--- title: FireRedVAD canonical_url: "https://www.modelscope.cn/models/xukaituo/FireRedVAD" md_url: "https://www.modelscope.cn/models/xukaituo/FireRedVAD.md" repository: xukaituo/FireRedVAD last_updated: 2026-03-03 license: "Apache License 2.0" pipeline_tag: voice-activity-detection tasks: - voice-activity-detection library_name: - pytorch frameworks: - PyTorch downloads: 42837 stars: 0 --- # FireRedVAD > FireRedVAD - xukaituo 在 ModelScope 开源的模型。FireRedVAD: A SOTA Industrial-Grade Voice Activity Detection & Audio Event Detection xukaituo/FireRedVAD 是 ModelScope 魔搭社区上的voice-activity-detection模型，采用 Apache License 2.0 许可。 - **Repository**: xukaituo/FireRedVAD - **License**: Apache License 2.0 - **Tasks**: voice-activity-detection - **Downloads**: 42837 - **Stars**: 0 - **Last updated**: 2026-03-03 Source: https://www.modelscope.cn/models/xukaituo/FireRedVAD ---

FireRedVAD: A SOTA Industrial-Grade
Voice Activity Detection & Audio Event Detection

[[Code]](https://github.com/FireRedTeam/FireRedVAD) [[HuggingFace]](https://huggingface.co/FireRedTeam/FireRedVAD) [[ModelScope]](https://www.modelscope.cn/models/xukaituo/FireRedVAD) FireRedVAD is a state-of-the-art (SOTA) industrial-grade Voice Activity Detection (VAD) and Audio Event Detection (AED) solution. FireRedVAD supports non-streaming/streaming VAD and non-streaming AED. It supports speech/singing/music detection in 100+ languages. Non-streaming VAD achieves 97.57% F1 on FLEURS-VAD-102, outperforming Silero-VAD, TEN-VAD, FunASR-VAD and WebRTC-VAD. ## 🔥 News - [2026.03.03] We release FireRedVAD as a standalone repository, along with model weights and inference code. - [2026.02.12] We release [FireRedASR2S](https://github.com/FireRedTeam/FireRedASR2S) (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code. ## Method DFSMN-based non-streaming/streaming Voice Activity Detection and Audio Event Detection. ## Evaluation ### FireRedVAD We evaluate FireRedVAD on FLEURS-VAD-102, a multilingual VAD benchmark covering 102 languages. FireRedVAD achieves SOTA performance, outperforming Silero-VAD, TEN-VAD, FunASR-VAD, and WebRTC-VAD. |Metric\Model|FireRedVAD|[Silero-VAD](https://github.com/snakers4/silero-vad)|[TEN-VAD](https://github.com/TEN-framework/ten-vad)|[FunASR-VAD](https://modelscope.cn/models/iic/speech_fsmn_vad_zh-cn-16k-common-pytorch)|[WebRTC-VAD](https://github.com/wiseman/py-webrtcvad)| |:-------:|:-----:|:------:|:------:|:------:|:------:| |AUC-ROC↑ |**99.60**|97.99|97.81|- |- | |F1 score↑ |**97.57**|95.95|95.19|90.91|52.30| |False Alarm Rate↓ |**2.69** |9.41 |15.47|44.03|2.83 | |Miss Rate↓|3.62 |3.95 |2.95 |0.42 |64.15| *FLEURS-VAD-102: We randomly selected ~100 audio files per language from [FLEURS test set](https://huggingface.co/datasets/google/fleurs), resulting in 9,443 audio files with manually annotated binary VAD labels (speech=1, silence=0). This VAD testset will be open sourced (coming soon). Note: FunASR-VAD achieves low Miss Rate but at the cost of high False Alarm Rate (44.03%), indicating over-prediction of speech segments. ## Quick Start ### Setup 1. Create a clean Python environment: ```bash $ conda create --name fireredvad python=3.10 $ conda activate fireredvad $ git clone https://github.com/FireRedTeam/FireRedVAD.git $ cd FireRedVAD # or fireredvad ``` 2. Install dependencies and set up PATH and PYTHONPATH: ```bash $ pip install -r requirements.txt $ export PATH=$PWD/fireredvad/bin/:$PATH $ export PYTHONPATH=$PWD/:$PYTHONPATH ``` 3. Download models: ```bash # Download via ModelScope (recommended for users in China) pip install -U modelscope modelscope download --model xukaituo/FireRedVAD --local_dir ./pretrained_models/FireRedVAD # Download via Hugging Face pip install -U "huggingface_hub[cli]" huggingface-cli download