WHISPER-SMALL - GGUF Quantized Models
Quantized versions of openai/whisper-small in GGUF format.
Directory Structure
small/
├── whisper-small-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-small-q*.gguf
Format Compatibility
-
Root directory (
whisper-small-*.gguf): Use with Candle (Rust ML framework)- Tensor names include
model.prefix (e.g.,model.encoder.conv1.weight) - Compatible with Neurolang application
- Requires
config-small.jsonandtokenizer-small.json
- Tensor names include
-
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
- Tensor names without
Available Formats
| Format | Quality | Use Case |
|---|---|---|
| q2_k | Smallest | Extreme compression |
| q3_k | Small | Mobile devices |
| q4_0 | Good | Legacy compatibility |
| q4_k | Good | Recommended for production |
| q4_1 | Good+ | Legacy with bias |
| q5_0 | Very Good | Legacy compatibility |
| q5_k | Very Good | High quality |
| q5_1 | Very Good+ | Legacy with bias |
| q6_k | Excellent | Near-lossless |
| q8_0 | Excellent | Minimal loss, benchmarking |
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 small \
--model-id oxide-lab/whisper-small-GGUF \
With whisper.cpp (C++)
# Use models from whisper.cpp/ subdirectory
./whisper.cpp/build/bin/whisper-cli \
--model models/openai/small/whisper.cpp/whisper-small-q4_k.gguf \
--file audio.wav
Recommended Format
For most use cases, we recommend q4_k format as it provides the best balance of:
- Size reduction (~65% smaller)
- Quality (minimal degradation)
- Speed (faster inference than higher quantizations)
Quantization Details
- Source Model: openai/whisper-small
- Quantization Methods:
- Candle GGUF (root directory): Python-based. Directly PyTorch → GGUF
- Adds
model.prefix to tensor names for Candle compatibility
- Adds
- whisper.cpp GGML (whisper.cpp/ subdirectory): whisper-quantize tool
- Uses original tensor names without prefix
- Candle GGUF (root directory): Python-based. Directly PyTorch → GGUF
- Format: GGUF (GGML Universal Format) for both directories
- Total Formats: 10 quantization levels (q2_k through q8_0)
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}
}