# Copyright (c) 2020 PaddlePaddle Authors. 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 librosa import numpy as np import paddle from librosa.util import pad_center from paddle import nn from paddle.nn import functional as F from scipy import signal __all__ = ["quantize", "dequantize", "STFT", "MelScale"] def quantize(values, n_bands): """Linearlly quantize a float Tensor in [-1, 1) to an interger Tensor in [0, n_bands). Parameters ----------- values : Tensor [dtype: flaot32 or float64] The floating point value. n_bands : int The number of bands. The output integer Tensor's value is in the range [0, n_bans). Returns ---------- Tensor [dtype: int 64] The quantized tensor. """ quantized = paddle.cast((values + 1.0) / 2.0 * n_bands, "int64") return quantized def dequantize(quantized, n_bands, dtype=None): """Linearlly dequantize an integer Tensor into a float Tensor in the range [-1, 1). Parameters ----------- quantized : Tensor [dtype: int] The quantized value in the range [0, n_bands). n_bands : int Number of bands. The input integer Tensor's value is in the range [0, n_bans). dtype : str, optional Data type of the output. Returns ----------- Tensor The dequantized tensor, dtype is specified by `dtype`. If `dtype` is not specified, the default float data type is used. """ dtype = dtype or paddle.get_default_dtype() value = (paddle.cast(quantized, dtype) + 0.5) * (2.0 / n_bands) - 1.0 return value class STFT(nn.Layer): """A module for computing stft transformation in a differentiable way. Parameters ------------ n_fft : int Number of samples in a frame. hop_length : int Number of samples shifted between adjacent frames. win_length : int Length of the window. window : str, optional Name of window function, see `scipy.signal.get_window` for more details. Defaults to "hanning". center : bool If True, the signal y is padded so that frame D[:, t] is centered at y[t * hop_length]. If False, then D[:, t] begins at y[t * hop_length]. Defaults to True. pad_mode : string or function If center=True, this argument is passed to np.pad for padding the edges of the signal y. By default (pad_mode="reflect"), y is padded on both sides with its own reflection, mirrored around its first and last sample respectively. If center=False, this argument is ignored. Notes ----------- It behaves like ``librosa.core.stft``. See ``librosa.core.stft`` for more details. Given a audio which ``T`` samples, it the STFT transformation outputs a spectrum with (C, frames) and complex dtype, where ``C = 1 + n_fft / 2`` and ``frames = 1 + T // hop_lenghth``. Ony ``center`` and ``reflect`` padding is supported now. """ def __init__(self, n_fft, hop_length=None, win_length=None, window="hanning", center=True, pad_mode="reflect"): super().__init__() # By default, use the entire frame if win_length is None: win_length = n_fft # Set the default hop, if it's not already specified if hop_length is None: hop_length = int(win_length // 4) self.hop_length = hop_length self.n_bin = 1 + n_fft // 2 self.n_fft = n_fft self.center = center self.pad_mode = pad_mode # calculate window window = signal.get_window(window, win_length, fftbins=True) # pad window to n_fft size if n_fft != win_length: window = pad_center(window, n_fft, mode="constant") # lpad = (n_fft - win_length) // 2 # rpad = n_fft - win_length - lpad # window = np.pad(window, ((lpad, pad), ), 'constant') # calculate weights # r = np.arange(0, n_fft) # M = np.expand_dims(r, -1) * np.expand_dims(r, 0) # w_real = np.reshape(window * # np.cos(2 * np.pi * M / n_fft)[:self.n_bin], # (self.n_bin, 1, self.n_fft)) # w_imag = np.reshape(window * # np.sin(-2 * np.pi * M / n_fft)[:self.n_bin], # (self.n_bin, 1, self.n_fft)) weight = np.fft.fft(np.eye(n_fft))[:self.n_bin] w_real = weight.real w_imag = weight.imag w = np.concatenate([w_real, w_imag], axis=0) w = w * window w = np.expand_dims(w, 1) weight = paddle.cast(paddle.to_tensor(w), paddle.get_default_dtype()) self.register_buffer("weight", weight) def forward(self, x): """Compute the stft transform. Parameters ------------ x : Tensor [shape=(B, T)] The input waveform. Returns ------------ real : Tensor [shape=(B, C, frames)] The real part of the spectrogram. imag : Tensor [shape=(B, C, frames)] The image part of the spectrogram. """ x = paddle.unsqueeze(x, axis=1) if self.center: x = F.pad( x, [self.n_fft // 2, self.n_fft // 2], data_format='NCL', mode=self.pad_mode) # to BCT, C=1 out = F.conv1d(x, self.weight, stride=self.hop_length) real, imag = paddle.chunk(out, 2, axis=1) # BCT return real, imag def power(self, x): """Compute the power spectrum. Parameters ------------ x : Tensor [shape=(B, T)] The input waveform. Returns ------------ Tensor [shape=(B, C, T)] The power spectrum. """ real, imag = self.forward(x) power = real**2 + imag**2 return power def magnitude(self, x): """Compute the magnitude of the spectrum. Parameters ------------ x : Tensor [shape=(B, T)] The input waveform. Returns ------------ Tensor [shape=(B, C, T)] The magnitude of the spectrum. """ power = self.power(x) magnitude = paddle.sqrt(power) # TODO(chenfeiyu): maybe clipping return magnitude class MelScale(nn.Layer): def __init__(self, sr, n_fft, n_mels, fmin, fmax): super().__init__() mel_basis = librosa.filters.mel(sr, n_fft, n_mels, fmin, fmax) # self.weight = paddle.to_tensor(mel_basis) weight = paddle.to_tensor(mel_basis, dtype=paddle.get_default_dtype()) self.register_buffer("weight", weight) def forward(self, spec): # (n_mels, n_freq) * (batch_size, n_freq, n_frames) mel = paddle.matmul(self.weight, spec) return mel