obj.__name__}"
+    NODE_CLASS_MAPPINGS[obj.UNIQUE_NAME] = obj
+    NODE_DISPLAY_NAME_MAPPINGS[obj.UNIQUE_NAME] = obj.DISPLAY_NAME
+
+init_logger.info(f"Registering {len(NODE_CLASS_MAPPINGS)} node(s).")
+init_logger.debug(f"{list(NODE_DISPLAY_NAME_MAPPINGS.values())}")
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/example_workflows/audio_batch_select_example.jpg b/example_workflows/audio_batch_select_example.jpg
new file mode 100644
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diff --git a/example_workflows/audio_batch_select_example.json b/example_workflows/audio_batch_select_example.json
new file mode 100644
index 0000000..17d4101
--- /dev/null
+++ b/example_workflows/audio_batch_select_example.json
@@ -0,0 +1 @@
+{"id":"c006d818-3f65-40be-857c-e20ee6fe6016","revision":0,"last_node_id":12,"last_link_id":6,"nodes":[{"id":1,"type":"LoadAudio","pos":[2029.5302734375,607.4900512695312],"size":[350,150],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[1]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.34","widget_ue_connectable":{},"Node name for S&R":"LoadAudio"},"widgets_values":["Palpatine_1.wav",null,null],"color":"#233","bgcolor":"#355"},{"id":2,"type":"LoadAudio","pos":[2030,810],"size":[350,150],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[2]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.34","widget_ue_connectable":{},"Node name for S&R":"LoadAudio"},"widgets_values":["Palpatine_2.wav",null,null],"color":"#233","bgcolor":"#355"},{"id":4,"type":"LoadAudio","pos":[2030,1020],"size":[350,150],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[4]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.34","widget_ue_connectable":{},"Node name for S&R":"LoadAudio"},"widgets_values":["aud-sample-vs-1.wav",null,null],"color":"#233","bgcolor":"#355"},{"id":3,"type":"SET_AudioBatch","pos":[2459.5302734375,757.4900512695312],"size":[146.42578125,46],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"audio1","name":"audio1","type":"AUDIO","link":1},{"localized_name":"audio2","name":"audio2","type":"AUDIO","link":2}],"outputs":[{"localized_name":"audio_batch","name":"audio_batch","type":"AUDIO","links":[3]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","widget_ue_connectable":{},"Node name for S&R":"SET_AudioBatch"},"color":"#2a363b","bgcolor":"#3f5159"},{"id":5,"type":"SET_AudioBatch","pos":[2650,860],"size":[146.42578125,46],"flags":{},"order":8,"mode":0,"inputs":[{"localized_name":"audio1","name":"audio1","type":"AUDIO","link":3},{"localized_name":"audio2","name":"audio2","type":"AUDIO","link":4}],"outputs":[{"localized_name":"audio_batch","name":"audio_batch","type":"AUDIO","links":[5]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","widget_ue_connectable":{},"Node name for S&R":"SET_AudioBatch"},"color":"#2a363b","bgcolor":"#3f5159"},{"id":10,"type":"MarkdownNote","pos":[2450,570],"size":[210,110],"flags":{},"order":4,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["## This node will create a batch from the first 2"],"color":"#432","bgcolor":"#653"},{"id":11,"type":"MarkdownNote","pos":[2600,960],"size":[210,110],"flags":{},"order":5,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["## You can easily extend the batch"],"color":"#432","bgcolor":"#653"},{"id":12,"type":"MarkdownNote","pos":[2880,680],"size":[210,110],"flags":{},"order":6,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["## Select which audio you need here"],"color":"#432","bgcolor":"#653"},{"id":9,"type":"MarkdownNote","pos":[1770,610],"size":[230,150],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"properties":{},"widgets_values":["# Load 3 audio files\n\nAudios in the batch will have the same number of channels, the same sample rate and the same duration.\n\nThis is all adapted by the `Batch Audio` nodes."],"color":"#432","bgcolor":"#653"},{"id":6,"type":"SET_SelectAudioFromBatch","pos":[2840,860],"size":[350,106],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"audio_batch","name":"audio_batch","type":"AUDIO","link":5},{"localized_name":"index","name":"index","type":"INT","widget":{"name":"index"},"link":null},{"localized_name":"behavior_out_of_range","name":"behavior_out_of_range","type":"COMBO","widget":{"name":"behavior_out_of_range"},"link":null},{"localized_name":"silence_duration_seconds","name":"silence_duration_seconds","type":"FLOAT","widget":{"name":"silence_duration_seconds"},"link":null}],"outputs":[{"localized_name":"selected_audio","name":"selected_audio","type":"AUDIO","links":[6]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","widget_ue_connectable":{},"Node name for S&R":"SET_SelectAudioFromBatch"},"widgets_values":[2,"silence_original_length",1],"color":"#2a363b","bgcolor":"#3f5159"},{"id":7,"type":"PreviewAudio","pos":[3220,860],"size":[270,88],"flags":{},"order":10,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":6},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.3.39","widget_ue_connectable":{},"Node name for S&R":"PreviewAudio"},"widgets_values":[],"color":"#233","bgcolor":"#355"}],"links":[[1,1,0,3,0,"AUDIO"],[2,2,0,3,1,"AUDIO"],[3,3,0,5,0,"AUDIO"],[4,4,0,5,1,"AUDIO"],[5,5,0,6,0,"AUDIO"],[6,6,0,7,0,"AUDIO"]],"groups":[],"config":{},"extra":{"ue_links":[],"links_added_by_ue":[],"ds":{"scale":1.0313326743041535,"offset":[-1630.6237988782925,-363.19559199608597]}},"version":0.4}
