uz-whisper-small-stt-v2

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

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

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

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.