Multimodal-RAG / core /data_processing /audio_processor.py
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# core/data_processing/audio_processor.py
import os
from typing import List, Dict, Any
from utils.logger import logger
from pydub import AudioSegment
from pydub.silence import split_on_silence
from config.settings import settings
class AudioProcessor:
def __init__(self, min_silence_len: int = 1000, silence_thresh_db: int = -40, target_sr: int = 16000):
self.min_silence_len = min_silence_len
self.silence_thresh_db = silence_thresh_db
self.target_sr = target_sr
logger.info(f"AudioProcessor initialized (min_silence_len={min_silence_len}ms, silence_thresh_db={silence_thresh_db}dB).")
def process(self, file_path: str) -> List[Dict[str, Any]]:
try:
logger.info(f"Processing audio file: {file_path}")
audio = AudioSegment.from_file(file_path)
if audio.frame_rate != self.target_sr:
audio = audio.set_frame_rate(self.target_sr)
audio_segments = split_on_silence(
audio,
min_silence_len=self.min_silence_len,
silence_thresh=self.silence_thresh_db,
keep_silence=500
)
chunks = []
audio_chunks_dir = os.path.join(settings.CHUNKS_DIR, "audio")
os.makedirs(audio_chunks_dir, exist_ok=True)
for i, segment in enumerate(audio_segments):
segment_id = f"{os.path.basename(file_path).split('.')[0]}_chunk_audio_{i}"
chunk_file_path = os.path.join(audio_chunks_dir, f"{segment_id}.wav")
# Save segments into data/processed/chunks
segment.export(chunk_file_path, format="wav")
metadata = {
"source_id": os.path.basename(file_path),
"type": "audio",
"chunk_id": segment_id,
"chunk_data_path": chunk_file_path,
"duration_ms": len(segment)
}
chunks.append({
"content": chunk_file_path,
"metadata": metadata
})
logger.info(f"Generated {len(chunks)} audio segments from {file_path}")
return chunks
except FileNotFoundError:
logger.error(f"Audio file not found: {file_path}. Please ensure ffmpeg is installed and accessible.")
return []
except Exception as e:
logger.error(f"Error processing audio file {file_path}: {e}")
return []