< p align = "center" >
< img src = "./docs/images/PaddleSpeech_logo.png" / >
< / p >
< div align = "center" >
< h3 >
< a href = "#quick-start" > Quick Start < / a >
| < a href = "#tutorials" > Tutorials < / a >
| < a href = "#model-list" > Models List < / a >
< / div >
------------------------------------------------------------------------------------
<!-- -
from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readmes-readable.md
1.What is this repo or project? (You can reuse the repo description you used earlier because this section doesn’
2.How does it work?
3.Who will use this repo or project?
4.What is the goal of this project?
-->
**PaddleSpeech** is an open-source toolkit on [PaddlePaddle ](https://github.com/PaddlePaddle/Paddle ) platform for a variety of critical tasks in speech, with the state-of-art and influential models.
##### Speech-to-Text
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th > Input Audio < / th >
< th width = "550" > Recognition Result < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td align = "center" >
< a href = "https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav" rel = "nofollow" >
< img align = "center" src = "./docs/images/audio_icon.png" width = "200 style=" max-width: 100 % ; " > < / a > < br >
< / td >
< td > I knocked at the door on the ancient side of the building.< / td >
< / tr >
< tr >
< td align = "center" >
< a href = "https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav" rel = "nofollow" >
< img align = "center" src = "./docs/images/audio_icon.png" width = "200" style = "max-width: 100%;" > < / a > < br >
< / td >
< td > 我认为跑步最重要的就是给我带来了身体健康。< / td >
< / tr >
< / tbody >
< / table >
< / div >
##### Text-to-Speech
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th > < img width = "200" height = "1" > Input Text < img width = "200" height = "1" > < / th >
< th > Synthetic Audio< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > Life was like a box of chocolates, you never know what you're gonna get.< / td >
< td align = "center" >
< a href = "https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/transformer_tts_ljspeech_ckpt_0.4_waveflow_ljspeech_ckpt_0.3/001.wav" rel = "nofollow" >
< img align = "center" src = "./docs/images/audio_icon.png" width = "200" style = "max-width: 100%;" > < / a > < br >
< / td >
< / tr >
< tr >
< td > 早上好， ， < / td >
< td align = "center" >
< a href = "https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/parakeet_espnet_fs2_pwg_demo/tn_g2p/parakeet/001.wav" rel = "nofollow" >
< img align = "center" src = "./docs/images/audio_icon.png" width = "200" style = "max-width: 100%;" > < / a > < br >
< / td >
< / tr >
< / tbody >
< / table >
< / div >
For more synthesized audios, please refer to [PaddleSpeech Text-to-Speech samples ](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html ).
Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. To be more specific, this toolkit features at:
- **Fast and Light-weight**: we provide high-speed and ultra-lightweight models that are convenient for industrial deployment.
- **Rule-based Chinese frontend**: our frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
- **Varieties of Functions that Vitalize both Industrial and Academia**:
- *Implementation of critical audio tasks*: this toolkit contains audio functions like Speech Translation, Automatic Speech Recognition, Text-to-Speech Synthesis, Voice Cloning, etc.
- *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of the speech tasks, and uses mainstream datasets like LibriSpeech, LJSpeech, AIShell, CSMSC, etc. See also [model list ](#model-list ) for more details.
- *Cascaded models application*: as an extension of the application of traditional audio tasks, we combine the workflows of aforementioned tasks with other fields like Natural language processing (NLP), like Punctuation Restoration.
## Installation
The base environment in this page is
- Ubuntu 16.04
- python>=3.7
- paddlepaddle>=2.2.0
If you want to set up PaddleSpeech in other environment, please see the [installation ](./docs/source/install.md ) documents for all the alternatives.
## Quick Start
Developers can have a try of our model with only a few lines of code.
A tiny DeepSpeech2 **Speech-to-Text** model training on toy set of LibriSpeech:
```shell
cd examples/tiny/s0/
# source the environment
source path.sh
source ../../../utils/parse_options.sh
# prepare data
bash ./local/data.sh
# train model, all `ckpt` under `exp` dir, if you use paddlepaddle-gpu, you can set CUDA_VISIBLE_DEVICES before the train script
./local/train.sh conf/deepspeech2.yaml deepspeech2 offline
# avg n best model to get the test model, in this case, n = 1
avg.sh best exp/deepspeech2/checkpoints 1
# evaluate the test model
./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 offline
```
For **Text-To-Speech** , try pretrained FastSpeech2 + Parallel WaveGAN on CSMSC:
```shell
cd examples/csmsc/tts3
# download the pretrained models and unaip them
wget https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
unzip pwg_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip fastspeech2_nosil_baker_ckpt_0.4.zip
# source the environment
source path.sh
# run end-to-end synthesize
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
--fastspeech2-config=fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
--fastspeech2-checkpoint=fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
--fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
--pwg-config=pwg_baker_ckpt_0.4/pwg_default.yaml \
--pwg-checkpoint=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
--pwg-stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
--text=${BIN_DIR}/../sentences.txt \
--output-dir=exp/default/test_e2e \
--inference-dir=exp/default/inference \
--phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
```
If you want to try more functions like training and tuning, please see [Speech-to-Text Quick Start ](./docs/source/asr/quick_start.md ) and [Text-To-Speech Quick Start ](./docs/source/tts/quick_start.md ).
