| import librosa
|
| import librosa.display
|
| import numpy as np
|
| import matplotlib.pyplot as plt
|
| import io
|
| from PIL import Image
|
|
|
|
|
| SR = 16000
|
| N_FFT = 1024
|
| HOP_LENGTH = 512
|
| N_MELS = 128
|
| TARGET_DURATION = 5.0
|
| TARGET_LENGTH = int(TARGET_DURATION * SR)
|
|
|
| def preprocess_audio(file_path):
|
|
|
| y, sr = librosa.load(file_path, sr=None, mono=True)
|
|
|
|
|
| peak = np.abs(y).max()
|
| if peak > 0:
|
| y = y / peak * 0.99
|
|
|
|
|
| if sr != SR:
|
| y = librosa.resample(y, orig_sr=sr, target_sr=SR)
|
|
|
|
|
| chunks = []
|
| for start in range(0, len(y), TARGET_LENGTH):
|
| chunk = y[start:start + TARGET_LENGTH]
|
| if len(chunk) < TARGET_LENGTH:
|
| chunk = np.pad(chunk, (0, TARGET_LENGTH - len(chunk)), mode="constant")
|
|
|
|
|
| S = librosa.feature.melspectrogram(
|
| y=chunk, sr=SR, n_fft=N_FFT, hop_length=HOP_LENGTH, n_mels=N_MELS
|
| )
|
| S_dB = librosa.power_to_db(S, ref=np.max)
|
|
|
|
|
| fig = plt.figure(figsize=(3, 3))
|
| librosa.display.specshow(S_dB, sr=SR, hop_length=HOP_LENGTH, cmap="magma")
|
| plt.axis("off")
|
|
|
| buf = io.BytesIO()
|
| plt.savefig(buf, format="png", bbox_inches="tight", pad_inches=0)
|
| plt.close(fig)
|
|
|
| buf.seek(0)
|
| img = Image.open(buf).convert("RGBA")
|
| chunks.append(img)
|
|
|
| return chunks |
