(简体中文|English)

FunASR hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model, researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun！

Highlights | News | Installation | Quick Start | Tutorial | Runtime | Model Zoo | Contact

• FunASR is a fundamental speech recognition toolkit that offers a variety of features, including speech recognition (ASR), Voice Activity Detection (VAD), Punctuation Restoration, Language Models, Speaker Verification, Speaker Diarization and multi-talker ASR. FunASR provides convenient scripts and tutorials, supporting inference and fine-tuning of pre-trained models.
• We have released a vast collection of academic and industrial pretrained models on the ModelScope and huggingface, which can be accessed through our Model Zoo. The representative Paraformer-large, a non-autoregressive end-to-end speech recognition model, has the advantages of high accuracy, high efficiency, and convenient deployment, supporting the rapid construction of speech recognition services. For more details on service deployment, please refer to the service deployment document.

• 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.
• 2024/03/05：Added support for the Whisper-large-v3 model, a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. It can be downloaded from themodelscope, and openai.
• 2024/03/05: Offline File Transcription Service 4.4, Offline File Transcription Service of English 1.5，Real-time Transcription Service 1.9 released，docker image supports ARM64 platform, update modelscope；(docs)
• 2024/01/30：funasr-1.0 has been released (docs)
• 2024/01/30：emotion recognition models are new supported. model link, modified from repo.
• 2024/01/25: Offline File Transcription Service 4.2, Offline File Transcription Service of English 1.3 released，optimized the VAD (Voice Activity Detection) data processing method, significantly reducing peak memory usage, memory leak optimization; Real-time Transcription Service 1.7 released，optimizatized the client-side；(docs)
• 2024/01/09: The Funasr SDK for Windows version 2.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin 4.1, The offline file transcription service (CPU) of English 1.2, The real-time transcription service (CPU) of Mandarin 1.6. For more details, please refer to the official documentation or release notes(FunASR-Runtime-Windows)
• 2024/01/03: File Transcription Service 4.0 released, Added support for 8k models, optimized timestamp mismatch issues and added sentence-level timestamps, improved the effectiveness of English word FST hotwords, supported automated configuration of thread parameters, and fixed known crash issues as well as memory leak problems, refer to (docs).
• 2024/01/03: Real-time Transcription Service 1.6 released，The 2pass-offline mode supports Ngram language model decoding and WFST hotwords, while also addressing known crash issues and memory leak problems, (docs)
• 2024/01/03: Fixed known crash issues as well as memory leak problems, (docs).
• 2023/12/04: The Funasr SDK for Windows version 1.0 has been released, featuring support for The offline file transcription service (CPU) of Mandarin, The offline file transcription service (CPU) of English, The real-time transcription service (CPU) of Mandarin. For more details, please refer to the official documentation or release notes(FunASR-Runtime-Windows)
• 2023/11/08: The offline file transcription service 3.0 (CPU) of Mandarin has been released, adding punctuation large model, Ngram language model, and wfst hot words. For detailed information, please refer to docs.
• 2023/10/17: The offline file transcription service (CPU) of English has been released. For more details, please refer to (docs).
• 2023/10/13: SlideSpeech: A large scale multi-modal audio-visual corpus with a significant amount of real-time synchronized slides.
• 2023/10/10: The ASR-SpeakersDiarization combined pipeline Paraformer-VAD-SPK is now released. Experience the model to get recognition results with speaker information.
• 2023/10/07: FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec.
• 2023/09/01: The offline file transcription service 2.0 (CPU) of Mandarin has been released, with added support for ffmpeg, timestamp, and hotword models. For more details, please refer to (docs).
• 2023/08/07: The real-time transcription service (CPU) of Mandarin has been released. For more details, please refer to (docs).
• 2023/07/17: BAT is released, which is a low-latency and low-memory-consumption RNN-T model. For more details, please refer to (BAT).
• 2023/06/26: ASRU2023 Multi-Channel Multi-Party Meeting Transcription Challenge 2.0 completed the competition and announced the results. For more details, please refer to (M2MeT2.0).

pip3 install -U funasr

Or install from source code

git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./

Install modelscope for the pretrained models (Optional)

pip3 install -U modelscope

FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the Model License Agreement. Below are some representative models, for more models please refer to the Model Zoo.

