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"""
Audio Preprocessing for Model Inference
This module handles loading audio files and converting them to spectrograms
for input to the emotion prediction models.
"""
import librosa
import numpy as np
import torch
from PIL import Image
class AudioPreprocessor:
"""
Preprocessor for converting audio files to model-ready spectrograms.
"""
def __init__(self,
sample_rate=22050,
duration=30,
n_mels=128,
hop_length=512,
n_fft=2048,
fmin=20,
fmax=8000,
image_size=224):
"""
Initialize audio preprocessor.
Args:
sample_rate: Audio sampling rate (Hz)
duration: Audio clip duration (seconds)
n_mels: Number of mel-frequency bins
hop_length: Hop length for STFT
n_fft: FFT window size
fmin: Minimum frequency
fmax: Maximum frequency
image_size: Target image size for model input (224 for ViT)
"""
self.sample_rate = sample_rate
self.duration = duration
self.n_mels = n_mels
self.hop_length = hop_length
self.n_fft = n_fft
self.fmin = fmin
self.fmax = fmax
self.image_size = image_size
# ImageNet normalization (used by ViT)
self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
def load_audio(self, audio_path):
"""
Load audio file.
Args:
audio_path: Path to audio file
Returns:
audio: Audio waveform
sr: Sample rate
"""
try:
audio, sr = librosa.load(
audio_path,
sr=self.sample_rate,
duration=self.duration,
mono=True
)
# Pad or truncate to exact duration
target_length = self.sample_rate * self.duration
if len(audio) < target_length:
audio = np.pad(audio, (0, target_length - len(audio)))
else:
audio = audio[:target_length]
return audio, sr
except Exception as e:
raise RuntimeError(f"Failed to load audio from {audio_path}: {e}")
def audio_to_melspectrogram(self, audio):
"""
Convert audio waveform to mel spectrogram.
Args:
audio: Audio waveform
Returns:
mel_spec: Mel spectrogram in dB scale
"""
# Compute mel spectrogram
mel_spec = librosa.feature.melspectrogram(
y=audio,
sr=self.sample_rate,
n_mels=self.n_mels,
n_fft=self.n_fft,
hop_length=self.hop_length,
fmin=self.fmin,
fmax=self.fmax
)
# Convert to dB scale
mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)
return mel_spec_db
def spectrogram_to_image(self, mel_spec):
"""
Convert mel spectrogram to RGB image tensor for ViT input.
Args:
mel_spec: Mel spectrogram (n_mels, time_steps)
Returns:
image_tensor: Tensor of shape (3, 224, 224) normalized for ViT
"""
# Normalize to [0, 1]
spec_min = mel_spec.min()
spec_max = mel_spec.max()
spec_norm = (mel_spec - spec_min) / (spec_max - spec_min + 1e-8)
# Resize to 224x224 using PIL
spec_pil = Image.fromarray((spec_norm * 255).astype(np.uint8))
spec_resized = spec_pil.resize(
(self.image_size, self.image_size),
Image.Resampling.BILINEAR
)
# Convert back to numpy and normalize
spec_array = np.array(spec_resized).astype(np.float32) / 255.0
# Convert grayscale to RGB by replicating channels
spec_rgb = np.stack([spec_array, spec_array, spec_array], axis=0)
# Convert to torch tensor
image_tensor = torch.from_numpy(spec_rgb).float()
# Apply ImageNet normalization
image_tensor = (image_tensor - self.imagenet_mean) / self.imagenet_std
return image_tensor
def preprocess(self, audio_path):
"""
Complete preprocessing pipeline: audio file -> model-ready tensor.
Args:
audio_path: Path to audio file
Returns:
image_tensor: Tensor of shape (3, 224, 224) ready for model input
mel_spec: Raw mel spectrogram (for visualization)
"""
# Load audio
audio, _ = self.load_audio(audio_path)
# Convert to mel spectrogram
mel_spec = self.audio_to_melspectrogram(audio)
# Convert to image tensor
image_tensor = self.spectrogram_to_image(mel_spec)
return image_tensor, mel_spec
def preprocess_batch(self, audio_paths):
"""
Preprocess multiple audio files.
Args:
audio_paths: List of audio file paths
Returns:
batch_tensor: Tensor of shape (batch_size, 3, 224, 224)
mel_specs: List of mel spectrograms
"""
tensors = []
mel_specs = []
for audio_path in audio_paths:
tensor, mel_spec = self.preprocess(audio_path)
tensors.append(tensor)
mel_specs.append(mel_spec)
# Stack into batch
batch_tensor = torch.stack(tensors, dim=0)
return batch_tensor, mel_specs
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