## Model List
PaddleSpeech supports a series of most popular models, summarized in [released models ](./docs/source/released_model.md ) with available pretrained models.
Speech-to-Text module contains *Acoustic Model* and *Language Model* , with the following details:
<!-- -
The current hyperlinks redirect to [Previous Parakeet ](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples ).
-->
< table style = "width:100%" >
< thead >
< tr >
< th > Speech-to-Text Module Type< / th >
< th > Dataset< / th >
< th > Model Type< / th >
< th > Link< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td rowspan = "3" > Acoustic Model< / td >
< td rowspan = "2" > Aishell< / td >
< td > DeepSpeech2 RNN + Conv based Models< / td >
< td >
< a href = "./examples/aishell/s0" > deepspeech2-aishell< / a >
< / td >
< / tr >
< tr >
< td > Transformer based Attention Models < / td >
< td >
< a href = "./examples/aishell/s1" > u2.transformer.conformer-aishell< / a >
< / td >
< / tr >
< tr >
< td > Librispeech< / td >
< td > Transformer based Attention Models < / td >
< td >
< a href = "./examples/librispeech/s0" > deepspeech2-librispeech< / a > / < a href = "./examples/librispeech/s1" > transformer.conformer.u2-librispeech< / a > / < a href = "./examples/librispeech/s2" > transformer.conformer.u2-kaldi-librispeech< / a >
< / td >
< / td >
< / tr >
< tr >
< td > Alignment< / td >
< td > THCHS30< / td >
< td > MFA< / td >
< td >
< a href = ".examples/thchs30/a0" > mfa-thchs30< / a >
< / td >
< / tr >
< tr >
< td rowspan = "2" > Language Model< / td >
< td colspan = "2" > Ngram Language Model< / td >
< td >
< a href = "./examples/other/ngram_lm" > kenlm< / a >
< / td >
< / tr >
< tr >
< td > TIMIT< / td >
< td > Unified Streaming & Non-streaming Two-pass< / td >
< td >
< a href = "./examples/timit/s1" > u2-timit< / a >
< / td >
< / tr >
< / tbody >
< / table >
PaddleSpeech Text-to-Speech mainly contains three modules: *Text Frontend* , *Acoustic Model* and *Vocoder* . Acoustic Model and Vocoder models are listed as follow:
< table >
< thead >
< tr >
< th > Text-to-Speech Module Type < img width = "110" height = "1" > < / th >
< th > Model Type < / th >
< th > < img width = "50" height = "1" > Dataset < img width = "50" height = "1" > < / th >
< th > < img width = "101" height = "1" > Link < img width = "105" height = "1" > < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > Text Frontend< / td >
< td colspan = "2" >   < / td >
< td >
< a href = "./examples/other/tn" > tn< / a > / < a href = "./examples/other/g2p" > g2p< / a >
< / td >
< / tr >
< tr >
< td rowspan = "4" > Acoustic Model< / td >
< td > Tacotron2< / td >
< td rowspan = "2" > LJSpeech< / td >
< td >
< a href = "./examples/ljspeech/tts0" > tacotron2-ljspeech< / a >
< / td >
< / tr >
< tr >
< td > TransformerTTS< / td >
< td >
< a href = "./examples/ljspeech/tts1" > transformer-ljspeech< / a >
< / td >
< / tr >
< tr >
< td > SpeedySpeech< / td >
< td > CSMSC< / td >
< td >
< a href = "./examples/csmsc/tts2" > speedyspeech-csmsc< / a >
< / td >
< / tr >
< tr >
< td > FastSpeech2< / td >
< td > AISHELL-3 / VCTK / LJSpeech / CSMSC< / td >
< td >
< a href = "./examples/aishell3/tts3" > fastspeech2-aishell3< / a > / < a href = "./examples/vctk/tts3" > fastspeech2-vctk< / a > / < a href = "./examples/ljspeech/tts3" > fastspeech2-ljspeech< / a > / < a href = "./examples/csmsc/tts3" > fastspeech2-csmsc< / a >
< / td >
< / tr >
< tr >
< td rowspan = "2" > Vocoder< / td >
< td > WaveFlow< / td >
< td > LJSpeech< / td >
< td >
< a href = "./examples/ljspeech/voc0" > waveflow-ljspeech< / a >
< / td >
< / tr >
< tr >
< td > Parallel WaveGAN< / td >
< td > LJSpeech / VCTK / CSMSC< / td >
< td >
< a href = "./examples/ljspeech/voc1" > PWGAN-ljspeech< / a > / < a href = "./examples/vctk/voc1" > PWGAN-vctk< / a > / < a href = "./examples/csmsc/voc1" > PWGAN-csmsc< / a >
< / td >
< / tr >
< tr >
< td rowspan = "2" > Voice Cloning< / td >
< td > GE2E< / td >
< td > AISHELL-3, etc.< / td >
< td >
< a href = "./examples/other/ge2e" > ge2e< / a >
< / td >
