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maqsudxo1ja/uz-whisper-small-stt-v2

sourceHugging Faceupdated 2mo agoView on Hugging Face

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Model Card

Uzbek Whisper Small STT v2

An Uzbek automatic speech recognition model based on Whisper Small, fine-tuned on Mozilla Common Voice 26 and converted to CTranslate2 FP16 for efficient inference with faster-whisper.

Evaluation

Metric	Value
Training epochs	3
Optimization steps	9,195
Test samples	12,405
Test WER	22.1379%
Test loss	0.2795

Results were measured on the Uzbek test split of Mozilla Common Voice 26. Lower WER is better.

Installation

bash

pip install faster-whisper

Usage

NVIDIA GPU

python

from faster_whisper import WhisperModel

model = WhisperModel(
    "maqsudxo1ja/uz-whisper-small-stt-v2",
    device="cuda",
    compute_type="float16",
)

segments, info = model.transcribe(
    "audio.wav",
    language="uz",
    beam_size=5,
)

text = "".join(segment.text for segment in segments).strip()
print(text)

CPU

python

from faster_whisper import WhisperModel

model = WhisperModel(
    "maqsudxo1ja/uz-whisper-small-stt-v2",
    device="cpu",
    compute_type="int8",
)

Model Format

The main branch contains CTranslate2 model files and should be loaded with faster-whisper. It is not a standard Transformers checkpoint.

The previous Transformers-format revision is preserved in the `backup-transformers-before-finetune-20260810` branch.

Limitations

Performance may vary across microphones, background noise, speaking styles, dialects, and audio domains that differ from Mozilla Common Voice. Validate the model on representative production audio before deployment.