Create Audio_into_chunks.py

Audio_into_chunks.py

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+import os
+from pydub import AudioSegment
+import whisper
+from deep_translator import GoogleTranslator
+
+
+# @title Audio into chunks
+def audio_into_chunks_transcribe_translate(audio_file,lang):
+  chunk_length_seconds=11
+  output_format="wav"
+  # Check if file exists
+  if not os.path.exists(audio_file):
+    raise ValueError(f"FLAC file not found: {audio_file}")
+  Transcribe_Text=[]
+  # Load the FLAC audio
+  audio_segment = AudioSegment.from_file(audio_file, format="flac")
+  #load Model For Transcribe
+  model = whisper.load_model("medium")
+  # Get total audio duration in milliseconds
+  total_duration_ms = audio_segment.duration_seconds * 1000
+
+  # Calculate chunk duration in milliseconds
+  chunk_duration_ms = chunk_length_seconds * 1000
+
+  # Split audio into chunks
+  start_time = 0
+  chunk_num = 1
+  while start_time < total_duration_ms:
+    # Get the end time for the current chunk
+    end_time = min(start_time + chunk_duration_ms, total_duration_ms)
+
+    # Extract the current chunk
+    chunk = audio_segment[start_time:end_time]
+
+    # Generate output filename with sequential numbering
+    output_filename = f"{os.path.splitext(os.path.basename(audio_file))[0]}_chunk_{chunk_num}.{output_format}"
+
+    # Export the chunk as the specified format
+    chunk.export(output_filename, format=output_format)
+
+    # Update start time for the next chunk
+    start_time += chunk_duration_ms
+    chunk_num += 1
+    #transcribe Chunks
+    result = model.transcribe(output_filename)
+    #translate the transcribe data
+    translator=GoogleTranslator(source='auto',target=lang)
+    data_trans=translator.translate(result['text'])
+    Transcribe_Text.append(data_trans)
+    print(data_trans)
+    print(result['text'])
+
+  return Transcribe_Text
+  print(f"FLAC file '{flac_filepath}' successfully split into {chunk_num - 1} chunks.")
+
+