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| # Copyright 2022 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import warnings | |
| from ...configuration_utils import ConfigMixin, register_to_config | |
| from ...schedulers.scheduling_utils import SchedulerMixin | |
| warnings.filterwarnings("ignore") | |
| import numpy as np # noqa: E402 | |
| try: | |
| import librosa # noqa: E402 | |
| _librosa_can_be_imported = True | |
| _import_error = "" | |
| except Exception as e: | |
| _librosa_can_be_imported = False | |
| _import_error = ( | |
| f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it." | |
| ) | |
| from PIL import Image # noqa: E402 | |
| class Mel(ConfigMixin, SchedulerMixin): | |
| """ | |
| Parameters: | |
| 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 | |
| """ | |
| config_name = "mel_config.json" | |
| 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, | |
| ): | |
| 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.set_resolution(x_res, y_res) | |
| self.audio = None | |
| if not _librosa_can_be_imported: | |
| raise ValueError(_import_error) | |
| def set_resolution(self, x_res: int, y_res: int): | |
| """Set resolution. | |
| Args: | |
| x_res (`int`): x resolution of spectrogram (time) | |
| y_res (`int`): y resolution of spectrogram (frequency bins) | |
| """ | |
| 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 | |
| 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 | |