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released_model.md 28.89 KB

# Released Models

## Speech-to-Text Models

### Speech Recognition Model
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link | Inference Type | static_model | 
:-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----:  | :-----:  | :-----: | :-----: | :-----: |
[Ds2 Online Wenetspeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr0/asr0_deepspeech2_online_wenetspeech_ckpt_1.0.4.model.tar.gz) | Wenetspeech Dataset | Char-based | 1.2 GB  | 2 Conv + 5 LSTM layers | 0.152 (test\_net, w/o LM) <br> 0.2417 (test\_meeting, w/o LM) <br> 0.053 (aishell, w/ LM) |-| 10000 h | - | onnx/inference/python |-|
[Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz) | Aishell Dataset | Char-based | 491 MB  | 2 Conv + 5 LSTM layers | 0.0666 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) | onnx/inference/python |-|
[Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz)| Aishell Dataset | Char-based | 1.4 GB | 2 Conv + 5 bidirectional LSTM layers| 0.0554 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) | inference/python |-|
[Conformer Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz) | WenetSpeech Dataset | Char-based | 457 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.11 (test\_net) 0.1879 (test\_meeting) |-| 10000 h |- | python |-|
[Conformer U2PP Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_u2pp_wenetspeech_ckpt_1.3.0.model.tar.gz) | WenetSpeech Dataset | Char-based | 540 MB  | Encoder:Conformer, Decoder:BiTransformer, Decoding method: Attention rescoring| 0.047198 (aishell test\_-1) 0.059212 (aishell test\_16) |-| 10000 h |- | python |[FP32](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_u2pp_wenetspeech_ckpt_1.3.0.model.tar.gz) </br>[INT8](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/static/asr1_chunk_conformer_u2pp_wenetspeech_static_quant_1.3.0.model.tar.gz) |
[Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.0544 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) | python |-|
[Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_1.0.1.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0460 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) | python |-|
[Transformer Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 128 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0523 || 151 h | [Transformer  Aishell ASR1](../../examples/aishell/asr1) | python |-|
[Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_offline_librispeech_ckpt_1.0.1.model.tar.gz)| Librispeech Dataset | Char-based | 1.3 GB | 2 Conv + 5 bidirectional LSTM layers| - |0.0467| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) | inference/python |-|
[Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0338 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) | python |-|
[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 |-|
[Conformer TALCS ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/tal_cs/asr1/asr1_conformer_talcs_ckpt_1.4.0.model.tar.gz) | TALCS Dataset | subword-based | 470 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0844 | 587 h | [Conformer TALCS ASR1](../../examples/tal_cs/asr1) | 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_1.3.1.model.tar.gz) | wav2vec2 | Librispeech and LV-60k Dataset (5.3w h) | Librispeech (960 h) | 718 MB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | - | 0.0189 | [Wav2vecASR Librispeech ASR3](../../examples/librispeech/asr3) |
[Wav2vec2-large-wenetspeech-self Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2-large-wenetspeech-self_ckpt_1.3.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | - | 714 MB |Pre-trained Wav2vec2.0 Model | - | - | - | 
[Wav2vec2ASR-large-aishell1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | aishell1 (train set) | 1.18 GB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | 0.0510 | - | - |

### Whisper Model
Demo Link | Training Data | Size | Descriptions | CER | Model 
:-----------: | :-----:| :-------: | :-----: | :-----: |:---------:|
[Whisper](../../demos/whisper) | 680kh from internet | large: 5.8G,</br>medium: 2.9G,</br>small: 923M,</br>base: 277M,</br>tiny: 145M | Encoder:Transformer,</br> Decoder:Transformer, </br>Decoding method: </br>Greedy search | 0.027 </br>(large, Librispeech) | [whisper-large](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-large-model.tar.gz) </br>[whisper-medium](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-model.tar.gz) </br>[whisper-medium-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-medium-en-model.tar.gz) </br>[whisper-small](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-model.tar.gz) </br>[whisper-small-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-small-en-model.tar.gz) </br>[whisper-base](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-model.tar.gz) </br>[whisper-base-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-base-en-model.tar.gz) </br>[whisper-tiny](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-model.tar.gz) </br>[whisper-tiny-English-only](https://paddlespeech.bj.bcebos.com/whisper/whisper_model_20221122/whisper-tiny-en-model.tar.gz)