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diff --git a/example_workflows/resample_force_stereo.json b/example_workflows/resample_force_stereo.json
new file mode 100644
index 0000000..2769275
--- /dev/null
+++ b/example_workflows/resample_force_stereo.json
@@ -0,0 +1 @@
+{"id":"f8820f6e-7280-4a6e-858d-4aa7fd65e8aa","revision":0,"last_node_id":10,"last_link_id":9,"nodes":[{"id":3,"type":"SET_AudioResampler","pos":[2450,450],"size":[270,58],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":1},{"localized_name":"target_sample_rate","name":"target_sample_rate","type":"INT","widget":{"name":"target_sample_rate"},"link":null}],"outputs":[{"localized_name":"audio_out","name":"audio_out","type":"AUDIO","links":[4]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","Node name for S&R":"SET_AudioResampler","widget_ue_connectable":{}},"widgets_values":[44100],"color":"#2a363b","bgcolor":"#3f5159"},{"id":5,"type":"SET_AudioChannelConverter","pos":[2750,450],"size":[270.740234375,58],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":4},{"localized_name":"channel_conversion","name":"channel_conversion","type":"COMBO","widget":{"name":"channel_conversion"},"link":null}],"outputs":[{"localized_name":"audio_out","name":"audio_out","type":"AUDIO","links":[6]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","Node name for 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S&R":"LoadAudio","widget_ue_connectable":{}},"widgets_values":["Palpatine_1.wav",null,null],"color":"#233","bgcolor":"#355"},{"id":7,"type":"LoadAudio","pos":[2050,850],"size":[350,150],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[7]}],"properties":{"cnr_id":"comfy-core","ver":"0.3.34","Node name for S&R":"LoadAudio","widget_ue_connectable":{}},"widgets_values":["Palpatine_1.wav",null,null],"color":"#233","bgcolor":"#355"},{"id":6,"type":"SaveAudio","pos":[3080,450],"size":[270,112],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":6},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.3.39","Node name for S&R":"SaveAudio","widget_ue_connectable":{}},"widgets_values":["audio/ComfyUI"],"color":"#233","bgcolor":"#355"},{"id":9,"type":"SaveAudio","pos":[3080,850],"size":[270,112],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":9},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.3.39","Node name for S&R":"SaveAudio","widget_ue_connectable":{}},"widgets_values":["audio/ComfyUI"],"color":"#233","bgcolor":"#355"},{"id":4,"type":"SET_AudioChannelConvResampler","pos":[2580,850],"size":[327.1470642089844,82],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":7},{"localized_name":"channel_conversion","name":"channel_conversion","type":"COMBO","widget":{"name":"channel_conversion"},"link":null},{"localized_name":"target_sample_rate","name":"target_sample_rate","type":"INT","widget":{"name":"target_sample_rate"},"link":null}],"outputs":[{"localized_name":"audio_out","name":"audio_out","type":"AUDIO","links":[9]}],"properties":{"aux_id":"set-soft/ComfyUI-AudioBatch","ver":"65e3da3da285b8593e74f138d114ec0b576a22bd","Node name for S&R":"SET_AudioChannelConvResampler","widget_ue_connectable":{}},"widgets_values":["force_stereo",44100],"color":"#2a363b","bgcolor":"#3f5159"},{"id":10,"type":"MarkdownNote","pos":[2060,240],"size":[530,130],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[],"properties":{"widget_ue_connectable":{}},"widgets_values":["# Forcing channels and sample rate\n\n## These two examples will force stereo output sampled at 44100 Hz"],"color":"#432","bgcolor":"#653"}],"links":[[1,1,0,3,0,"AUDIO"],[4,3,0,5,0,"AUDIO"],[6,5,0,6,0,"AUDIO"],[7,7,0,4,0,"AUDIO"],[9,4,0,9,0,"AUDIO"]],"groups":[],"config":{},"extra":{"ue_links":[],"ds":{"scale":1.0313326743041535,"offset":[-1112.3182273496227,-193.07144659156816]},"links_added_by_ue":[]},"version":0.4}
diff --git a/nodes_audio.py b/nodes_audio.py
new file mode 100644
index 0000000..05b3932
--- /dev/null
+++ b/nodes_audio.py
@@ -0,0 +1,450 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) 2025 Salvador E. Tropea
+# Copyright (c) 2025 Instituto Nacional de Tecnologïa Industrial
+# License: GPL-3.0
+# Project: ComfyUI-AudioBatch
