192 lines
5.7 KiB
Python
192 lines
5.7 KiB
Python
# 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 paddle
|
|
from paddle import nn
|
|
from paddle.nn import functional as F
|
|
from scipy import signal
|
|
import numpy as np
|
|
|
|
__all__ = ["quantize", "dequantize", "STFT"]
|
|
|
|
|
|
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".
|
|
|
|
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, win_length, window="hanning"):
|
|
super(STFT, self).__init__()
|
|
self.hop_length = hop_length
|
|
self.n_bin = 1 + n_fft // 2
|
|
self.n_fft = n_fft
|
|
|
|
# calculate window
|
|
window = signal.get_window(window, win_length)
|
|
if n_fft != win_length:
|
|
pad = (n_fft - win_length) // 2
|
|
window = np.pad(window, ((pad, 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, 1, self.n_fft))
|
|
w_imag = np.reshape(window *
|
|
np.sin(-2 * np.pi * M / n_fft)[:self.n_bin],
|
|
(self.n_bin, 1, 1, self.n_fft))
|
|
|
|
w = np.concatenate([w_real, w_imag], axis=0)
|
|
self.weight = paddle.cast(
|
|
paddle.to_tensor(w), paddle.get_default_dtype())
|
|
|
|
def forward(self, x):
|
|
"""Compute the stft transform.
|
|
|
|
Parameters
|
|
------------
|
|
x : Tensor [shape=(B, T)]
|
|
The input waveform.
|
|
|
|
Returns
|
|
------------
|
|
real : Tensor [shape=(B, C, 1, frames)]
|
|
The real part of the spectrogram.
|
|
|
|
imag : Tensor [shape=(B, C, 1, frames)]
|
|
The image part of the spectrogram.
|
|
"""
|
|
# x(batch_size, time_steps)
|
|
# pad it first with reflect mode
|
|
# TODO(chenfeiyu): report an issue on paddle.flip
|
|
pad_start = paddle.reverse(x[:, 1:1 + self.n_fft // 2], axis=[1])
|
|
pad_stop = paddle.reverse(x[:, -(1 + self.n_fft // 2):-1], axis=[1])
|
|
x = paddle.concat([pad_start, x, pad_stop], axis=-1)
|
|
|
|
# to BC1T, C=1
|
|
x = paddle.unsqueeze(x, axis=[1, 2])
|
|
out = F.conv2d(x, self.weight, stride=(1, self.hop_length))
|
|
real, imag = paddle.chunk(out, 2, axis=1) # BC1T
|
|
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, 1, T)]
|
|
The power spectrum.
|
|
"""
|
|
real, imag = self(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, 1, T)]
|
|
The magnitude of the spectrum.
|
|
"""
|
|
power = self.power(x)
|
|
magnitude = paddle.sqrt(power)
|
|
return magnitude
|