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build
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dist
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*.egg-info/
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from typing import Tuple
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import numpy as np
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import paddle
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from paddle import Tensor
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from paddle import nn
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from paddle.nn import functional as F
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def frame(x: Tensor,
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num_samples: Tensor,
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win_length: int,
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hop_length: int,
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clip: bool = True) -> Tuple[Tensor, Tensor]:
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"""Extract frames from audio.
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Parameters
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----------
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x : Tensor
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Shape (N, T), batched waveform.
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num_samples : Tensor
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Shape (N, ), number of samples of each waveform.
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win_length : int
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Window length.
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hop_length : int
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Number of samples shifted between ajancent frames.
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clip : bool, optional
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Whether to clip audio that does not fit into the last frame, by
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default True
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Returns
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-------
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frames : Tensor
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Shape (N, T', win_length).
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num_frames : Tensor
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Shape (N, ) number of valid frames
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"""
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assert hop_length <= win_length
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num_frames = (num_samples - win_length) // hop_length
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padding = (0, 0)
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if not clip:
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num_frames += 1
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# NOTE: pad hop_length - 1 to the right to ensure that there is at most
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# one frame dangling to the righe edge
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padding = (0, hop_length - 1)
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weight = paddle.eye(win_length).unsqueeze(1)
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frames = F.conv1d(x.unsqueeze(1),
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weight,
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padding=padding,
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stride=(hop_length, ))
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return frames, num_frames
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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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clip: bool
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Whether to clip audio is necesaary.
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"""
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def __init__(self,
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n_fft: int,
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hop_length: int,
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win_length: int,
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window_type: str = None,
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clip: bool = True):
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super().__init__()
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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.clip = clip
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# calculate window
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if window_type is None:
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window = np.ones(win_length)
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elif window_type == "hann":
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window = np.hanning(win_length)
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elif window_type == "hamming":
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window = np.hamming(win_length)
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else:
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raise ValueError("Not supported yet!")
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if win_length < n_fft:
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window = F.pad(window, (0, n_fft - win_length))
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elif win_length > n_fft:
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window = window[:n_fft]
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# (n_bins, n_fft) complex
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kernel_size = min(n_fft, win_length)
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weight = np.fft.fft(np.eye(n_fft))[:self.n_bin, :kernel_size]
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w_real = weight.real
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w_imag = weight.imag
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# (2 * n_bins, kernel_size)
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w = np.concatenate([w_real, w_imag], axis=0)
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w = w * window
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# (2 * n_bins, 1, kernel_size) # (C_out, C_in, kernel_size)
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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: Tensor, num_samples: Tensor) -> Tuple[Tensor, Tensor]:
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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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num_samples : Tensor
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Number of samples of each waveform.
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Returns
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------------
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D : Tensor
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Shape(N, T', n_bins, 2) Spectrogram.
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num_frames: Tensor
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Shape (N,) number of samples of each spectrogram
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"""
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num_frames = (num_samples - self.win_length) // self.hop_length
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padding = (0, 0)
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if not self.clip:
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num_frames += 1
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padding = (0, self.hop_length - 1)
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batch_size, _, _ = paddle.shape(x)
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x = x.unsqueeze(-1)
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D = F.conv1d(self.weight,
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x,
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stride=(self.hop_length, ),
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padding=padding,
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data_format="NLC")
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D = paddle.reshape(D, [batch_size, -1, self.n_bin, 2])
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return D, num_frames
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