+# From code generated by Gemini 2.5 Pro
+import torch
+import torchaudio.transforms as T
+from .utils.logger import main_logger
+
+logger = main_logger
+BASE_CATEGORY = "audio"
+BATCH_CATEGORY = "batch"
+CONV_CATEGORY = "conversion"
+
+
+def convert_batch_to_stereo_tensor(audio_waveform_mono_batch: torch.Tensor) -> torch.Tensor:
+    """ Converts a batch of mono audio tensors (B, 1, N) to stereo (B, 2, N). """
+    if audio_waveform_mono_batch.ndim != 3 or audio_waveform_mono_batch.shape[1] != 1:
+        # This could also happen if an input was (N) or (1,N) and wasn't unsqueezed to (B,1,N) yet
+        raise ValueError("Input for stereo conversion must be a batch of mono audio (B, 1, N), "
+                         f"got {audio_waveform_mono_batch.shape}")
+    return audio_waveform_mono_batch.repeat(1, 2, 1)  # (B, 1, N) -> (B, 2, N)
+
+
+class AudioBatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio1": ("AUDIO",),
+                "audio2": ("AUDIO",),
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio_batch",)
+    FUNCTION = "batch_audio"
+    CATEGORY = BASE_CATEGORY + "/" + BATCH_CATEGORY
+    DESCRIPTION = "Audio batch creator"
+    UNIQUE_NAME = "SET_AudioBatch"
+    DISPLAY_NAME = "Batch Audios"
+
+    def _preprocess_waveform_batch(
+        self,
+        waveform: torch.Tensor,  # (B, C_in, N_in)
+        original_sr: int,
+        target_sr: int,
+        target_channels: int,
+        target_samples: int,
+        reference_dtype: torch.dtype,
+        reference_device: torch.device
+    ) -> torch.Tensor:
+        """
+        Helper function to resample, change channels, and pad a batch of waveforms.
+        Returns a tensor of shape (B, target_channels, target_samples).
+        """
+        # Ensure correct device and dtype
+        processed_wf = waveform.to(device=reference_device, dtype=reference_dtype)
+        current_batch_size, current_channels, current_samples = processed_wf.shape
+
+        # 1. Resample if necessary
+        if original_sr != target_sr:
+            logger.debug(f"Resampling batch from {original_sr} to {target_sr}. Input shape: {processed_wf.shape}")
+            # Resample expects (..., time)
+            # For (B, C, N), we can reshape to (B*C, N), resample, then reshape back.
+            # Or, if T.Resample handles batch dims appropriately (some versions might if C=1 or applied per channel).
+            # Let's reshape for robustness with T.Resample.
+            resampler = T.Resample(orig_freq=original_sr, new_freq=target_sr, dtype=reference_dtype).to(reference_device)
+
+            if current_channels == 1:
+                # Reshape (B, 1, N) to (B, N) for resampler, then unsqueeze back
+                processed_wf_reshaped = processed_wf.squeeze(1)  # (B, N)
+                resampled_wf_reshaped = resampler(processed_wf_reshaped)  # (B, N_new)
+                processed_wf = resampled_wf_reshaped.unsqueeze(1)  # (B, 1, N_new)
+            else:  # Multi-channel (e.g., stereo)
+                # Resample each channel in the batch separately
+                # This is more complex if T.Resample doesn't broadcast correctly.
+                # A common way: permute to (C, B, N), reshape to (C*B, N), resample, reshape back.
+                # Or loop (less efficient for GPU tensors).
+                # Let's try reshaping to (B*C, N)
+                original_shape = processed_wf.shape
+                processed_wf_flat_batch_channel = processed_wf.reshape(-1, current_samples)  # (B*C, N)
+                resampled_wf_flat = resampler(processed_wf_flat_batch_channel)  # (B*C, N_new)
+                # Reshape back to (B, C, N_new)
+                processed_wf = resampled_wf_flat.reshape(original_shape[0], original_shape[1], -1)
+
+            current_samples = processed_wf.shape[2]  # Update sample count
+            logger.debug(f"Batch after resampling: shape={processed_wf.shape}")
+
+        # 2. Adjust number of channels
+        if current_channels == 1 and target_channels == 2:
+            processed_wf = convert_batch_to_stereo_tensor(processed_wf)  # (B, 1, N) -> (B, 2, N)
+            logger.debug(f"Batch converted to stereo: shape={processed_wf.shape}")
+        elif current_channels == 2 and target_channels == 1:
+            # Example: Convert stereo to mono by averaging (can make this an option)
+            processed_wf = processed_wf.mean(dim=1, keepdim=True)  # (B, 2, N) -> (B, 1, N)
+            logger.debug(f"Batch converted to mono (avg): shape={processed_wf.shape}")
+        elif current_channels != target_channels:
+            # Fallback or error for other unsupported channel conversions (e.g. 5.1 to stereo)
+            # For now, if shapes don't match after mono/stereo adjustment, it might error later.