FireRedTeam/FireRedVAD --local-dir ./pretrained_models/FireRedVAD ``` 4. Convert your audio to **16kHz 16-bit mono PCM** format if needed: ```bash $ ffmpeg -i -ar 16000 -ac 1 -acodec pcm_s16le -f wav ``` ### Script Usage ```bash $ cd examples $ bash inference_vad.sh $ bash inference_stream_vad.sh $ bash inference_aed.sh ``` ### Command-line Usage Set up `PATH` and `PYTHONPATH` first: `export PATH=$PWD/fireredvad/bin/:$PATH; export PYTHONPATH=$PWD/:$PYTHONPATH` ```bash $ vad.py --help $ vad.py --use_gpu 0 --model_dir pretrained_models/FireRedVAD/VAD --smooth_window_size 5 --speech_threshold 0.4 \ --min_speech_frame 20 --max_speech_frame 3000 --min_silence_frame 10 --merge_silence_frame 0 \ --extend_speech_frame 0 --chunk_max_frame 30000 --write_textgrid 1 \ --wav_path assets/hello_zh.wav --output out/vad.txt --save_segment_dir out/vad $ stream_vad.py --help $ stream_vad.py --use_gpu 0 --model_dir pretrained_models/FireRedVAD/Stream-VAD --smooth_window_size 5 --speech_threshold 0.3 \ --pad_start_frame 5 --min_speech_frame 8 --max_speech_frame 2000 --min_silence_frame 20 \ --chunk_max_frame 30000 --write_textgrid 1 \ --wav_path assets/hello_en.wav --output out/vad.txt --save_segment_dir out/stream_vad $ aed.py --help $ aed.py --use_gpu 0 --model_dir pretrained_models/FireRedVAD/AED --smooth_window_size 5 --speech_threshold 0.4 \ --singing_threshold 0.5 --music_threshold 0.5 --min_event_frame 20 --max_event_frame 3000 \ --min_silence_frame 10 --merge_silence_frame 0 --extend_speech_frame 0 --chunk_max_frame 30000 --write_textgrid 1 \ --wav_path assets/event.wav --output out/aed.txt --save_segment_dir out/aed ``` ### Python API Usage Set up `PYTHONPATH` first: `export PYTHONPATH=$PWD/:$PYTHONPATH` #### Non-streaming VAD ```python from fireredvad import FireRedVad, FireRedVadConfig vad_config = FireRedVadConfig( use_gpu=False, smooth_window_size=5, speech_threshold=0.4, min_speech_frame=20, max_speech_frame=2000, min_silence_frame=20, merge_silence_frame=0, extend_speech_frame=0, chunk_max_frame=30000) vad = FireRedVad.from_pretrained("pretrained_models/FireRedVAD/VAD", vad_config) result, probs = vad.detect("assets/hello_zh.wav") print(result) # {'dur': 2.32, 'timestamps': [(0.44, 1.82)], 'wav_path': 'assets/hello_zh.wav'} ``` #### Streaming VAD ```python from fireredvad import FireRedStreamVad, FireRedStreamVadConfig vad_config=FireRedStreamVadConfig( use_gpu=False, smooth_window_size=5, speech_threshold=0.4, pad_start_frame=5, min_speech_frame=8, max_speech_frame=2000, min_silence_frame=20, chunk_max_frame=30000) stream_vad = FireRedStreamVad.from_pretrained("pretrained_models/FireRedVAD/Stream-VAD", vad_config) frame_results, result = stream_vad.detect_full("assets/hello_en.wav") print(result) # {'dur': 2.24, 'timestamps': [(0.28, 1.83)], 'wav_path': 'assets/hello_en.wav'} ``` #### Non-streaming AED ```python from fireredvad import FireRedAed, FireRedAedConfig aed_config=FireRedAedConfig( use_gpu=False, smooth_window_size=5, speech_threshold=0.4, singing_threshold=0.5, music_threshold=0.5, min_event_frame=20, max_event_frame=2000, min_silence_frame=20, merge_silence_frame=0, extend_speech_frame=0, chunk_max_frame=30000) aed = FireRedAed.from_pretrained("pretrained_models/FireRedVAD/AED", aed_config) result, probs = aed.detect("assets/event.wav") print(result) # {'dur': 22.016, 'event2timestamps': {'speech': [(0.4, 3.56), (3.66, 9.08), (9.27, 9.77), (10.78, 21.76)], 'singing': [(1.79, 19.96), (19.97, 22.016)], 'music': [(0.09, 12.32), (12.33, 22.016)]}, 'event2ratio': {'speech': 0.848, 'singing': 0.905, 'music': 0.991}, 'wav_path': 'assets/event.wav'} ``` ## FAQ **Q: What audio format is supported?** 16kHz 16-bit mono PCM wav. Use ffmpeg to convert other formats: `ffmpeg -i -ar 16000 -ac 1 -acodec pcm_s16le -f wav `