Azerbaijani Whisper Turbo

Fine-tuned openai/whisper-large-v3-turbo for Azerbaijani automatic speech recognition.

Performance

This model achieves 8% lower WER than whisper-large-v3 with nearly 2x faster inference.

Evaluated on FLEURS Azerbaijani test set.

Usage

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

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

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.

Requirements

pip install transformers torch soundfile librosa

Which model to choose?

Benchmark Details

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

License

Apache 2.0