### Language Model based on NGram
|Language Model | Training Data | Token-based | Size | Descriptions|
| :------------: | :------------: | :------------: | :------------: | :------------: |
[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings

### Speech Translation Models

| Model | Training Data | Token-based | Size | Descriptions | BLEU | Example Link |
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| (only for CLI)[Transformer FAT-ST MTL En-Zh](https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz) | Ted-En-Zh| Spm| | Encoder:Transformer, Decoder:Transformer, <br />Decoding method: Attention | 20.80 | [Transformer Ted-En-Zh ST1](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/ted_en_zh/st1) |

## Text-to-Speech Models

### Acoustic Models
Model Type | Dataset| Example Link | Pretrained Models|Static / ONNX / Paddle-Lite Models|Size (static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
Tacotron2|LJSpeech|[tacotron2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip)|||
Tacotron2|CSMSC|[tacotron2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts0)|[tacotron2_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip)|[tacotron2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_static_0.2.0.zip)|103MB|
TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)|||
SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2)|[speedyspeech_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip)|[speedyspeech_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_static_0.2.0.zip) </br> [speedyspeech_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_onnx_0.2.0.zip) </br> [speedyspeech_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_pdlite_1.3.0.zip)|13MB|
FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_static_0.2.0.zip) </br> [fastspeech2_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip) </br> [fastspeech2_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_pdlite_1.3.0.zip)|157MB|
FastSpeech2-Conformer| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_conformer_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_conformer_baker_ckpt_0.5.zip)|||
FastSpeech2-CNNDecoder| CSMSC| [fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)| [fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip) |  [fastspeech2_cnndecoder_csmsc_static_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_static_1.0.0.zip) </br>[fastspeech2_cnndecoder_csmsc_streaming_static_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_static_1.0.0.zip)  </br>[fastspeech2_cnndecoder_csmsc_onnx_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_onnx_1.0.0.zip)  </br>[fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip)  </br> [fastspeech2_cnndecoder_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_pdlite_1.3.0.zip) </br> [fastspeech2_cnndecoder_csmsc_streaming_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_pdlite_1.3.0.zip)| 84MB|
FastSpeech2| AISHELL-3 |[fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3)|[fastspeech2_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_ckpt_1.1.0.zip)|[fastspeech2_aishell3_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_static_1.1.0.zip) </br> [fastspeech2_aishell3_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_onnx_1.1.0.zip) </br> [fastspeech2_aishell3_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_pdlite_1.3.0.zip) |147MB|
FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts3)|[fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip)|[fastspeech2_ljspeech_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_ljspeech_static_1.1.0.zip) </br> [fastspeech2_ljspeech_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_ljspeech_onnx_1.1.0.zip) </br> [fastspeech2_ljspeech_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_ljspeech_pdlite_1.3.0.zip)|145MB|
FastSpeech2| VCTK |[fastspeech2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_vctk_ckpt_1.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_ckpt_1.2.0.zip)|[fastspeech2_vctk_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_static_1.1.0.zip) </br> [fastspeech2_vctk_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_onnx_1.1.0.zip) </br> [fastspeech2_vctk_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_pdlite_1.3.0.zip)| 145MB|
FastSpeech2| ZH_EN |[fastspeech2-zh_en](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/zh_en_tts/tts3)|[fastspeech2_mix_ckpt_1.2.0.zip](https://paddlespeech.bj.bcebos.com/t2s/chinse_english_mixed/models/fastspeech2_mix_ckpt_1.2.0.zip)|[fastspeech2_mix_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/t2s/chinse_english_mixed/models/fastspeech2_mix_static_0.2.0.zip) </br> [fastspeech2_mix_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/t2s/chinse_english_mixed/models/fastspeech2_mix_onnx_0.2.0.zip) | 145MB|
FastSpeech2| male-zh ||[fastspeech2_male_zh_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_zh_ckpt_1.4.0.zip)|[fastspeech2_male_zh_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_zh_static_1.4.0.zip) </br> [fastspeech2_male_zh_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_zh_onnx_1.4.0.zip) |146MB|
FastSpeech2| male-en ||[fastspeech2_male_en_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_en_ckpt_1.4.0.zip)|[fastspeech2_male_en_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_en_static_1.4.0.zip) </br> [fastspeech2_male_en_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_en_onnx_1.4.0.zip) |145MB|
FastSpeech2| male-mix ||[fastspeech2_male_mix_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_mix_ckpt_1.4.0.zip)|[fastspeech2_male_mix_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_mix_static_1.4.0.zip) </br> [fastspeech2_male_mix_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_mix_onnx_1.4.0.zip) |146MB|
FastSpeech2| Cantonese |[fastspeech2-canton](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/canton/tts3)|[fastspeech2_canton_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_canton_ckpt_1.4.0.zip)|[fastspeech2_canton_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_canton_static_1.4.0.zip)</br>[fastspeech2_canton_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_canton_onnx_1.4.0.zip)|146MB|

