# Released Models
## Speech-to-Text Models
### Speech Recognition Model
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link
:-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----: | :-----: | :-----:
[Ds2 Online Aishell ASR0 Model ](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz ) | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h | [D2 Online Aishell ASR0 ](../../examples/aishell/asr0 )
[Ds2 Offline Aishell ASR0 Model ](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz )| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h | [Ds2 Offline Aishell ASR0 ](../../examples/aishell/asr0 )
[Conformer Online Aishell ASR1 Model ](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz ) | Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0594 |-| 151 h | [Conformer Online Aishell ASR1 ](../../examples/aishell/asr1 )
[Conformer Offline Aishell ASR1 Model ](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz ) | Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0547 |-| 151 h | [Conformer Offline Aishell ASR1 ](../../examples/aishell/asr1 )
[Transformer Aishell ASR1 Model ](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.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 )
[Conformer Librispeech ASR1 Model ](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz ) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0337 | 960 h | [Conformer Librispeech ASR1 ](../../example/librispeech/asr1 )
[Transformer Librispeech ASR1 Model ](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/transformer.model.tar.gz ) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1 ](../../example/librispeech/asr1 )
[Transformer Librispeech ASR2 Model ](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.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 ](../../example/librispeech/asr2 )
### 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 |
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| [Transformer FAT-ST MTL En-Zh ](https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.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 Models|Size (static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
Tacotron2|LJSpeech|[tacotron2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3.zip)|||
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_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip)|[speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip)|12MB|
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_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.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| AISHELL-3 |[fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3)|[fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip)|||
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| VCTK |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip)|||
### Vocoders
Model Type | Dataset| Example Link | Pretrained Models| Static 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)|5.1MB|
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)|||
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)|||
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)|||
|Multi Band MelGAN |CSMSC|[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [mb_melgan_baker_ckpt_0.5.zip ](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_ckpt_0.5.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_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_static_0.5.zip) |8.2MB|
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)|50MB|
### 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 + Tactron2| AISHELL-3 |[ge2e-tactron2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc0)|[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_0.3.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
:-------------:| :------------:| :-----: | :-----:
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 )
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 )
## 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)
## Speech Recognition Model from paddle 1.8
| Acoustic Model |Training Data| Token-based | Size | Descriptions | CER | WER | Hours of speech |
| :-----:| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| [Ds2 Offline Aishell model ](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz ) | Aishell Dataset | Char-based | 234 MB | 2 Conv + 3 bidirectional GRU layers | 0.0804 | — | 151 h |
| [Ds2 Offline Librispeech model ](https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz ) | Librispeech Dataset | Word-based | 307 MB | 2 Conv + 3 bidirectional sharing weight RNN layers | — | 0.0685 | 960 h |
| [Ds2 Offline Baidu en8k model ](https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz ) | Baidu Internal English Dataset | Word-based | 273 MB | 2 Conv + 3 bidirectional GRU layers |— | 0.0541 | 8628 h|