From 2a66366be4c2715870e4859fd5a5db6e8a9dc00a Mon Sep 17 00:00:00 2001 From: chenmengzheAAA <123789350+chenmengzheAAA@users.noreply.github.com> Date: 星期四, 14 九月 2023 19:00:17 +0800 Subject: [PATCH] Merge pull request #956 from alibaba-damo-academy/chenmengzheAAA-patch-4 --- docs/model_zoo/modelscope_models.md | 48 ++++++++++++++++++++++++------------------------ 1 files changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/model_zoo/modelscope_models.md b/docs/model_zoo/modelscope_models.md index e844209..93f1b72 100644 --- a/docs/model_zoo/modelscope_models.md +++ b/docs/model_zoo/modelscope_models.md @@ -1,18 +1,23 @@ -# Pretrained Models on ModelScope +([绠�浣撲腑鏂嘳(./modelscope_models_zh.md)|English) + +# Pretrained Models Released on ModelScope ## Model License -- Apache License 2.0 +You are free to use, copy, modify, and share FunASR models under the conditions of this agreement. You should indicate the model source and author information when using, copying, modifying and sharing FunASR models. You should keep the relevant names of models in [FunASR software].. Full model license could see [license](https://github.com/alibaba-damo-academy/FunASR/blob/main/MODEL_LICENSE) + +## Model Usage +Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html) ## Model Zoo Here we provided several pretrained models on different datasets. The details of models and datasets can be found on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition). -### Speech Recognition Models -#### Paraformer Models +### Speech Recognition +#### Paraformer | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |:--------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:--------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| | [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-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 would 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](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://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 | @@ -24,7 +29,7 @@ | [ParaformerBert-aishell2](https://www.modelscope.cn/models/damo/speech_paraformerbert_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | CN | AISHELL-2 (1000hours) | 5212 | 64M | Offline | | -#### UniASR Models +#### UniASR [Unify Streaming and Non-streaming] | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |:-------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:---------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| @@ -48,7 +53,7 @@ | [UniASR Turkish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary) | TR | Alibaba Speech Data (1000 hours) | 1582 | 95M | Online | UniASR streaming offline unifying models | -#### Conformer Models +#### Conformer | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| @@ -56,12 +61,7 @@ | [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 | | [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) | EN | Alibaba Speech Data (10000hours) | 4199 | 220M | Offline | Duration of input wav <= 20s | - -#### RNN-T Models - -### Multi-talker Speech Recognition Models - -#### MFCCA Models +### Multi-talker Speech Recognition | Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | |:-------------------------------------------------------------------------------------------------------------:|:--------:|:------------------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| @@ -69,19 +69,20 @@ -### Voice Activity Detection Models +### Voice Activity Detection | Model Name | Training Data | Parameters | Sampling Rate | Notes | |:----------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------| | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) | Alibaba Speech Data (5000hours) | 0.4M | 16000 | | | [FSMN-VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-8k-common/summary) | Alibaba Speech Data (5000hours) | 0.4M | 8000 | | -### Punctuation Restoration Models +### Punctuation Restoration -| Model Name | Training Data | Parameters | Vocab Size| Offline/Online | Notes | -|:--------------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------| -| [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 | -| [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 | +| Model Name | Language | Training Data | Parameters | Vocab Size| Offline/Online | Notes | +|:--------------------------------------------------------------------------------------------------------------------------:|:---------|:----------------------------:|:----------:|:----------:|:--------------:|:------| +| [CT-Transformer-Large](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) | CN & EN | Alibaba Text Data | 1.1G | 471067 | Offline | large offline punctuation model | +| [CT-Transformer](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) | CN & EN | Alibaba Text Data | 291M | 272727 | Offline | offline punctuation model | +| [CT-Transformer-Realtime](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary) | CN & EN | Alibaba Text Data | 288M | 272727 | Online | online punctuation model | ### Language Models @@ -89,27 +90,26 @@ |:----------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:------| | [Transformer](https://www.modelscope.cn/models/damo/speech_transformer_lm_zh-cn-common-vocab8404-pytorch/summary) | Alibaba Speech Data (?hours) | 57M | 8404 | | -### Speaker Verification Models +### Speaker Verification | Model Name | Training Data | Parameters | Number Speaker | Notes | |:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:----------:|:----------:|:------| | [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 | Model Name | Training Data | Parameters | Notes | |:----------------------------------------------------------------------------------------------------------------:|:-------------------:|:----------:|:------| | [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 - +### Timestamp Prediction | 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 | -### Inverse Text Normalization (ITN) Models +### Inverse Text Normalization (ITN) | Model Name | Language | Parameters | Notes | |:----------------------------------------------------------------------------------------------------------------:|:--------:|:----------:|:-------------------------| -- Gitblit v1.9.1