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merge upstream develop

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lfchener 2020-12-10 03:37:56 +00:00
parent f375792c51 07ce84c680
commit e30d7ad48f
4 changed files with 161 additions and 236 deletions
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13
parakeet/models/waveflow.py
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@ -141,6 +141,15 @@ class ResidualBlock(nn.Layer):
raise ValueError("Only use start sequence at evaluation mode.")
self._conv_buffer = None
# NOTE: call self.conv's weight norm hook expliccitly since
# its weight will be visited directly in `add_input` without
# calling its `__call__` method. If we do not trigger the weight
# norm hook, the weight may be outdated. e.g. after loading from
# a saved checkpoint
# see also: https://github.com/pytorch/pytorch/issues/47588
for hook in self.conv._forward_pre_hooks.values():
hook(self.conv, None)
def add_input(self, x_row, condition_row):
"""Compute the output for a row and update the buffer.
@ -158,10 +167,6 @@ class ResidualBlock(nn.Layer):
self._update_buffer(x_row)
rw = self.rw
# call self.conv's weight norm hook expliccitly since its __call__
# method is not called here
for hook in self.conv._forward_pre_hooks.values():
hook(self.conv, self._conv_buffer)
x_row = F.conv2d(
self._conv_buffer,
self.conv.weight,

344
parakeet/models/wavenet.py
View File

@ -12,9 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import division
import math
import time
from typing import Union, Sequence
from tqdm import trange
import numpy as np
@ -25,36 +25,7 @@ import paddle.fluid.initializer as I
import paddle.fluid.layers.distributions as D
from parakeet.modules.conv import Conv1dCell
__all__ = ["ConditionalWavenet"]
def quantize(values, n_bands):
"""Linearlly quantize a float Tensor in [-1, 1) to an interger Tensor in [0, n_bands).
Args:
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: the quantized tensor, dtype: int64.
"""
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).
Args:
quantized (Tensor): dtype: int64. 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).
Returns:
Tensor: the dequantized tensor, dtype is specified by dtype.
"""
dtype = dtype or paddle.get_default_dtype()
value = (paddle.cast(quantized, dtype) + 0.5) * (2.0 / n_bands) - 1.0
return value
from parakeet.modules.audio import quantize, dequantize, STFT
def crop(x, audio_start, audio_length):
@ -62,7 +33,7 @@ def crop(x, audio_start, audio_length):
Args:
x (Tensor): shape(B, C, T), dtype float32, the upsample condition.
audio_start (Tensor): shape(B,), dtype: int64, the index the starting point.
audio_start (Tensor): shape(B, ), dtype: int64, the index the starting point.
audio_length (int): the length of the audio (number of samples it contaions).
Returns:
@ -81,9 +52,52 @@ def crop(x, audio_start, audio_length):
return out
class UpsampleNet(nn.LayerList):
def __init__(self, upscale_factors=[16, 16]):
"""UpsamplingNet.
It consists of several layers of Conv2DTranspose. Each Conv2DTranspose layer upsamples the time dimension by its `stride` times. And each Conv2DTranspose's filter_size at frequency dimension is 3.
Args:
upscale_factors (list[int], optional): time upsampling factors for each Conv2DTranspose Layer. The `UpsampleNet` contains len(upscale_factor) Conv2DTranspose Layers. Each upscale_factor is used as the `stride` for the corresponding Conv2DTranspose. Defaults to [16, 16].
Note:
np.prod(upscale_factors) should equals the `hop_length` of the stft transformation used to extract spectrogram features from audios. For example, 16 * 16 = 256, then the spectram extracted using a stft transformation whose `hop_length` is 256. See `librosa.stft` for more details.
"""
super(UpsampleNet, self).__init__()
self.upscale_factors = list(upscale_factors)
self.upscale_factor = 1
for item in upscale_factors:
self.upscale_factor *= item
for factor in self.upscale_factors:
self.append(
nn.utils.weight_norm(
nn.Conv2DTranspose(1, 1,
kernel_size=(3, 2 * factor),
stride=(1, factor),
padding=(1, factor // 2))))
def forward(self, x):
"""Compute the upsampled condition.
Args:
x (Tensor): shape(B, F, T), dtype float32, the condition (mel spectrogram here.) (F means the frequency bands). In the internal Conv2DTransposes, the frequency dimension is treated as `height` dimension instead of `in_channels`.
