# 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