### Vocoders
Model Type | Dataset| Example Link | Pretrained Models| Static / ONNX / Paddle-Lite Models|Size (static)
:-----:| :-----:| :-----: | :-----:| :-----:| :-----:
WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip)|||
Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip) </br> [pwgan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_csmsc_onnx_0.2.0.zip) </br> [pwgan_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_csmsc_pdlite_1.3.0.zip)|4.8MB|
Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)|[pwgan_ljspeech_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_ljspeech_static_1.1.0.zip) </br> [pwgan_ljspeech_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_ljspeech_onnx_1.1.0.zip) </br> [pwgan_ljspeech_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_ljspeech_pdlite_1.3.0.zip)|4.8MB|
Parallel WaveGAN| AISHELL-3 |[PWGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1)|[pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip)| [pwgan_aishell3_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_aishell3_static_1.1.0.zip) </br> [pwgan_aishell3_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_aishell3_onnx_1.1.0.zip) </br> [pwgan_aishell3_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_aishell3_pdlite_1.3.0.zip)|4.8MB|
Parallel WaveGAN| VCTK |[PWGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1)|[pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip)|[pwgan_vctk_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_vctk_static_1.1.0.zip) </br> [pwgan_vctk_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_vctk_onnx_1.1.0.zip) </br> [pwgan_vctk_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_vctk_pdlite_1.3.0.zip)|4.8MB|
|Multi Band MelGAN | CSMSC |[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [mb_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip) <br>[mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip)|[mb_melgan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip) </br> [mb_melgan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip) </br> [mb_melgan_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_pdlite_1.3.0.zip)|7.6MB|
Style MelGAN | CSMSC |[Style MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc4)|[style_melgan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip)| | |
HiFiGAN | CSMSC |[HiFiGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc5)|[hifigan_csmsc_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip)|[hifigan_csmsc_static_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip) </br> [hifigan_csmsc_onnx_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip) </br> [hifigan_csmsc_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_pdlite_1.3.0.zip)|46MB|
HiFiGAN | LJSpeech |[HiFiGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc5)|[hifigan_ljspeech_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip)|[hifigan_ljspeech_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_static_1.1.0.zip) </br> [hifigan_ljspeech_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_onnx_1.1.0.zip) </br> [hifigan_ljspeech_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_pdlite_1.3.0.zip) |49MB|
HiFiGAN | AISHELL-3 |[HiFiGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc5)|[hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip)|[hifigan_aishell3_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_static_1.1.0.zip) </br> [hifigan_aishell3_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_onnx_1.1.0.zip) </br> [hifigan_aishell3_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_pdlite_1.3.0.zip)|46MB|
HiFiGAN | VCTK |[HiFiGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc5)|[hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip)|[hifigan_vctk_static_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_static_1.1.0.zip) </br> [hifigan_vctk_onnx_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_onnx_1.1.0.zip) </br> [hifigan_vctk_pdlite_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_pdlite_1.3.0.zip)|46MB|
WaveRNN | CSMSC |[WaveRNN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc6)|[wavernn_csmsc_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip)|[wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip)|18MB|
Parallel WaveGAN| Male ||[pwg_male_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_male_ckpt_1.4.0.zip)|[pwgan_male_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_male_static_1.4.0.zip) </br> [pwgan_male_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwgan_male_onnx_1.4.0.zip)|4.8M|
HiFiGAN| Male ||[hifigan_male_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_male_ckpt_1.4.0.zip)|[hifigan_male_static_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_male_static_1.4.0.zip) </br> [hifigan_male_onnx_1.4.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_male_onnx_1.4.0.zip)|46M|

