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# Pretrained Models on Huggingface
## Model License
- Apache License 2.0
## Model Zoo
Here we provided several pretrained models on different datasets. The details of models and datasets can be found on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition).
### Speech Recognition Models
#### Paraformer Models
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:-----------------------------------------------------------------------:|:--------:|:----------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [Paraformer-large](https://huggingface.co/funasr/paraformer-large) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Offline | Duration of input wav <= 20s |
[//]: # (| [Paraformer-large-long]&#40;https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary&#41; | CN & EN | Alibaba Speech Data &#40;60000hours&#41; | 8404 | 220M | Offline | Which ould deal with arbitrary length input wav |)
[//]: # (| [paraformer-large-contextual]&#40;https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary&#41; | CN & EN | Alibaba Speech Data &#40;60000hours&#41; | 8404 | 220M | Offline | Which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords. |)
[//]: # (| [Paraformer]&#40;https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary&#41; | CN & EN | Alibaba Speech Data &#40;50000hours&#41; | 8358 | 68M | Offline | Duration of input wav <= 20s |)
[//]: # (| [Paraformer-online]&#40;https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary&#41; | CN & EN | Alibaba Speech Data &#40;50000hours&#41; | 8404 | 68M | Online | Which could deal with streaming input |)
[//]: # (| [Paraformer-tiny]&#40;https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary&#41; | CN | Alibaba Speech Data &#40;200hours&#41; | 544 | 5.2M | Offline | Lightweight Paraformer model which supports Mandarin command words recognition |)
[//]: # (| [Paraformer-aishell]&#40;https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-aishell1-pytorch/summary&#41; | CN | AISHELL &#40;178hours&#41; | 4234 | 43M | Offline | |)
[//]: # (| [ParaformerBert-aishell]&#40;https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary&#41; | CN | AISHELL &#40;178hours&#41; | 4234 | 43M | Offline | |)
[//]: # (| [Paraformer-aishell2]&#40;https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary&#41; | CN | AISHELL-2 &#40;1000hours&#41; | 5212 | 64M | Offline | |)
[//]: # (| [ParaformerBert-aishell2]&#40;https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary&#41; | CN | AISHELL-2 &#40;1000hours&#41; | 5212 | 64M | Offline | |)
#### UniASR Models
[//]: # (| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |)
[//]: # (|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|)
[//]: # (| [UniASR]&#40;https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary&#41; | CN & EN | Alibaba Speech Data &#40;60000hours&#41; | 8358 | 100M | Online | UniASR streaming offline unifying models |)
[//]: # (| [UniASR-large]&#40;https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary&#41; | CN & EN | Alibaba Speech Data &#40;60000hours&#41; | 8358 | 220M | Offline | UniASR streaming offline unifying models |)
[//]: # (| [UniASR Burmese]&#40;https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary&#41; | Burmese | Alibaba Speech Data &#40;? hours&#41; | 696 | 95M | Online | UniASR streaming offline unifying models |)
[//]: # (| [UniASR Hebrew]&#40;https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary&#41; | Hebrew | Alibaba Speech Data &#40;? hours&#41; | 1085 | 95M | Online | UniASR streaming offline unifying models |)
[//]: # (| [UniASR Urdu]&#40;https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary&#41; | Urdu | Alibaba Speech Data &#40;? hours&#41; | 877 | 95M | Online | UniASR streaming offline unifying models |)
#### Conformer Models
[//]: # (| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |)
[//]: # (|:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|)
[//]: # (| [Conformer]&#40;https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary&#41; | CN | AISHELL &#40;178hours&#41; | 4234 | 44M | Offline | Duration of input wav <= 20s |)
[//]: # (| [Conformer]&#40;https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary&#41; | CN | AISHELL-2 &#40;1000hours&#41; | 5212 | 44M | Offline | Duration of input wav <= 20s |)