Returns:
Tensor: shape(B, F, T * upscale_factor), dtype float32, the upsampled condition.
"""
x = paddle.unsqueeze(x, 1)
for sublayer in self:
x = F.leaky_relu(sublayer(x), 0.4)
x = paddle.squeeze(x, 1)
return x
class ResidualBlock(nn.Layer):
def __init__(self, residual_channels, condition_dim, filter_size,
dilation):
def __init__(self,
residual_channels: int,
condition_dim: int,
filter_size: Union[int, Sequence[int]],
dilation: int):
"""A Residual block in wavenet. It does not have parametric residual or skip connection. It consists of a Conv1DCell and an Conv1D(filter_size = 1) to integrate the condition.
Args:
@ -108,7 +122,7 @@ class ResidualBlock(nn.Layer):
std = math.sqrt(1 / condition_dim)
condition_proj = Conv1dCell(condition_dim, dilated_channels, (1,),
weight_attr=I.Normal(scale=std))
weight_attr=I.Normal(scale=std))
self.condition_proj = nn.utils.weight_norm(condition_proj)
self.filter_size = filter_size
@ -121,20 +135,13 @@ class ResidualBlock(nn.Layer):
"""Conv1D gated-tanh Block.
Args:
x (Tensor): shape(B, C_res, T), the input. (B stands for batch_size,
C_res stands for residual channels, T stands for time steps.)
dtype float32.
condition (Tensor, optional): shape(B, C_cond, T), the condition,
it has been upsampled in time steps, so it has the same time
steps as the input does.(C_cond stands for the condition's channels).
Defaults to None.
x (Tensor): shape(B, C_res, T), the input. (B stands for batch_size, C_res stands for residual channels, T stands for time steps.) dtype float32.
condition (Tensor, optional): shape(B, C_cond, T), the condition, it has been upsampled in time steps, so it has the same time steps as the input does.(C_cond stands for the condition's channels). Defaults to None.
Returns:
(residual, skip_connection)
residual (Tensor): shape(B, C_res, T), the residual, which is used
as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res, T), the skip connection.
This output is accumulated with that of other ResidualBlocks.
residual (Tensor): shape(B, C_res, T), the residual, which is used as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res, T), the skip connection. This output is accumulated with that of other ResidualBlocks.
"""
h = x
@ -155,30 +162,22 @@ class ResidualBlock(nn.Layer):
return residual, skip_connection
def start_sequence(self):
"""
Prepare the ResidualBlock to generate a new sequence. This method
should be called before starting calling `add_input` multiple times.
"""Prepare the ResidualBlock to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
self.conv.start_sequence()
self.condition_proj.start_sequence()
def add_input(self, x, condition=None):
"""
Add a step input. This method works similarily with `forward` but
in a `step-in-step-out` fashion.
"""Add a step input. This method works similarily with `forward` but in a `step-in-step-out` fashion.
Args:
x (Variable): shape(B, C_res), input for a step, dtype float32.
condition (Variable, optional): shape(B, C_cond). condition for a
step, dtype float32. Defaults to None.
x (Tensor): shape(B, C_res), input for a step, dtype float32.
condition (Tensor, optional): shape(B, C_cond). condition for a step, dtype float32. Defaults to None.
Returns:
(residual, skip_connection)
residual (Variable): shape(B, C_res), the residual for a step,
which is used as the input to the next layer of ResidualBlock.
skip_connection (Variable): shape(B, C_res), the skip connection
for a step. This output is accumulated with that of other
ResidualBlocks.
residual (Tensor): shape(B, C_res), the residual for a step, which is used as the input to the next layer of ResidualBlock.
skip_connection (Tensor): shape(B, C_res), the skip connection for a step. This output is accumulated with that of other ResidualBlocks.
"""
h = x
@ -200,22 +199,24 @@ class ResidualBlock(nn.Layer):
class ResidualNet(nn.LayerList):
def __init__(self, n_loop, n_layer, residual_channels, condition_dim,
filter_size):
"""The residual network in wavenet. It consists of `n_layer` stacks,
each of which consists of `n_loop` ResidualBlocks.
def __init__(self,
n_stack: int,
n_loop: int,
residual_channels: int,
condition_dim: int,
filter_size: int):
"""The residual network in wavenet. It consists of `n_layer` stacks, each of which consists of `n_loop` ResidualBlocks.
