Quick Start | Tutorials | Models List

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PaddleSpeech is an open-source toolkit on PaddlePaddle 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.

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 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 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:

cd examples/tiny/asr0/
# 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:

cd examples/csmsc/tts3
# download the pretrained models and unaip them
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip
unzip pwg_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/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 and Text-to-Speech Quick Start.

Model List

PaddleSpeech supports a series of most popular models, summarized in released models 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
	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	AISHELL-3, etc.	ge2e
	GE2E + Tactron2	AISHELL-3	ge2e-tactron2-aishell3
	GE2E + FastSpeech2	AISHELL-3	ge2e-fastspeech2-aishell3

Tutorials

Normally, Speech SoTA 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
• Quick Start
  • Dependencies and Installation
  • Quick Start of Speech-to-Text
  • Quick Start of Text-to-Speech
• Speech-to-Text
  • Models Introduction
  • Data Preparation
  • Data Augmentation Pipeline
  • Features
  • Ngram LM
• Text-to-Speech
  • Introduction
  • Advanced Usage
  • Chinese Rule Based Text Frontend
  • Test Audio Samples and PaddleSpeech VS. Espnet
• Released Models

The TTS module is originally called Parakeet, and now merged with DeepSpeech. If you are interested in academic research about this function, please see TTS research overview. Also, this document is a good guideline for the pipeline components.

FAQ and Contributing

You are warmly welcome to submit questions in discussions and bug reports in 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.

@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.

PaddleSpeech depends on a lot of open source repositories. See references for more information.