+            # A more robust node would handle various conversions or error clearly.
+            logger.warning(f"Unhandled channel conversion from {current_channels} to {target_channels}."
+                           f" Resulting channels: {processed_wf.shape[1]}")
+            # If, for example, target is 2, and current is 5, how to downmix?
+            # For this node's purpose (batching existing audio), major downmixing is out of scope.
+            # We primarily handle mono <-> stereo alignment.
+            if processed_wf.shape[1] != target_channels:
+                raise ValueError(f"Cannot align channels: input has {processed_wf.shape[1]} after initial processing, "
+                                 f"target is {target_channels}")
+
+        # 3. Pad length if necessary
+        if current_samples < target_samples:
+            padding_needed = target_samples - current_samples
+            # Pad only the last dimension (samples)
+            processed_wf = torch.nn.functional.pad(processed_wf, (0, padding_needed))
+            logger.debug(f"Batch padded: shape={processed_wf.shape}")
+        elif current_samples > target_samples:  # Should not happen if target_samples is max length
+            logger.warning(f"Waveform has {current_samples} samples, but target is {target_samples}. "
+                           "This indicates an issue in target_samples calculation.")
+            # Truncate as a fallback, though logic should prevent this.
+            processed_wf = processed_wf[..., :target_samples]
+
+        # Final check for shape
+        if processed_wf.shape[1] != target_channels or processed_wf.shape[2] != target_samples:
+            raise RuntimeError(f"Internal Preprocessing Error: Waveform shape {processed_wf.shape} "
+                               f"does not match target ({current_batch_size}, {target_channels}, {target_samples}).")
+
+        return processed_wf
+
+    def batch_audio(self, audio1: dict, audio2: dict):
+        waveform1_orig = audio1['waveform']  # (B1, C1, N1)
+        sr1 = audio1['sample_rate']
+        waveform2_orig = audio2['waveform']  # (B2, C2, N2)
+        sr2 = audio2['sample_rate']
+
+        logger.debug(f"Audio1 input: shape={waveform1_orig.shape}, sr={sr1}, dtype={waveform1_orig.dtype}, "
+                     f"device={waveform1_orig.device}")
+        logger.debug(f"Audio2 input: shape={waveform2_orig.shape}, sr={sr2}, dtype={waveform2_orig.dtype}, "
+                     f"device={waveform2_orig.device}")
+
+        # Use properties of audio1 as the reference for the output batch
+        # (e.g., device, dtype, and target sample rate)
+        reference_device = waveform1_orig.device
+        reference_dtype = waveform1_orig.dtype
+        target_sr = sr1  # All audio will be resampled to sr1
+
+        # Determine target number of channels for the batch (max of inputs, ensure mono/stereo alignment)
+        c1 = waveform1_orig.shape[1]
+        c2 = waveform2_orig.shape[1]
+        # If one is mono and other is stereo, output will be stereo. Otherwise, max (handles both mono or both stereo).
+        if (c1 == 1 and c2 == 2) or (c1 == 2 and c2 == 1) or (c1 == 2 and c2 == 2):
+            target_channels = 2
+        elif c1 == 1 and c2 == 1:
+            target_channels = 1
+        else:
+            # For other multi-channel counts (e.g. 5.1), this simple logic might not be ideal.
+            # For now, default to max, but this could be an error or require downmixing node.
+            logger.warning(f"Complex channel counts detected (C1={c1}, C2={c2}). Defaulting to max channels ({max(c1,c2)}) "
+                           "and hoping for downstream compatibility or further processing. "
+                           "Explicit mono/stereo alignment is preferred for this node.")
