| from typing import Optional |
| from config import config |
| import numpy as np |
| import librosa |
| from PIL import Image |
| import soundfile as sf |
|
|
| import warnings |
| warnings.filterwarnings("ignore", category=UserWarning, module='librosa') |
|
|
| class Mel: |
| def __init__( |
| self, |
| file_path: str = None, |
| spectrogram: Optional[np.ndarray] = None, |
| image: Image.Image = None, |
| x_res: int = config.image_size, |
| y_res: int = config.image_size, |
| sample_rate: int = config.sample_rate, |
| n_fft: int = 2048, |
| hop_length: int = 882, |
| top_db: int = 80, |
| n_iter: int = 32, |
| ): |
| self.hop_length = hop_length |
| self.sr = sample_rate |
| self.n_fft = n_fft |
| self.top_db = top_db |
| self.n_iter = n_iter |
| self.x_res = x_res |
| self.y_res = y_res |
| self.n_mels = self.y_res |
| self.slice_size = self.x_res * self.hop_length - 1 |
| self.file_path = file_path |
| self.spectrogram = spectrogram |
| self.image = image |
|
|
| if file_path is not None and not isinstance(file_path, str): |
| raise ValueError("file_path must be a string") |
| if spectrogram is not None and not isinstance(spectrogram, np.ndarray): |
| raise ValueError("spectrogram must be an ndarray") |
| if image is not None and not isinstance(image, Image.Image): |
| raise ValueError("image must be a PIL Image") |
|
|
| if file_path is not None: |
| self.load_file() |
| elif image is not None: |
| self.load_spectrogram() |
| elif spectrogram is not None: |
| self.load_image() |
| else: |
| print("Both file path and image are None!") |
|
|
| def load_file(self): |
| try: |
| |
| if ".wav" in self.file_path: |
| audio, _ = librosa.load(self.file_path, mono=True, sr=self.sr) |
| |
| if len(audio) < self.x_res * self.hop_length: |
| audio = np.concatenate([audio, np.zeros((self.x_res * self.hop_length - len(audio),))]) |
| |
| S = librosa.feature.melspectrogram( |
| y=audio, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels, fmax=self.sr//2 |
| ) |
| log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db) |
| log_S = log_S[:self.y_res, :self.x_res] |
| self.spectrogram = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8) |
| self.image = Image.fromarray(self.spectrogram) |
| except Exception as e: |
| print(f"Error loading {self.file_path}: {e}") |
|
|
| def load_spectrogram(self): |
| self.spectrogram = np.array(self.image) |
|
|
| def load_image(self): |
| self.spectrogram = self.spectrogram.astype("uint8") |
| self.image = Image.fromarray(self.spectrogram) |
|
|
| def get_spectrogram(self): |
| return self.spectrogram |
|
|
| def get_image(self): |
| return self.image |
|
|
| def get_audio(self): |
| log_S = self.spectrogram.astype("float") * self.top_db / 255 - self.top_db |
| S = librosa.db_to_power(log_S) |
| audio = librosa.feature.inverse.mel_to_audio( |
| S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter |
| ) |
| return Audio(audio, rate=self.sr) |
|
|
| def save_audio(self): |
| audio = self.get_audio() |
| sf.write(config.generated_track_path, audio.data, audio.rate) |
| print(f"Audio saved to {config.generated_track_path}") |
|
|
| def plot_spectrogram(self): |
| plt.figure(figsize=(10, 4)) |
| plt.imshow(self.spectrogram, aspect='auto', origin='lower', cmap='viridis') |
| plt.colorbar(label='Magnitude') |
| plt.title('Mel Spectrogram') |
| plt.xlabel('Time (frames)') |
| plt.ylabel('Frequency (Mel bins)') |
| plt.tight_layout() |
| plt.show() |
|
|
