From 32d2b3ec153e53176da710ebcc0aba5669effd8a Mon Sep 17 00:00:00 2001 From: yhliang <429259365@qq.com> Date: 星期四, 27 四月 2023 17:45:00 +0800 Subject: [PATCH] update m2met2 docs --- docs/modelscope_models.md | 18 +++++++++--------- 1 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/modelscope_models.md b/docs/modelscope_models.md index d066a94..5f94a09 100644 --- a/docs/modelscope_models.md +++ b/docs/modelscope_models.md @@ -1,4 +1,4 @@ -# Pretrained models on ModelScope +# Pretrained Models on ModelScope ## Model License - Apache License 2.0 @@ -13,9 +13,9 @@ |:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| | [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 | | @@ -27,8 +27,8 @@ | 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](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 | @@ -47,9 +47,9 @@ #### 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銆丄ISHELL-4銆丼imudata (917hours) | 4950 | 45M | Offline | Duration of input wav <= 20s, channel of input wav <= 8 channel +| 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銆丄ISHELL-4銆丼imudata (917hours) | 4950 | 45M | Offline | Duration of input wav <= 20s, channel of input wav <= 8 channel | @@ -80,7 +80,7 @@ | [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 | |:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------| -- Gitblit v1.9.1