+            target_channels = max(c1, c2)
+
+        # Determine target number of samples (length) for the batch
+        # First, calculate lengths *after* potential resampling
+        n1_orig = waveform1_orig.shape[2]
+        n2_orig = waveform2_orig.shape[2]
+
+        n1_after_resample = n1_orig  # No resampling for audio1 as it's the target_sr reference
+        n2_after_resample = n2_orig
+        if sr2 != target_sr:
+            n2_after_resample = int(n2_orig * (target_sr / sr2))
+
+        target_samples = max(n1_after_resample, n2_after_resample)
+        logger.debug(f"Unified Batch Params: SR={target_sr}, Channels={target_channels}, Samples={target_samples}")
+
+        # Preprocess both waveform batches
+        wf1_processed = self._preprocess_waveform_batch(waveform1_orig, sr1, target_sr, target_channels, target_samples,
+                                                        reference_dtype, reference_device)
+        wf2_processed = self._preprocess_waveform_batch(waveform2_orig, sr2, target_sr, target_channels, target_samples,
+                                                        reference_dtype, reference_device)
+
+        # wf1_processed is (B1, target_channels, target_samples)
+        # wf2_processed is (B2, target_channels, target_samples)
+
+        # Concatenate along the batch dimension (dim=0)
+        try:
+            batched_waveform_final = torch.cat((wf1_processed, wf2_processed), dim=0)
+        except RuntimeError as e:
+            logger.error(f"Error during final torch.cat: {e}")
+            logger.error(f"Processed Waveform1 shape: {wf1_processed.shape}, dtype: {wf1_processed.dtype}, "
+                         f"device: {wf1_processed.device}")
+            logger.error(f"Processed Waveform2 shape: {wf2_processed.shape}, dtype: {wf2_processed.dtype}, "
+                         f"device: {wf2_processed.device}")
+            raise  # Re-raise to make error visible
+
+        logger.info(f"Final batched audio: shape={batched_waveform_final.shape}, sr={target_sr}")
+
+        output_audio = {
+            "waveform": batched_waveform_final,
+            "sample_rate": target_sr
+        }
+        return (output_audio,)
+
+
+class SelectAudioFromBatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio_batch": ("AUDIO",),  # Expects {'waveform': (B, C, N), 'sample_rate': int}
+                "index": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 0xffffffffffffffff,  # Effectively unbounded, but UI might cap
+                    "step": 1,
+                    "display": "number"
+                }),
+                "behavior_out_of_range": (["silence_original_length", "silence_fixed_length", "error"], {
+                    "default": "silence_original_length"
+                }),
+                "silence_duration_seconds": ("FLOAT", {  # Only used if behavior is "silence_fixed_length"
+                    "default": 1.0,
+                    "min": 0.01,
+                    "max": 3600.0,  # 1 hour
+                    "step": 0.1,
+                    "display": "number"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("selected_audio",)
+    FUNCTION = "select_audio"
+    CATEGORY = BASE_CATEGORY + "/" + BATCH_CATEGORY
+    DESCRIPTION = "Selects an audio from a batch"
+    UNIQUE_NAME = "SET_SelectAudioFromBatch"
+    DISPLAY_NAME = "Select Audio from Batch"
+
+    def select_audio(self, audio_batch: dict, index: int, behavior_out_of_range: str, silence_duration_seconds: float):
+
+        waveform_batch = audio_batch['waveform']  # (B, C, N)
+        sample_rate = audio_batch['sample_rate']
+
+        batch_size, num_channels, num_samples_per_item = waveform_batch.shape
+
+        logger.debug(f"Input audio batch: shape={waveform_batch.shape}, sr={sample_rate}, index={index}")
+        logger.debug(f"Out of range behavior: {behavior_out_of_range}, silence duration: {silence_duration_seconds}s")
+
+        selected_waveform = None
+
+        if 0 <= index < batch_size:
+            # Valid index, select the audio
+            # Slicing with index:index+1 keeps the batch dimension, resulting in (1, C, N)
+            selected_waveform = waveform_batch[index:index+1, :, :]
+            logger.info(f"Selected audio at index {index}. Shape: {selected_waveform.shape}")
+        else:
+            # Index is out of range
+            msg = f"Index {index} is out of range for batch of size {batch_size}."
+            logger.warning(msg)
+
+            if behavior_out_of_range == "error":
+                raise ValueError(msg)
+            elif behavior_out_of_range == "silence_original_length":
+                logger.info(f"Outputting silence with original length: {num_samples_per_item} samples, "
+                            f"{num_channels} channels.")
+                selected_waveform = torch.zeros((1, num_channels if num_channels > 0 else 1, num_samples_per_item),
+                                                dtype=waveform_batch.dtype, device=waveform_batch.device)
+            elif behavior_out_of_range == "silence_fixed_length":
+                silence_samples = int(silence_duration_seconds * sample_rate)
+                if silence_samples <= 0:  # Ensure positive sample count
+                    silence_samples = 1
+                    logger.warning(f"Calculated silence samples is <=0 ({silence_samples} from "
+                                   f"{silence_duration_seconds}s). Using 1 sample.")
+                logger.info(f"Outputting silence with fixed length: {silence_samples} samples, {num_channels} channels.")
+                selected_waveform = torch.zeros((1, num_channels if num_channels > 0 else 1, silence_samples),
+                                                dtype=waveform_batch.dtype, device=waveform_batch.device)
+
+        if selected_waveform is None:  # Should not happen if logic above is complete
+            logger.error("Selected waveform is None unexpectedly. Defaulting to silence.")
+            selected_waveform = torch.zeros((1, num_channels if num_channels > 0 else 1, 1),
+                                            dtype=waveform_batch.dtype, device=waveform_batch.device)
+
+        output_audio = {
+            "waveform": selected_waveform,  # Shape (1, C, N_selected)
+            "sample_rate": sample_rate
+        }
+
+        return (output_audio,)
+
+
+class AudioChannelConverter:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "channel_conversion": (["keep", "stereo_to_mono", "mono_to_stereo", "force_mono", "force_stereo"],
+                                       {"default": "keep"}),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio_out",)
+    FUNCTION = "convert_channels"
+    CATEGORY = BASE_CATEGORY + "/" + CONV_CATEGORY
+    DESCRIPTION = "Converts audio channels (mono/stereo/multi-channel handling)."
