Audio
Mozilla TTS
Mozilla's deep-learning text-to-speech library with Tacotron, Glow-TTS and neural vocoders.
★ 10,2 k étoilesMPL-2.0
Voir le guide →WaveRNN est un projet de audio open source, créé par fatchord (fatchord/WaveRNN sur GitHub). Il compte 2 191 étoiles sur GitHub et 685 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 2 juillet 2022.
WaveRNN vocoder plus Tacotron TTS in PyTorch: train your own neural speech synthesis on LJSpeech.
Decide whether you want instant TTS from the pretrained models or to train on your own voice dataset.
Clone the repository and install the requirements with pip, with PyTorch and CUDA available.
For training, download the LJSpeech dataset, point the hyperparameters at your wav path, and run preprocessing.
Generate speech with the quick start script, or train Tacotron and then WaveRNN following the documented order.
Combines a Tacotron acoustic model with the WaveRNN vocoder so text goes in and waveform audio comes out.
A WaveRNN model (mixture-of-logistics output) and a Tacotron model, both trained on LJSpeech, live in the pretrained folder.
quick_start.py synthesises the default sentences, accepts custom input text, and can run unbatched for better audio quality, saving wavs and attention plots.
Preprocess, train Tacotron, optionally force a GTA dataset mid-training, train WaveRNN with or without GTA, then generate.
Dataset paths and training settings are centralised in hparams.py for easy adaptation to other corpora.
A samples page linked from the README lets you hear output quality before installing anything.
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.txtGenerate the default sentences into a quick_start output folder.
python quick_start.pyPass your own text, optionally unbatched for higher quality.
python quick_start.py -u --input_text "What will happen if I run this command?"It is maintained by fatchord, building on the WaveRNN and Tacotron papers and earlier open implementations credited in the README.
Yes, it is MIT-licensed, including use of the bundled pretrained models.
No. Everything runs locally in PyTorch; there are no hosted APIs or keys.
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.
Clone the repo, pip install -r requirements.txt, and run python quick_start.py to hear the pretrained system immediately.
Mozilla's deep-learning text-to-speech library with Tacotron, Glow-TTS and neural vocoders.
Unofficial PyTorch implementation of VALL-E, the neural codec language model for zero-shot TTS
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Les autres projets de la catégorie Audio avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si WaveRNN ne vous convient pas.
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Projets de la catégorie Audio proches dans le catalogue, les plus proches en étoiles de WaveRNN — continuez à explorer de part et d'autre.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.