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| | | ([简体中文](./README_zh.md)|English) |
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| | | # FunASR: A Fundamental End-to-End Speech Recognition Toolkit |
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| | | [//]: # (# FunASR: A Fundamental End-to-End Speech Recognition Toolkit) |
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| | | [](https://github.com/Akshay090/svg-banners) |
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| | | [](https://pypi.org/project/funasr/) |
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| | | <a name="whats-new"></a> |
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| | | ## What's new: |
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| | | - 2024/06/27: Offline File Transcription Service GPU 1.0 released, supporting dynamic batch processing and multi-threading concurrency. In the long audio test set, the single-thread RTF is 0.0076, and multi-threads' speedup is 1200+ (compared to 330+ on CPU); ref to ([docs](runtime/readme.md)) |
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| | | - 2024/05/15：emotion recognition models are new supported. [emotion2vec+large](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary)，[emotion2vec+base](https://modelscope.cn/models/iic/emotion2vec_plus_base/summary)，[emotion2vec+seed](https://modelscope.cn/models/iic/emotion2vec_plus_seed/summary). currently supports the following categories: 0: angry 1: happy 2: neutral 3: sad 4: unknown. |
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| | | - 2024/05/15: Offline File Transcription Service 4.5, Offline File Transcription Service of English 1.6，Real-time Transcription Service 1.10 released，adapting to FunASR 1.0 model structure；([docs](runtime/readme.md)) |
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| | | - 2024/03/05：Added the Qwen-Audio and Qwen-Audio-Chat large-scale audio-text multimodal models, which have topped multiple audio domain leaderboards. These models support speech dialogue, [usage](examples/industrial_data_pretraining/qwen_audio). |
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| | | <a name="Installation"></a> |
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| | | ## Installation |
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| | | - Requirements |
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| | | ```text |
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| | | python>=3.8 |
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| | | torch>=1.13 |
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| | | torchaudio |
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| | | ``` |
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| | | |
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| | | - Install for pypi |
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| | | ```shell |
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| | | pip3 install -U funasr |
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| | | ``` |
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| | | Or install from source code |
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| | | - Or install from source code |
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| | | ``` sh |
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| | | git clone https://github.com/alibaba/FunASR.git && cd FunASR |
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| | | pip3 install -e ./ |
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| | | ``` |
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| | | Install modelscope for the pretrained models (Optional) |
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| | | - Install modelscope or huggingface_hub for the pretrained models (Optional) |
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| | | ```shell |
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| | | pip3 install -U modelscope |
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| | | pip3 install -U modelscope huggingface_hub |
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| | | ``` |
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| | | |
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| | | ## Model Zoo |
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| | | | <nobr>paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗](https://huggingface.co/funasr/paraformer-zh-streaming) )</nobr> | speech recognition, streaming | 60000 hours, Mandarin | 220M | |
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| | | | paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗](https://huggingface.co/funasr/paraformer-en) ) | speech recognition, without timestamps, non-streaming | 50000 hours, English | 220M | |
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| | | | conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗](https://huggingface.co/funasr/conformer-en) ) | speech recognition, non-streaming | 50000 hours, English | 220M | |
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| | | | ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗](https://huggingface.co/funasr/ct-punc) ) | punctuation restoration | 100M, Mandarin and English | 1.1G | |
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| | | | ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗](https://huggingface.co/funasr/ct-punc) ) | punctuation restoration | 100M, Mandarin and English | 290M | |
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| | | | fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗](https://huggingface.co/funasr/fsmn-vad) ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M | |
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| | | | fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗](https://huggingface.co/funasr/fa-zh) ) | timestamp prediction | 5000 hours, Mandarin | 38M | |
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| | | | cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗](https://huggingface.co/funasr/campplus) ) | speaker verification/diarization | 5000 hours | 7.2M | |
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| | | | Whisper-large-v2 <br> ([⭐](https://www.modelscope.cn/models/iic/speech_whisper-large_asr_multilingual/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M | |
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| | | | Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M | |
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| | | | Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | audio-text multimodal models (pretraining) | multilingual | 8B | |
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| | | | Qwen-Audio-Chat <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo_chat.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio-Chat) ) | audio-text multimodal models (chat) | multilingual | 8B | |
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| | | | emotion2vec+large <br> ([⭐](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary) [🤗](https://huggingface.co/emotion2vec/emotion2vec_plus_large) ) | speech emotion recongintion | 40000 hours | 300M | |
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| | | | Whisper-large-v2 <br> ([⭐](https://www.modelscope.cn/models/iic/speech_whisper-large_asr_multilingual/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M | |
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| | | | Whisper-large-v3 <br> ([⭐](https://www.modelscope.cn/models/iic/Whisper-large-v3/summary) [🍀](https://github.com/openai/whisper) ) | speech recognition, with timestamps, non-streaming | multilingual | 1550 M | |
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| | | | Qwen-Audio <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio) ) | audio-text multimodal models (pretraining) | multilingual | 8B | |
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| | | | Qwen-Audio-Chat <br> ([⭐](examples/industrial_data_pretraining/qwen_audio/demo_chat.py) [🤗](https://huggingface.co/Qwen/Qwen-Audio-Chat) ) | audio-text multimodal models (chat) | multilingual | 8B | |
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| | | | emotion2vec+large <br> ([⭐](https://modelscope.cn/models/iic/emotion2vec_plus_large/summary) [🤗](https://huggingface.co/emotion2vec/emotion2vec_plus_large) ) | speech emotion recongintion | 40000 hours | 300M | |
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| | | ``` |
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| | | Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word. |
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| | | <details><summary>More Examples</summary> |
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| | | ### Voice Activity Detection (Non-Streaming) |
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| | | ```python |
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| | | from funasr import AutoModel |
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| | | res = model.generate(input=(wav_file, text_file), data_type=("sound", "text")) |
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| | | print(res) |
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| | | ``` |
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| | | ### Speech Emotion Recognition |
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| | | ```python |
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| | | from funasr import AutoModel |
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| | | model = AutoModel(model="emotion2vec_plus_large") |
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| | | wav_file = f"{model.model_path}/example/test.wav" |
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| | | res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False) |
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| | | print(res) |
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| | | ``` |
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| | | More usages ref to [docs](docs/tutorial/README_zh.md), |
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| | | more examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining) |
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| | | </details> |
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| | | |
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| | | ## Export ONNX |
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