ComfyUI Qwen3 ASR

A high-performance ComfyUI integration for the Qwen3-ASR model family. This extension provides state-of-the-art speech-to-text transcription, language identification, and precise word-level timestamps using the novel Qwen3 Forced Aligner.

Features

• High Accuracy: Supports Qwen3-ASR 0.6B and 1.7B models.
• Multilingual: Supports 52 languages and dialects with automatic language detection.
• Word-Level Timestamps: Optional integration with Qwen3-ForcedAligner-0.6B.
• Flexible Precision: Support for bf16, fp16, and fp32 to balance VRAM and speed.
• Automatic Resampling: Internally handles audio resampling to 16kHz for optimal model performance.
• FlashAttention 2: Integrated support for FlashAttention 2 to significantly reduce VRAM usage and accelerate inference.

Preview

Installation

Manual Installation

1. Navigate to your ComfyUI custom_nodes directory:

   cd ComfyUI/custom_nodes
   

2. Clone this repository:

   git clone https://github.com/kaushiknishchay/ComfyUI-Qwen3-ASR
   

3. Install the dependencies using your ComfyUI Python executable:

   # For portable versions, use the full path to your python.exe
   python.exe -m pip install -r ComfyUI-Qwen3-ASR/requirements.txt
   

4. (Recommended) Install FlashAttention 2 for maximum performance:

   # For FlashAttention 2 (requires compatible NVIDIA GPU)
   python.exe -m pip install -U flash-attn --no-build-isolation
   

   OR

Install via ComfyUI Manager

• Search ComfyUI-Qwen3-ASR by Kaushik

Model Setup

Models must be placed in the models/diffusion_models/Qwen3-ASR/ directory. Each model should be in its own subfolder containing the full weights and configuration.

Recommended Directory Structure:

ComfyUI/models/diffusion_models/Qwen3-ASR/
├── Qwen3-ASR-1.7B/
│   ├── config.json
│   ├── model.safetensors
│   └── ...
├── Qwen3-ASR-0.6B/
│   └── ...
└── Qwen3-ForcedAligner-0.6B/
    └── ...

Downloading Models

You can use the huggingface-cli to download the models directly into the correct folders:

huggingface-cli download Qwen/Qwen3-ASR-1.7B --local-dir models/diffusion_models/Qwen3-ASR/Qwen3-ASR-1.7B
huggingface-cli download Qwen/Qwen3-ForcedAligner-0.6B --local-dir models/diffusion_models/Qwen3-ASR/Qwen3-ForcedAligner-0.6B

Node Usage

1. Qwen3 ASR Transcriber

The main node for generating transcriptions.

Inputs:

• audio: The audio input from a "Load Audio" node.
• model_name: Select the ASR model folder from the dropdown.
• language: Set to auto for automatic detection, or specify a language (e.g., English, Chinese).
• device: cuda (recommended) or cpu.
• precision: bf16 (recommended for RTX 30/40 series), fp16, or fp32.
• max_new_tokens: Maximum length of the generated text.
• flash_attention_2: Enable Flash Attention 2 for faster inference and lower VRAM usage.
• chunk_size: Process audio in chunks of this many seconds (default: 30). Set to 0 to disable.
• overlap: Overlap between chunks in seconds to maintain context (default: 2).
• forced_aligner (Optional): Connect the output of a Qwen3 Forced Aligner Config node here to enable timestamps.

Outputs:

• text: The raw transcription text.
• timestamps: A formatted string containing word-level timestamps (requires the Forced Aligner).

2. Qwen3 Forced Aligner Config

A configuration node that prepares the alignment model.

Inputs:

• model_name: Select the Qwen3-ForcedAligner-0.6B folder.
• device: Should match the Transcriber node.
• precision: Should match the Transcriber node.
• flash_attention_2: Enable Flash Attention 2 for the aligner model to improve performance and reduce memory footprint.

Outputs:

• aligner_config: Connect this to the forced_aligner input on the Transcriber node.

Troubleshooting

• Python 3.13 Issues: If you encounter an UnboundLocalError related to lazy_loader, ensure you have updated the package:

  python.exe -m pip install -U lazy-loader
  

• VRAM Usage: The 1.7B model requires approximately 4-6GB of VRAM in bf16 mode. If you run out of memory, try the 0.6B model or use cpu mode.

License

This project is licensed under the MIT License. The Qwen3 models are subject to the Qwen License Agreement.