### Voice Cloning
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----: | 
GE2E| AISHELL-3, etc. |[ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e)|[ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip)
GE2E + Tacotron2| AISHELL-3 |[ge2e-Tacotron2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc0)|[tacotron2_aishell3_ckpt_vc0_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_vc0_0.2.0.zip)
GE2E + FastSpeech2 | AISHELL-3  |[ge2e-fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc1)|[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip)

## Audio Classification Models

Model Type | Dataset| Example Link | Pretrained Models | Static Models 
:-------------:| :------------:| :-----: | :-----: | :-----:
PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz)(18M), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz)(19M), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz)(289M) 
PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)

## Speaker Verification Models

Model Type | Dataset| Example Link | Pretrained Models | Static Models 
:-------------:| :------------:| :-----: | :-----: | :-----:
ECAPA-TDNN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1.tar.gz) | -

## Punctuation Restoration Models
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----:
Ernie Linear | IWLST2012_zh |[iwslt2012_punc0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/iwslt2012/punc0)|[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip)

一键复制编辑原始数据按行查看历史

提交于 2023-03-10 11:30 . [ASR] add wav2vec2 aishell model result, test=asr (#3012)

Released Models

Speech-to-Text Models

Speech Recognition Model

Acoustic Model	Training Data	Token-based	Size	Descriptions	CER	WER	Hours of speech	Example Link	Inference Type	static_model
Ds2 Online Wenetspeech ASR0 Model	Wenetspeech Dataset	Char-based	1.2 GB	2 Conv + 5 LSTM layers	0.152 (test_net, w/o LM) 0.2417 (test_meeting, w/o LM) 0.053 (aishell, w/ LM)	-	10000 h	-	onnx/inference/python	-
Ds2 Online Aishell ASR0 Model	Aishell Dataset	Char-based	491 MB	2 Conv + 5 LSTM layers	0.0666	-	151 h	D2 Online Aishell ASR0	onnx/inference/python	-
Ds2 Offline Aishell ASR0 Model	Aishell Dataset	Char-based	1.4 GB	2 Conv + 5 bidirectional LSTM layers	0.0554	-	151 h	Ds2 Offline Aishell ASR0	inference/python	-
Conformer Online Wenetspeech ASR1 Model	WenetSpeech Dataset	Char-based	457 MB	Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring	0.11 (test_net) 0.1879 (test_meeting)	-	10000 h	-	python	-
Conformer U2PP Online Wenetspeech ASR1 Model	WenetSpeech Dataset	Char-based	540 MB	Encoder:Conformer, Decoder:BiTransformer, Decoding method: Attention rescoring	0.047198 (aishell test_-1) 0.059212 (aishell test_16)	-	10000 h	-	python	FP32 INT8
Conformer Online Aishell ASR1 Model	Aishell Dataset	Char-based	189 MB	Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring	0.0544	-	151 h	Conformer Online Aishell ASR1	python	-
Conformer Offline Aishell ASR1 Model	Aishell Dataset	Char-based	189 MB	Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring	0.0460	-	151 h	Conformer Offline Aishell ASR1	python	-
Transformer Aishell ASR1 Model	Aishell Dataset	Char-based	128 MB	Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring	0.0523		151 h	Transformer Aishell ASR1	python	-
Ds2 Offline Librispeech ASR0 Model	Librispeech Dataset	Char-based	1.3 GB	2 Conv + 5 bidirectional LSTM layers	-	0.0467	960 h	Ds2 Offline Librispeech ASR0	inference/python	-
Conformer Librispeech ASR1 Model	Librispeech Dataset	subword-based	191 MB	Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring	-	0.0338	960 h	Conformer Librispeech ASR1	python	-
Transformer Librispeech ASR1 Model	Librispeech Dataset	subword-based	131 MB	Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring	-	0.0381	960 h	Transformer Librispeech ASR1	python	-
Transformer Librispeech ASR2 Model	Librispeech Dataset	subword-based	131 MB	Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM	-	0.0240	960 h	Transformer Librispeech ASR2	python	-
Conformer TALCS ASR1 Model	TALCS Dataset	subword-based	470 MB	Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring	-	0.0844	587 h	Conformer TALCS ASR1	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	wav2vec2	Librispeech and LV-60k Dataset (5.3w h)	-	1.18 GB	Pre-trained Wav2vec2.0 Model	-	-	-
Wav2vec2ASR-large-960h-librispeech Model	wav2vec2	Librispeech and LV-60k Dataset (5.3w h)	Librispeech (960 h)	718 MB	Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search	-	0.0189	Wav2vecASR Librispeech ASR3
Wav2vec2-large-wenetspeech-self Model	wav2vec2	Wenetspeech Dataset (1w h)	-	714 MB	Pre-trained Wav2vec2.0 Model	-	-	-
Wav2vec2ASR-large-aishell1 Model	wav2vec2	Wenetspeech Dataset (1w h)	aishell1 (train set)	1.18 GB	Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search	0.0510	-	-

