---
license: apache-2.0
datasets:
- asierhv/composite_corpus_eu_v2.1
language:
- eu
metrics:
- wer
model-index:
- name: Whisper Tiny Basque
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 18.0
      type: mozilla-foundation/common_voice_18_0
      config: eu
      split: test
      args:
        language: eu
    metrics:
      - name: Test WER
        type: wer
        value: 13.56
base_model:
- xezpeleta/whisper-tiny-eu
---

# Whisper Basque (eu) - CTranslate2 Conversion

**This is a CTranslate2 conversion of [xezpeleta/whisper-tiny-eu](https://huggingface.co./xezpeleta/whisper-tiny-eu) designed for use with faster-whisper.**

## Model Details
- **Base Model:** OpenAI Whisper Tiny (original model card: [whisper-tiny](https://huggingface.co./openai/whisper-tiny))
- **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](https://github.com/SYSTRAN/faster-whisper)
- **WER:** 13.56 on Mozilla Common Voice 18.0

## Usage with faster-whisper

First install required packages:
```bash
pip install faster-whisper
```

Then use the following code snippet:

```py
from faster_whisper import WhisperModel

# Load the model (FP16 precision)
model = WhisperModel("xezpeleta/whisper-tiny-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 **13.56% 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-tiny-eu \
                           --output_dir whisper-tiny-eu-ct2 \
                           --copy_files tokenizer.json preprocessor_config.json \
                           --quantization float16
```