IndicWhisper GGML (whisper.cpp format)
This repository contains GGML model files for Vignesh-M/Indic-whisper.
Why .bin instead of .gguf?
While large language model runtimes (like llama.cpp) have migrated to the newer GGUF format, whisper.cpp historically and currently uses the GGML binary format specification for its speech-to-text model files.
Because convert-h5-to-ggml.py generates native GGML models rather than GGUF models, these files use the .bin extension (matching official whisper.cpp models like ggml-base.bin). This is the correct native format expected by your whisper-server sidecar.
Compatibility Note
The original PyTorch Transformers checkpoint from Hugging Face is not directly compatible with whisper.cpp or whisper-server. These files have been converted using whisper.cpp's convert-h5-to-ggml.py script into the bin format accepted by whisper-server builds.
Files Available
ggml-indic-whisper-medium-f16.bin: FP16 half-precision model (~1.5 GB). This model offers the highest accuracy matching the original fine-tune. Recommended for systems with modern GPUs (RTX 4000/5000) where native FP16 Tensor Cores are available.ggml-indic-whisper-medium-q8_0.bin: Q8_0 (8-bit quantized) model (~823 MB). This model offers nearly identical accuracy (>99% of FP16) with half the memory footprint and faster execution on CPU.
Conversion & Quantization Process
The models were converted using an optimized version of the convert-h5-to-ggml.py script and quantized using the whisper-quantize tool:
python3 convert-h5-to-ggml.py [model_dir] [whisper_pkg_dir] [out_dir]
whisper-quantize ggml-indic-whisper-medium-f16.bin ggml-indic-whisper-medium-q8_0.bin q8_0
Supported Languages
Supports many of the required Indic languages through the Whisper multilingual tokenizer/model family, but language coverage and accuracy should be verified per target language.