Quick Start | Tutorials | Models List

------------------------------------------------------------------------------------ ![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) **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
Input Audio Recognition Result

I knocked at the door on the ancient side of the building.

我认为跑步最重要的就是给我带来了身体健康。
##### Text-to-Speech
Input Text Synthetic Audio
Life was like a box of chocolates, you never know what you're gonna get.
早上好，今天是2020/10/29，最低温度是-3°C。
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_models.md) with available pretrained models. Speech-to-Text module contains *Acoustic Model* and *Language Model*, with the following details:
Speech-to-Text Module Type Dataset Model Type Link
Acoustic Model Aishell DeepSpeech2 RNN + Conv based Models deepspeech2-aishell
Transformer based Attention Models u2.transformer.conformer-aishell
Librispeech Transformer based Attention Models deepspeech2-librispeech / transformer.conformer.u2-librispeech / transformer.conformer.u2-kaldi-librispeech
Alignment THCHS30 MFA mfa-thchs30
Language Model Ngram Language Model kenlm
TIMIT Unified Streaming & Non-streaming Two-pass u2-timit
PaddleSpeech Text-to-Speech mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
Text-to-Speech Module Type Model Type Dataset Link
Text Frontend   tn / g2p
Acoustic Model Tacotron2 LJSpeech tacotron2-ljspeech
TransformerTTS transformer-ljspeech
SpeedySpeech CSMSC speedyspeech-csmsc
FastSpeech2 AISHELL-3 / VCTK / LJSpeech / CSMSC fastspeech2-aishell3 / fastspeech2-vctk / fastspeech2-ljspeech / fastspeech2-csmsc
Vocoder WaveFlow LJSpeech waveflow-ljspeech
Parallel WaveGAN LJSpeech / VCTK / CSMSC PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc
Voice Cloning GE2E AISHELL-3, etc. ge2e
GE2E + Tactron2 AISHELL-3 ge2e-tactron2-aishell3
## 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! ## Citation 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.