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 and audio, 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). ##### Speech Translation
Input Audio Translations Result

我 在 这栋 建筑 的 古老 门上 敲门。
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: - **Ease of Use**: low barries to install, and [CLI](#quick-start) is available to quick-start your journey. - **Align to the State-of-the-Art**: we provide high-speed and ultra-lightweight models, and also cutting edge technology. - **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 Audio Classification, Speech Translation, Automatic Speech Recognition, Text-to-Speech Synthesis, 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 typical traditional audio tasks, we combine the workflows of the aforementioned tasks with other fields like Natural language processing (NLP) and Computer Vision (CV). ## Installation We strongly recommend our users to install PaddleSpeech in *Linux* with *python>=3.7* and *paddlepaddle>=2.2.0*, where `paddlespeech` can be easily installed with `pip`: ```python pip install paddlespeech ``` If you want to set up in other environment, please see the [installation](./docs/source/install.md) for all the alternatives. ## Quick Start Developers can have a try of our models with [PaddleSpeech Command Line](./paddlespeech/cli/README.md). Change `--input` to test your own audio/text. **Audio Classification** ```shell paddlespeech cls --input input.wav ``` **Automatic Speech Recognition** ```shell paddlespeech asr --lang zh --input input_16k.wav ``` **Speech Translation** (English to Chinese) ```shell paddlespeech st --input input_16k.wav ``` **Text-to-Speech** ```shell paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！" --output output.wav ``` If you want to try more functions like training and tuning, please have a look at [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. They are summarized in [released models](./docs/source/released_model.md) and attached with available pretrained models. **Speech-to-Text** 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
**Text-to-Speech** in PaddleSpeech 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
Transformer TTS 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
Multi Band MelGAN CSMSC Multi Band MelGAN-csmsc
Voice Cloning GE2E Librispeech, etc. ge2e
GE2E + Tactron2 AISHELL-3 ge2e-tactron2-aishell3
GE2E + FastSpeech2 AISHELL-3 ge2e-fastspeech2-aishell3
**Others**
Task Dataset Model Type Link
Audio Classification ESC-50 PANN pann-esc50
Speech Translation (English to Chinese) TED En-Zh Transformer + ASR MTL transformer-ted
FAT + Transformer + ASR MTL fat-st-ted
## Tutorials Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](https://paperswithcode.com/area/audio) and [Music SoTA](https://paperswithcode.com/area/music) give you an overview of the hot academic topics in the related area. 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) - [Installation](./docs/source/install.md) - Speech-to-Text - [Quick Start of Speech-to-Text](./docs/source/asr/quick_start.md) - [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 - [Quick Start of Text-to-Speech](./docs/source/tts/quick_start.md) - [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) - Audio Classification - Speech Translation - [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://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) 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.