**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for two critical tasks in Speech - **Automatic Speech Recognition (ASR)** and **Text-To-Speech Synthesis (TTS)**, with modules involving state-of-art and influential models.
**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 module, and deployment. Besides, this toolkit also features at:
- **Fast and Light-weight**: we provide a high-speed and ultra-lightweight model that is convenient for industrial deployment.
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 Research**:
- *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of both ASR and TTS, and uses datasets like LibriSpeech, LJSpeech, AIShell, etc. See also [model lists](#models-list) for more details.
- *Support of ASR streaming and non-streaming data*: This toolkit contains non-streaming/streaming models like [DeepSpeech2](http://proceedings.mlr.press/v48/amodei16.pdf), [Transformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100) and [U2](https://arxiv.org/pdf/2012.05481.pdf).
- **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
``` shell
# 1. Install essential libraries and paddlepaddle first.
@ -75,10 +68,13 @@ The base environment in this page is
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.
> Note: `ckptfile` should be replaced by real path that represents files or folders later. Similarly, `exp/default` is the folder that contains the pretrained models.
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.
Try a tiny ASR DeepSpeech2 model training on toy set of LibriSpeech:
Developers can have a try of our model with only a few lines of code.
A tiny *ASR* DeepSpeech2 model training on toy set of LibriSpeech:
- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/) and our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
For *TTS*, 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:
@ -104,14 +101,17 @@ If you want to try more functions like training and tuning, please see [ASR gett
## Models List
PaddleSpeech supports a series of most popular models, summarized in [released models](./docs/source/released_model.md) with available pretrained models.
PaddleSpeech ASR supports a lot of mainstream models, which are summarized as follow. For more information, please refer to [ASR Models](./docs/source/asr/released_model.md).
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).
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>
@ -125,7 +125,7 @@ The current hyperlinks redirect to [Previous Parakeet](https://github.com/Paddle
<tr>
<tdrowspan="6">Acoustic Model</td>
<tdrowspan="4">Aishell</td>
<td>2 Conv + 5 LSTM layers with only forward direction</td>
<td>2 Conv + 5 LSTM layers with only forward direction</td>
Hui Zhang
3 years ago
committed by
GitHub