Instructions to use developerabu/IndicF5-MNN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- F5-TTS
How to use developerabu/IndicF5-MNN 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 MNN
MNN exports of ai4bharat/IndicF5 (DiT + Vocos), including fp16 and int8 weight-quantized variants.
Files
DiT
| File | Approx. size |
|---|---|
indicf5_dit_fp32.mnn |
~1.3 GB |
indicf5_dit_fp16.mnn |
~646 MB |
indicf5_dit_int8wq.mnn |
~347 MB |
indicf5_dit_fp16_int8wq.mnn |
~646 MB |
Vocos
| File | Notes |
|---|---|
indicf5_vocos_fp32.mnn |
static |
indicf5_vocos_fp32_dynamic.mnn |
variable mel frames (recommended for audio) |
indicf5_vocos_fp16.mnn |
|
indicf5_vocos_int8wq.mnn |
static |
indicf5_vocos_int8wq_dynamic.mnn |
variable mel frames |
indicf5_vocos_fp16_int8wq.mnn |
Sample WAVs (fp32 / int8wq Vocos decode) are under samples/. Bench numbers: quant_benchmark.json.
Notes
- Prefer
*_dynamic.mnnVocos models for full-length audio (callresizeTensorwith a tuple shape, thenresizeSession). - Path used for samples: PyTorch mel → MNN Vocos.
- Still not realtime on Apple M1 CPU for full DiT ODE + Vocos.
Attribution
Derived from ai4bharat/IndicF5. Follow the base model license/terms for redistribution and use.
Model tree for developerabu/IndicF5-MNN
Base model
ai4bharat/IndicF5