Args:
n_stack (int): number of stacks in the `ResidualNet`.
n_loop (int): number of ResidualBlocks in a stack.
n_layer (int): number of stacks in the `ResidualNet`.
residual_channels (int): channels of each `ResidualBlock`'s input.
condition_dim (int): channels of the condition.
filter_size (int): filter size of the internal Conv1DCell of each
`ResidualBlock`.
filter_size (int): filter size of the internal Conv1DCell of each `ResidualBlock`.
"""
super(ResidualNet, self).__init__()
# double the dilation at each layer in a loop(n_loop layers)
dilations = [2**i for i in range(n_loop)] * n_layer
# double the dilation at each layer in a stack
dilations = [2**i for i in range(n_loop)] * n_stack
self.context_size = 1 + sum(dilations)
for dilation in dilations:
self.append(ResidualBlock(residual_channels, condition_dim, filter_size, dilation))
@ -223,13 +224,8 @@ class ResidualNet(nn.LayerList):
def forward(self, x, condition=None):
"""
Args:
x (Tensor): shape(B, C_res, T), dtype float32, the input.
(B stands for batch_size, C_res stands for residual channels,
T stands for time steps.)
condition (Tensor, optional): shape(B, C_cond, T), dtype float32,
the condition, it has been upsampled in time steps, so it has
the same time steps as the input does.(C_cond stands for the
condition's channels) Defaults to None.
x (Tensor): shape(B, C_res, T), dtype float32, the input. (B stands for batch_size, C_res stands for residual channels, T stands for time steps.)
condition (Tensor, optional): shape(B, C_cond, T), dtype float32, the condition, it has been upsampled in time steps, so it has the same time steps as the input does.(C_cond stands for the condition's channels) Defaults to None.
Returns:
skip_connection (Tensor): shape(B, C_res, T), dtype float32, the output.
@ -244,24 +240,20 @@ class ResidualNet(nn.LayerList):
return skip_connections
def start_sequence(self):
"""Prepare the ResidualNet to generate a new sequence. This method
should be called before starting calling `add_input` multiple times.
"""Prepare the ResidualNet to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
for block in self:
block.start_sequence()
def add_input(self, x, condition=None):
"""Add a step input. This method works similarily with `forward` but
in a `step-in-step-out` fashion.
"""Add a step input. This method works similarily with `forward` but in a `step-in-step-out` fashion.
Args:
x (Tensor): shape(B, C_res), dtype float32, input for a step.
condition (Tensor, optional): shape(B, C_cond), dtype float32,
condition for a step. Defaults to None.
condition (Tensor, optional): shape(B, C_cond), dtype float32, condition for a step. Defaults to None.
Returns:
skip_connection (Tensor): shape(B, C_res), dtype float32, the
output for a step.
skip_connection (Tensor): shape(B, C_res), dtype float32, the output for a step.
"""
for i, func in enumerate(self):
@ -275,31 +267,19 @@ class ResidualNet(nn.LayerList):
class WaveNet(nn.Layer):
def __init__(self, n_loop, n_layer, residual_channels, output_dim,
def __init__(self, n_stack, n_loop, residual_channels, output_dim,
condition_dim, filter_size, loss_type, log_scale_min):
"""Wavenet that transform upsampled mel spectrogram into waveform.
Args:
n_stack (int): n_stack for the internal ResidualNet.
n_loop (int): n_loop for the internal ResidualNet.
n_layer (int): n_loop for the internal ResidualNet.
residual_channels (int): the channel of the input.
output_dim (int): the channel of the output distribution.
condition_dim (int): the channel of the condition.
filter_size (int): the filter size of the internal ResidualNet.
loss_type (str): loss type of the wavenet. Possible values are
'softmax' and 'mog'.
If `loss_type` is 'softmax', the output is the logits of the
catrgotical(multinomial) distribution, `output_dim` means the
number of classes of the categorical distribution.
