Update README.md

README.md

@@ -13,44 +13,44 @@ 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)
+     #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)