(简体中文|English|日本語)
SenseVoice is a speech foundation model with multiple speech understanding capabilities, including automatic speech recognition (ASR), spoken language identification (LID), speech emotion recognition (SER), and audio event detection (AED).
Model Zoo: modelscope, huggingface
Online Demo: modelscope demo, huggingface space
SenseVoice focuses on high-accuracy multilingual speech recognition, speech emotion recognition, and audio event detection.
We compared the performance of multilingual speech recognition between SenseVoice and Whisper on open-source benchmark datasets, including AISHELL-1, AISHELL-2, Wenetspeech, LibriSpeech, and Common Voice. In terms of Chinese and Cantonese recognition, the SenseVoice-Small model has advantages.
Due to the current lack of widely-used benchmarks and methods for speech emotion recognition, we conducted evaluations across various metrics on multiple test sets and performed a comprehensive comparison with numerous results from recent benchmarks. The selected test sets encompass data in both Chinese and English, and include multiple styles such as performances, films, and natural conversations. Without finetuning on the target data, SenseVoice was able to achieve and exceed the performance of the current best speech emotion recognition models.
Furthermore, we compared multiple open-source speech emotion recognition models on the test sets, and the results indicate that the SenseVoice-Large model achieved the best performance on nearly all datasets, while the SenseVoice-Small model also surpassed other open-source models on the majority of the datasets.
Although trained exclusively on speech data, SenseVoice can still function as a standalone event detection model. We compared its performance on the environmental sound classification ESC-50 dataset against the widely used industry models BEATS and PANN. The SenseVoice model achieved commendable results on these tasks. However, due to limitations in training data and methodology, its event classification performance has some gaps compared to specialized AED models.
The SenseVoice-Small model deploys a non-autoregressive end-to-end architecture, resulting in extremely low inference latency. With a similar number of parameters to the Whisper-Small model, it infers more than 5 times faster than Whisper-Small and 15 times faster than Whisper-Large.
pip install -r requirements.txtSupports input of audio in any format and of any duration.
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = AutoModel(
model=model_dir,
trust_remote_code=True,
remote_code="./model.py",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device="cuda:0",
)
# en
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
use_itn=True,
batch_size_s=60,
merge_vad=True, #
merge_length_s=15,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)model_dir: The name of the model, or the path to the model on the local disk.trust_remote_code:
True, it means that the model's code implementation is loaded from remote_code, which specifies the exact location of the model code (for example, model.py in the current directory). It supports absolute paths, relative paths, and network URLs.False, it indicates that the model's code implementation is the integrated version within FunASR. At this time, modifications made to model.py in the current directory will not be effective, as the version loaded is the internal one from FunASR. For the model code, click here to view.vad_model: This indicates the activation of VAD (Voice Activity Detection). The purpose of VAD is to split long audio into shorter clips. In this case, the inference time includes both VAD and SenseVoice total consumption, and represents the end-to-end latency. If you wish to test the SenseVoice model's inference time separately, the VAD model can be disabled.vad_kwargs: Specifies the configurations for the VAD model. max_single_segment_time: denotes the maximum duration for audio segmentation by the vad_model, with the unit being milliseconds (ms).use_itn: Whether the output result includes punctuation and inverse text normalization.batch_size_s: Indicates the use of dynamic batching, where the total duration of audio in the batch is measured in seconds (s).merge_vad: Whether to merge short audio fragments segmented by the VAD model, with the merged length being merge_length_s, in seconds (s).ban_emo_unk: Whether to ban the output of the emo_unk token.If all inputs are short audios (<30s), and batch inference is needed to speed up inference efficiency, the VAD model can be removed, and batch_size can be set accordingly.
model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="zh", # "zh", "en", "yue", "ja", "ko", "nospeech"
use_itn=False,
batch_size=64,
)For more usage, please refer to docs
Supports input of audio in any format, with an input duration limit of 30 seconds or less.
from model import SenseVoiceSmall
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
m.eval()
res = m.inference(
data_in=f"{kwargs['model_path']}/example/en.mp3",
language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
use_itn=False,
ban_emo_unk=False,
**kwargs,
)
text = rich_transcription_postprocess(res[0][0]["text"])
print(text)# pip3 install -U funasr funasr-onnx
from pathlib import Path
from funasr_onnx import SenseVoiceSmall
from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = SenseVoiceSmall(model_dir, batch_size=10, quantize=True)
# inference
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]
res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess(i) for i in res])Note: ONNX model is exported to the original model directory.
