
TVPaint Animation
The digital solution for your professional 2D animation projects.

Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis.

HiFi-GAN is a Generative Adversarial Network (GAN)-based model designed for efficient and high-fidelity speech synthesis. It addresses limitations in prior GAN-based speech synthesis methods, which often struggle to match the audio quality of autoregressive or flow-based models. HiFi-GAN focuses on modeling the periodic patterns inherent in speech audio to enhance sample quality. The architecture leverages generators and discriminators optimized for audio waveforms, allowing for fast audio generation. The model is implemented using PyTorch and is designed for researchers and developers looking to improve the speed and quality of speech synthesis systems. Pretrained models are available for various datasets, including LJ Speech and VCTK, enabling quick experimentation and deployment.
HiFi-GAN is a Generative Adversarial Network (GAN)-based model designed for efficient and high-fidelity speech synthesis.
Explore all tools that specialize in synthesize speech. This domain focus ensures HiFi-GAN delivers optimized results for this specific requirement.
Explore all tools that specialize in mel-spectrogram inversion. This domain focus ensures HiFi-GAN delivers optimized results for this specific requirement.
Generates high-quality speech audio using a GAN-based architecture that models periodic patterns in audio.
Generates audio samples at a fraction of the time compared to autoregressive models.
Accurately converts mel-spectrograms into high-fidelity speech waveforms.
The universal model with discriminator weights can be used as a base for transfer learning to other datasets.
A compact version of HiFi-GAN that can run efficiently on CPUs with comparable quality to autoregressive models.
Clone the repository from GitHub.
Install the required Python packages using `pip install -r requirements.txt`.
Download and extract the LJ Speech dataset and move the wav files to the `LJSpeech-1.1/wavs` directory.
To train the model, run `python train.py --config config_v1.json`.
To use pretrained models, download them from the provided links and place them in the appropriate directories.
For inference from a WAV file, create a `test_files` directory, copy the wav files into it, and run `python inference.py --checkpoint_file [generator checkpoint file path]`.
For end-to-end speech synthesis, create a `test_mel_files` directory, copy generated mel-spectrogram files into it, and run `python inference_e2e.py --checkpoint_file [generator checkpoint file path]`.
All Set
Ready to go
Verified feedback from other users.
"HiFi-GAN is highly praised for its speed and ability to generate high-fidelity speech, making it suitable for various real-time applications."
Post questions, share tips, and help other users.

The digital solution for your professional 2D animation projects.

Empowering independent artists with digital music distribution, publishing administration, and promotional tools.

Convert creative micro-blogs into high-performance web presences using generative AI and Automattic's core infrastructure.

Fashion design technology software and machinery for apparel product development.

Instantly turns any text to natural sounding speech for listening online or generating downloadable audio.

Professional studio-quality AI headshot generator for individuals and teams.