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PaddleSpeech/README.md

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English | [简体中文](README_ch.md)
# PaddleSpeech
<p align="center">
<img src="./docs/images/PaddleSpeech_log.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>
------------------------------------------------------------------------------------
3 years ago
![License](https://img.shields.io/badge/license-Apache%202-red.svg)
![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
![support os](https://img.shields.io/badge/os-linux-yellow.svg)
<!---
why they should use your module,
how they can install it,
how they can use it
-->
**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for a variety of critical tasks in speech, with state-of-art and influential models.
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 (TN) 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 (ST), Automatic Speech Recognition (ASR), Text-To-Speech Synthesis (TTS), Voice Cloning(VC), Punctuation Restoration, 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 lists](#models-list) for more details.
- *Cross-domain application*: as an extension of the application of traditional audio tasks, we combine the aforementioned tasks with other fields like NLP.
Let's install PaddleSpeech with only a few lines of code!
>Note: The official name is still deepspeech. 2021/10/26
If you are using Ubuntu, PaddleSpeech can be set up with pip installation (with root privilege).
```shell
git clone https://github.com/PaddlePaddle/DeepSpeech.git
cd DeepSpeech
pip install -e .
```
## Table of Contents
The contents of this README is as follow:
- [Alternative Installation](#alternative-installation)
- [Quick Start](#quick-start)
- [Models List](#models-list)
- [Tutorials](#tutorials)
- [FAQ and Contributing](#faq-and-contributing)
- [License](#license)
- [Acknowledgement](#acknowledgement)
## Alternative Installation
The base environment in this page is
- Ubuntu 16.04
- python>=3.7
- paddlepaddle==2.1.2
If you want to set up PaddleSpeech in other environment, please see the [ASR installation](docs/source/asr/install.md) and [TTS installation](docs/source/tts/install.md) documents for all the alternatives.
## Quick Start
> Note: the current links to `English ASR` and `English TTS` are not valid.
3 years ago
Just a quick test of our functions: [English ASR](link/hubdetail?name=deepspeech2_aishell&en_category=AutomaticSpeechRecognition) and [English TTS](link/hubdetail?name=fastspeech2_baker&en_category=TextToSpeech) by typing message or upload your own audio file.
Developers can have a try of our model with only a few lines of code.
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A tiny **ASR** DeepSpeech2 model training on toy set of LibriSpeech:
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```bash
cd examples/tiny/s0/
# source the environment
source path.sh
# prepare librispeech dataset
bash local/data.sh
# evaluate your ckptfile model file
bash local/test.sh conf/deepspeech2.yaml ckptfile offline
```
For **TTS**, try pretrained FastSpeech2 + Parallel WaveGAN on CSMSC:
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```bash
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 \
--device="gpu" \
--phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
```
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
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If you want to try more functions like training and tuning, please see [ASR getting started](docs/source/asr/getting_started.md) and [TTS Basic Use](/docs/source/tts/basic_usage.md).
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## Models List
PaddleSpeech supports a series of most popular models, summarized in [released models](./docs/source/released_model.md) with available pretrained models.
ASR 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).
-->
> Note: The `Link` should be code path rather than download links.
<table>
<thead>
<tr>
<th>ASR Module Type</th>
<th>Dataset</th>
<th>Model Type</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="6">Acoustic Model</td>
<td rowspan="4" >Aishell</td>
<td >2 Conv + 5 LSTM layers with only forward direction</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds_online.5rnn.debug.tar.gz">Ds2 Online Aishell Model</a>
</td>
</tr>
<tr>
<td>2 Conv + 3 bidirectional GRU layers</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds2.offline.cer6p65.release.tar.gz">Ds2 Offline Aishell Model</a>
</td>
</tr>
<tr>
<td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention + CTC</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz">Conformer Offline Aishell Model</a>
</td>
</tr>
<tr>
<td >Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a>
</td>
</tr>
<tr>
<td rowspan="2"> Librispeech</td>
<td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
<td> <a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a> </td>
</tr>
<tr>
<td>Encoder:Transformer, Decoder:Transformer, Decoding method: Attention</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/transformer.release.tar.gz">Transformer Librispeech Model</a>
</td>
</tr>
<tr>
<td rowspan="3">Language Model</td>
<td >CommonCrawl(en.00)</td>
<td >English Language Model</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm">English Language Model</a>
</td>
</tr>
<tr>
<td rowspan="2">Baidu Internal Corpus</td>
<td>Mandarin Language Model Small</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm">Mandarin Language Model Small</a>
</td>
</tr>
<tr>
<td >Mandarin Language Model Large</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm">Mandarin Language Model Large</a>
</td>
</tr>
</tbody>
</table>
PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
<table>
<thead>
<tr>
<th>TTS Module Type</th>
<th>Model Type</th>
<th>Dataset</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td> Text Frontend</td>
<td colspan="2"> &emsp; </td>
<td>
<a href = "./examples/other/text_frontend">chinese-fronted</a>
</td>
</tr>
<tr>
<td rowspan="7">Acoustic Model</td>
<td >Tacotron2</td>
<td rowspan="2" >LJSpeech</td>
<td>
<a href = "./examples/ljspeech/tts0">tacotron2-vctk</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 rowspan="4">FastSpeech2</td>
<td>AISHELL-3</td>
<td>
<a href = "./examples/aishell3/tts3">fastspeech2-aishell3</a>
</td>
</tr>
<tr>
<td>VCTK</td>
<td> <a href = "./examples/vctk/tts3">fastspeech2-vctk</a> </td>
</tr>
<tr>
<td>LJSpeech</td>
<td> <a href = "./examples/ljspeech/tts3">fastspeech2-ljspeech</a> </td>
</tr>
<tr>
<td>CSMSC</td>
<td>
<a href = "./examples/csmsc/tts3">fastspeech2-csmsc</a>
</td>
</tr>
<tr>
<td rowspan="4">Vocoder</td>
<td >WaveFlow</td>
<td >LJSpeech</td>
<td>
<a href = "./examples/ljspeech/voc0">waveflow-ljspeech</a>
</td>
</tr>
<tr>
<td rowspan="3">Parallel WaveGAN</td>
<td >LJSpeech</td>
<td>
<a href = "./examples/ljspeech/voc1">PWGAN-ljspeech</a>
</td>
</tr>
<tr>
<td >VCTK</td>
<td>
<a href = "./examples/vctk/voc1">PWGAN-vctk</a>
</td>
</tr>
<tr>
<td >CSMSC</td>
<td>
<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. If you want to focus on the two tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
The original ASR module is based on [Baidu's DeepSpeech](https://arxiv.org/abs/1412.5567) which is an independent product named [DeepSpeech](https://deepspeech.readthedocs.io). However, the toolkit aligns almost all the SoTA modules in the pipeline. Specifically, these modules are
3 years ago
3 years ago
* [Data Prepration](docs/source/asr/data_preparation.md)
* [Data Augmentation](docs/source/asr/augmentation.md)
* [Ngram LM](docs/source/asr/ngram_lm.md)
* [Benchmark](docs/source/asr/benchmark.md)
* [Relased Model](docs/source/asr/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/DeepSpeech/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.
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## FAQ and Contributing
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You are warmly welcome to submit questions in [discussions](https://github.com/PaddlePaddle/DeepSpeech/discussions) and bug reports in [issues](https://github.com/PaddlePaddle/DeepSpeech/issues)! Also, we highly appreciate if you would like to contribute to this project!
3 years ago
## License
PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).
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
3 years ago
## Acknowledgement
PaddleSpeech depends on a lot of open source repos. See [references](docs/source/asr/reference.md) for more information.