license: mit
quantized_by: Pomni
language:
- en
base_model:
- distil-whisper/distil-medium.en
pipeline_tag: automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_13_0
- facebook/voxpopuli
- LIUM/tedlium
- MLCommons/peoples_speech
- speechcolab/gigaspeech
- edinburghcstr/ami
tags:
- whisper.cpp
- ggml
- whisper
- audio
- speech
- voice
- distil
Distil-Medium.en quants
This is a repository of GGML quants for distil-medium.en (a Whisper-based transcription model), for use with whisper.cpp.
If you are looking for a program to run this model with, then I would recommend EasyWhisper UI, as it is user-friendly, has a GUI, and automates a lot of the hard stuff for you.
List of Quants
I am still in the middle of creating quants right now. The most important quants that I deem to be "useful in real-world usage" (being able to provide acceptably accurate transcriptions) are already up however.
Clicking on a link will download the corresponding quant instantly.
| Link | Quant | Size | Notes |
|---|---|---|---|
| GGML | F32 | 1.58 GB | Likely overkill. |
| GGML | F16 | 794 MB | Performs better than Q8_0 for noisy audio and music. |
| GGML | Q8_0 | 430 MB | Sweet spot; superficial quality loss at nearly double the speed. |
| GGML | Q6_K | 336 MB | |
| GGML | Q5_K | 284 MB | |
| GGML | Q5_1 | 308 MB | |
| GGML | Q5_0 | 284 MB | Last "good" quant; anything below loses quality rapidly. |
| GGML | Q4_K | 235 MB | Might not have lost too much quality, but I'm not sure. |
| GGML | Q4_1 | 491 MB | |
| GGML | Q4_0 | 444 MB | |
| GGML | Q3_K | 345 MB |
The F32 quant was taken from distil-whisper/distil-medium.en/ggml-medium-32-2.en.fp32.bin, and the F16 quant was taken from distil-whisper/distil-medium.en/ggml-medium-32-2.en.bin.
Questions you may have
Why do the "K-quants" not work for me?
My guess is that your GPU might be too old to recognize them, considering that I have gotten the same error on my GTX 1080. If you would like to run them regardless, you can try switching to CPU inference.
Are the K-quants "S", "M", or "L"?
The quantizer I was using was not specific about this, so I do not know about this either.
What program did you use to make these quants?
I used whisper.cpp v1.7.6 on Windows x64, leveraging CUDA 12.4.0.