Model	Params	WER	CER
whisper-small (baseline)	242M	52.17%	14.52%
whisper-medium (baseline)	769M	34.54%	9.00%
whisper-large-v3 (baseline)	1543M	21.00%	5.51%
whisper-large-v3-turbo (baseline)	809M	22.99%	6.55%
azerbaijani-whisper-small	242M	20.54%	5.72%
azerbaijani-whisper-turbo	809M	13.17%	3.45%

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

Evaluated on FLEURS Azerbaijani test set.

Usage

python

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?

Model	Best for	WER	Speed
azerbaijani-whisper-small	CPU deployment, edge devices, low-resource environments	20.54%	Fast on CPU
azerbaijani-whisper-turbo	GPU deployment, real-time transcription, highest accuracy	13.17%	Very fast on GPU

Benchmark Details

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

Model	Params	WER	CER	RTF (GPU)
whisper-tiny	38M	104.48%	53.93%	0.033
whisper-base	73M	82.63%	30.35%	0.032
whisper-small	242M	52.17%	14.52%	0.053
whisper-medium	769M	34.54%	9.00%	0.097
whisper-large-v3	1543M	21.00%	5.51%	0.129
whisper-large-v3-turbo	809M	22.99%	6.55%	0.024
azerbaijani-whisper-small	242M	20.54%	5.72%	~0.05
azerbaijani-whisper-turbo	809M	13.17%	3.45%	~0.024

LocalDoc/
azerbaijani-whisper-turbo

Azerbaijani Whisper Turbo

Performance

Usage

Requirements

Which model to choose?

Benchmark Details

License

Apache 2.0