FunASR: A Fundamental End-to-End Speech Recognition Toolkit
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 | Runtime | Model Zoo | Contact
Highlights
- 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.
Installation
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
Model Zoo
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 Huggingface model zoo link, ⭐ represents the ModelScope model zoo link)
Model Name | Task Details | Training Data | Parameters |
---|---|---|---|
paraformer-zh (⭐ 🤗 ) |
speech recognition, with timestamps, non-streaming | 60000 hours, Mandarin | 220M |
( ⭐ 🤗 ) |
speech recognition, streaming | 60000 hours, Mandarin | 220M |
paraformer-en ( ⭐ 🤗 ) |
speech recognition, with timestamps, non-streaming | 50000 hours, English | 220M |
conformer-en ( ⭐ 🤗 ) |
speech recognition, non-streaming | 50000 hours, English | 220M |
ct-punc ( ⭐ 🤗 ) |
punctuation restoration | 100M, Mandarin and English | 1.1G |
fsmn-vad ( ⭐ 🤗 ) |
voice activity detection | 5000 hours, Mandarin and English | 0.4M |
fa-zh ( ⭐ 🤗 ) |
timestamp prediction | 5000 hours, Mandarin | 38M |
cam++ ( ⭐ 🤗 ) |
speaker verification/diarization | 5000 hours | 7.2M |
Quick Start
Below is a quick start tutorial. Test audio files (Mandarin, English).
Command-line usage
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
Speech Recognition (Non-streaming)
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", model_revision="v2.0.4",
vad_model="fsmn-vad", vad_model_revision="v2.0.4",
punc_model="ct-punc-c", punc_model_revision="v2.0.4",
# spk_model="cam++", spk_model_revision="v2.0.2",
)
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size_s=300,
hotword='魔搭')
print(res)
Note: model_hub
: represents the model repository, ms
stands for selecting ModelScope download, hf
stands for selecting Huggingface download.
Speech Recognition (Streaming)
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", model_revision="v2.0.4")
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.
Voice Activity Detection (Non-Streaming)
from funasr import AutoModel
model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
wav_file = f"{model.model_path}/example/asr_example.wav"
res = model.generate(input=wav_file)
print(res)
Voice Activity Detection (Streaming)
from funasr import AutoModel
chunk_size = 200 # ms
model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
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)
Punctuation Restoration
from funasr import AutoModel
model = AutoModel(model="ct-punc", model_revision="v2.0.4")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)
Timestamp Prediction
from funasr import AutoModel
model = AutoModel(model="fa-zh", model_revision="v2.0.4")
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 examples ref to docs
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