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| | | | [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 | |
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| | | |
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| | | #### Conformer Models |
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| | | #### Paraformer Models |
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| | | | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |
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| | | |:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| |
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| | | | [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 | |
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| | | | [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 | |
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| | | |
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| | | #### MFCCA Models |
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| | | | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |
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| | | |:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| |
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| | | | [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 |
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| | | |
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| | | #### RNN-T Models |
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| | | |
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| | |
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| | | |
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| | | | Model Name | Training Data | Parameters | Sampling Rate | Notes | |
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| | | |:----------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------| |
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| | | | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) | Alibaba Speech Data (?hours) | 0.4M | 16000 | | |
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| | | | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary) | Alibaba Speech Data (?hours) | 0.4M | 8000 | | |
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| | | | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) | Alibaba Speech Data (5000hours) | 0.4M | 16000 | | |
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| | | | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary) | Alibaba Speech Data (5000hours) | 0.4M | 8000 | | |
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| | | |
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| | | ### Punctuation Restoration Models |
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| | | |
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| | | | Model Name | Training Data | Parameters | Vocab Size| Offline/Online | Notes | |
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| | | |:--------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------| |
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| | | | [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) | Alibaba Speech Data (?hours) | 70M | 272727 | Offline | | |
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| | | | [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary) | Alibaba Speech Data (?hours) | 70M | 272727 | Online | | |
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| | | | [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 | |
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| | | | [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 | |
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| | | |
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| | | ### Language Models |
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| | | |
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