hifi-gan
Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
HiFi-GAN is optimized for generating high-fidelity speech efficiently and supports both training from scratch and fine-tuning with pretrained models.
Good fit when
- When you require real-time generation capabilities up to 167.9 times faster than real time on a single V100 GPU, or even 13.4 times faster on CPU than real time for the small footprint version
- If fine-tuning pretrained models for speech synthesis quality on unseen speakers is needed
Avoid when
- If your application prioritizes sample quality above speed, despite the competitive position of HiFi-GAN in both criteria
- When the computational resources for running models on a V100 GPU or leveraging the small footprint version's CPU efficiency are not available
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (784d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- 37 low (37 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install hifi-ganSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
HiFi-GAN is a GAN-based model aimed at achieving both efficient and high-fidelity speech synthesis.
Capability facts
- Languages
- python
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
HiFi GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our paper, we proposed HiFi GAN: a GAN based model capable of generating high fidelity speech efficiently.<br/ We provide our implementation and...
For agents
This page has a .md twin and JSON over the API.