From 24b341a7eb0ad72e021470b8f2d1ee1d0b29ea81 Mon Sep 17 00:00:00 2001 From: 游雁 <zhifu.gzf@alibaba-inc.com> Date: 星期日, 23 四月 2023 19:57:10 +0800 Subject: [PATCH] client websocket --- docs/modelscope_models.md | 4 ++-- 1 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/modelscope_models.md b/docs/modelscope_models.md index b35d625..b000fca 100644 --- a/docs/modelscope_models.md +++ b/docs/modelscope_models.md @@ -13,7 +13,7 @@ |:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| | [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-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 | @@ -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