Create app.py

app.py

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+import whisper
+import multiprocessing
+import os
+from pydub import AudioSegment
+from typing import List
+
+model = whisper.load_model("base")
+
+def convert_to_text(audio_path: str) -> str:
+  # Load the model outside the function if possible, so it's only loaded once
+  model = whisper.load_model("base")
+  
+  # Split the audio into segments/chunks
+  chunk_size = 30  # Length of each segment in seconds
+  audio_segments = split_audio(audio_path, chunk_size)
+  
+  
+  # Process segments in parallel using multiprocessing
+  """ctx = multiprocessing.get_context('spawn')
+  pool = ctx.Pool()
+  print("Starting the processes....")
+  results = pool.map(process_segment, audio_segments)
+  pool.close()
+  pool.join() """
+  
+  return audio_segments[0]
+  
+  # Combine the results
+  #text = ' '.join(results)
+  #return text
+
+import os
+from pydub import AudioSegment
+
+def split_audio(audio_path: str, chunk_size: int) -> List[str]:
+    # Create a directory to store the segmented audio files
+    output_dir = "segmented_audio"
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Open the audio file using pydub
+    audio = AudioSegment.from_file(audio_path)
+
+    # Calculate the number of chunks
+    duration = len(audio) / 1000  # Convert to seconds
+    num_chunks = int(duration / chunk_size)
+
+    print(f"Chunk : Duration : {duration} : Number : {num_chunks}")
+
+    # Split the audio into chunks
+    audio_segments = []
+    for i in range(num_chunks):
+        start_time = i * chunk_size * 1000  # Convert to milliseconds
+        end_time = (i + 1) * chunk_size * 1000
+
+        # Extract the chunk from the audio file
+        chunk = audio[start_time:end_time]
+
+        # Create a temporary file to store the chunk
+        chunk_path = os.path.join(output_dir, f"chunk_{i}.wav")
+        chunk.export(chunk_path, format="wav")
+
+        print(f"Chunk number {i} path : {chunk_path}")
+        audio_segments.append(chunk_path)
+      
+    print(f"Audio split into : {len(audio_segments)}")
+
+    return audio_segments
+
+
+def process_segment(segment_path: str, q) -> str:
+    # Load the model for each process if necessary
+    
+    print(f"Processing segment : {segment_path}")
+    
+    # Process the segment and return the transcribed text
+    result = model.transcribe(segment_path)
+    print(result['text'])
+    return result["text"]
+
+if __name__ == '__main__':
+  
+  path = '/content/The genius of Satya Nadella  Sam Altman and Lex Fridman.mp3'
+
+  seg = convert_to_text(path)
+  q = multiprocessing.Queue()
+  p = multiprocessing.Process(target=process_segment, args=(seg,q))
+  p.start()
+  print(q.get())
+  p.join()
+