A Whisper-medium model fine-tuned for Uzbek speech-to-text, with data augmentation aimed at telephone / call-center audio (narrowband, noise, reverb) and anti-hallucination training on non-speech segments.

Training data

Fine-tuned on the ~780-hour Uzbek STT dataset: Abduqayum/Uzbek-STT-Dataset-780h (audiobooks, podcasts and tech talks; transcriptions generated with Google Gemini). Base model: openai/whisper-medium (via an Uzbek-adapted checkpoint).

Training highlights:

~2 epochs, bf16, effective batch 16, LR 1e-5.
Call-center augmentation on ~50% of clips: telephone narrowband (8 kHz codec + 300–3400 Hz band), background noise, light reverb, gain.
Anti-hallucination: ~3% synthetic non-speech clips with empty labels, so the model stays silent on silence/noise instead of generating text.

Evaluation (Word Error Rate)

Numbers are normalized before scoring: Uzbek references write numbers as digits (2010-yilda) while the model speaks them as words (ikki ming o'ninchi yilda), so digits are converted to Uzbek words and apostrophes/punctuation/case are unified for a fair WER.

Dataset	Clips (test)	WER
FeruzaSpeech	899	7.88%
Common Voice 17.0 uz	12,348	13.37%
FLEURS uz	4,165	14.40%

Usage

python

from transformers import pipeline

asr = pipeline("automatic-speech-recognition",
               model="Abduqayum/whisper-uzbek-medium-callcenter", device=0)

text = asr("audio.wav",
           generate_kwargs={"language": "uz", "task": "transcribe"})["text"]
print(text)

faster-whisper (with anti-hallucination decoding)

Convert with ct2-transformers-converter, then:

python

from faster_whisper import WhisperModel
m = WhisperModel("path/to/ct2-model", device="cuda", compute_type="float16")
segments, _ = m.transcribe(
    "audio.wav", language="uz",
    vad_filter=True, condition_on_previous_text=False,
    no_speech_threshold=0.6, temperature=0,
)
print(" ".join(s.text for s in segments).strip())

For stereo call recordings, split the left/right channels and transcribe each separately.

Limitations

Training transcriptions are model-generated (Gemini), so a small amount of label noise is expected.
Tuned for Uzbek Latin script; Cyrillic output is not targeted.