---
language:
- az
license: apache-2.0
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- azerbaijani
- asr
- speech
- fine-tuned
base_model: openai/whisper-large-v3-turbo
datasets:
- LocalDoc/azerbaijani_asr
- LocalDoc/fleurs-azerbaijani-asr
metrics:
- wer
- cer
model-index:
- name: azerbaijani-whisper-turbo
  results:
  - task:
      type: automatic-speech-recognition
      name: Speech Recognition
    dataset:
      type: LocalDoc/fleurs-azerbaijani-asr
      name: FLEURS Azerbaijani
      split: test
    metrics:
    - type: wer
      value: 13.17
      name: WER
    - type: cer
      value: 3.45
      name: CER
---

# Azerbaijani Whisper Turbo

Fine-tuned [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) for Azerbaijani automatic speech recognition.

## Performance

| 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](https://huggingface.co/LocalDoc/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](https://huggingface.co/datasets/LocalDoc/fleurs-azerbaijani-asr) 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

```bash
pip install transformers torch soundfile librosa
```

## Which model to choose?

| Model | Best for | WER | Speed |
|-------|----------|-----|-------|
| [azerbaijani-whisper-small](https://huggingface.co/LocalDoc/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](https://huggingface.co/datasets/LocalDoc/fleurs-azerbaijani-asr) 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** |

## License

Apache 2.0
---