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| [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://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-实时/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://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-实时/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 |