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"""
Audio processing utilities
"""
import numpy as np
import librosa
import soundfile as sf
from typing import Tuple, Optional
import logging
logger = logging.getLogger(__name__)
class AudioProcessor:
def __init__(self):
self.sample_rate = 16000 # Default sample rate for models
def load_audio(self, file_path: str) -> Tuple[np.ndarray, int]:
"""Load audio file and convert to appropriate format"""
try:
audio, sr = librosa.load(file_path, sr=self.sample_rate)
return audio, sr
except Exception as e:
logger.error(f"Failed to load audio: {str(e)}")
raise
def save_audio(self, audio_array: np.ndarray, output_path: str, sample_rate: int = None):
"""Save audio array to file"""
try:
sr = sample_rate or self.sample_rate
sf.write(output_path, audio_array, sr)
logger.info(f"Audio saved to {output_path}")
except Exception as e:
logger.error(f"Failed to save audio: {str(e)}")
raise
def normalize_audio(self, audio: np.ndarray) -> np.ndarray:
"""Normalize audio to [-1, 1] range"""
return audio / np.max(np.abs(audio))
def trim_silence(self, audio: np.ndarray, threshold: float = 0.01) -> np.ndarray:
"""Remove silence from beginning and end"""
return librosa.effects.trim(audio, top_db=20, frame_length=512, hop_length=256)[0]
def change_speed(self, audio: np.ndarray, speed_factor: float) -> np.ndarray:
"""Change playback speed without changing pitch"""
return librosa.effects.time_stretch(audio, rate=speed_factor)
def change_pitch(self, audio: np.ndarray, n_steps: float) -> np.ndarray:
"""Change pitch by n semitones"""
return librosa.effects.pitch_shift(audio, sr=self.sample_rate, n_steps=n_steps)
def get_spectrogram(self, audio: np.ndarray) -> np.ndarray:
"""Generate spectrogram for visualization"""
return librosa.stft(audio)
def get_tempo(self, audio: np.ndarray) -> float:
"""Estimate tempo (BPM)"""
tempo, _ = librosa.beat.beat_track(y=audio, sr=self.sample_rate)
return tempo
def apply_fade(self, audio: np.ndarray, fade_in: float = 0.1, fade_out: float = 0.1) -> np.ndarray:
"""Apply fade in/out"""
fade_in_samples = int(fade_in * self.sample_rate)
fade_out_samples = int(fade_out * self.sample_rate)
if fade_in_samples > 0:
fade_in_curve = np.linspace(0, 1, fade_in_samples)
audio[:fade_in_samples] *= fade_in_curve
if fade_out_samples > 0:
fade_out_curve = np.linspace(1, 0, fade_out_samples)
audio[-fade_out_samples:] *= fade_out_curve
return audio |