(Note: ⭐ represents the ModelScope model zoo, 🤗 represents the Huggingface model zoo, 🍀 represents the OpenAI model zoo)

Below is a quick start tutorial. Test audio files (Mandarin, English).

funasr   model=paraformer-zh   vad_model="fsmn-vad"   punc_model="ct-punc"   input=asr_example_zh.wav

Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: wav_id wav_pat

from funasr import AutoModel
# paraformer-zh is a multi-functional asr model
# use vad, punc, spk or not as you need
model = AutoModel(model="paraformer-zh",  vad_model="fsmn-vad",  punc_model="ct-punc", 
                  # spk_model="cam  ", 
                  )
res = model.generate(input=f"{model.model_path}/example/asr_example.wav", 
                     batch_size_s=300, 
                     hotword='魔搭')
print(res)

Note: hub: represents the model repository, ms stands for selecting ModelScope download, hf stands for selecting Huggingface download.

from funasr import AutoModel

chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention

model = AutoModel(model="paraformer-zh-streaming")

import soundfile
import os

wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms

cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride 1)
for i in range(total_chunk_num):
    speech_chunk = speech[i*chunk_stride:(i 1)*chunk_stride]
    is_final = i == total_chunk_num - 1
    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
    print(res)

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.

from funasr import AutoModel

model = AutoModel(model="fsmn-vad")
wav_file = f"{model.model_path}/example/vad_example.wav"
res = model.generate(input=wav_file)
print(res)

Note: The output format of the VAD model is: [[beg1, end1], [beg2, end2], ..., [begN, endN]], where begN/endN indicates the starting/ending point of the N-th valid audio segment, measured in milliseconds.

from funasr import AutoModel

chunk_size = 200 # ms
model = AutoModel(model="fsmn-vad")

import soundfile

wav_file = f"{model.model_path}/example/vad_example.wav"
speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)

cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride 1)
for i in range(total_chunk_num):
    speech_chunk = speech[i*chunk_stride:(i 1)*chunk_stride]
    is_final = i == total_chunk_num - 1
    res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
    if len(res[0]["value"]):
        print(res)

Note: The output format for the streaming VAD model can be one of four scenarios:

• [[beg1, end1], [beg2, end2], .., [begN, endN]]：The same as the offline VAD output result mentioned above.
• [[beg, -1]]：Indicates that only a starting point has been detected.
• [[-1, end]]：Indicates that only an ending point has been detected.
• []：Indicates that neither a starting point nor an ending point has been detected.

The output is measured in milliseconds and represents the absolute time from the starting point.

from funasr import AutoModel

model = AutoModel(model="ct-punc")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)

from funasr import AutoModel

model = AutoModel(model="fa-zh")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)

More usages ref to docs, more examples ref to demo

funasr-export   model=paraformer   quantize=false   device=cpu

from funasr import AutoModel

model = AutoModel(model="paraformer", device="cpu")

res = model.export(quantize=False)

# pip3 install -U funasr-onnx
from funasr_onnx import Paraformer
model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model = Paraformer(model_dir, batch_size=1, quantize=True)

wav_path = ['~/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']

result = model(wav_path)
print(result)

More examples ref to demo

FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment:

• File transcription service, Mandarin, CPU version, done
• The real-time transcription service, Mandarin (CPU), done
• File transcription service, English, CPU version, done
• File transcription service, Mandarin, GPU version, in progress
• and more.

For more detailed information, please refer to the service deployment documentation.

If you encounter problems in use, you can directly raise Issues on the github page.

You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion.

DingTalk group	WeChat group

The contributors can be found in contributors list

This project is licensed under The MIT License. FunASR also contains various third-party components and some code modified from other repos under other open source licenses. The use of pretraining model is subject to model license

@inproceedings{gao2023funasr,
  author={Zhifu Gao and Zerui Li and Jiaming Wang and Haoneng Luo and Xian Shi and Mengzhe Chen and Yabin Li and Lingyun Zuo and Zhihao Du and Zhangyu Xiao and Shiliang Zhang},
  title={FunASR: A Fundamental End-to-End Speech Recognition Toolkit},
  year={2023},
  booktitle={INTERSPEECH},
}
@inproceedings{An2023bat,
  author={Keyu An and Xian Shi and Shiliang Zhang},
  title={BAT: Boundary aware transducer for memory-efficient and low-latency ASR},
  year={2023},
  booktitle={INTERSPEECH},
}
@inproceedings{gao22b_interspeech,
  author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
  title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={2063--2067},
  doi={10.21437/Interspeech.2022-9996}
}
@inproceedings{shi2023seaco,
  author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
  title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
  year={2023},
  booktitle={ICASSP2024}
}