Initial Commit 🚀 🚀

api/utils/audio_converter.py

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+#!/usr/bin/env python3
+"""Utility script to convert audio file sample rates using Pipecat's resampler."""
+
+import asyncio
+import sys
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+# Add pipecat to path
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "pipecat" / "src"))
+
+from pipecat.audio.utils import create_file_resampler
+
+
+async def convert_audio_sample_rate(input_path: str, output_sample_rates: list[int]):
+    """Convert an audio file to different sample rates.
+
+    Args:
+        input_path: Path to the input audio file
+        output_sample_rates: List of target sample rates to convert to
+    """
+    input_file = Path(input_path)
+    if not input_file.exists():
+        print(f"Error: Input file '{input_path}' not found")
+        return
+
+    # Load the audio file using soundfile
+    print(f"Loading audio file: {input_path}")
+    audio_data, original_sample_rate = sf.read(input_path, dtype="int16")
+
+    print(f"Original sample rate: {original_sample_rate} Hz")
+    print(f"Shape: {audio_data.shape}")
+    print(f"Duration: {len(audio_data) / original_sample_rate:.2f} seconds")
+
+    # Convert to mono if stereo
+    if len(audio_data.shape) > 1 and audio_data.shape[1] > 1:
+        print("Converting to mono...")
+        audio_data = np.mean(audio_data, axis=1).astype(np.int16)
+
+    # Convert numpy array to bytes for resampler
+    raw_audio = audio_data.tobytes()
+
+    # Create resampler
+    resampler = create_file_resampler()
+
+    # Convert to each target sample rate
+    for target_rate in output_sample_rates:
+        print(f"\nConverting to {target_rate} Hz...")
+
+        # Resample the audio
+        resampled_audio = await resampler.resample(
+            raw_audio, original_sample_rate, target_rate
+        )
+
+        # Convert bytes back to numpy array
+        resampled_data = np.frombuffer(resampled_audio, dtype=np.int16)
+
+        # Generate output filename
+        output_name = input_file.stem.replace("24000", str(target_rate))
+        if "24000" not in input_file.stem:
+            output_name = f"{input_file.stem}-{target_rate}-mono"
+
+        # Save as MP3 using ffmpeg if available, otherwise WAV
+        output_path = input_file.parent / f"{output_name}.mp3"
+        wav_path = input_file.parent / f"{output_name}.wav"
+
+        # First save as WAV
+        sf.write(wav_path, resampled_data, target_rate, subtype="PCM_16")
+        print(f"Saved WAV: {wav_path}")
+
+
+async def main():
+    """Main function to convert the office ambience file."""
+    input_file = "/Users/abhishekkumar/Projects/dograh/dograh/api/assets/office-ambience-24000-mono.mp3"
+    target_rates = [8000, 16000]
+
+    await convert_audio_sample_rate(input_file, target_rates)
+    print("\nConversion complete!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())