released_model.md

Released Models

!!! Since PaddlePaddle support 0-D tensor from 2.5.0, PaddleSpeech Static model will not work for it, please re-export static model.

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	0.051968	-	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	-	-
Hubert-large-lv60 Model	hubert	LV-60k Dataset	-	1.18 GB	Pre-trained hubert Model	-	-	-
Hubert-large-100h-librispeech Model	hubert	LV-60k Dataset	librispeech train-clean-100	1.27 GB	Encoder: Hubert, Decoder: Linear + CTC, Decoding method: Greedy search	-	0.0587	HubertASR Librispeech ASR4

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