Whisper Model

Demo Link	Training Data	Size	Descriptions	CER	Model
Whisper	680kh from internet	large: 5.8G,medium: 2.9G,small: 923M,base: 277M,tiny: 145M	Encoder:Transformer, Decoder:Transformer, Decoding method: Greedy search	0.027 (large, Librispeech)	whisper-large whisper-medium whisper-medium-English-only whisper-small whisper-small-English-only whisper-base whisper-base-English-only whisper-tiny whisper-tiny-English-only

Language Model based on NGram

Language Model	Training Data	Token-based	Size	Descriptions
English LM	CommonCrawl(en.00)	Word-based	8.3 GB	Pruned with 0 1 1 1 1; About 1.85 billion n-grams; 'trie' binary with '-a 22 -q 8 -b 8'
Mandarin LM Small	Baidu Internal Corpus	Char-based	2.8 GB	Pruned with 0 1 2 4 4; About 0.13 billion n-grams; 'probing' binary with default settings
Mandarin LM Large	Baidu Internal Corpus	Char-based	70.4 GB	No Pruning; About 3.7 billion n-grams; 'probing' binary with default settings

Speech Translation Models

Model	Training Data	Token-based	Size	Descriptions	BLEU	Example Link
(only for CLI)Transformer FAT-ST MTL En-Zh	Ted-En-Zh	Spm		Encoder:Transformer, Decoder:Transformer, Decoding method: Attention	20.80	Transformer Ted-En-Zh ST1

