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import os
import librosa
import numpy as np
from tabulate import tabulate
import soundfile as sf
import scipy.ndimage
import itertools
from tqdm import tqdm
import torch
import torchaudio
class AudioProcessor:
def __init__(self, audio_file):
self.path = audio_file
self.name = os.path.splitext(os.path.basename(audio_file))[0]
self.format = os.path.splitext(os.path.basename(audio_file))[1]
self.duration = librosa.get_duration(path=audio_file)
self.sample_rate = librosa.get_samplerate(audio_file)
self.changes = []
self.optimized_params = None
self.load_details()
# File information methods
def load_details(self):
"""Save the attributes of the audio file."""
data = [
["File Name", self.name],
["File Format", self.format],
["Duration", f"{self.duration} seconds"],
["Sample Rate", f"{self.sample_rate} Hz"]
]
table = tabulate(data, headers=["Attribute", "Value"], tablefmt="outline")
self.changes.append(table)
return table
def display_details(self):
"""Display the details of the audio file."""
print(self.changes[-1])
def display_changes(self):
"""Display the changes made to the audio file side by side."""
self._clean_duplicates_changes()
if len(self.changes) == 1:
self.display_details()
else:
table1 = self.changes[0].split('\n')
table2 = self.changes[-1].split('\n')
combined_table = []
for line1, line2 in zip(table1, table2):
combined_table.append([line1, '===>', line2])
print(tabulate(combined_table, tablefmt="plain"))
def _clean_duplicates_changes(self):
"""Remove duplicate consecutive changes from the audio file."""
self.changes = [change for i, change in enumerate(self.changes)
if i == 0 or change != self.changes[i-1]]
# Audio processing methods
def load_as_array(self, sample_rate: int = 16000) -> np.ndarray:
"""
Load an audio file and convert it into a NumPy array.
Parameters
----------
sample_rate : int, optional
The sample rate to which the audio will be resampled (default is 16000 Hz).
Returns
-------
np.ndarray
A NumPy array containing the audio data.
"""
try:
audio, sr = librosa.load(self.path, sr=sample_rate)
self.sample_rate = sr
return audio
except Exception as e:
raise RuntimeError(f"Failed to load audio file: {e}")
def resample_wav(self) -> str:
output_path = os.path.join('resampled_files', f'{self.name}.wav')
try:
audio, sr = librosa.load(self.path)
resampled_audio = librosa.resample(y=audio, orig_sr=sr, target_sr=16000)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
sf.write(output_path, resampled_audio, 16000)
self._update_file_info(output_path)
return output_path
except Exception as e:
raise RuntimeError(f"Failed to resample audio file: {e}")
def convert_to_wav(self):
"""
Converts an audio file to WAV format.
Returns
-------
str
The path to the converted audio file.
"""
output_path = os.path.join('converted_files', f'{self.name}.wav')
try:
os.makedirs(os.path.dirname(output_path), exist_ok=True)
audio, sr = librosa.load(self.path, sr=16000)
sf.write(output_path, audio, 16000)
self._update_file_info(output_path)
return output_path
except Exception as e:
raise RuntimeError(f"Failed to convert audio file to WAV: {e}")
def enhance_audio(self, noise_reduce_strength=0.5, voice_enhance_strength=1.5, volume_boost=1.2):
"""
Enhance audio quality by reducing noise and clarifying voices.
"""
try:
y, sr = librosa.load(self.path, sr=16000)
y_enhanced = self._enhance_audio_sample(y, noise_reduce_strength, voice_enhance_strength, volume_boost)
output_path = os.path.join('enhanced_files', f'{self.name}_enhanced.wav')
os.makedirs(os.path.dirname(output_path), exist_ok=True)
sf.write(output_path, y_enhanced, sr)
self._update_file_info(output_path)
return output_path
except Exception as e:
raise RuntimeError(f"Failed to enhance audio: {e}")
def _compute_spectral_contrast(self, y, sr, n_bands=6, fmin=200.0, quantile=0.02, hop_length=512):
"""
Compute spectral contrast using librosa.
