reorg content graph

open_notebook/graphs/content_processing/audio.py

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+import os
+from math import ceil
+
+from loguru import logger
+from pydub import AudioSegment
+
+from open_notebook.graphs.content_processing.state import SourceState
+
+# todo: add a speechtotext model to the config
+# future: parallelize the transcription process
+
+
+def split_audio(input_file, segment_length_minutes=15, output_prefix=None):
+    """
+    Split an audio file into segments of specified length.
+
+    Args:
+        input_file (str): Path to the input audio file
+        segment_length_minutes (int): Length of each segment in minutes
+        output_dir (str): Directory to save the segments (defaults to input file's directory)
+        output_prefix (str): Prefix for output files (defaults to input filename)
+
+    Returns:
+        list: List of paths to the created segment files
+    """
+    # Convert input file to absolute path
+    input_file = os.path.abspath(input_file)
+
+    output_dir = os.path.dirname(input_file)
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Set up output prefix
+    if output_prefix is None:
+        output_prefix = os.path.splitext(os.path.basename(input_file))[0]
+
+    # Load the audio file
+    audio = AudioSegment.from_file(input_file)
+
+    # Calculate segment length in milliseconds
+    segment_length_ms = segment_length_minutes * 60 * 1000
+
+    # Calculate number of segments
+    total_segments = ceil(len(audio) / segment_length_ms)
+    logger.debug(f"Splitting file: {input_file} into {total_segments} segments")
+
+    # List to store output file paths
+    output_files = []
+
+    # Split the audio into segments
+    for i in range(total_segments):
+        # Calculate start and end times for this segment
+        start_time = i * segment_length_ms
+        end_time = min((i + 1) * segment_length_ms, len(audio))
+
+        # Extract segment
+        segment = audio[start_time:end_time]
+
+        # Generate output filename
+        # Format: prefix_001.mp3 (padding with zeros ensures correct ordering)
+        output_filename = f"{output_prefix}_{str(i+1).zfill(3)}.mp3"
+        output_path = os.path.join(output_dir, output_filename)
+
+        # Export segment
+        segment.export(output_path, format="mp3")
+
+        output_files.append(output_path)
+
+        # Optional progress indication
+        logger.debug(f"Exported segment {i+1}/{total_segments}: {output_filename}")
+
+    return output_files
+
+
+def extract_audio(data: SourceState):
+    input_audio_path = data.get("file_path")
+    from openai import OpenAI
+
+    client = OpenAI()
+    audio_files = []
+
+    try:
+        audio_files = split_audio(input_audio_path)
+        transcriptions = []
+
+        for audio_file in audio_files:
+            with open(audio_file, "rb") as audio:
+                transcription = client.audio.transcriptions.create(
+                    model="whisper-1", file=audio
+                )
+                transcriptions.append(transcription.text)
+
+        return {"content": " ".join(transcriptions)}
+
+    except Exception as e:
+        logger.error(f"Error transcribing audio: {str(e)}")
+        logger.exception(e)
+        raise  # Re-raise the exception after logging
+
+    finally:
+        for file in audio_files:
+            try:
+                os.remove(file)
+            except OSError as e:
+                logger.error(f"Error removing temporary file {file}: {str(e)}")