Instructions to use developerabu/IndicF5-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- F5-TTS
How to use developerabu/IndicF5-ONNX 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 ONNX
ONNX exports of ai4bharat/IndicF5 (DiT denoiser + Vocos vocoder) for local/CPU inference with ONNX Runtime.
Files
| File | Notes |
|---|---|
indicf5_dit_fp32_static.onnx |
DiT, fp32, static shapes (~1.3 GB) |
indicf5_vocos_fp32.onnx |
Vocos, fp32, dynamic frames |
indicf5_vocos_fp32_static.onnx |
Vocos, fp32, static |
indicf5_vocos_fp16.onnx |
Vocos, fp16 |
indicf5_vocos_int8.onnx |
Vocos, int8 |
Sample WAVs and mel inputs are under samples/ (Hindi + Tamil).
Notes
- Base model: IndicF5 (~0.35B F5-TTS-style TTS for Indian languages).
- Typical path used here: PyTorch/ONNX DiT mel → ONNX Vocos decode.
- Not realtime on Apple M1 CPU in our benches (DiT dominates).
Attribution
Derived from ai4bharat/IndicF5. Follow the base model license/terms for redistribution and use.
Model tree for developerabu/IndicF5-ONNX
Base model
ai4bharat/IndicF5