Azerbaijani Whisper Small

Fine-tuned openai/whisper-small for Azerbaijani automatic speech recognition.

Performance

This model achieves better quality than whisper-large-v3 while being 6x smaller.

Evaluated on FLEURS Azerbaijani test set.

Usage

pip install --upgrade transformers

import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import soundfile as sf
import numpy as np

processor = WhisperProcessor.from_pretrained("LocalDoc/azerbaijani-whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("LocalDoc/azerbaijani-whisper-small")

audio, sr = sf.read("audio.wav")

if len(audio.shape) > 1:
    audio = audio.mean(axis=1)

audio = librosa.resample(np.asarray(audio, dtype=np.float32), orig_sr=sr, target_sr=16000)
sr = 16000

inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
forced_ids = processor.get_decoder_prompt_ids(language="az", task="transcribe")

with torch.no_grad():
    ids = model.generate(inputs.input_features, forced_decoder_ids=forced_ids)

text = processor.batch_decode(ids, skip_special_tokens=True)[0]
print(text)

Note: Audio must be 16kHz mono. If your audio has a different sample rate, use librosa.resample() as shown above. Passing audio without resampling will produce incorrect results.

Requirements

pip install transformers torch soundfile librosa

Benchmark Details

All models evaluated on FLEURS Azerbaijani test split (921 samples) with the same normalization (lowercase, no punctuation).

License

Apache 2.0