README.md

---
library_name: transformers
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
model-index:
- name: whisper-fa-tinyyy
  results: []
license: mit
language:
- fa
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-fa-tinyyy

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0246

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0186        | 0.9998 | 2357 | 0.0246          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1

## how to use the model in colab:
    
    # Install required packages
    !pip install torch torchaudio transformers pydub google-colab
    
    import torch
    from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
    from pydub import AudioSegment
    import os
    from google.colab import files
    
    # Load the model and processor
    model_id = "hackergeek98/whisper-fa-tinyyy"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
    processor = AutoProcessor.from_pretrained(model_id)
    
    # Create pipeline
    whisper_pipe = pipeline(
        "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=0 if torch.cuda.is_available() else -1
    )
    
    # Convert audio to WAV format
    def convert_to_wav(audio_path):
        audio = AudioSegment.from_file(audio_path)
        wav_path = "converted_audio.wav"
        audio.export(wav_path, format="wav")
        return wav_path
    
    # Split long audio into chunks
    def split_audio(audio_path, chunk_length_ms=30000):  # Default: 30 sec per chunk
        audio = AudioSegment.from_wav(audio_path)
        chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
        chunk_paths = []
        
        for i, chunk in enumerate(chunks):
            chunk_path = f"chunk_{i}.wav"
            chunk.export(chunk_path, format="wav")
            chunk_paths.append(chunk_path)
        
        return chunk_paths
    
    # Transcribe a long audio file
    def transcribe_long_audio(audio_path):
        wav_path = convert_to_wav(audio_path)
        chunk_paths = split_audio(wav_path)
        transcription = ""
        
        for chunk in chunk_paths:
            result = whisper_pipe(chunk)
            transcription += result["text"] + "\n"
            os.remove(chunk)  # Remove processed chunk
        
        os.remove(wav_path)  # Cleanup original file
        
        # Save transcription to a text file
        text_path = "transcription.txt"
        with open(text_path, "w") as f:
            f.write(transcription)
        
        return text_path
    
    # Upload and process audio in Colab
    uploaded = files.upload()
    audio_file = list(uploaded.keys())[0]
    transcription_file = transcribe_long_audio(audio_file)
    
    # Download the transcription file
    files.download(transcription_file)