Higher contrast generally indicates clearer speech separation from background.
"""
S = np.abs(librosa.stft(y, hop_length=hop_length))
contrast = librosa.feature.spectral_contrast(
S=S,
sr=sr,
n_bands=n_bands,
fmin=fmin,
quantile=quantile,
hop_length=hop_length
)
return np.mean(contrast)
def optimize_enhancement_parameters(self, step=0.25, max_iterations=50, sample_duration=30):
"""
Find optimal parameters for audio enhancement using grid search on a sample.
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
y_orig, sr = librosa.load(self.path, duration=sample_duration)
y_orig_tensor = torch.tensor(y_orig, device=device)
param_ranges = [
np.arange(0.25, 1.5, step), # noise_reduce_strength
np.arange(1.0, 3.0, step), # voice_enhance_strength
np.arange(1.0, 2.0, step) # volume_boost
]
best_score = float('-inf')
best_params = None
total_iterations = min(max_iterations, len(list(itertools.product(*param_ranges))))
for params in tqdm(itertools.islice(itertools.product(*param_ranges), max_iterations),
total=total_iterations,
desc="Searching for optimal parameters"):
y_enhanced = self._enhance_audio_sample(y_orig, *params)
y_enhanced_tensor = torch.tensor(y_enhanced, device=device)
# Correlation between original and enhanced audio
min_length = min(len(y_orig_tensor), len(y_enhanced_tensor))
y_orig_trimmed = y_orig_tensor[:min_length]
y_enhanced_trimmed = y_enhanced_tensor[:min_length]
correlation = torch.corrcoef(torch.stack([y_orig_trimmed, y_enhanced_trimmed]))[0, 1].item()
# Spectral contrast improvement
contrast_orig = self._compute_spectral_contrast(y_orig, sr)
contrast_enhanced = self._compute_spectral_contrast(y_enhanced, sr)
contrast_improvement = contrast_enhanced - contrast_orig
score = (0.3 * correlation) + (0.7 * contrast_improvement)
if score > best_score:
best_score = score
best_params = params
self.optimized_params = best_params
return best_params
def _enhance_audio_sample(self, y, noise_reduce_strength=0.5, voice_enhance_strength=1.5, volume_boost=1.2):
"""
Enhance an audio sample by reducing noise and enhancing voice clarity.
Parameters
----------
y : np.ndarray
Input audio signal
noise_reduce_strength : float
Strength of noise reduction (default: 0.5)
voice_enhance_strength : float
Strength of voice enhancement (default: 1.5)
volume_boost : float
Volume boost factor (default: 1.2)
Returns
-------
np.ndarray
Enhanced audio signal
"""
# STFT
S = librosa.stft(y, n_fft=2048)
S_mag, S_phase = np.abs(S), np.angle(S)
S_filtered = scipy.ndimage.median_filter(S_mag, size=(1, 31))
# Noise reduction mask
mask = np.clip((S_mag - S_filtered) / (S_mag + 1e-10), 0, 1) ** noise_reduce_strength
S_denoised = S_mag * mask * np.exp(1j * S_phase)
# Inverse STFT
y_denoised = librosa.istft(S_denoised)
# Harmonic-percussive separation and enhancement
y_harmonic, y_percussive = librosa.effects.hpss(y_denoised)
y_enhanced = (y_harmonic * voice_enhance_strength + y_percussive) * volume_boost
return librosa.util.normalize(y_enhanced, norm=np.inf, threshold=1.0)
# Helper method
def _update_file_info(self, new_path):
"""Update file information after processing."""
self.path = new_path
self.sample_rate = librosa.get_samplerate(new_path)
self.format = os.path.splitext(new_path)[1]
self.duration = librosa.get_duration(path=new_path)
self.load_details() |