oxide-lab/whisper-medium-GGUF
2840
WHISPER-MEDIUM - GGUF Quantized Models
Quantized versions of openai/whisper-medium in GGUF format.
Directory Structure
medium/
├── whisper-medium-q*.gguf # Candle-compatible GGUF models (root)
├── config.json # Model configuration for Candle
├── tokenizer.json # Tokenizer for Candle
└── whisper.cpp/ # whisper.cpp-compatible models
└── whisper-medium-q*.gguf
Format Compatibility
- Root directory (
whisper-medium-*.gguf): Use with Candle (Rust ML framework) - Tensor names include
model.prefix (e.g.,model.encoder.conv1.weight) - Requires
config-medium.jsonandtokenizer-medium.json
- whisper.cpp/ directory: Use with whisper.cpp (C++ implementation)
- Tensor names without
model.prefix (e.g.,encoder.conv1.weight) - Compatible with whisper.cpp CLI tools
- Both directories contain
.gguffiles, not.binfiles
Available Formats
Usage
With Candle (Rust)
For this model, you need to modify the example code in candle. To try whisper in candle faster and easier, it's better to use the tiny model → https://huggingface.co/oxide-lab/whisper-tiny-GGUF
Command line example:
# Run Candle Whisper with local quantized model
cargo run --example whisper --release -- \
--features symphonia \
--quantized \
--model medium \
--model-id oxide-lab/whisper-medium-GGUF \With whisper.cpp (C++)
# Use models from whisper.cpp/ subdirectory
./whisper.cpp/build/bin/whisper-cli \
--model models/openai/medium/whisper.cpp/whisper-medium-q4_k.gguf \
--file audio.wavRecommended Format
For most use cases, we recommend q4_k format as it provides the best balance of:
- Size reduction (~65% mediumer)
- Quality (minimal degradation)
- Speed (faster inference than higher quantizations)
Quantization Details
- Source Model: openai/whisper-medium
- Quantization Methods:
- Candle GGUF (root directory): Python-based. Directly PyTorch → GGUF
- Adds
model.prefix to tensor names for Candle compatibility - whisper.cpp GGML (whisper.cpp/ subdirectory): whisper-quantize tool
- Uses original tensor names without prefix
- Format: GGUF (GGML Universal Format) for both directories
- Total Formats: 10 quantization levels (q2k through q80)
License
Same as the original Whisper model (MIT License).
Citation
@misc{radford2022whisper,
doi = {10.48550/ARXIV.2212.04356},
url = {https://arxiv.org/abs/2212.04356},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}