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