---
license: mit
datasets:
- mozilla-foundation/common_voice_11_0
language:
- fa
metrics:
- wer
base_model:
- openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
library_name: transformers
---
how to use the model in colab:

     #start
     pip install torch torchaudio transformers librosa gradio
     from transformers import WhisperProcessor, WhisperForConditionalGeneration
     import torch
    
      #Load your fine-tuned Whisper model and processor
      model_name = "hackergeek98/tinyyyy_whisper"
      processor = WhisperProcessor.from_pretrained(model_name)
      model = WhisperForConditionalGeneration.from_pretrained(model_name)
    
      #Force the model to transcribe in Persian
      model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fa", task="transcribe")
      
      #Move model to GPU if available
      device = "cuda" if torch.cuda.is_available() else "cpu"
      model.to(device)
      import librosa
      
      def transcribe_audio(audio_file):
          # Load audio file using librosa (supports multiple formats)
          audio_data, sampling_rate = librosa.load(audio_file, sr=16000)  # Resample to 16kHz
      
          # Preprocess the audio
          inputs = processor(audio_data, sampling_rate=sampling_rate, return_tensors="pt").input_features.to(device)
      
          # Generate transcription
          with torch.no_grad():
              predicted_ids = model.generate(inputs)
      
          # Decode the transcription
          transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
          return transcription
      from google.colab import files
      
      #Upload an audio file
      uploaded = files.upload()
      audio_file = list(uploaded.keys())[0]
      
      #Transcribe the audio
      transcription = transcribe_audio(audio_file)
      print("Transcription:", transcription)