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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import numpy as np | |
| import torch | |
| # ZERO = 1e-12 | |
| def gaussian_normalize_mel_channel(mel, mu, sigma): | |
| """ | |
| Shift to Standorm Normal Distribution | |
| Args: | |
| mel: (n_mels, frame_len) | |
| mu: (n_mels,), mean value | |
| sigma: (n_mels,), sd value | |
| Return: | |
| Tensor like mel | |
| """ | |
| mu = np.expand_dims(mu, -1) | |
| sigma = np.expand_dims(sigma, -1) | |
| return (mel - mu) / sigma | |
| def de_gaussian_normalize_mel_channel(mel, mu, sigma): | |
| """ | |
| Args: | |
| mel: (n_mels, frame_len) | |
| mu: (n_mels,), mean value | |
| sigma: (n_mels,), sd value | |
| Return: | |
| Tensor like mel | |
| """ | |
| mu = np.expand_dims(mu, -1) | |
| sigma = np.expand_dims(sigma, -1) | |
| return sigma * mel + mu | |
| def decompress(audio_compressed, bits): | |
| mu = 2**bits - 1 | |
| audio = np.sign(audio_compressed) / mu * ((1 + mu) ** np.abs(audio_compressed) - 1) | |
| return audio | |
| def compress(audio, bits): | |
| mu = 2**bits - 1 | |
| audio_compressed = np.sign(audio) * np.log(1 + mu * np.abs(audio)) / np.log(mu + 1) | |
| return audio_compressed | |
| def label_to_audio(quant, bits): | |
| classes = 2**bits | |
| audio = 2 * quant / (classes - 1.0) - 1.0 | |
| return audio | |
| def audio_to_label(audio, bits): | |
| """Normalized audio data tensor to digit array | |
| Args: | |
| audio (tensor): audio data | |
| bits (int): data bits | |
| Returns: | |
| array<int>: digit array of audio data | |
| """ | |
| classes = 2**bits | |
| # initialize an increasing array with values from -1 to 1 | |
| bins = np.linspace(-1, 1, classes) | |
| # change value in audio tensor to digits | |
| quant = np.digitize(audio, bins) - 1 | |
| return quant | |
| def label_to_onehot(x, bits): | |
| """Converts a class vector (integers) to binary class matrix. | |
| Args: | |
| x: class vector to be converted into a matrix | |
| (integers from 0 to num_classes). | |
| num_classes: total number of classes. | |
| Returns: | |
| A binary matrix representation of the input. The classes axis | |
| is placed last. | |
| """ | |
| classes = 2**bits | |
| result = torch.zeros((x.shape[0], classes), dtype=torch.float32) | |
| for i in range(x.shape[0]): | |
| result[i, x[i]] = 1 | |
| output_shape = x.shape + (classes,) | |
| output = torch.reshape(result, output_shape) | |
| return output | |