LibreAI / Audio / WaveRNN
// audio · fatchord/WaveRNN

WaveRNN

WaveRNN es un proyecto de audio de código abierto de fatchord (fatchord/WaveRNN en GitHub). Tiene 2.191 estrellas en GitHub y 685 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 2 de julio de 2022.

WaveRNN vocoder plus Tacotron TTS in PyTorch: train your own neural speech synthesis on LJSpeech.

★ 2,2k estrellas⑂ 685 bifurcacionesMITactualizado jul 2022
▦ visual: el README de este proyecto no incluye ninguna captura incrustable,
así que aquí va un marcador. La guía de abajo está completa de todos modos.
// datos del proyecto

Datos del proyecto

Repositorio
fatchord/WaveRNN
Estrellas en GitHub
2.191
Bifurcaciones
685
Licencia
MIT
Última actualización en GitHub
2 de julio de 2022
Categoría
Audio
Tipo de ficha
Guía de instalación completa
Sitio oficial
Official site
// cómo funciona

Cómo funciona

  1. Clarify your goal

    Decide whether you want instant TTS from the pretrained models or to train on your own voice dataset.

  2. Get the project

    Clone the repository and install the requirements with pip, with PyTorch and CUDA available.

  3. Install and configure

    For training, download the LJSpeech dataset, point the hyperparameters at your wav path, and run preprocessing.

  4. Run your first task

    Generate speech with the quick start script, or train Tacotron and then WaveRNN following the documented order.

// características

Por qué gusta tanto

Complete TTS pipeline

Combines a Tacotron acoustic model with the WaveRNN vocoder so text goes in and waveform audio comes out.

Pretrained models included

A WaveRNN model (mixture-of-logistics output) and a Tacotron model, both trained on LJSpeech, live in the pretrained folder.

Quick start generation

quick_start.py synthesises the default sentences, accepts custom input text, and can run unbatched for better audio quality, saving wavs and attention plots.

Documented training order

Preprocess, train Tacotron, optionally force a GTA dataset mid-training, train WaveRNN with or without GTA, then generate.

Configurable hyperparameters

Dataset paths and training settings are centralised in hparams.py for easy adaptation to other corpora.

Audio samples published

A samples page linked from the README lets you hear output quality before installing anything.

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// ejecútalo

Ejecútalo tú mismo

  1. Clone and install requirements

    Make sure PyTorch 1 with CUDA is installed first, per the README.

    git clone https://github.com/fatchord/WaveRNN.git
    cd WaveRNN
    pip install -r requirements.txt
  2. Quick start with pretrained models

    Generate the default sentences into a quick_start output folder.

    python quick_start.py
  3. Generate custom sentences

    Pass your own text, optionally unbatched for higher quality.

    python quick_start.py -u --input_text "What will happen if I run this command?"
// casos de uso

Qué puedes crear

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// preguntas frecuentes

Preguntas frecuentes

Who maintains it?

It is maintained by fatchord, building on the WaveRNN and Tacotron papers and earlier open implementations credited in the README.

Is it free to use?

Yes, it is MIT-licensed, including use of the bundled pretrained models.

Does it need a paid API key?

No. Everything runs locally in PyTorch; there are no hosted APIs or keys.

How does it compare to a well-known alternative?

HiFi-GAN is a GAN vocoder focused on mel inversion; WaveRNN pairs its autoregressive vocoder with a full Tacotron TTS front end and its own training pipeline in one repo.

How do I get started?

Clone the repo, pip install -r requirements.txt, and run python quick_start.py to hear the pretrained system immediately.

// sigue explorando

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// alternativas

Alternativas en Audio

Los demás proyectos de Audio con más estrellas del catálogo de LibreAI: la lista realista de alternativas si WaveRNN no es lo que buscas.

// más en audio

Más en Audio

Proyectos de Audio cercanos en el catálogo, los más próximos en estrellas a WaveRNN: sigue explorando a ambos lados.

Consigue WaveRNN

Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.