FUNASR训练
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shixian.shi
2023-05-05 653fffdf29fc77ea9203d0cffdcc760f55a61dd5 
 docs/modelscope_models.md


@@ -1,4 +1,4 @@


# Pretrained models on ModelScope


# Pretrained Models on ModelScope




## Model License


-  Apache License 2.0


@@ -8,13 +8,14 @@




### Speech Recognition Models


#### Paraformer Models




|                                                                     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 ould deal with arbitrary length input wav                                                                                 |


| [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-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://modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1/summary)           | CN & EN  | Alibaba Speech Data (50000hours) |    8404    |    68M    |     Online     | Which could deal with streaming input                                                                                           |


|          [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-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     |                                                                                                                                 |


@@ -23,36 +24,63 @@






#### UniASR Models


|                                                               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 (60000hours) |    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 (60000hours) |    8358    |   220M    |    Offline     | UniASR streaming offline unifying models                                                                                                    |


|           [UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary)           | Burmese  |  Alibaba Speech Data (? 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)           |  Hebrew  |  Alibaba Speech Data (? 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)                  |   Urdu   |  Alibaba Speech Data (? hours)   |    877     |    95M    |     Online     | UniASR streaming offline unifying models                                                                                                    |




|                                                                    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                                                                                                    |








#### Conformer Models


#### Paraformer Models




|                                                       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                                                                                                    |






#### RNN-T Models




### Multi-talker Speech Recognition Models




#### MFCCA Models




|                                                  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 Models




|                                           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 (?hours) |    0.4M    |     16000     |       |


|   [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary)        | Alibaba Speech Data (?hours) |    0.4M    |     8000      |       |


| [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 Models




|                                                         Model Name                                                         |        Training Data         | Parameters | Vocab Size| Offline/Online | Notes |


|:--------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------|


|      [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)      | Alibaba Speech Data (?hours) |    70M     |    272727     |    Offline     |       |


| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary)      | Alibaba Speech Data (?hours) |    70M     |    272727     |     Online     |       |


|      [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary)      | Alibaba Text Data |    70M     |    272727     |    Offline     |   offline punctuation model    |


| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary)      | Alibaba Text Data |    70M     |    272727     |     Online     |  online punctuation model     |




### Language Models




@@ -62,14 +90,36 @@




### Speaker Verification Models




|                                                  Model Name                                                   |   Training Data   | Parameters | Vocab Size | Notes |


|                                                  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 (?hours)  |   17.5M    |    3465    |       |


| [Xvector](https://www.modelscope.cn/models/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/summary) | CallHome (?hours) |    61M     |    6135    |       |


| [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 Models


### Speaker Diarization Models




|                                                    Model Name                                                    |    Training Data    | Parameters | Notes |


|:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------|


| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/summary) | AliMeeting (?hours) |   40.5M    |       |


| [SOND](https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary)    |  CallHome (?hours)  |     12M     |       |


| [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 Models




|                                                    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) Models




|                                                    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    | 1.28M | 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 | InverASR 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    | 1.28M | ITN, ASR post processing |


| [Spanish](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-es/summary) |    ES    | 1.28M | 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    | 1.28M | InverASR post processing |


| [German](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-de/summary)|    GE    | 1.28M | ITN, ASR post processing |