from pathlib import Path
from funasr_torch import SenseVoiceSmall
from funasr_torch.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = SenseVoiceSmall(model_dir, batch_size=10, device="cuda:0")
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]
res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess(i) for i in res])Note: Libtorch model is exported to the original model directory.
export SENSEVOICE_DEVICE=cuda:0
fastapi run --port 50000git clone https://github.com/alibaba/FunASR.git && cd FunASR
pip3 install -e ./Data examples
{"key": "YOU0000008470_S0000238_punc_itn", "text_language": "<|en|>", "emo_target": "<|NEUTRAL|>", "event_target": "<|Speech|>", "with_or_wo_itn": "<|withitn|>", "target": "Including legal due diligence, subscription agreement, negotiation.", "source": "/cpfs01/shared/Group-speech/beinian.lzr/data/industrial_data/english_all/audio/YOU0000008470_S0000238.wav", "target_len": 7, "source_len": 140}
{"key": "AUD0000001556_S0007580", "text_language": "<|en|>", "emo_target": "<|NEUTRAL|>", "event_target": "<|Speech|>", "with_or_wo_itn": "<|woitn|>", "target": "there is a tendency to identify the self or take interest in what one has got used to", "source": "/cpfs01/shared/Group-speech/beinian.lzr/data/industrial_data/english_all/audio/AUD0000001556_S0007580.wav", "target_len": 18, "source_len": 360}
Full ref to data/train_example.jsonl
Description：
key: audio file unique IDsource：path to the audio filesource_len：number of fbank frames of the audio filetarget：transcriptiontarget_len：length of targettext_language：language id of the audio fileemo_target：emotion label of the audio fileevent_target：event label of the audio filewith_or_wo_itn：whether includes punctuation and inverse text normalizationtrain_text.txt
BAC009S0764W0121 甚至出现交易几乎停滞的情况
BAC009S0916W0489 湖北一公司以员工名义贷款数十员工负债千万
asr_example_cn_en 所有只要处理 data 不管你是做 machine learning 做 deep learning 做 data analytics 做 data science 也好 scientist 也好通通都要都做的基本功啊那 again 先先对有一些>也许对
ID0012W0014 he tried to think how it could betrain_wav.scp
BAC009S0764W0121 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav
BAC009S0916W0489 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0916W0489.wav
asr_example_cn_en https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_cn_en.wav
ID0012W0014 https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wavtrain_text_language.txt
The language ids include <|zh|>、<|en|>、<|yue|>、<|ja|> and <|ko|>.
BAC009S0764W0121 <|zh|>
BAC009S0916W0489 <|zh|>
asr_example_cn_en <|zh|>
ID0012W0014 <|en|>train_emo.txt
The emotion labels include<|HAPPY|>、<|SAD|>、<|ANGRY|>、<|NEUTRAL|>、<|FEARFUL|>、<|DISGUSTED|> and <|SURPRISED|>.
BAC009S0764W0121 <|NEUTRAL|>
BAC009S0916W0489 <|NEUTRAL|>
asr_example_cn_en <|NEUTRAL|>
ID0012W0014 <|NEUTRAL|>train_event.txt
The event labels include<|BGM|>、<|Speech|>、<|Applause|>、<|Laughter|>、<|Cry|>、<|Sneeze|>、<|Breath|> and <|Cough|>.
BAC009S0764W0121 <|Speech|>
BAC009S0916W0489 <|Speech|>
asr_example_cn_en <|Speech|>
ID0012W0014 <|Speech|>Command
# generate train.jsonl and val.jsonl from wav.scp, text.txt, text_language.txt, emo_target.txt, event_target.txt
sensevoice2jsonl
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt", "../../../data/list/train_text_language.txt", "../../../data/list/train_emo.txt", "../../../data/list/train_event.txt"]'
++data_type_list='["source", "target", "text_language", "emo_target", "event_target"]'
++jsonl_file_out="../../../data/list/train.jsonl"If there is no train_text_language.txt, train_emo_target.txt and train_event_target.txt, the language, emotion and event label will be predicted automatically by using the SenseVoice model.
# generate train.jsonl and val.jsonl from wav.scp and text.txt
sensevoice2jsonl
++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]'
++data_type_list='["source", "target"]'
++jsonl_file_out="../../../data/list/train.jsonl"
++model_dir='iic/SenseVoiceSmall'Ensure to modify the train_tool in finetune.sh to the absolute path of funasr/bin/train_ds.py from the FunASR installation directory you have set up earlier.
bash finetune.shpython webui.py
If you encounter problems in use, you can directly raise Issues on the github page.
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