+    UNIQUE_NAME = "SET_AudioChannelConverter"
+    DISPLAY_NAME = "Audio Channel Converter"
+
+    def convert_channels(self, audio: dict, channel_conversion: str):
+        waveform = audio['waveform']  # (B, C, T)
+        sample_rate = audio['sample_rate']
+
+        original_batch_size, original_channels, original_samples = waveform.shape
+        logger.debug(f"Input audio: {original_batch_size}B, {original_channels}C, {original_samples}T @ {sample_rate}Hz")
+        logger.debug(f"Channel conversion mode: {channel_conversion}")
+
+        output_waveform = waveform.clone()  # Start with a copy
+
+        if channel_conversion == "keep":
+            # No change needed, but log if > 2 channels
+            if original_channels > 2:
+                logger.warning(f"Channel mode 'keep': Input has {original_channels} channels. Outputting all "
+                               f"{original_channels} channels.")
+            # output_waveform remains as is
+
+        elif channel_conversion == "stereo_to_mono" or channel_conversion == "force_mono":
+            if original_channels == 1:
+                logger.debug("Input is already mono. No change for stereo_to_mono/force_mono.")
+                # output_waveform remains as is
+            else:  # Stereo or Multi-channel to Mono
+                if original_channels > 2 and channel_conversion == "stereo_to_mono":
+                    logger.warning(f"Channel mode 'stereo_to_mono': Input has {original_channels} channels. "
+                                   "Averaging all to mono.")
+                elif channel_conversion == "force_mono":
+                    logger.info(f"Channel mode 'force_mono': Input has {original_channels} channels. Averaging all to mono.")
+                # Average across the channel dimension (dim=1)
+                output_waveform = torch.mean(waveform, dim=1, keepdim=True)
+                logger.debug(f"Converted to mono. New shape: {output_waveform.shape}")
+
+        elif channel_conversion == "mono_to_stereo" or channel_conversion == "force_stereo":
+            if original_channels == 2:
+                logger.debug("Input is already stereo. No change for mono_to_stereo/force_stereo.")
+                # output_waveform remains as is
+            elif original_channels == 1:  # Mono to Stereo
+                # Duplicate the mono channel
+                output_waveform = waveform.repeat(1, 2, 1)
+                logger.debug(f"Converted mono to stereo by duplication. New shape: {output_waveform.shape}")
+            else:  # Multi-channel (>2) to Stereo
+                if channel_conversion == "mono_to_stereo":
+                    logger.warning(f"Channel mode 'mono_to_stereo': Input has {original_channels} channels. "
+                                   "Taking the first channel and duplicating it to create stereo.")
+                elif channel_conversion == "force_stereo":
+                    logger.info(f"Channel mode 'force_stereo': Input has {original_channels} channels. "
+                                "Taking the first channel and duplicating it to create stereo.")
+                output_waveform = waveform[:, 0:1, :].repeat(1, 2, 1)  # Take first channel, make it (B,1,T), then repeat
+                logger.debug(f"Converted {original_channels}ch to stereo. New shape: {output_waveform.shape}")
+        else:
+            logger.error(f"Unknown channel_conversion mode: {channel_conversion}. Returning original.")
+            # output_waveform remains as is (original waveform)
+
+        processed_audio_dict = {"waveform": output_waveform, "sample_rate": sample_rate}
+        return (processed_audio_dict,)
+
+
+class AudioResampler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "target_sample_rate": ("INT", {"default": 0, "min": 0, "max": 192000, "step": 100}),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio_out",)
+    FUNCTION = "resample_audio"
+    CATEGORY = BASE_CATEGORY + "/" + CONV_CATEGORY
+    DESCRIPTION = "Resamples audio to a target sample rate using torchaudio."
+    UNIQUE_NAME = "SET_AudioResampler"
+    DISPLAY_NAME = "Audio Resampler"
+
+    def resample_audio(self, audio: dict, target_sample_rate: int):
+        waveform = audio['waveform']  # (B, C, T)
+        original_sample_rate = audio['sample_rate']
+
+        logger.debug(f"Input audio SR: {original_sample_rate}Hz, Target SR: {target_sample_rate}Hz")
+
+        if target_sample_rate == 0 or target_sample_rate == original_sample_rate:
+            logger.info(f"Target sample rate ({target_sample_rate}Hz) is 0 or matches original ({original_sample_rate}Hz). "
+                        "Skipping resampling.")
+            return (audio,)  # Return original audio dict
+
+        try:
+            # Ensure waveform is on the CPU for torchaudio transforms if it might be on GPU
+            # Though many torchaudio ops support GPU tensors. Resample does.