Text-to-Speech Models

Acoustic Models

Model Type	Dataset	Example Link	Pretrained Models	Static / ONNX / Paddle-Lite Models	Size (static)
Tacotron2	LJSpeech	tacotron2-ljspeech	tacotron2_ljspeech_ckpt_0.2.0.zip
Tacotron2	CSMSC	tacotron2-csmsc	tacotron2_csmsc_ckpt_0.2.0.zip	tacotron2_csmsc_static_0.2.0.zip	103MB
TransformerTTS	LJSpeech	transformer-ljspeech	transformer_tts_ljspeech_ckpt_0.4.zip
SpeedySpeech	CSMSC	speedyspeech-csmsc	speedyspeech_csmsc_ckpt_0.2.0.zip	speedyspeech_csmsc_static_0.2.0.zip speedyspeech_csmsc_onnx_0.2.0.zip speedyspeech_csmsc_pdlite_1.3.0.zip	13MB
FastSpeech2	CSMSC	fastspeech2-csmsc	fastspeech2_nosil_baker_ckpt_0.4.zip	fastspeech2_csmsc_static_0.2.0.zip fastspeech2_csmsc_onnx_0.2.0.zip fastspeech2_csmsc_pdlite_1.3.0.zip	157MB
FastSpeech2-Conformer	CSMSC	fastspeech2-csmsc	fastspeech2_conformer_baker_ckpt_0.5.zip
FastSpeech2-CNNDecoder	CSMSC	fastspeech2-csmsc	fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip	fastspeech2_cnndecoder_csmsc_static_1.0.0.zip fastspeech2_cnndecoder_csmsc_streaming_static_1.0.0.zip fastspeech2_cnndecoder_csmsc_onnx_1.0.0.zip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip fastspeech2_cnndecoder_csmsc_pdlite_1.3.0.zip fastspeech2_cnndecoder_csmsc_streaming_pdlite_1.3.0.zip	84MB
FastSpeech2	AISHELL-3	fastspeech2-aishell3	fastspeech2_aishell3_ckpt_1.1.0.zip	fastspeech2_aishell3_static_1.1.0.zip fastspeech2_aishell3_onnx_1.1.0.zip fastspeech2_aishell3_pdlite_1.3.0.zip	147MB
FastSpeech2	LJSpeech	fastspeech2-ljspeech	fastspeech2_nosil_ljspeech_ckpt_0.5.zip	fastspeech2_ljspeech_static_1.1.0.zip fastspeech2_ljspeech_onnx_1.1.0.zip fastspeech2_ljspeech_pdlite_1.3.0.zip	145MB
FastSpeech2	VCTK	fastspeech2-vctk	fastspeech2_vctk_ckpt_1.2.0.zip	fastspeech2_vctk_static_1.1.0.zip fastspeech2_vctk_onnx_1.1.0.zip fastspeech2_vctk_pdlite_1.3.0.zip	145MB
FastSpeech2	ZH_EN	fastspeech2-zh_en	fastspeech2_mix_ckpt_1.2.0.zip	fastspeech2_mix_static_0.2.0.zip fastspeech2_mix_onnx_0.2.0.zip	145MB
FastSpeech2	male-zh		fastspeech2_male_zh_ckpt_1.4.0.zip	fastspeech2_male_zh_static_1.4.0.zip fastspeech2_male_zh_onnx_1.4.0.zip	146MB
FastSpeech2	male-en		fastspeech2_male_en_ckpt_1.4.0.zip	fastspeech2_male_en_static_1.4.0.zip fastspeech2_male_en_onnx_1.4.0.zip	145MB
FastSpeech2	male-mix		fastspeech2_male_mix_ckpt_1.4.0.zip	fastspeech2_male_mix_static_1.4.0.zip fastspeech2_male_mix_onnx_1.4.0.zip	146MB
FastSpeech2	Cantonese	fastspeech2-canton	fastspeech2_canton_ckpt_1.4.0.zip	fastspeech2_canton_static_1.4.0.zip fastspeech2_canton_onnx_1.4.0.zip	146MB

