Update README.md

README.md

@@ -15,44 +15,4 @@ this model trained on validation segment of data for one epoch with 0.05 loss an
 
 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)
+