+            # For consistency or if issues arise:
+            # device = waveform.device
+            # waveform_cpu = waveform.cpu()
+            # resampler = T.Resample(orig_freq=original_sample_rate, new_freq=target_sample_rate).to(device)
+            # resampled_waveform = resampler(waveform)
+            resampler = T.Resample(orig_freq=original_sample_rate, new_freq=target_sample_rate,
+                                   dtype=waveform.dtype  # Preserve dtype
+                                   ).to(waveform.device)  # Perform resampling on the tensor's current device
+
+            resampled_waveform = resampler(waveform)
+            logger.info(f"Resampled audio from {original_sample_rate}Hz to {target_sample_rate}Hz. "
+                        f"Original shape: {waveform.shape}, New shape: {resampled_waveform.shape}")
+            processed_audio_dict = {"waveform": resampled_waveform, "sample_rate": target_sample_rate}
+            return (processed_audio_dict,)
+
+        except Exception as e:
+            logger.error(f"Error during resampling: {e}", exc_info=True)
+            # Fallback: return original audio if resampling fails
+            return (audio,)
+
+
+class AudioProcessAdvanced:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "channel_conversion": (["keep", "stereo_to_mono", "mono_to_stereo", "force_mono", "force_stereo"],
+                                       {"default": "keep"}),
+                "target_sample_rate": ("INT", {"default": 0, "min": 0, "max": 192000, "step": 100}),
+            },
+        }
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio_out",)
+    FUNCTION = "process_audio"
+    CATEGORY = BASE_CATEGORY + "/" + CONV_CATEGORY
+    DESCRIPTION = "Applies channel conversion and resampling to audio."
+    UNIQUE_NAME = "SET_AudioChannelConvResampler"
+    DISPLAY_NAME = "Audio Channel Conv and Resampler"
+
+    def process_audio(self, audio: dict, channel_conversion: str, target_sample_rate: int):
+        # Instantiate helper classes (or move their logic directly here)
+        channel_converter_node = AudioChannelConverter()
+        resampler_node = AudioResampler()
+
+        # 1. Channel Conversion
+        (audio_after_channels,) = channel_converter_node.convert_channels(audio, channel_conversion)
+
+        # 2. Resampling
+        (audio_after_resample,) = resampler_node.resample_audio(audio_after_channels, target_sample_rate)
+
+        return (audio_after_resample,)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..353d544
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+[project]
+name = "audio-batch"
+description = "Audio batch creation, extraction, resample, mono and stereo conversion."
+version = "1.0.0"
+license = { file = "LICENSE" }
+dependencies = []
+
+[project.urls]
+Repository = "https://github.com/set-soft/ComfyUI-AudioBatch"
+
+[tool.comfy]
+PublisherId = "set-soft"
+DisplayName = "Audio Batch"
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..76cf8ed
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+torch
+torchaudio
+numpy
+
+# non essential dependencies:
+colorama
diff --git a/utils/ansi.py b/utils/ansi.py
new file mode 100644
index 0000000..5e7d0cc
--- /dev/null
+++ b/utils/ansi.py
@@ -0,0 +1,113 @@
+# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
+'''
+This module generates ANSI character codes to printing colors to terminals.
+See: http://en.wikipedia.org/wiki/ANSI_escape_code
+'''
+import sys
+import os
+
+CSI = '\033['
+OSC = '\033]'
+BEL = '\a'
+is_a_tty = sys.stderr.isatty() and os.name == 'posix'
+
+
+def code_to_chars(code):
+    return CSI + str(code) + 'm' if is_a_tty else ''
+
+
+def set_title(title):
+    return OSC + '2;' + title + BEL
+
+
+def clear_screen(mode=2):
+    return CSI + str(mode) + 'J'
+
+
+def clear_line(mode=2):
+    return CSI + str(mode) + 'K'
+
+
+class AnsiCodes(object):
+    def __init__(self):
+        # the subclasses declare class attributes which are numbers.
+        # Upon instantiation we define instance attributes, which are the same
+        # as the class attributes but wrapped with the ANSI escape sequence
+        for name in dir(self):
+            if not name.startswith('_'):
+                value = getattr(self, name)
+                setattr(self, name, code_to_chars(value))
+
+
+class AnsiCursor(object):
+    def UP(self, n=1):
+        return CSI + str(n) + 'A'
+
+    def DOWN(self, n=1):
+        return CSI + str(n) + 'B'
+
+    def FORWARD(self, n=1):
+        return CSI + str(n) + 'C'
+
+    def BACK(self, n=1):
+        return CSI + str(n) + 'D'
+
+    def POS(self, x=1, y=1):
+        return CSI + str(y) + ';' + str(x) + 'H'
+
+
+class AnsiFore(AnsiCodes):
+    BLACK = 30
+    RED = 31
+    GREEN = 32
+    YELLOW = 33
+    BLUE = 34
+    MAGENTA = 35
+    CYAN = 36
+    WHITE = 37
+    RESET = 39
+
+    # These are fairly well supported, but not part of the standard.
+    LIGHTBLACK_EX = 90
+    LIGHTRED_EX = 91
+    LIGHTGREEN_EX = 92
+    LIGHTYELLOW_EX = 93
+    LIGHTBLUE_EX = 94
+    LIGHTMAGENTA_EX = 95
+    LIGHTCYAN_EX = 96
+    LIGHTWHITE_EX = 97
+
+
+class AnsiBack(AnsiCodes):
+    BLACK = 40
+    RED = 41
+    GREEN = 42
+    YELLOW = 43
+    BLUE = 44
+    MAGENTA = 45
+    CYAN = 46
+    WHITE = 47
+    RESET = 49
+
+    # These are fairly well supported, but not part of the standard.