< / tr >
< tr >
< td > GE2E + Tactron2< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vc0" > ge2e-tactron2-aishell3< / a >
< / td >
< / td >
< / tr >
< / tbody >
< / table >
## Tutorials
Normally, [Speech SoTA ](https://paperswithcode.com/area/speech ) gives you an overview of the hot academic topics in speech. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
- [Overview ](./docs/source/introduction.md )
- Quick Start
- [Dependencies ](./docs/source/dependencies.md ) and [Installation ](./docs/source/install.md )
- [Quick Start of Speech-to-Text ](./docs/source/asr/quick_start.md )
- [Quick Start of Text-to-Speech ](./docs/source/tts/quick_start.md )
- Speech-to-Text
- [Models Introduction ](./docs/source/asr/models_introduction.md )
- [Data Preparation ](./docs/source/asr/data_preparation.md )
- [Data Augmentation Pipeline ](./docs/source/asr/augmentation.md )
- [Features ](./docs/source/asr/feature_list.md )
- [Ngram LM ](./docs/source/asr/ngram_lm.md )
- Text-to-Speech
- [Introduction ](./docs/source/tts/models_introduction.md )
- [Advanced Usage ](./docs/source/tts/advanced_usage.md )
- [Chinese Rule Based Text Frontend ](./docs/source/tts/zh_text_frontend.md )
- [Test Audio Samples ](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html ) and [PaddleSpeech VS. Espnet ](https://paddlespeech.readthedocs.io/en/latest/tts/demo_2.html )
- [Released Models ](./docs/source/released_model.md )
The TTS module is originally called [Parakeet ](https://github.com/PaddlePaddle/Parakeet ), and now merged with DeepSpeech. If you are interested in academic research about this function, please see [TTS research overview ](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview ). Also, [this document ](https://paddleparakeet.readthedocs.io/en/latest/released_models.html ) is a good guideline for the pipeline components.
## FAQ and Contributing
You are warmly welcome to submit questions in [discussions ](https://github.com/PaddlePaddle/PaddleSpeech/discussions ) and bug reports in [issues ](https://github.com/PaddlePaddle/PaddleSpeech/issues )! Also, we highly appreciate if you would like to contribute to this project!
E2E/Streaming Transformer/Conformer ASR (#578)
* add cmvn and label smoothing loss layer
* add layer for transformer
* add glu and conformer conv
* add torch compatiable hack, mask funcs
* not hack size since it exists
* add test; attention
* add attention, common utils, hack paddle
* add audio utils
* conformer batch padding mask bug fix #223
* fix typo, python infer fix rnn mem opt name error and batchnorm1d, will be available at 2.0.2
* fix ci
* fix ci
* add encoder
* refactor egs
* add decoder
* refactor ctc, add ctc align, refactor ckpt, add warmup lr scheduler, cmvn utils
* refactor docs
* add fix
* fix readme
* fix bugs, refactor collator, add pad_sequence, fix ckpt bugs
* fix docstring
* refactor data feed order
* add u2 model
* refactor cmvn, test
* add utils
* add u2 config
* fix bugs
* fix bugs
* fix autograd maybe has problem when using inplace operation
* refactor data, build vocab; add format data
* fix text featurizer
* refactor build vocab
* add fbank, refactor feature of speech
* refactor audio feat
* refactor data preprare
* refactor data
* model init from config
* add u2 bins
* flake8
* can train
* fix bugs, add coverage, add scripts
* test can run
* fix data
* speed perturb with sox
* add spec aug
* fix for train
* fix train logitc
* fix logger
* log valid loss, time dataset process
* using np for speed perturb, remove some debug log of grad clip
* fix logger
* fix build vocab
* fix logger name
* using module logger as default
* fix
* fix install
* reorder imports
* fix board logger
* fix logger
* kaldi fbank and mfcc
* fix cmvn and print prarams
* fix add_eos_sos and cmvn
* fix cmvn compute
* fix logger and cmvn
* fix subsampling, label smoothing loss, remove useless
* add notebook test
* fix log
* fix tb logger
* multi gpu valid
* fix log
* fix log
* fix config
* fix compute cmvn, need paddle 2.1
* add cmvn notebook
* fix layer tools
