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Jeremy-p/whisper-small-qc-fr-ct2-int8
whisper-small-qc-fr-ct2-int8 is a automatic speech recognition model from Jeremy-p. Use it when you need speech turned into text. The card lists the license as mit.
CTranslate2 int8 quantization of Jeremy-p/whisper-small-qc-fr, openai/whisper-small fully fine-tuned on a Quebec-French-weighted French/English mix (fr sampling probability 0.90 / en 0.10). 242 MB, for use with faster…
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From the Hugging Face model README
CTranslate2 int8 quantization of Jeremy-p/whisper-small-qc-fr,
openai/whisper-small fully fine-tuned on a Quebec-French-weighted French/English mix
(fr sampling probability 0.90 / en 0.10). 242 MB, for use with faster-whisper.
Full fp32 fine-tune (fp16-autocast mixed precision), gradient checkpointing, 2-GPU DDP.
French and English training data mixed via
datasets.interleave_datasets(probabilities=[0.90, 0.10], stopping_strategy="first_exhausted").
Labels truncated/filtered at a 448-token max length. Quantized to int8 via
ct2-transformers-converter after fine-tuning.
French, n=100:
| Model | WER |
|---|---|
openai/whisper-small (stock) | 49.0% |
faster-whisper base int8 | 66.9% |
Qwen3-ASR-0.6B | 40.3% |
| this model, fp32 | 37.9% |
| this model, ct2 int8 | 35.1% |
English, n=324:
| Model | WER |
|---|---|
openai/whisper-small (stock) | 5.67% |
faster-whisper base int8 | 6.13% |
Qwen3-ASR-0.6B | 2.34% |
| this model, fp32 | 3.51% |
| this model, ct2 int8 | 3.69% |
int8 quantization costs roughly 0.2pt WER on English relative to fp32 (3.51% → 3.69%, ~5% relative).
from faster_whisper import WhisperModel
model = WhisperModel("Jeremy-p/whisper-small-qc-fr-ct2-int8", compute_type="int8")
segments, info = model.transcribe("audio.wav", language="fr")
print("".join(seg.text for seg in segments))
The fp32 Transformers checkpoint is at Jeremy-p/whisper-small-qc-fr.
MIT