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cfb5e7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | """Audio processing module for Myanmar Ghost project."""
import logging
from pathlib import Path
from typing import Optional, Tuple
import librosa
import numpy as np
import soundfile as sf
from scipy.signal import butter, filtfilt
logger = logging.getLogger(__name__)
class AudioProcessor:
"""Process audio files for Myanmar speech recognition."""
def __init__(
self,
sample_rate: int = 16000,
n_fft: int = 512,
hop_length: int = 160,
n_mels: int = 80,
):
self.sample_rate = sample_rate
self.n_fft = n_fft
self.hop_length = hop_length
self.n_mels = n_mels
def load_audio(self, path: str) -> Tuple[np.ndarray, int]:
"""Load audio file and resample to target sample rate."""
audio, sr = librosa.load(path, sr=self.sample_rate)
logger.info(f"Loaded audio from {path}: {len(audio)} samples at {sr}Hz")
return audio, sr
def normalize_audio(self, audio: np.ndarray) -> np.ndarray:
"""Normalize audio to [-1, 1] range."""
max_val = np.abs(audio).max()
if max_val > 0:
audio = audio / max_val
return audio
def remove_silence(
self,
audio: np.ndarray,
threshold_db: float = -40,
min_silence_duration: float = 0.3,
) -> np.ndarray:
"""Remove silence from audio based on energy threshold."""
intervals = librosa.effects.split(
audio,
top_db=-threshold_db,
frame_length=self.n_fft,
hop_length=self.hop_length,
)
if len(intervals) == 0:
return audio
min_samples = int(min_silence_duration * self.sample_rate)
non_silent = []
for start, end in intervals:
if end - start >= min_samples:
non_silent.append(audio[start:end])
if non_silent:
return np.concatenate(non_silent)
return audio
def apply_bandpass_filter(
self,
audio: np.ndarray,
low_freq: float = 80,
high_freq: float = 7500,
) -> np.ndarray:
"""Apply bandpass filter to focus on speech frequencies."""
nyquist = self.sample_rate / 2
low = low_freq / nyquist
high = high_freq / nyquist
if low < 0:
low = 0.001
if high > 1:
high = 0.999
b, a = butter(4, [low, high], btype="band")
filtered = filtfilt(b, a, audio)
return filtered
def reduce_noise(
self,
audio: np.ndarray,
noise_profile: Optional[np.ndarray] = None,
) -> np.ndarray:
"""Reduce background noise using spectral subtraction."""
if noise_profile is None:
noise_profile = audio[: int(0.1 * self.sample_rate)]
noise_spectrum = np.abs(np.fft.rfft(noise_profile))
noise_magnitude = np.mean(noise_spectrum, axis=0)
audio_spectrum = np.abs(np.fft.rfft(audio))
cleaned = np.maximum(
audio_spectrum - noise_magnitude[:, None],
audio_spectrum * 0.1,
)
cleaned = cleaned * np.exp(1j * np.fft.rfft(audio).angle())
return np.fft.irfft(cleaned)
def extract_mel_spectrogram(self, audio: np.ndarray) -> np.ndarray:
"""Extract mel spectrogram features."""
mel_spec = librosa.feature.melspectrogram(
y=audio,
sr=self.sample_rate,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
)
log_mel = librosa.power_to_db(mel_spec, ref=np.max)
return log_mel
def extract_prosody_features(self, audio: np.ndarray) -> dict:
"""Extract prosodic features (pitch, energy, speaking rate)."""
pitches, magnitudes = librosa.piptrack(
y=audio,
sr=self.sample_rate,
n_fft=self.n_fft,
hop_length=self.hop_length,
)
pitch_values = []
for i in range(pitches.shape[1]):
index = magnitudes[:, i].argmax()
pitch = pitches[index, i]
if pitch > 0:
pitch_values.append(pitch)
rms = librosa.feature.rms(y=audio, hop_length=self.hop_length)[0]
return {
"mean_pitch": np.mean(pitch_values) if pitch_values else 0,
"pitch_std": np.std(pitch_values) if pitch_values else 0,
"pitch_range": (np.min(pitch_values) if pitch_values else 0,
np.max(pitch_values) if pitch_values else 0),
"mean_energy": np.mean(rms),
"energy_std": np.std(rms),
}
def process_file(
self,
input_path: str,
output_path: str,
remove_silence: bool = True,
apply_filter: bool = True,
) -> dict:
"""Process a single audio file."""
audio, sr = self.load_audio(input_path)
audio = self.normalize_audio(audio)
if apply_filter:
audio = self.apply_bandpass_filter(audio)
if remove_silence:
audio = self.remove_silence(audio)
prosody = self.extract_prosody_features(audio)
sf.write(output_path, audio, self.sample_rate)
logger.info(f"Saved processed audio to {output_path}")
return {
"input_path": input_path,
"output_path": output_path,
"duration": len(audio) / self.sample_rate,
"prosody": prosody,
}
def batch_process(
self,
input_dir: str,
output_dir: str,
pattern: str = "*.wav",
) -> list:
"""Process all audio files in a directory."""
input_path = Path(input_dir)
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
results = []
for file_path in input_path.glob(pattern):
out_file = output_path / file_path.name
result = self.process_file(str(file_path), str(out_file))
results.append(result)
return results
def create_processor(config: dict = None) -> AudioProcessor:
"""Factory function to create AudioProcessor from config."""
if config is None:
config = {}
return AudioProcessor(
sample_rate=config.get("sample_rate", 16000),
n_fft=config.get("n_fft", 512),
hop_length=config.get("hop_length", 160),
n_mels=config.get("n_mels", 80),
)
if __name__ == "__main__":
processor = create_processor()
print("AudioProcessor initialized successfully")
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