* fix compute cmvn
* add rtf
* fix decoding
* fix layer tools
* fix log, add avg script
* more avg and test info
* fix dataset pickle problem; using 2.1 paddle; num_workers can > 0; ckpt save in exp dir;fix setup.sh;
* add vimrc
* refactor tiny script, add transformer and stream conf
* spm demo; librisppech scripts and confs
* fix log
* add librispeech scripts
* refactor data pipe; fix conf; fix u2 default params
* fix bugs
* refactor aishell scripts
* fix test
* fix cmvn
* fix s0 scripts
* fix ds2 scripts and bugs
* fix dev & test dataset filter
* fix dataset filter
* filter dev
* fix ckpt path
* filter test, since librispeech will cause OOM, but all test wer will be worse, since mismatch train with test
* add comment
* add syllable doc
* fix ds2 configs
* add doc
* add pypinyin tools
* fix decoder using blank_id=0
* mmseg with pybind11
* format code
4 years ago
## Citation
E2E/Streaming Transformer/Conformer ASR (#578)
* add cmvn and label smoothing loss layer
* add layer for transformer
* add glu and conformer conv
* add torch compatiable hack, mask funcs
* not hack size since it exists
* add test; attention
* add attention, common utils, hack paddle
* add audio utils
* conformer batch padding mask bug fix #223
* fix typo, python infer fix rnn mem opt name error and batchnorm1d, will be available at 2.0.2
* fix ci
* fix ci
* add encoder
* refactor egs
* add decoder
* refactor ctc, add ctc align, refactor ckpt, add warmup lr scheduler, cmvn utils
* refactor docs
* add fix
* fix readme
* fix bugs, refactor collator, add pad_sequence, fix ckpt bugs
* fix docstring
* refactor data feed order
* add u2 model
* refactor cmvn, test
* add utils
* add u2 config
* fix bugs
* fix bugs
* fix autograd maybe has problem when using inplace operation
* refactor data, build vocab; add format data
* fix text featurizer
* refactor build vocab
* add fbank, refactor feature of speech
* refactor audio feat
* refactor data preprare
* refactor data
* model init from config
* add u2 bins
* flake8
* can train
* fix bugs, add coverage, add scripts
* test can run
* fix data
* speed perturb with sox
* add spec aug
* fix for train
* fix train logitc
* fix logger
* log valid loss, time dataset process
* using np for speed perturb, remove some debug log of grad clip
* fix logger
* fix build vocab
* fix logger name
* using module logger as default
* fix
* fix install
* reorder imports
* fix board logger
* fix logger
* kaldi fbank and mfcc
* fix cmvn and print prarams
* fix add_eos_sos and cmvn
* fix cmvn compute
* fix logger and cmvn
* fix subsampling, label smoothing loss, remove useless
* add notebook test
* fix log
* fix tb logger
* multi gpu valid
* fix log
* fix log
* fix config
* fix compute cmvn, need paddle 2.1
* add cmvn notebook
* fix layer tools
* fix compute cmvn
* add rtf
* fix decoding
* fix layer tools
* fix log, add avg script
* more avg and test info
* fix dataset pickle problem; using 2.1 paddle; num_workers can > 0; ckpt save in exp dir;fix setup.sh;
* add vimrc
* refactor tiny script, add transformer and stream conf
* spm demo; librisppech scripts and confs
* fix log
* add librispeech scripts
* refactor data pipe; fix conf; fix u2 default params
* fix bugs
* refactor aishell scripts
* fix test
* fix cmvn
* fix s0 scripts
* fix ds2 scripts and bugs
* fix dev & test dataset filter
* fix dataset filter
* filter dev
* fix ckpt path
* filter test, since librispeech will cause OOM, but all test wer will be worse, since mismatch train with test
* add comment
* add syllable doc
* fix ds2 configs
* add doc
* add pypinyin tools
* fix decoder using blank_id=0
* mmseg with pybind11
* format code
4 years ago
To cite PaddleSpeech for research, please use the following format.
```tex
@misc {ppspeech2021,
title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleSpeech}},
year={2021}
}
```
## License and Acknowledge
PaddleSpeech is provided under the [Apache-2.0 License ](./LICENSE ).
PaddleSpeech depends on a lot of open source repositories. See [references ](./docs/source/reference.md ) for more information.