#### RNN-T Models
### Multi-talker Speech Recognition Models
#### MFCCA Models
[//]: # (| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |)
[//]: # (|:-------------------------------------------------------------------------------------------------------------:|:--------:|:------------------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|)
[//]: # (| [MFCCA]&#40;https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary&#41; | CN | AliMeeting、AISHELL-4、Simudata &#40;917hours&#41; | 4950 | 45M | Offline | Duration of input wav <= 20s, channel of input wav <= 8 channel |)
### Voice Activity Detection Models
| Model Name | Training Data | Parameters | Sampling Rate | Notes |
|:----------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------|
| [FSMN-VAD](https://huggingface.co/funasr/FSMN-VAD) | Alibaba Speech Data (5000hours) | 0.4M | 16000 | |
[//]: # (| [FSMN-VAD]&#40;https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary&#41; | Alibaba Speech Data &#40;5000hours&#41; | 0.4M | 8000 | |)
### Punctuation Restoration Models
| Model Name | Training Data | Parameters | Vocab Size| Offline/Online | Notes |
|:--------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------|
| [CT-Transformer](https://huggingface.co/funasr/CT-Transformer-punc) | Alibaba Text Data | 70M | 272727 | Offline | offline punctuation model |
[//]: # (| [CT-Transformer]&#40;https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary&#41; | Alibaba Text Data | 70M | 272727 | Online | online punctuation model |)
### Language Models
[//]: # (| Model Name | Training Data | Parameters | Vocab Size | Notes |)
[//]: # (|:----------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:------|)
[//]: # (| [Transformer]&#40;https://www.modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary&#41; | Alibaba Speech Data &#40;?hours&#41; | 57M | 8404 | |)
### Speaker Verification Models
[//]: # (| Model Name | Training Data | Parameters | Number Speaker | Notes |)
[//]: # (|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:----------:|:----------:|:------|)
[//]: # (| [Xvector]&#40;https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary&#41; | CNCeleb &#40;1,200 hours&#41; | 17.5M | 3465 | Xvector, speaker verification, Chinese |)
[//]: # (| [Xvector]&#40;https://www.modelscope.cn/models/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/summary&#41; | CallHome &#40;60 hours&#41; | 61M | 6135 | Xvector, speaker verification, English |)
### Speaker diarization Models
[//]: # (| Model Name | Training Data | Parameters | Notes |)
[//]: # (|:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------|)
[//]: # (| [SOND]&#40;https://www.modelscope.cn/models/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/summary&#41; | AliMeeting &#40;120 hours&#41; | 40.5M | Speaker diarization, profiles and records, Chinese |)
[//]: # (| [SOND]&#40;https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary&#41; | CallHome &#40;60 hours&#41; | 12M | Speaker diarization, profiles and records, English |)
### Timestamp Prediction Models
[//]: # (| Model Name | Language | Training Data | Parameters | Notes |)
[//]: # (|:--------------------------------------------------------------------------------------------------:|:--------------:|:-------------------:|:----------:|:------|)
[//]: # (| [TP-Aligner]&#40;https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary&#41; | CN | Alibaba Speech Data &#40;50000hours&#41; | 37.8M | Timestamp prediction, Mandarin, middle size |)
model_zoo/modelscope_models.md
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([简体中文](./modelscope_models_zh.md)|English)
# Pretrained Models Released on ModelScope
## Model License
You are free to use, copy, modify, and share FunASR models under the conditions of this agreement. You should indicate the model source and author information when using, copying, modifying and sharing FunASR models. You should keep the relevant names of models in [FunASR software].. Full model license could see [license](https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE)
## Model Usage
Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
## Model Zoo
Here we provided several pretrained models on different datasets. The details of models and datasets can be found on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition).
