# DrumScript/audio_processor/audio_loader.py
"""
This module handles loading and basic normalisation of audio files.
It also contains the main execution block to demonstrate the workflow.
"""
from __future__ import annotations
import argparse # for command-line argument parsing
import librosa
import numpy as np
from drumscript.audio_processor.tempo_detector import estimate_tempo
from drumscript.notation_generator.constants import SAMPLE_RATE
# --- Define functions --------------------------------------------------------------------------------------------
# 1. Load audio file : -------------------------------------------------------------------------------
[docs]
def load_audio(audio_path: str, sr: int | None = None) -> tuple[np.ndarray, int]:
"""
Load an audio file and optionally resample it.
:param audio_path: Path to the audio file (.wav, .mp3, .flac, .ogg, etc.).
:type audio_path: str
:param sr: Target sample rate in Hz.
- ``None`` (default): load at the file's native sample rate (no resampling).
- An integer (e.g. ``44100``): resample to that rate.
:type sr: int or None
:return: Tuple of (audio_data, sample_rate).
:rtype: tuple[np.ndarray, int]
:raises FileNotFoundError: If the file does not exist.
**Examples:**
Load at native sample rate (for exploration / notebooks)::
import drumscript as ds
audio, sr = ds.load_audio("my_drum_loop.wav")
print(f"Native rate: {sr} Hz")
Load and resample to 44100 Hz (for the transcription pipeline)::
from drumscript.notation_generator.constants import SAMPLE_RATE
audio, sr = ds.load_audio("my_drum_loop.wav", sr=SAMPLE_RATE)
"""
# sample_rate = SAMPLE_RATE
try:
audio_data, sample_rate = librosa.load(
audio_path, sr=sr
) # The librosa.load_audio() fct handles wide variety of audio formats, including .mp3, .wav, .flac, .ogg, etc.
# return audio_data, sr
return audio_data, sample_rate
except FileNotFoundError:
print(f"Error: Audio file not found at {audio_path}")
raise
except Exception as e:
print(f"Error loading audio file {audio_path}: {e}")
raise
# 2. Normalise audio file : -------------------------------------------------------------------------------
[docs]
def normalise_audio(audio_data: np.ndarray) -> np.ndarray:
"""
Normalises the audio data to a range between -1.0 and 1.0.
:param audio_data: The input audio time series.
:type audio_data: np.ndarray
:return: The normalised audio time series.
:rtype: np.ndarray
"""
if audio_data.size == 0:
return audio_data # Return empty array if input is empty
max_abs_val = np.max(np.abs(audio_data))
if max_abs_val > 0:
normalised_data = audio_data / max_abs_val
else:
normalised_data = audio_data # Already zero or empty
return normalised_data
# 3. Audio playback function: -------------------------------------------------------------------------------
# def _prompt_user_for_playback() -> bool:
# """
# Prompts the user whether to play the audio and returns their choice.
#
#
#
# Returns:
# bool: True if the user wants to play, False otherwise.
# """
# response = input("Audio loaded. Would you like to play it? (yes/no): ").strip().lower()
# return response == 'yes' or response == 'y'
# def play_audio(audio_data: np.ndarray, sr: int):
# """
# Plays the provided audio data with an option to stop via user input.
# """
# def stop_playback_on_enter():
# input("Audio is playing. Press Enter to stop...\n")
# sd.stop()
# print("Playback stopped by user.")
# print("\nPlaying loaded audio...")
# sd.play(audio_data, sr)
#
# listener_thread = threading.Thread(target=stop_playback_on_enter, daemon=True)
# listener_thread.start()
# sd.wait()
# print("Audio playback finished.")
# =========================================================================================================w
# MAIN BLOCK
if __name__ == "__main__":
from drumscript.audio_processor.tempo_detector import estimate_tempo
# --------------------------------------------------------------------------uncomment during testing
# from datetime import datetime
# print("\n# ------------------------------------------------------------------------------------")
# datetimestamp = datetime.now()
# print(f'\ndate/time: {datetimestamp}')
# --------------------------------------------------------------------------------------------------
print("Running audio_loader.py example with actual MP3/WAV...") # FUTURE: Find way to encode this so it prints the file path provided in CLI
# Set up argument parser
parser = argparse.ArgumentParser(description="Load and process an audio file for DrumScript.")
parser.add_argument("audio_path", type=str, help="Path to the audio file to be processed.")
# Parse the command-line arguments
args = parser.parse_args()
audio_path = args.audio_path
try:
print(f"Attempting to load: {audio_path}")
audio, sr = load_audio(audio_path, sr=SAMPLE_RATE)
normalised_audio = normalise_audio(audio)
normalised_max = np.max(np.abs(normalised_audio))
assert np.isclose(normalised_max, 1.0) or np.isclose(normalised_max, 0.0), "Normalisation failed!"
bpm = estimate_tempo(normalised_audio, sr)
# print(f"Estimated Tempo (Tempogram-First): {int(round(bpm))} BPM")
print(f"Loaded audio: Shape={audio.shape}, Sample Rate={sr}, Tempo={bpm:.2f}, Duration={len(audio) / sr:.2f} seconds")
# if _prompt_user_for_playback():
# play_audio(normalised_audio, sr)
except Exception as e:
print(f"\nAn unexpected error occurred during the example execution: {e}")
# Uncomment to use, for clearer error logs
# print("\n# ------------------------------------------------------------------------------------")
DrumScript