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xezpeleta/
whisper-large-v3-eu-ct2

Name: whisper-large-v3-eu-ct2
Availability: InStock

asierhv/composite_corpus_eu_v2.1

xezpeleta/whisper-large-v3-eu

finetune

apache-2.0

model-index

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Whisper Basque (eu) - CTranslate2 Conversion

This is a CTranslate2 conversion of xezpeleta/whisper-large-v3-eu designed for use with faster-whisper.

Model Details

Base Model: OpenAI Whisper Base (original model card: whisper-large-v3)
Finetuned for: Basque (eu) speech recognition
Dataset: asierhv/composite_corpus_eu_v2.1 (Mozilla Common Voice 18.0 + Basque Parliament + OpenSLR)
Conversion Format: CTranslate2 (optimized for inference)
Compatibility: Designed for use with faster-whisper
WER: 4.84% on Mozilla Common Voice 18.0

Usage with faster-whisper

First install required packages:

pip install faster-whisper

Then use the following code snippet:

from faster_whisper import WhisperModel

# Load the model (FP16 precision)
model = WhisperModel("xezpeleta/whisper-large-v3-eu-ct2", device="cuda", compute_type="float16")

# Transcribe audio file
segments, info = model.transcribe("audio.mp3", language="eu")

# Print transcription
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

Evaluation

The model achieves 4.84% Word Error Rate (WER) on the Basque test split of Mozilla Common Voice 18.0.

Conversion details

Converted from the original HuggingFace model using:

bash

ct2-transformers-converter --model xezpeleta/whisper-large-v3-eu \
                           --output_dir whisper-large-v3-eu-ct2 \
                           --copy_files tokenizer.json preprocessor_config.json \
                           --quantization float16