From b8818991c0344282ba33aec0a7d28f455dc701eb Mon Sep 17 00:00:00 2001 From: Mingxue-Xu <92848346+Mingxue-Xu@users.noreply.github.com> Date: Thu, 28 Oct 2021 20:25:10 +0800 Subject: [PATCH] Update README.md Correct the mistakes mentioned by @zh794390558. --- README.md | 94 +++++++++++++++++++++++++++---------------------------- 1 file changed, 47 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index 468f42a6..7060a655 100644 --- a/README.md +++ b/README.md @@ -9,55 +9,48 @@ English | [简体中文](README_ch.md)

-

- Quick Start - | Tutorials - | Models List - +

+ 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 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. -# install prerequisites -sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev libsndfile1 -# `pip install paddlepaddle-gpu` instead if you are using GPU. -pip install paddlepaddle - -# 2.Then install PaddleSpeech. +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](#installation) +- [Alternative Installation](#alternative-installation) - [Quick Start](#quick-start) - [Models List](#models-list) - [Tutorials](#tutorials) @@ -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: ```shell cd examples/tiny/s0/ @@ -90,12 +86,13 @@ bash local/data.sh bash local/test.sh conf/deepspeech2.yaml ckptfile offline ``` -For TTS, try FastSpeech2 on LJSpeech: -- 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: ```shell bash ./local/preprocess.sh conf/default.yaml -bash ./local/synthesize_e2e.sh conf/default.yaml exp/default ckptfile +bash ./local/synthesize_e2e.sh conf/default.yaml ./model/fastspeech2/snapshot_iter_100000.pdz ./model/pwg/pwg_snapshot_iter_400000.pdz ``` @@ -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: +> Note: The `Link` should be code path rather than download links. + + @@ -125,7 +125,7 @@ The current hyperlinks redirect to [Previous Parakeet](https://github.com/Paddle - + @@ -200,7 +200,7 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model @@ -208,41 +208,41 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model - + - + @@ -250,26 +250,26 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model @@ -277,14 +277,14 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model
Acoustic Model Aishell2 Conv + 5 LSTM layers with only forward direction 2 Conv + 5 LSTM layers with only forward direction Ds2 Online Aishell Model Text Frontend   - chinese-fronted + chinese-fronted
Tacotron2 LJSpeech - tacotron2-vctk + tacotron2-vctk
TransformerTTS - transformer-ljspeech + transformer-ljspeech
SpeedySpeech CSMSC - speedyspeech-csmsc + speedyspeech-csmsc
FastSpeech2 AISHELL-3 - fastspeech2-aishell3 + fastspeech2-aishell3
VCTK fastspeech2-vctk fastspeech2-vctk
LJSpeech fastspeech2-ljspeech fastspeech2-ljspeech
CSMSC - fastspeech2-csmsc + fastspeech2-csmsc
WaveFlow LJSpeech - waveflow-ljspeech + waveflow-ljspeech
Parallel WaveGAN LJSpeech - PWGAN-ljspeech + PWGAN-ljspeech
VCTK - PWGAN-vctk + PWGAN-vctk
CSMSC - PWGAN-csmsc + PWGAN-csmsc
GE2E AISHELL-3, etc. - ge2e + ge2e
GE2E + Tactron2 AISHELL-3 - ge2e-tactron2-aishell3 + ge2e-tactron2-aishell3