Vocoders

Model Type	Dataset	Example Link	Pretrained Models	Static / ONNX / Paddle-Lite Models	Size (static)
WaveFlow	LJSpeech	waveflow-ljspeech	waveflow_ljspeech_ckpt_0.3.zip
Parallel WaveGAN	CSMSC	PWGAN-csmsc	pwg_baker_ckpt_0.4.zip	pwg_baker_static_0.4.zip pwgan_csmsc_onnx_0.2.0.zip pwgan_csmsc_pdlite_1.3.0.zip	4.8MB
Parallel WaveGAN	LJSpeech	PWGAN-ljspeech	pwg_ljspeech_ckpt_0.5.zip	pwgan_ljspeech_static_1.1.0.zip pwgan_ljspeech_onnx_1.1.0.zip pwgan_ljspeech_pdlite_1.3.0.zip	4.8MB
Parallel WaveGAN	AISHELL-3	PWGAN-aishell3	pwg_aishell3_ckpt_0.5.zip	pwgan_aishell3_static_1.1.0.zip pwgan_aishell3_onnx_1.1.0.zip pwgan_aishell3_pdlite_1.3.0.zip	4.8MB
Parallel WaveGAN	VCTK	PWGAN-vctk	pwg_vctk_ckpt_0.5.zip	pwgan_vctk_static_1.1.0.zip pwgan_vctk_onnx_1.1.0.zip pwgan_vctk_pdlite_1.3.0.zip	4.8MB
Multi Band MelGAN	CSMSC	MB MelGAN-csmsc	mb_melgan_csmsc_ckpt_0.1.1.zip mb_melgan_baker_finetune_ckpt_0.5.zip	mb_melgan_csmsc_static_0.1.1.zip mb_melgan_csmsc_onnx_0.2.0.zip mb_melgan_csmsc_pdlite_1.3.0.zip	7.6MB
Style MelGAN	CSMSC	Style MelGAN-csmsc	style_melgan_csmsc_ckpt_0.1.1.zip
HiFiGAN	CSMSC	HiFiGAN-csmsc	hifigan_csmsc_ckpt_0.1.1.zip	hifigan_csmsc_static_0.1.1.zip hifigan_csmsc_onnx_0.2.0.zip hifigan_csmsc_pdlite_1.3.0.zip	46MB
HiFiGAN	LJSpeech	HiFiGAN-ljspeech	hifigan_ljspeech_ckpt_0.2.0.zip	hifigan_ljspeech_static_1.1.0.zip hifigan_ljspeech_onnx_1.1.0.zip hifigan_ljspeech_pdlite_1.3.0.zip	49MB
HiFiGAN	AISHELL-3	HiFiGAN-aishell3	hifigan_aishell3_ckpt_0.2.0.zip	hifigan_aishell3_static_1.1.0.zip hifigan_aishell3_onnx_1.1.0.zip hifigan_aishell3_pdlite_1.3.0.zip	46MB
HiFiGAN	VCTK	HiFiGAN-vctk	hifigan_vctk_ckpt_0.2.0.zip	hifigan_vctk_static_1.1.0.zip hifigan_vctk_onnx_1.1.0.zip hifigan_vctk_pdlite_1.3.0.zip	46MB
WaveRNN	CSMSC	WaveRNN-csmsc	wavernn_csmsc_ckpt_0.2.0.zip	wavernn_csmsc_static_0.2.0.zip	18MB
Parallel WaveGAN	Male		pwg_male_ckpt_1.4.0.zip	pwgan_male_static_1.4.0.zip pwgan_male_onnx_1.4.0.zip	4.8M
HiFiGAN	Male		hifigan_male_ckpt_1.4.0.zip	hifigan_male_static_1.4.0.zip hifigan_male_onnx_1.4.0.zip	46M

Voice Cloning

Model Type	Dataset	Example Link	Pretrained Models
GE2E	AISHELL-3, etc.	ge2e	ge2e_ckpt_0.3.zip
GE2E + Tacotron2	AISHELL-3	ge2e-Tacotron2-aishell3	tacotron2_aishell3_ckpt_vc0_0.2.0.zip
GE2E + FastSpeech2	AISHELL-3	ge2e-fastspeech2-aishell3	fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip

Audio Classification Models

Model Type	Dataset	Example Link	Pretrained Models	Static Models
PANN	Audioset	audioset_tagging_cnn	panns_cnn6.pdparams, panns_cnn10.pdparams, panns_cnn14.pdparams	panns_cnn6_static.tar.gz(18M), panns_cnn10_static.tar.gz(19M), panns_cnn14_static.tar.gz(289M)
PANN	ESC-50	pann-esc50	esc50_cnn6.tar.gz, esc50_cnn10.tar.gz, esc50_cnn14.tar.gz

Speaker Verification Models

Model Type	Dataset	Example Link	Pretrained Models	Static Models
ECAPA-TDNN	VoxCeleb	voxceleb_ecapatdnn	ecapatdnn.tar.gz	-

Punctuation Restoration Models

Model Type	Dataset	Example Link	Pretrained Models
Ernie Linear	IWLST2012_zh	iwslt2012_punc0	ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip

Python

1

https://gitee.com/justwdh/DeepSpeech.git

git@gitee.com:justwdh/DeepSpeech.git

justwdh

DeepSpeech

DeepSpeech

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