### Speech Recognition
#### Paraformer
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Offline | Duration of input wav <= 20s |
| [Paraformer-large-long](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Offline | Which would deal with arbitrary length input wav |
| [Paraformer-large-en-long](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) | EN | Alibaba Speech Data (50000hours) | 10020 | 220M | Offline | Which would deal with arbitrary length input wav |
| [Paraformer-large-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Offline | Supporting speaker diarizatioin for ASR results based on paraformer-large-long |
| [Paraformer-large-contextual](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Offline | Which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords. |
| [Paraformer](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary) | CN & EN | Alibaba Speech Data (50000hours) | 8358 | 68M | Offline | Duration of input wav <= 20s |
| [Paraformer-online](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary) | CN & EN | Alibaba Speech Data (50000hours) | 8404 | 68M | Online | Which could deal with streaming input |
| [Paraformer-large-online](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) | CN & EN | Alibaba Speech Data (60000hours) | 8404 | 220M | Online | Which could deal with streaming input |
| [Paraformer-tiny](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary) | CN | Alibaba Speech Data (200hours) | 544 | 5.2M | Offline | Lightweight Paraformer model which supports Mandarin command words recognition |
| [Paraformer-aishell](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-aishell1-pytorch/summary) | CN | AISHELL (178hours) | 4234 | 43M | Offline | |
| [ParaformerBert-aishell](https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | CN | AISHELL (178hours) | 4234 | 43M | Offline | |
| [Paraformer-aishell2](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | CN | AISHELL-2 (1000hours) | 5212 | 64M | Offline | |
| [ParaformerBert-aishell2](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | CN | AISHELL-2 (1000hours) | 5212 | 64M | Offline | |
#### UniASR [Unify Streaming and Non-streaming]
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:-------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:---------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [UniASR](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary) | CN & EN | Alibaba Speech Data (60000 hours) | 8358 | 100M | Online | UniASR streaming offline unifying models |
| [UniASR-large](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary) | CN & EN | Alibaba Speech Data (60000 hours) | 8358 | 220M | Offline | UniASR streaming offline unifying models |
| [UniASR English](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary) | EN | Alibaba Speech Data (10000 hours) | 1080 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Russian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary) | RU | Alibaba Speech Data (5000 hours) | 1664 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Japanese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary) | JA | Alibaba Speech Data (5000 hours) | 5977 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Korean](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary) | KO | Alibaba Speech Data (2000 hours) | 6400 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Cantonese (CHS)](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/summary) | Cantonese (CHS) | Alibaba Speech Data (5000 hours) | 1468 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Indonesian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary) | ID | Alibaba Speech Data (1000 hours) | 1067 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Vietnamese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary) | VI | Alibaba Speech Data (1000 hours) | 1001 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Spanish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary) | ES | Alibaba Speech Data (1000 hours) | 3445 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Portuguese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary) | PT | Alibaba Speech Data (1000 hours) | 1617 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR French](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary) | FR | Alibaba Speech Data (1000 hours) | 3472 | 95M | Online | UniASR streaming online unifying models |
| [UniASR German](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary) | GE | Alibaba Speech Data (1000 hours) | 3690 | 95M | Online | UniASR streaming online unifying models |
| [UniASR Persian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary) | FA | Alibaba Speech Data (1000 hours) | 1257 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary) | MY | Alibaba Speech Data (1000 hours) | 696 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Hebrew](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary) | HE | Alibaba Speech Data (1000 hours) | 1085 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Urdu](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary) | UR | Alibaba Speech Data (1000 hours) | 877 | 95M | Online | UniASR streaming offline unifying models |
| [UniASR Turkish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary) | TR | Alibaba Speech Data (1000 hours) | 1582 | 95M | Online | UniASR streaming offline unifying models |
#### Conformer
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | CN | AISHELL (178hours) | 4234 | 44M | Offline | Duration of input wav <= 20s |
| [Conformer](https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | CN | AISHELL-2 (1000hours) | 5212 | 44M | Offline | Duration of input wav <= 20s |
| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) | EN | Alibaba Speech Data (10000hours) | 4199 | 220M | Offline | Duration of input wav <= 20s |
### Multi-talker Speech Recognition
| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |
|:-------------------------------------------------------------------------------------------------------------:|:--------:|:------------------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [MFCCA](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary) | CN | AliMeeting、AISHELL-4、Simudata (917hours) | 4950 | 45M | Offline | Duration of input wav <= 20s, channel of input wav <= 8 channel |
### Voice Activity Detection
| Model Name | Training Data | Parameters | Sampling Rate | Notes |
|:----------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------|
| [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) | Alibaba Speech Data (5000hours) | 0.4M | 16000 | |
| [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary) | Alibaba Speech Data (5000hours) | 0.4M | 8000 | |
### Punctuation Restoration
| Model Name | Language | Training Data | Parameters | Vocab Size| Offline/Online | Notes |
|:--------------------------------------------------------------------------------------------------------------------------:|:---------|:----------------------------:|:----------:|:----------:|:--------------:|:------|
| [CT-Transformer-Large](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) | CN & EN | Alibaba Text Data(100M) | 1.1G | 471067 | Offline | large offline punctuation model |
| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) | CN & EN | Alibaba Text Data(70M) | 291M | 272727 | Offline | offline punctuation model |
| [CT-Transformer-Realtime](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary) | CN & EN | Alibaba Text Data(70M) | 288M | 272727 | Online | online punctuation model |
### Language Models
| Model Name | Training Data | Parameters | Vocab Size | Notes |
|:----------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:------|
| [Transformer](https://www.modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary) | Alibaba Speech Data (?hours) | 57M | 8404 | |
### Speaker Verification
| Model Name | Training Data | Parameters | Number Speaker | Notes |
|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:----------:|:----------:|:------|
| [Xvector](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary) | CNCeleb (1,200 hours) | 17.5M | 3465 | Xvector, speaker verification, Chinese |
| [Xvector](https://www.modelscope.cn/models/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/summary) | CallHome (60 hours) | 61M | 6135 | Xvector, speaker verification, English |
### Speaker Diarization
| Model Name | Training Data | Parameters | Notes |
|:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------|
| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/summary) | AliMeeting (120 hours) | 40.5M | Speaker diarization, profiles and records, Chinese |
| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary) | CallHome (60 hours) | 12M | Speaker diarization, profiles and records, English |
### Timestamp Prediction
| Model Name | Language | Training Data | Parameters | Notes |
|:--------------------------------------------------------------------------------------------------:|:--------------:|:-------------------:|:----------:|:------|
| [TP-Aligner](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) | CN | Alibaba Speech Data (50000hours) | 37.8M | Timestamp prediction, Mandarin, middle size |
### Inverse Text Normalization (ITN)
| Model Name | Language | Parameters | Notes |
|:----------------------------------------------------------------------------------------------------------------:|:--------:|:----------:|:-------------------------|
| [English](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-en/summary) | EN | 1.54M | ITN, ASR post-processing |
| [Russian](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ru/summary) | RU | 17.79M | ITN, ASR post-processing |
| [Japanese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ja/summary) | JA | 6.8M | ITN, ASR post-processing |
| [Korean](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ko/summary) | KO | 1.28M | ITN, ASR post-processing |
| [Indonesian](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-id/summary) | ID | 2.06M | ITN, ASR post-processing |
| [Vietnamese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-vi/summary) | VI | 0.92M | ITN, ASR post-processing |
| [Tagalog](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-tl/summary) | TL | 0.65M | ITN, ASR post-processing |
| [Spanish](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-es/summary) | ES | 1.32M | ITN, ASR post-processing |
| [Portuguese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-pt/summary) | PT | 1.28M | ITN, ASR post-processing |
| [French](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-fr/summary) | FR | 4.39M | ITN, ASR post-processing |
| [German](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-de/summary)| GE | 3.95M | ITN, ASR post-processing |
model_zoo/modelscope_models_zh.md
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(简体中文|[English](./modelscope_models.md))
# ModelScope上的预训练模型
## 模型许可协议
您可以在本协议的条件下自由使用、复制、修改和分享FunASR模型。在使用、复制、修改和分享FunASR模型时，您应当标明模型来源和作者信息。您应当在[FunASR软件]中保留相关模型的名称。完整的模型许可证请参见 [模型许可协议](https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE)
## 模型用法
模型用法参考[文档](funasr/quick_start_zh.md)
## 模型仓库
这里我们提供了在不同数据集上预训练的模型。模型和数据集的详细信息可在 [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)中找到.
