Audio
fastrtc
FastRTC turns any Python function into a real-time audio or video stream over WebRTC or WebSockets
★ 4,6 k étoilesMIT
Voir le guide →hifi-gan est un projet de audio open source, créé par jik876 (jik876/hifi-gan sur GitHub). Il compte 2 373 étoiles sur GitHub et 560 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 27 juillet 2024.
GAN vocoder that turns mel-spectrograms into high-fidelity speech audio, fast enough for real-time use.
Decide whether you need inference with a pretrained checkpoint, fine-tuning on your own voice data, or training from scratch.
Clone the repository and install the Python requirements; for training, download the LJ Speech dataset and place its wavs as documented.
Download a pretrained model folder that matches your use case, such as a universal model or one fine-tuned for Tacotron2 mel-spectrograms.
Run inference from wav files or mel-spectrograms, or start training with one of the bundled V1, V2, or V3 configurations.
A GAN generator designed around periodic patterns in audio, producing 22.05 kHz speech the paper reports as close to human quality in listening tests.
Checkpoints for LJSpeech, VCTK, and a universal model, including versions fine-tuned on Tacotron2-generated mel-spectrograms.
inference.py reconstructs audio from wav-derived features, while inference_e2e.py synthesises speech directly from mel-spectrogram files for end-to-end TTS.
Generate mel-spectrograms with a model such as Tacotron2, place them in the fine-tuning dataset folder, and continue training with the fine-tuning flag.
Bundled config_v1, config_v2, and config_v3 JSON files trade model size and speed against quality.
The universal model ships with discriminator weights intended as a starting point for adapting to new datasets.
Python 3.6 or newer is required; install the dependencies from requirements.txt.
git clone https://github.com/jik876/hifi-gan.git
cd hifi-gan
pip install -r requirements.txtTrain with a bundled config, or fine-tune a checkpoint on your own mel-spectrograms.
python train.py --config config_v1.json
python train.py --fine_tuning True --config config_v1.jsonPut wav files in test_files (or mel files in test_mel_files) and generate audio with a checkpoint.
python inference.py --checkpoint_file [generator checkpoint file path]
python inference_e2e.py --checkpoint_file [generator checkpoint file path]It is maintained by jik876 (Jungil Kong) and collaborators, as the official open-source release accompanying the HiFi-GAN paper.
Yes. It is MIT-licensed, including the provided pretrained models, so research and commercial use are permitted.
No. HiFi-GAN runs entirely locally in PyTorch; there are no API keys or hosted services involved.
WaveGlow and MelGAN are earlier neural vocoders the authors drew on; HiFi-GAN focuses on modelling periodic audio patterns to reach higher fidelity at much faster than real-time generation.
Clone the repo, pip install -r requirements.txt, download a pretrained model, and run inference.py or inference_e2e.py with its checkpoint.
FastRTC turns any Python function into a real-time audio or video stream over WebRTC or WebSockets
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.