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@ -86,3 +86,9 @@ Here we provided several pretrained models on different datasets. The details of
|:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------|
| [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 |