From d5784e3444ff891b92c681d866f1d527a25cb299 Mon Sep 17 00:00:00 2001 From: zhifu gao <zhifu.gzf@alibaba-inc.com> Date: 星期日, 23 四月 2023 15:51:59 +0800 Subject: [PATCH] Merge pull request #404 from alibaba-damo-academy/main --- docs/huggingface_models.md | 126 ++++++++++++++++++++++++++++++++++++++++++ 1 files changed, 126 insertions(+), 0 deletions(-) diff --git a/docs/huggingface_models.md b/docs/huggingface_models.md new file mode 100644 index 0000000..ad367de --- /dev/null +++ b/docs/huggingface_models.md @@ -0,0 +1,126 @@ +# Pretrained Models on Huggingface + +## Model License +- Apache License 2.0 + +## 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 + +| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes | +|:-----------------------------------------------------------------------:|:--------:|:----------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| +| [Paraformer-large](https://huggingface.co/funasr/paraformer-large) | 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](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 |) + +[//]: # (| [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 | |) + +[//]: # (| [Paraformer-aishell2](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/summary) | CN | AISHELL-2 (1000hours) | 5212 | 64M | Offline | |) + +[//]: # (| [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 + +[//]: # (| 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 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 |) + +#### Conformer Models + +[//]: # (| Model Name | Language | Training Data | Vocab Size | Parameter | Offline/Online | Notes |) + +[//]: # (|:----------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|) + +[//]: # (| [Conformer](https://modelscope.cn/models/damo/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/summary) | CN | AISHELL (178hours) | 4234 | 44M | Offline | Duration of input wav <= 20s |) + +[//]: # (| [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 |) + + +#### RNN-T Models + +### Multi-talker Speech Recognition Models + +#### 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 |) + + + +### Voice Activity Detection Models + +| Model Name | Training Data | Parameters | Sampling Rate | Notes | +|:----------------------------------------------------:|:----------------------------:|:----------:|:-------------:|:------| +| [FSMN-VAD](https://huggingface.co/funasr/FSMN-VAD) | 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 + +| Model Name | Training Data | Parameters | Vocab Size| Offline/Online | Notes | +|:--------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:--------------:|:------| +| [CT-Transformer](https://huggingface.co/funasr/CT-Transformer-punc) | 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 |) + +### Language Models + +[//]: # (| Model Name | Training Data | Parameters | Vocab Size | Notes |) + +[//]: # (|:----------------------------------------------------------------------------------------------------------------------:|:----------------------------:|:----------:|:----------:|:------|) + +[//]: # (| [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 + +[//]: # (| 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 + +[//]: # (| 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 + +[//]: # (| 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 |) -- Gitblit v1.9.1