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, ): """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 """ 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.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, 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.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.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