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# *****************************************************************************
# Copyright (c) 2019 fatchord (https://github.com/fatchord)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# *****************************************************************************
import torch
import torchaudio
from processing import normalized_waveform_to_bits
from torch import Tensor
from torchaudio.models.wavernn import WaveRNN
def _fold_with_overlap(x: Tensor, timesteps: int, overlap: int) -> Tensor:
r"""Fold the tensor with overlap for quick batched inference.
Overlap will be used for crossfading in xfade_and_unfold().
x = [[h1, h2, ... hn]]
Where each h is a vector of conditioning channels
Eg: timesteps=2, overlap=1 with x.size(1)=10
folded = [[h1, h2, h3, h4],
[h4, h5, h6, h7],
[h7, h8, h9, h10]]
Args:
x (tensor): Upsampled conditioning channels of size (1, timesteps, channel).
timesteps (int): Timesteps for each index of batch.
overlap (int): Timesteps for both xfade and rnn warmup.
Return:
folded (tensor): folded tensor of size (n_folds, timesteps + 2 * overlap, channel).
"""
_, channels, total_len = x.size()
# Calculate variables needed
n_folds = (total_len - overlap) // (timesteps + overlap)
extended_len = n_folds * (overlap + timesteps) + overlap
remaining = total_len - extended_len
# Pad if some time steps poking out
if remaining != 0:
n_folds += 1
padding = timesteps + 2 * overlap - remaining
x = torch.nn.functional.pad(x, (0, padding))
folded = torch.zeros((n_folds, channels, timesteps + 2 * overlap), device=x.device)
# Get the values for the folded tensor
for i in range(n_folds):
start = i * (timesteps + overlap)
end = start + timesteps + 2 * overlap
folded[i] = x[0, :, start:end]
return folded
def _xfade_and_unfold(y: Tensor, overlap: int) -> Tensor:
r"""Applies a crossfade and unfolds into a 1d array.
y = [[seq1],
[seq2],
[seq3]]
Apply a gain envelope at both ends of the sequences
y = [[seq1_in, seq1_timesteps, seq1_out],
[seq2_in, seq2_timesteps, seq2_out],
[seq3_in, seq3_timesteps, seq3_out]]
Stagger and add up the groups of samples:
[seq1_in, seq1_timesteps, (seq1_out + seq2_in), seq2_timesteps, ...]
Args:
y (Tensor): Batched sequences of audio samples of size
(num_folds, channels, timesteps + 2 * overlap).
overlap (int): Timesteps for both xfade and rnn warmup.
Returns:
unfolded waveform (Tensor) : waveform in a 1d tensor of size (channels, total_len).
"""
num_folds, channels, length = y.shape
timesteps = length - 2 * overlap
total_len = num_folds * (timesteps + overlap) + overlap
# Need some silence for the rnn warmup
silence_len = overlap // 2
fade_len = overlap - silence_len
silence = torch.zeros((silence_len), dtype=y.dtype, device=y.device)
linear = torch.ones((silence_len), dtype=y.dtype, device=y.device)
# Equal power crossfade
t = torch.linspace(-1, 1, fade_len, dtype=y.dtype, device=y.device)
fade_in = torch.sqrt(0.5 * (1 + t))
fade_out = torch.sqrt(0.5 * (1 - t))
# Concat the silence to the fades
fade_in = torch.cat([silence, fade_in])
fade_out = torch.cat([linear, fade_out])
# Apply the gain to the overlap samples
y[:, :, :overlap] *= fade_in
y[:, :, -overlap:] *= fade_out
unfolded = torch.zeros((channels, total_len), dtype=y.dtype, device=y.device)
# Loop to add up all the samples
for i in range(num_folds):
start = i * (timesteps + overlap)
end = start + timesteps + 2 * overlap
unfolded[:, start:end] += y[i]
return unfolded
class WaveRNNInferenceWrapper(torch.nn.Module):
def __init__(self, wavernn: WaveRNN):
super().__init__()
self.wavernn_model = wavernn
def forward(
self, specgram: Tensor, mulaw: bool = True, batched: bool = True, timesteps: int = 100, overlap: int = 5
) -> Tensor:
r"""Inference function for WaveRNN.
Based on the implementation from
https://github.com/fatchord/WaveRNN/blob/master/models/fatchord_version.py.
Currently only supports multinomial sampling.
Args:
specgram (Tensor): spectrogram of size (n_mels, n_time)
mulaw (bool, optional): Whether to perform mulaw decoding (Default: ``True``).
batched (bool, optional): Whether to perform batch prediction. Using batch prediction
will significantly increase the inference speed (Default: ``True``).
timesteps (int, optional): The time steps for each batch. Only used when `batched`
is set to True (Default: ``100``).
overlap (int, optional): The overlapping time steps between batches. Only used when
`batched` is set to True (Default: ``5``).
Returns:
waveform (Tensor): Reconstructed waveform of size (1, n_time, ).
1 represents single channel.
"""
specgram = specgram.unsqueeze(0)
if batched:
specgram = _fold_with_overlap(specgram, timesteps, overlap)
output = self.wavernn_model.infer(specgram).cpu()
if mulaw:
output = normalized_waveform_to_bits(output, self.wavernn_model.n_bits)
output = torchaudio.functional.mu_law_decoding(output, self.wavernn_model.n_classes)
if batched:
output = _xfade_and_unfold(output, overlap)
else:
output = output[0]
return output
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