### 语音识别模型
#### Paraformer模型
| 模型名字 | 语言 | 训练数据 | 词典大小 | 参数量 | 非实时/实时 | 备注 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:-----------------:|:----:|:-------:|:---------------------------|
| [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | 中文和英文 | 阿里巴巴语音数据（60000小时） | 8404 | 220M | 非实时 | 输入wav文件持续时间不超过20秒 |
| [Paraformer-large长音频版本](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) | 中文和英文 | 阿里巴巴语音数据（60000小时） | 8404 | 220M | 非实时 | 能够处理任意长度的输入wav文件 |
| [Paraformer-large-en长音频版本](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) | 英文 | 阿里巴巴语音数据（50000小时） | 10020 | 220M | 非实时 | 能够处理任意长度的输入wav文件 |
| [Paraformer-large-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) | 中文和英文 | 阿里巴巴语音数据（60000小时） | 8404 | 220M | 非实时 | 在长音频功能的基础上添加说话人识别功能 |
| [Paraformer-large热词](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary) | 中文和英文 | 阿里巴巴语音数据（60000小时） | 8404 | 220M | 非实时 | 基于激励增强的热词定制支持，可以提高热词的召回率和准确率，输入wav文件持续时间不超过20秒 |
| [Paraformer](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary) | 中文和英文 | 阿里巴巴语音数据（50000小时） | 8358 | 68M | 离线 | 输入wav文件持续时间不超过20秒 |
| [Paraformer实时](https://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary) | 中文和英文 | 阿里巴巴语音数据 (50000hours) | 8404 | 68M | 实时 | 能够处理流式输入 |
| [Paraformer-large实时](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) | 中文和英文 | 阿里巴巴语音数据 (60000hours) | 8404 | 220M | 实时 | 能够处理流式输入 |
| [Paraformer-tiny](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary) | 中文 | 阿里巴巴语音数据 (200hours) | 544 | 5.2M | 非实时 | 轻量级Paraformer模型，支持普通话命令词识别 |
| [Paraformer-aishell](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-aishell1-pytorch/summary) | 中文 | AISHELL (178hours) | 4234 | 43M | 非实时 | 学术模型 |
| [ParaformerBert-aishell](https://modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | 中文 | AISHELL (178hours) | 4234 | 43M | 非实时 | 学术模型 |
| [Paraformer-aishell2](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | 中文 | AISHELL-2 (1000hours) | 5212 | 64M | 非实时 | 学术模型 |
| [ParaformerBert-aishell2](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | 中文 | AISHELL-2 (1000hours) | 5212 | 64M | 非实时 | 学术模型 |
#### UniASR模型
| 模型名字 | 语言 | 训练数据 | Vocab Size | Parameter | 非实时/实时 | 备注 |
|:---------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [UniASR](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 100M | 实时 | 流式离线一体化模型 |
| [UniASR-large](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 220M | 非实时 | 流式离线一体化模型 |
| [UniASR English](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary) | 英文 | 阿里巴巴语音数据 (10000 小时) | 1080 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Russian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary) | 俄语 | 阿里巴巴语音数据 (5000 小时) | 1664 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Japanese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary) | 日语 | 阿里巴巴语音数据 (5000 小时) | 5977 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Korean](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary) | 韩语 | 阿里巴巴语音数据 (2000 小时) | 6400 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Cantonese (CHS)](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/summary) | 粤语（简体中文） | 阿里巴巴语音数据 (5000 小时) | 1468 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Indonesian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary) | 印尼语 | 阿里巴巴语音数据 (1000 小时) | 1067 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Vietnamese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary) | 越南语 | 阿里巴巴语音数据 (1000 小时) | 1001 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Spanish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary) | 西班牙语 | 阿里巴巴语音数据 (1000 小时) | 3445 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Portuguese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary) | 葡萄牙语 | 阿里巴巴语音数据 (1000 小时) | 1617 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR French](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary) | 法语 | 阿里巴巴语音数据 (1000 小时) | 3472 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR German](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary) | 德语 | 阿里巴巴语音数据 (1000 小时) | 3690 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Persian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary) | 波斯语 | 阿里巴巴语音数据 (1000 小时) | 1257 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary) | 缅甸语 | 阿里巴巴语音数据 (1000 小时) | 696 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Hebrew](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary) | 希伯来语 | 阿里巴巴语音数据 (1000 小时) | 1085 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Urdu](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary) | 乌尔都语 | 阿里巴巴语音数据 (1000 小时) | 877 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Turkish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary) | 土耳其语 | 阿里巴巴语音数据 (1000 小时) | 1582 | 95M | 实时 | 流式离线一体化模型 |