If `loss_type` is mog(mixture of gaussians), the output is the
parameters of a mixture of gaussians, which consists of weight
(in the form of logit) of each gaussian distribution and its
mean and log standard deviaton. So when `loss_type` is 'mog',
`output_dim` should be perfectly divided by 3.
log_scale_min (int): the minimum value of log standard deviation
of the output gaussian distributions. Note that this value is
only used for computing loss if `loss_type` is 'mog', values
less than `log_scale_min` is clipped when computing loss.
loss_type (str): loss type of the wavenet. Possible values are 'softmax' and 'mog'. If `loss_type` is 'softmax', the output is the logits of the catrgotical(multinomial) distribution, `output_dim` means the number of classes of the categorical distribution. If `loss_type` is mog(mixture of gaussians), the output is the parameters of a mixture of gaussians, which consists of weight(in the form of logit) of each gaussian distribution and its mean and log standard deviaton. So when `loss_type` is 'mog', `output_dim` should be perfectly divided by 3.
log_scale_min (int): the minimum value of log standard deviation of the output gaussian distributions. Note that this value is only used for computing loss if `loss_type` is 'mog', values less than `log_scale_min` is clipped when computing loss.
"""
super(WaveNet, self).__init__()
if loss_type not in ["softmax", "mog"]:
@ -312,7 +292,7 @@ class WaveNet(nn.Layer):
"with Mixture of Gaussians(mog) output, the output dim must be divisible by 3, but get {}".format(output_dim))
self.embed = nn.utils.weight_norm(nn.Linear(1, residual_channels), dim=-1)
self.resnet = ResidualNet(n_loop, n_layer, residual_channels,
self.resnet = ResidualNet(n_stack, n_loop, residual_channels,
condition_dim, filter_size)
self.context_size = self.resnet.context_size
@ -334,12 +314,10 @@ class WaveNet(nn.Layer):
Args:
x (Tensor): shape(B, T), dtype float32, the input waveform.
condition (Tensor, optional): shape(B, C_cond, T), dtype float32,
the upsampled condition. Defaults to None.
condition (Tensor, optional): shape(B, C_cond, T), dtype float32, the upsampled condition. Defaults to None.
Returns:
Tensor: shape(B, T, C_output), dtype float32, the parameter of
the output distributions.
Tensor: shape(B, T, C_output), dtype float32, the parameter of the output distributions.
"""
# Causal Conv
@ -362,24 +340,19 @@ class WaveNet(nn.Layer):
return y
def start_sequence(self):
"""Prepare the WaveNet to generate a new sequence. This method should
be called before starting calling `add_input` multiple times.
"""Prepare the WaveNet to generate a new sequence. This method should be called before starting calling `add_input` multiple times.
"""
self.resnet.start_sequence()
def add_input(self, x, condition=None):
"""compute the output distribution (represented by its parameters) for
a step. It works similarily with the `forward` method but in a
`step-in-step-out` fashion.
"""compute the output distribution (represented by its parameters) for a step. It works similarily with the `forward` method but in a `step-in-step-out` fashion.
Args:
x (Tensor): shape(B,), dtype float32, a step of the input waveform.
condition (Tensor, optional): shape(B, C_cond, ), dtype float32, a
step of the upsampled condition. Defaults to None.
condition (Tensor, optional): shape(B, C_cond, ), dtype float32, a step of the upsampled condition. Defaults to None.
Returns:
Tensor: shape(B, C_output), dtype float32, the parameter of the
output distributions.
Tensor: shape(B, C_output), dtype float32, the parameter of the output distributions.
"""
# Causal Conv
if self.loss_type == "softmax":
@ -402,12 +375,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a categorial distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the logits of the
output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the logits of the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -420,15 +389,14 @@ class WaveNet(nn.Layer):
label = paddle.unsqueeze(quantized, -1)
loss = F.softmax_with_cross_entropy(y, label)
reduced_loss = paddle.reduce_mean(loss)
reduced_loss = paddle.mean(loss)
return reduced_loss
def sample_from_softmax(self, y):
"""Sample from the output distribution where the output distribution is
a categorical distriobution.
"""Sample from the output distribution where the output distribution is a categorical distriobution.
