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[doc] release wav2vec2ASR and wav2vec2.0 model, update Recent Update #2527

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Oct 14, 2022
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[doc] release wav2vec2ASR and wav2vec2.0 model, update Recent Update #2527

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dbe8cee
release wav2vec2ASR and wav2vec2.0 model, update Recent Update
Zth9730 Oct 13, 2022
f292941
update reference.md and released_model.md
Zth9730 Oct 13, 2022
49c0cf9
format reference.md
Zth9730 Oct 14, 2022
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -157,6 +157,7 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
- 🧩 *Cascaded models application*: as an extension of the typical traditional audio tasks, we combine the workflows of the aforementioned tasks with other fields like Natural language processing (NLP) and Computer Vision (CV).

### Recent Update
- 👑 2022.10.11: Add [Wav2vec2ASR](./examples/librispeech/asr3), wav2vec2.0 fine-tuning for ASR on LibriSpeech.
- 🔥 2022.09.26: Add Voice Cloning, TTS finetune, and ERNIE-SAT in [PaddleSpeech Web Demo](./demos/speech_web).
- ⚡ 2022.09.09: Add AISHELL-3 Voice Cloning [example](./examples/aishell3/vc2) with ECAPA-TDNN speaker encoder.
- ⚡ 2022.08.25: Release TTS [finetune](./examples/other/tts_finetune/tts3) example.
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1 change: 1 addition & 0 deletions README_cn.md
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Expand Up @@ -179,6 +179,7 @@
</div>

### 近期更新
- 👑 2022.10.11: 新增 [Wav2vec2ASR](./examples/librispeech/asr3), 在 LibriSpeech 上针对ASR任务对wav2vec2.0 的fine-tuning.
- 🔥 2022.09.26: 新增 Voice Cloning, TTS finetune 和 ERNIE-SAT 到 [PaddleSpeech 网页应用](./demos/speech_web)。
- ⚡ 2022.09.09: 新增基于 ECAPA-TDNN 声纹模型的 AISHELL-3 Voice Cloning [示例](./examples/aishell3/vc2)。
- ⚡ 2022.08.25: 发布 TTS [finetune](./examples/other/tts_finetune/tts3) 示例。
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6 changes: 6 additions & 0 deletions docs/source/reference.md
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Expand Up @@ -28,6 +28,8 @@ We borrowed a lot of code from these repos to build `model` and `engine`, thanks
* [speechbrain](https://github.com/speechbrain/speechbrain/blob/develop/LICENSE)
- Apache-2.0 License
- ECAPA-TDNN SV model
- ASR with CTC and pre-trained wav2vec2 models.


* [chainer](https://github.com/chainer/chainer/blob/master/LICENSE)
- MIT License
Expand All @@ -43,3 +45,7 @@ We borrowed a lot of code from these repos to build `model` and `engine`, thanks

* [g2pW](https://github.com/GitYCC/g2pW/blob/master/LICENCE)
- Apache-2.0 license

*[transformers](https://github.com/huggingface/transformers)
- Apache-2.0 License
- Wav2vec2.0
6 changes: 6 additions & 0 deletions docs/source/released_model.md
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Expand Up @@ -18,6 +18,12 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
[Transformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1](../../examples/librispeech/asr1) | python |
[Transformer Librispeech ASR2 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h | [Transformer Librispeech ASR2](../../examples/librispeech/asr2) | python |

### Self-Supervised Pre-trained Model
Model | Pre-Train Method | Pre-Train Data | Finetune Data | Size | Descriptions | CER | WER | Example Link |
:-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----: | :-----: | :-----: |
[Wav2vec2-large-960h-lv60-self Model](https://paddlespeech.bj.bcebos.com/wav2vec/wav2vec2-large-960h-lv60-self.pdparams) | wav2vec2 | Librispeech and LV-60k Dataset (5.3w h) | - | 1.18 GB |Pre-trained Wav2vec2.0 Model | - | - | - |
[Wav2vec2ASR-large-960h-librispeech Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr3/wav2vec2ASR-large-960h-librispeech_ckpt_1.3.0.model.tar.gz) | wav2vec2 | Librispeech and LV-60k Dataset (5.3w h) | Librispeech (960 h) | 1.18 GB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | - | 0.0189 | [Wav2vecASR Librispeech ASR3](../../examples/librispeech/asr3) |

### Language Model based on NGram
Language Model | Training Data | Token-based | Size | Descriptions
:------------:| :------------:|:------------: | :------------: | :------------:
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