#### Conformer模型
| 模型名字 | 语言 | 训练数据 | Vocab Size | Parameter | 非实时/实时 | 备注 |
|:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | 中文 | AISHELL (178hours) | 4234 | 44M | 非实时 | 输入wav文件持续时间不超过20秒 |
| [Conformer](https://www.modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | 中文 | AISHELL-2 (1000hours) | 5212 | 44M | 非实时 | 输入wav文件持续时间不超过20秒 |
| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) | 英文 | 阿里巴巴语音数据 (10000hours) | 4199 | 220M | 非实时 | 输入wav文件持续时间不超过20秒 |
#### RNN-T 模型
### 多说话人语音识别模型
#### MFCCA模型
| 模型名字 | 语言 | 训练数据 | Vocab Size | Parameter | 非实时/实时 | 备注 |
|:-------------------------------------------------------------------------------------------------------------:|:--------:|:------------------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [MFCCA](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary) | 中文 | AliMeeting、AISHELL-4、Simudata (917hours) | 4950 | 45M | 非实时 | 输入音频的持续时间不超过20秒，输入音频的通道数不超过8通道。 |
### 语音端点检测模型
| 模型名字 | 训练数据 | 模型参数 | Sampling Rate | 备注 |
|:----------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------|
| [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) | 阿里巴巴语音数据 (5000hours) | 0.4M | 16000 | |
| [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary) | 阿里巴巴语音数据 (5000hours) | 0.4M | 8000 | |
### 标点恢复模型
| 模型名字 | 语言 | 训练数据 | 模型参数 | Vocab Size| 非实时/实时 | 备注 |
|:--------------------------------------------------------------------------------------------------------------------------:|:----------:|:----------------------------:|:----------:|:----------:|:--------------:|:--------|
| [CT-Transformer-Large](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) | 中文和英文 | Alibaba Text Data(100M) | 1.1G | 471067 | 非实时 | 支持中英文标点大模型 |
| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) | 中文和英文 | Alibaba Text Data(70M) | 291M | 272727 | 非实时 | 支持中英文标点 |
| [CT-Transformer-Realtime](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary) | 中文和英文 | Alibaba Text Data(70M) | 288M | 272727 | 实时 | VAD点实时标点 |
### 语音模型
| 模型名字 | 训练数据 | 模型参数 | 词典大小 | 备注 |
|:----------------------------------------------------------------------------------------------------------------------:|:---------:|:----------:|:----:|:------|
| [Transformer](https://www.modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary) | 阿里巴巴语音数据 | 57M | 8404 | |
### 说话人确认模型
| 模型名字 | 训练数据 | 模型参数 | Number Speaker | 备注 |
|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:----------:|:----------:|:------------|
| [Xvector](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary) | CNCeleb (1,200 小时) | 17.5M | 3465 | Xvector， 中文 |
| [Xvector](https://www.modelscope.cn/models/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/summary) | CallHome (60 小时) | 61M | 6135 | Xvector，英文 |
### 说话人日志模型
| 模型名字 | 训练数据 | 模型参数 | 备注 |
|:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:----|
| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/summary) | AliMeeting (120 小时) | 40.5M | 中文 |
| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary) | CallHome (60 小时) | 12M | 英文 |
### 时间戳预测模型
| 模型名字 | 语言 | 训练数据 | 模型参数 | 备注 |
|:--------------------------------------------------------------------------------------------------:|:--------------:|:-------------------:|:----------:|:---------|
| [TP-Aligner](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) |中文| 阿里巴巴语音数据 (50000hours) | 37.8M | 时间戳模型，中文 |
### 逆文本正则化
| 模型名字 | 语言 | 模型参数 | 备注 |
|:----------------------------------------------------------------------------------------------------------------:|:---:|:------:|:--------------|
| [English](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-en/summary) | EN | 1.54M | ITN，语音识别文本后处理 |
| [Russian](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ru/summary) | RU | 17.79M | ITN，语音识别文本后处理 |