Args:
y (Tensor): shape(B, T, C_output), the logits of the output distribution.
y (Tensor): shape(B, T, C_output), the logits of the output distribution
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -446,16 +414,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution. It is the concatenation of 3 parts,
the logits of every distribution, the mean of each distribution
and the log standard deviation of each distribution. Each part's
shape is (B, T, n_mixture), where `n_mixture` means the number
of Gaussians in the mixture.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution. It is the concatenation of 3 parts, the logits of every distribution, the mean of each distribution and the log standard deviation of each distribution. Each part's shape is (B, T, n_mixture), where `n_mixture` means the number of Gaussians in the mixture.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -483,22 +443,16 @@ class WaveNet(nn.Layer):
pdf_x = p_mixture * pdf_x
# pdf_x: [bs, len]
pdf_x = paddle.reduce_sum(pdf_x, -1)
pdf_x = paddle.sum(pdf_x, -1)
per_sample_loss = -paddle.log(pdf_x + 1e-9)
loss = paddle.reduce_mean(per_sample_loss)
loss = paddle.mean(per_sample_loss)
return loss
def sample_from_mog(self, y):
"""Sample from the output distribution where the output distribution is
a mixture of Gaussians.
"""Sample from the output distribution where the output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution. It is the concatenation of 3 parts, the
logits of every distribution, the mean of each distribution and the
log standard deviation of each distribution. Each part's shape is
(B, T, n_mixture), where `n_mixture` means the number of Gaussians
in the mixture.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution. It is the concatenation of 3 parts, the logits of every distribution, the mean of each distribution and the log standard deviation of each distribution. Each part's shape is (B, T, n_mixture), where `n_mixture` means the number of Gaussians in the mixture.
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -529,8 +483,7 @@ class WaveNet(nn.Layer):
def sample(self, y):
"""Sample from the output distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution.
Returns:
Tensor: shape(B, T), waveform sampled from the output distribution.
@ -544,12 +497,8 @@ class WaveNet(nn.Layer):
"""compute the loss where output distribution is a mixture of Gaussians.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of
the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that
the target's corresponding time index is one step ahead of the
output distribution. And output distribution whose input contains
padding is neglected in loss computation.
y (Tensor): shape(B, T, C_output), dtype float32, the parameterd of the output distribution.
t (Tensor): shape(B, T), dtype float32, the target audio. Note that the target's corresponding time index is one step ahead of the output distribution. And output distribution whose input contains padding is neglected in loss computation.
Returns:
Tensor: shape(1, ), dtype float32, the loss.
@ -560,64 +509,9 @@ class WaveNet(nn.Layer):
return self.compute_mog_loss(y, t)
class UpsampleNet(nn.LayerList):
def __init__(self, upscale_factors=[16, 16]):
"""UpsamplingNet.
It consists of several layers of Conv2DTranspose. Each Conv2DTranspose
layer upsamples the time dimension by its `stride` times. And each
Conv2DTranspose's filter_size at frequency dimension is 3.
Args:
upscale_factors (list[int], optional): time upsampling factors for
each Conv2DTranspose Layer. The `UpsampleNet` contains
len(upscale_factor) Conv2DTranspose Layers. Each upscale_factor
is used as the `stride` for the corresponding Conv2DTranspose.
Defaults to [16, 16].
Note:
np.prod(upscale_factors) should equals the `hop_length` of the stft
transformation used to extract spectrogram features from audios.
For example, 16 * 16 = 256, then the spectram extracted using a
stft transformation whose `hop_length` is 256. See `librosa.stft`
for more details.
"""
super(UpsampleNet, self).__init__()
self.upscale_factors = list(upscale_factors)
self.upscale_factor = 1
for item in upscale_factors:
self.upscale_factor *= item
for factor in self.upscale_factors:
self.append(
nn.utils.weight_norm(
nn.ConvTranspose2d(1, 1,
kernel_size=(3, 2 * factor),
stride=(1, factor),
padding=(1, factor // 2))))
def forward(self, x):
"""Compute the upsampled condition.
Args:
x (Tensor): shape(B, F, T), dtype float32, the condition
(mel spectrogram here.) (F means the frequency bands). In the
internal Conv2DTransposes, the frequency dimension is treated
as `height` dimension instead of `in_channels`.
Returns:
Tensor: shape(B, F, T * upscale_factor), dtype float32, the
upsampled condition.
"""
x = paddle.unsqueeze(x, 1)
for sublayer in self:
x = F.leaky_relu(sublayer(x), 0.4)
x = paddle.squeeze(x, 1)
return x
class ConditionalWavenet(nn.Layer):
def __init__(self, encoder, decoder):
"""Conditional Wavenet, which contains an UpsampleNet as the encoder
and a WaveNet as the decoder. It is an autoregressive model.
"""Conditional Wavenet, which contains an UpsampleNet as the encoder and a WaveNet as the decoder. It is an autoregressive model.
