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README
Apache-2.0

(简体中文|English)


PaddleSpeech 是基于飞桨 PaddlePaddle 的语音方向的开源模型库,用于语音和音频中的各种关键任务的开发,包含大量基于深度学习前沿和有影响力的模型,一些典型的应用示例如下:

语音识别
输入音频 识别结果

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。
语音翻译 (英译中)
输入音频 翻译结果

我 在 这栋 建筑 的 古老 门上 敲门。
语音合成
输入文本 合成音频
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。

更多合成音频,可以参考 PaddleSpeech 语音合成音频示例

特性:

本项目采用了易用、高效、灵活以及可扩展的实现,旨在为工业应用、学术研究提供更好的支持,实现的功能包含训练、推断以及测试模块,以及部署过程,主要包括

  • 📦 易用性: 安装门槛低,可使用 CLI 快速开始。
  • 🏆 对标 SoTA: 提供了高速、轻量级模型,且借鉴了最前沿的技术。
  • 💯 基于规则的中文前端: 我们的前端包含文本正则化和字音转换(G2P)。此外,我们使用自定义语言规则来适应中文语境。
  • 多种工业界以及学术界主流功能支持:
    • 🛎️ 典型音频任务: 本工具包提供了音频任务如音频分类、语音翻译、自动语音识别、文本转语音、语音合成等任务的实现。
    • 🔬 主流模型及数据集: 本工具包实现了参与整条语音任务流水线的各个模块,并且采用了主流数据集如 LibriSpeech、LJSpeech、AIShell、CSMSC,详情请见 模型列表
    • 🧩 级联模型应用: 作为传统语音任务的扩展,我们结合了自然语言处理、计算机视觉等任务,实现更接近实际需求的产业级应用。

近期更新:

  • 🤗 2021.12.14: 我们在 Hugging Face Spaces 上的 ASR 以及 TTS Demos 上线啦!
  • 👏🏻 2021.12.10: PaddleSpeech CLI 上线!覆盖了声音分类、语音识别、语音翻译(英译中)以及语音合成。

技术交流群

微信扫描二维码加入官方交流群,获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。

安装

我们强烈建议用户在 Linux 环境下,3.7 以上版本的 python 上安装 PaddleSpeech。这种情况下安装 paddlespeech 只需要一条 pip 命令:

pip install paddlepaddle paddlespeech

目前为止,Linux 支持声音分类、语音识别、语音合成和语音翻译四种功能,Mac OSX、 Windows 下暂不支持语音翻译功能。 想了解更多安装细节,可以参考安装文档

快速开始

安装完成后,开发者可以通过命令行快速开始,改变 --input 可以尝试用自己的音频或文本测试。

声音分类

paddlespeech cls --input input.wav

语音识别

paddlespeech asr --lang zh --input input_16k.wav

语音翻译 (English to Chinese)

paddlespeech st --input input_16k.wav

语音合成

paddlespeech tts --input "你好,欢迎使用百度飞桨深度学习框架!" --output output.wav

Note: 如果需要训练或者微调,请查看语音识别语音合成

模型列表

PaddleSpeech 支持很多主流的模型,并提供了预训练模型,详情请见模型列表

PaddleSpeech 的 语音识别 包含语音识别声学模型、语音识别语言模型和语音翻译, 详情如下:

语音识别模块种类 数据集 模型种类 链接
语音识别 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
对齐 THCHS30 MFA mfa-thchs30
语言模型 Ngram 语言模型 kenlm
TIMIT Unified Streaming & Non-streaming Two-pass u2-timit
语音翻译(英译中) TED En-Zh Transformer + ASR MTL transformer-ted
FAT + Transformer + ASR MTL fat-st-ted

PaddleSpeech 的 语音合成 主要包含三个模块:文本前端、声学模型和声码器。声学模型和声码器模型如下:

语音合成模块类型 模型种类 数据集 链接
文本前端 tn / g2p
声学模型 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
声码器 WaveFlow LJSpeech waveflow-ljspeech
Parallel WaveGAN LJSpeech / VCTK / CSMSC PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc
Multi Band MelGAN CSMSC Multi Band MelGAN-csmsc
声音克隆 GE2E Librispeech, etc. ge2e
GE2E + Tactron2 AISHELL-3 ge2e-tactron2-aishell3
GE2E + FastSpeech2 AISHELL-3 ge2e-fastspeech2-aishell3

声音分类

任务 数据集 模型种类 链接
声音分类 ESC-50 PANN pann-esc50

教程文档

对于 PaddleSpeech 的所关注的任务,以下指南有助于帮助开发者快速入门,了解语音相关核心思想。

语音合成模块最初被称为 Parakeet,现在与此仓库合并。如果您对该任务的学术研究感兴趣,请参阅 TTS 研究概述。此外,模型介绍 是了解语音合成流程的一个很好的指南。

引用

要引用 PaddleSpeech 进行研究,请使用以下格式进行引用。

@misc{ppspeech2021,
title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleSpeech}},
year={2021}
}

参与 PaddleSpeech 的开发

热烈欢迎您在Discussions 中提交问题,并在Issues 中指出发现的 bug。此外,我们非常希望您参与到 PaddleSpeech 的开发中!

贡献者

致谢

  • 非常感谢 yeyupiaoling 多年来的关注和建议,以及在诸多问题上的帮助。
  • 非常感谢 AK391 在 Huggingface Spaces 上使用 Gradio 对我们的语音合成功能进行网页版演示。
  • 非常感谢 mymagicpower 采用PaddleSpeech 对 ASR 的短语音长语音进行 Java 实现。

此外,PaddleSpeech 依赖于许多开源存储库。有关更多信息,请参阅 references

License

PaddleSpeech 在 Apache-2.0 许可 下提供。

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简介

DeepSpeech2是一个采用PaddlePaddle平台的端到端自动语音识别(ASR)引擎的开源项目 展开 收起
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