+    LIGHTBLACK_EX = 100
+    LIGHTRED_EX = 101
+    LIGHTGREEN_EX = 102
+    LIGHTYELLOW_EX = 103
+    LIGHTBLUE_EX = 104
+    LIGHTMAGENTA_EX = 105
+    LIGHTCYAN_EX = 106
+    LIGHTWHITE_EX = 107
+
+
+class AnsiStyle(AnsiCodes):
+    BRIGHT = 1
+    DIM = 2
+    NORMAL = 22
+    RESET_ALL = 0
+
+
+Fore = AnsiFore()
+Back = AnsiBack()
+Style = AnsiStyle()
+Cursor = AnsiCursor()
diff --git a/utils/logger.py b/utils/logger.py
new file mode 100644
index 0000000..ce927d5
--- /dev/null
+++ b/utils/logger.py
@@ -0,0 +1,84 @@
+# Copyright (c) 2025 Salvador E. Tropea
+# Copyright (c) 2025 Instituto Nacional de Tecnologïa Industrial
+# License: GPL-3.0
+# Project: ComfyUI-AudioBatch
+import os
+import sys
+import logging
+from .misc import NODES_NAME, NODES_DEBUG_VAR
+
+no_colorama = False
+try:
+    from colorama import init as colorama_init, Fore, Back, Style
+except ImportError:
+    no_colorama = True
+# If colorama isn't installed use an ANSI basic replacement
+if no_colorama:
+    from .ansi import Fore, Back, Style  # noqa: F811
+else:
+    colorama_init()
+
+
+class CustomFormatter(logging.Formatter):
+    """Logging Formatter to add colors"""
+
+    def __init__(self):
+        super(logging.Formatter, self).__init__()
+        white = Fore.WHITE + Style.BRIGHT
+        yellow = Fore.YELLOW + Style.BRIGHT
+        red = Fore.RED + Style.BRIGHT
+        red_alarm = Fore.RED + Back.WHITE + Style.BRIGHT
+        cyan = Fore.CYAN + Style.BRIGHT
+        reset = Style.RESET_ALL
+        # format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s "
+        #          "(%(filename)s:%(lineno)d)"
+        format = f"[{NODES_NAME} %(levelname)s] %(message)s (%(name)s - %(filename)s:%(lineno)d)"
+        format_simple = f"[{NODES_NAME}] %(message)s"
+
+        self.FORMATS = {
+            logging.DEBUG: cyan + format + reset,
+            logging.INFO: white + format_simple + reset,
+            logging.WARNING: yellow + format + reset,
+            logging.ERROR: red + format + reset,
+            logging.CRITICAL: red_alarm + format + reset
+        }
+
+    def format(self, record):
+        log_fmt = self.FORMATS.get(record.levelno)
+        formatter = logging.Formatter(log_fmt)
+        return formatter.format(record)
+
+
+print("Crea el logger")
+# Create a new logger
+logger = logging.getLogger(NODES_NAME)
+logger.propagate = False
+
+# Add handler if we don't have one.
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(CustomFormatter())
+    logger.addHandler(handler)
+
+# ######################
+# Logger setup
+# ######################
+# 1. Determine the ComfyUI global log level (influenced by --verbose)
+main_logger = logger
+comfy_root_logger = logging.getLogger('comfy')
+effective_comfy_level = logging.getLogger().getEffectiveLevel()
+# 2. Check our custom environment variable for more verbosity
+try:
+    nodes_debug_env = int(os.environ.get(NODES_DEBUG_VAR, "0"))
+    print(nodes_debug_env)
+except ValueError:
+    nodes_debug_env = 0
+# 3. Set node's logger level
+if nodes_debug_env:
+    main_logger.setLevel(logging.DEBUG - (nodes_debug_env - 1))
+    final_level_str = f"DEBUG (due to {NODES_DEBUG_VAR}={nodes_debug_env})"
+else:
+    main_logger.setLevel(effective_comfy_level)
+    final_level_str = logging.getLevelName(effective_comfy_level) + " (matching ComfyUI global)"
+_initial_setup_logger = logging.getLogger(NODES_NAME + ".setup")  # A temporary logger for this message
+_initial_setup_logger.debug(f"{NODES_NAME} logger level set to: {final_level_str}")
diff --git a/utils/misc.py b/utils/misc.py
new file mode 100644
index 0000000..4945c26
--- /dev/null
+++ b/utils/misc.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2025 Salvador E. Tropea
+# Copyright (c) 2025 Instituto Nacional de Tecnologïa Industrial
+# License: GPL-3.0
+# Project: ComfyUI-AudioBatch
+
+NODES_NAME = "AudioBatch"
+NODES_DEBUG_VAR = NODES_NAME.upper() + "_NODES_DEBUG"