---
license: mit
datasets:
- mozilla-foundation/common_voice_11_0
language:
- fa
metrics:
- wer
base_model:
- openai/whisper-large-v3-turbo
pipeline_tag: automatic-speech-recognition
library_name: transformers
tags:
- medical
---


training loss:
0.013100
validation loss:
0.043175
num. epoch: 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-persian-turbooo"
    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)