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| import warnings | |
| warnings.filterwarnings('ignore') | |
| import librosa | |
| import numpy as np | |
| from PIL import Image | |
| class Mel: | |
| def __init__( | |
| self, | |
| x_res=256, | |
| y_res=256, | |
| sample_rate=22050, | |
| n_fft=2048, | |
| hop_length=512, | |
| top_db=80, | |
| ): | |
| self.x_res = x_res | |
| self.y_res = y_res | |
| self.sr = sample_rate | |
| self.n_fft = n_fft | |
| self.hop_length = hop_length | |
| self.n_mels = self.y_res | |
| self.slice_size = self.x_res * self.hop_length - 1 | |
| self.fmax = self.sr / 2 | |
| self.top_db = top_db | |
| self.y = None | |
| def load_audio(self, audio_file): | |
| self.y, _ = librosa.load(audio_file, mono=True) | |
| def get_number_of_slices(self): | |
| return len(self.y) // self.slice_size | |
| def get_sample_rate(self): | |
| return self.sr | |
| def audio_slice_to_image(self, slice): | |
| S = librosa.feature.melspectrogram( | |
| y=self.y[self.slice_size * slice : self.slice_size * (slice + 1)], | |
| sr=self.sr, | |
| n_fft=self.n_fft, | |
| hop_length=self.hop_length, | |
| n_mels=self.n_mels, | |
| fmax=self.fmax, | |
| ) | |
| log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db) | |
| bytedata = ( | |
| ((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5 | |
| ).astype(np.uint8) | |
| image = Image.frombytes("L", log_S.shape, bytedata.tobytes()) | |
| return image | |
| def image_to_audio(self, image): | |
| bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape( | |
| (image.width, image.height) | |
| ) | |
| log_S = bytedata.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 | |
| ) | |
| return audio | |