| [Japanese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ja/summary) | JA | 6.8M | ITN，语音识别文本后处理 |
| [Korean](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ko/summary) | KO | 1.28M | ITN，语音识别文本后处理 |
| [Indonesian](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-id/summary) | ID | 2.06M | ITN，语音识别文本后处理 |
| [Vietnamese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-vi/summary) | VI | 0.92M | ITN，语音识别文本后处理 |
| [Tagalog](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-tl/summary) | TL | 0.65M | ITN，语音识别文本后处理 |
| [Spanish](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-es/summary) | ES | 1.32M | ITN，语音识别文本后处理 |
| [Portuguese](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-pt/summary) | PT | 1.28M | ITN，语音识别文本后处理 |
| [French](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-fr/summary) | FR | 4.39M | ITN，语音识别文本后处理 |
| [German](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-de/summary)| GE | 3.95M | ITN，语音识别文本后处理 |
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([简体中文](./readme_zh.md)|English)
# Model Zoo
## Model License
You are free to use, copy, modify, and share FunASR models under the conditions of this agreement. You should indicate the model source and author information when using, copying, modifying and sharing FunASR models. You should keep the relevant names of models in [FunASR software]. Full model license could see [license](https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE)
## Model Usage
Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)
## Model Zoo
Here we provided several pretrained models on different datasets. The details of models and datasets can be found on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition).
### Speech Recognition
#### Paraformer
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](./MODEL_LICENSE). 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 <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 60000 hours, Mandarin | 220M |
| paraformer-zh-spk <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) [🤗]() ) | speech recognition with speaker diarization, with timestamps, non-streaming | 60000 hours, Mandarin | 220M |
| <nobr>paraformer-zh-online <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() )</nobr> | speech recognition, streaming | 60000 hours, Mandarin | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 50000 hours, English | 220M |
| conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() ) | speech recognition, non-streaming | 50000 hours, English | 220M |
| ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() ) | punctuation restoration | 100M, Mandarin and English | 1.1G |
| fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M |
| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() ) | timestamp prediction | 5000 hours, Mandarin | 38M |
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(简体中文|[English](./readme.md))
# 模型仓库
## 模型许可协议
您可以在本协议的条件下自由使用、复制、修改和分享FunASR模型。在使用、复制、修改和分享FunASR模型时，您应当标明模型来源和作者信息。您应当在[FunASR软件]中保留相关模型的名称。完整的模型许可证请参见 [模型许可协议](https://github.com/modelscope/FunASR/blob/main/MODEL_LICENSE)
## 模型用法
模型用法参考[文档](funasr/quick_start_zh.md)
## 模型仓库
这里我们提供了在不同数据集上预训练的模型。模型和数据集的详细信息可在 [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition)中找到.
### 语音识别模型
#### Paraformer模型
（注：[🤗]()表示Huggingface模型仓库链接，[⭐]()表示ModelScope模型仓库链接）
| 模型名字 | 任务详情 | 训练数据 | 参数量 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------:|:------------:|:----:|
| paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | 语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M |
| SeACoParaformer-zh <br> ( [⭐](https://www.modelscope.cn/models/iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | 带热词功能的语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M |
| paraformer-zh-spk <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) [🤗]() ) | 分角色语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M |
| paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() ) | 语音识别，实时 | 60000小时，中文 | 220M |
| paraformer-zh-streaming-small <br> ( [⭐](https://www.modelscope.cn/models/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() ) | 语音识别，实时 | 60000小时，中文 | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | 语音识别，非实时 | 50000小时，英文 | 220M |