PaddleSpeech/parakeet/modules/audio.py

# 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