Args:
encoder (UpsampleNet): the UpsampleNet as the encoder.
@ -628,20 +522,15 @@ class ConditionalWavenet(nn.Layer):
self.decoder = decoder
def forward(self, audio, mel, audio_start):
"""Compute the output distribution given the mel spectrogram and the
input(for teacher force training).
"""Compute the output distribution given the mel spectrogram and the input(for teacher force training).
Args:
audio (Tensor): shape(B, T_audio), dtype float32, ground truth
waveform, used for teacher force training.
mel (Tensor): shape(B, F, T_mel), dtype float32, mel spectrogram.
Note that it is the spectrogram for the whole utterance.
audio_start (Tensor): shape(B, ), dtype: int, audio slices' start
positions for each utterance.
audio (Tensor): shape(B, T_audio), dtype float32, ground truth waveform, used for teacher force training.
mel (Tensor): shape(B, F, T_mel), dtype float32, mel spectrogram. Note that it is the spectrogram for the whole utterance.
audio_start (Tensor): shape(B, ), dtype: int, audio slices' start positions for each utterance.
Returns:
Tensor: shape(B, T_audio - 1, C_putput), parameters for the output
distribution.(C_output is the `output_dim` of the decoder.)
Tensor: shape(B, T_audio - 1, C_putput), parameters for the output distribution.(C_output is the `output_dim` of the decoder.)
"""
audio_length = audio.shape[1] # audio clip's length
condition = self.encoder(mel)
@ -655,12 +544,10 @@ class ConditionalWavenet(nn.Layer):
return y
def loss(self, y, t):
"""compute loss with respect to the output distribution and the targer
audio.
"""compute loss with respect to the output distribution and the targer audio.
Args:
y (Tensor): shape(B, T - 1, C_output), dtype float32, parameters of
the output distribution.
y (Tensor): shape(B, T - 1, C_output), dtype float32, parameters of the output distribution.
t (Tensor): shape(B, T), dtype float32, target waveform.
Returns:
@ -674,12 +561,10 @@ class ConditionalWavenet(nn.Layer):
"""Sample from the output distribution.
Args:
y (Tensor): shape(B, T, C_output), dtype float32, parameters of the
output distribution.
y (Tensor): shape(B, T, C_output), dtype float32, parameters of the output distribution.
Returns:
Tensor: shape(B, T), dtype float32, sampled waveform from the output
distribution.
Tensor: shape(B, T), dtype float32, sampled waveform from the output distribution.
"""
samples = self.decoder.sample(y)
return samples
@ -692,9 +577,7 @@ class ConditionalWavenet(nn.Layer):
mel (Tensor): shape(B, F, T), condition(mel spectrogram here).
Returns:
Tensor: shape(B, T * upsacle_factor), synthesized waveform.
(`upscale_factor` is the `upscale_factor` of the encoder
`UpsampleNet`)
Tensor: shape(B, T * upsacle_factor), synthesized waveform.(`upscale_factor` is the `upscale_factor` of the encoder `UpsampleNet`)
"""
condition = self.encoder(mel)
batch_size, _, time_steps = condition.shape
@ -712,6 +595,3 @@ class ConditionalWavenet(nn.Layer):
samples = paddle.concat(samples, -1)
return samples
# TODO WaveNetLoss

32
parakeet/modules/stft.py → parakeet/modules/audio.py
View File

@ -4,6 +4,38 @@ 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).
Args:
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: the quantized tensor, dtype: int64.
"""
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).
Args:
quantized (Tensor): dtype: int64. 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.
"""
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):
def __init__(self, n_fft, hop_length, win_length, window="hanning"):
"""A module for computing differentiable stft transform. See `librosa.stft` for more details.

8
parakeet/modules/conv.py
View File

@ -60,6 +60,14 @@ class Conv1dCell(nn.Conv1D):
if self.training:
raise Exception("only use start_sequence in evaluation")
self._buffer = None
# NOTE: call self's weight norm hook expliccitly since self.weight
# is visited directly in this method without calling self.__call__
# method. If we do not trigger the weight norm hook, the weight
# may be outdated. e.g. after loading from a saved checkpoint
# see also: https://github.com/pytorch/pytorch/issues/47588
for hook in self._forward_pre_hooks.values():
hook(self, None)
self._reshaped_weight = paddle.reshape(self.weight,
(self._out_channels, -1))