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1.What is this repo or project? (You can reuse the repo description you used earlier because this section doesn’t have to be long.)
2.How does it work?
3.Who will use this repo or project?
4.What is the goal of this project?
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**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 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 lists](#models-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.
# Community
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!
If you are from China, we strongly recommend you join our PaddleSpeech WeChat group. Scan the following WeChat QR code and get in touch with the other developers in this community!
For *Text-To-Speech*, try FastSpeech2 on LJSpeech:
- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/), our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
- The pretrained models are seperated into two parts: [fastspeech2_nosil_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) and [pwg_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip). Please download then unzip to `./model/fastspeech2` and `./model/pwg` respectively.
- Assume your path to the dataset is `~/datasets/LJSpeech-1.1` and `./ljspeech_alignment` accordingly, preprocess your data and then use our pretrained model to synthesize:
If you want to try more functions like training and tuning, please see [Speech-To-Text getting started](./docs/source/asr/getting_started.md) and [Text-To-Speech Basic Use](./docs/source/tts/basic_usage.md).
PaddleSpeech supports a series of most popular models, summarized in released models [Speech-To-Text](./docs/source/asr/released_model.md)/[Text-To-Speech](./docs/source/tts/released_models.md) with available pretrained models.
<ahref ="https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm">English Language Model</a>
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<td>Baidu Internal Corpus</td>
<td>Mandarin Language Model Small</td>
<td>
Mandarin Language Model <ahref ="https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm"> Small</a> / <ahref ="https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm">Large</a>
PaddleSpeech Text-To-Speech mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
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)
- [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)
