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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: int = 256, | |
| y_res: int = 256, | |
| sample_rate: int = 22050, | |
| n_fft: int = 2048, | |
| hop_length: int = 512, | |
| top_db: int = 80, | |
| n_iter: int = 32): | |
| """Class to convert audio to mel spectrograms and vice versa. | |
| Args: | |
| x_res (int): x resolution of spectrogram (time) | |
| y_res (int): y resolution of spectrogram (frequency bins) | |
| sample_rate (int): sample rate of audio | |
| n_fft (int): number of Fast Fourier Transforms | |
| hop_length (int): hop length (a higher number is recommended for lower than 256 y_res) | |
| top_db (int): loudest in decibels | |
| n_iter (int): number of iterations for Griffin Linn mel inversion | |
| """ | |
| 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.top_db = top_db | |
| self.n_iter = n_iter | |
| self.audio = None | |
| def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None): | |
| """Load audio. | |
| Args: | |
| audio_file (str): must be a file on disk due to Librosa limitation or | |
| raw_audio (np.ndarray): audio as numpy array | |
| """ | |
| if audio_file is not None: | |
| self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr) | |
| else: | |
| self.audio = raw_audio | |
| # Pad with silence if necessary. | |
| if len(self.audio) < self.x_res * self.hop_length: | |
| self.audio = np.concatenate([ | |
| self.audio, | |
| np.zeros((self.x_res * self.hop_length - len(self.audio), )) | |
| ]) | |
| def get_number_of_slices(self) -> int: | |
| """Get number of slices in audio. | |
| Returns: | |
| int: number of spectograms audio can be sliced into | |
| """ | |
| return len(self.audio) // self.slice_size | |
| def get_audio_slice(self, slice: int = 0) -> np.ndarray: | |
| """Get slice of audio. | |
| Args: | |
| slice (int): slice number of audio (out of get_number_of_slices()) | |
| Returns: | |
| np.ndarray: audio as numpy array | |
| """ | |
| return self.audio[self.slice_size * slice:self.slice_size * | |
| (slice + 1)] | |
| def get_sample_rate(self) -> int: | |
| """Get sample rate: | |
| Returns: | |
| int: sample rate of audio | |
| """ | |
| return self.sr | |
| def audio_slice_to_image(self, slice: int) -> Image.Image: | |
| """Convert slice of audio to spectrogram. | |
| Args: | |
| slice (int): slice number of audio to convert (out of get_number_of_slices()) | |
| Returns: | |
| PIL Image: grayscale image of x_res x y_res | |
| """ | |
| S = librosa.feature.melspectrogram(y=self.get_audio_slice(slice), | |
| sr=self.sr, | |
| n_fft=self.n_fft, | |
| hop_length=self.hop_length, | |
| n_mels=self.n_mels) | |
| 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.fromarray(bytedata) | |
| return image | |
| def image_to_audio(self, image: Image.Image) -> np.ndarray: | |
| """Converts spectrogram to audio. | |
| Args: | |
| image (PIL Image): x_res x y_res grayscale image | |
| Returns: | |
| audio (np.ndarray): raw audio | |
| """ | |
| bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape( | |
| (image.height, image.width)) | |
| 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, | |
| n_iter=self.n_iter) | |
| return audio | |