Instructions to use kumarx/indicf5-haryanvi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- F5-TTS
How to use kumarx/indicf5-haryanvi with F5-TTS:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
IndicF5 Haryanvi (Bangru) — public preview
This repository is the release destination for an experimental Bangru-focused Haryanvi adaptation of AI4Bharat/IndicF5.
Current status
The released IndicF5 checkpoint has been converted into the upstream F5 training format with full intended parameter coverage. A 22-prompt zero-shot baseline, a four-clip full-precision smoke test, a 200-update tiny overfit test, and checkpoint inference have passed.
A finished Haryanvi checkpoint is not published yet. Native-speaker listening approval and the pilot fine-tune remain required before release.
The reproducible source code is available at sauravtom/haryanvi-indicf5. The accompanying Space is kumarx/indicf5-haryanvi-demo.
Dataset scope
The public dataset identifies itself as the Bangru dialect of Haryanvi. This project does not claim coverage of every Haryanvi dialect. At the pinned revision, 2,766 metadata-linked rows passed automatic filtering, representing approximately 4.12 hours of audio. Native-speaker review is still a release gate.
Responsible use
Only use reference voices that you own or have explicit permission to clone. Unauthorized impersonation and voice cloning are prohibited.
Licenses and attribution
- Project release: Apache-2.0
- Base model: AI4Bharat IndicF5, MIT
- Fine-tuning dataset:
ankitdhiman/haryanvi-tts, Apache-2.0
The final model card will include training configuration, hardware, duration, evaluation methodology, listening results, known failures, and samples.
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