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maqsudxo1ja/
uz-whisper-small-stt-v2

Name: uz-whisper-small-stt-v2
Availability: InStock

automatic-speech-recognition

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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

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.