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
File size: 1,116 Bytes
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license: mit
library_name: onnx
tags:
- text-to-speech
- indic
- onnx
- vocos
- f5-tts
- indian-languages
pipeline_tag: text-to-speech
base_model: ai4bharat/IndicF5
---
# IndicF5 ONNX
ONNX exports of [ai4bharat/IndicF5](https://huggingface.co/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](https://huggingface.co/ai4bharat/IndicF5). Follow the base model license/terms for redistribution and use.
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