README.md

---
language: 
- fa
tags:
- audio
- automatic-speech-recognition
- open-persian-asr-leaderboard
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---

# C1Tech/whisper_base_persian

*C1Tech/whisper_base_persian* is a Persian ASR model based on Whisper architecture, fine-tuned on a large scale custom persian dataset. 

With only 74 million parameters, this model achieves state-of-the-art performance on Persian ASR tasks, outperforming larger models like openai Whisper Large-v3 (1550M parameters) and Meta Wav2Vec2-XLSR (300M parameters).

## Benchmark Performance
We evaluated the model on multiple Persian ASR benchmarks, including Common Voice, and fleurs. Results show that our model outperforms popular models like vosk, fast-conformer and openai's whisper on these benchmarks:
<p align="center" style="display: flex; justify-content: space-around;">  
<img src="assets/base_common-voice.png" alt="Model Image 1" width="45%" />
  <img src="assets/base_fleurs.png" alt="Model Image 2" width="45%" />
</p>

The benchmark results highlight the model's efficiency and accuracy, proving that high-quality Persian ASR is achievable even with a compact model.

For more detailed evaluation and comparison with other models, please refer to the [Open Persian ASR Leaderboard](https://huggingface.co/spaces/C1Tech/Open_Persian_ASR_Leaderboard).

## Usage

Whisper base is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers 
library. 

```bash
pip install --upgrade pip
pip install --upgrade transformers
```

The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe audios of arbitrary length:

```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "C1Tech/whisper_base_persian"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

```

To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:

```python
result = pipe("audio.mp3")
```

Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter:

```python
result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2)
```

Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous 
tokens. The following example demonstrates how to enable these heuristics:

```python
generate_kwargs = {
    "num_beams": 3,
    "condition_on_prev_tokens": False,
    "compression_ratio_threshold": 1.35,  # zlib compression ratio threshold (in token space)
    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    "logprob_threshold": -1.0,
    "no_speech_threshold": 0.6,
    "return_timestamps": True,
    "language": "fa"
}

result = pipe(sample, generate_kwargs=generate_kwargs)
```

Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument:

```python
result = pipe(sample, return_timestamps=True)
print(result["chunks"])
```

And for word-level timestamps:

```python
result = pipe(sample, return_timestamps="word")
print(result["chunks"])
```

---
For further information, keep in touch:
info@c1tech.group