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ba978fca
编写于
11月 03, 2021
作者:
小湉湉
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix mbmelgan static
上级
980944da
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
368 addition
and
27 deletion
+368
-27
parakeet/exps/fastspeech2/synthesize_e2e_melgan.py
parakeet/exps/fastspeech2/synthesize_e2e_melgan.py
+185
-0
parakeet/models/melgan/melgan.py
parakeet/models/melgan/melgan.py
+13
-3
parakeet/modules/kaiser.py
parakeet/modules/kaiser.py
+146
-0
parakeet/modules/pqmf.py
parakeet/modules/pqmf.py
+24
-24
未找到文件。
parakeet/exps/fastspeech2/synthesize_e2e_melgan.py
0 → 100644
浏览文件 @
ba978fca
# Copyright (c) 2021 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
argparse
import
logging
import
os
from
pathlib
import
Path
import
numpy
as
np
import
paddle
import
soundfile
as
sf
import
yaml
from
paddle
import
jit
from
paddle.static
import
InputSpec
from
yacs.config
import
CfgNode
from
parakeet.frontend.zh_frontend
import
Frontend
from
parakeet.models.fastspeech2
import
FastSpeech2
from
parakeet.models.fastspeech2
import
FastSpeech2Inference
from
parakeet.models.melgan
import
MelGANGenerator
from
parakeet.models.melgan
import
MelGANInference
from
parakeet.modules.normalizer
import
ZScore
def
evaluate
(
args
,
fastspeech2_config
,
melgan_config
):
# dataloader has been too verbose
logging
.
getLogger
(
"DataLoader"
).
disabled
=
True
# construct dataset for evaluation
sentences
=
[]
with
open
(
args
.
text
,
'rt'
)
as
f
:
for
line
in
f
:
utt_id
,
sentence
=
line
.
strip
().
split
()
sentences
.
append
((
utt_id
,
sentence
))
with
open
(
args
.
phones_dict
,
"r"
)
as
f
:
phn_id
=
[
line
.
strip
().
split
()
for
line
in
f
.
readlines
()]
vocab_size
=
len
(
phn_id
)
print
(
"vocab_size:"
,
vocab_size
)
odim
=
fastspeech2_config
.
n_mels
model
=
FastSpeech2
(
idim
=
vocab_size
,
odim
=
odim
,
**
fastspeech2_config
[
"model"
])
model
.
set_state_dict
(
paddle
.
load
(
args
.
fastspeech2_checkpoint
)[
"main_params"
])
model
.
eval
()
vocoder
=
MelGANGenerator
(
**
melgan_config
[
"generator_params"
])
vocoder
.
set_state_dict
(
paddle
.
load
(
args
.
melgan_checkpoint
)[
"generator_params"
])
vocoder
.
remove_weight_norm
()
vocoder
.
eval
()
print
(
"model done!"
)
frontend
=
Frontend
(
phone_vocab_path
=
args
.
phones_dict
)
print
(
"frontend done!"
)
stat
=
np
.
load
(
args
.
fastspeech2_stat
)
mu
,
std
=
stat
mu
=
paddle
.
to_tensor
(
mu
)
std
=
paddle
.
to_tensor
(
std
)
fastspeech2_normalizer
=
ZScore
(
mu
,
std
)
stat
=
np
.
load
(
args
.
melgan_stat
)
mu
,
std
=
stat
mu
=
paddle
.
to_tensor
(
mu
)
std
=
paddle
.
to_tensor
(
std
)
pwg_normalizer
=
ZScore
(
mu
,
std
)
fastspeech2_inference
=
FastSpeech2Inference
(
fastspeech2_normalizer
,
model
)
fastspeech2_inference
.
eval
()
fastspeech2_inference
=
jit
.
to_static
(
fastspeech2_inference
,
input_spec
=
[
InputSpec
([
-
1
],
dtype
=
paddle
.
int64
)])
paddle
.
jit
.
save
(
fastspeech2_inference
,
os
.
path
.
join
(
args
.
inference_dir
,
"fastspeech2"
))
fastspeech2_inference
=
paddle
.
jit
.
load
(
os
.
path
.
join
(
args
.
inference_dir
,
"fastspeech2"
))
mb_melgan_inference
=
MelGANInference
(
pwg_normalizer
,
vocoder
)
mb_melgan_inference
.
eval
()
mb_melgan_inference
=
jit
.
to_static
(
mb_melgan_inference
,
input_spec
=
[
InputSpec
([
-
1
,
80
],
dtype
=
paddle
.
float32
),
])
paddle
.
jit
.
save
(
mb_melgan_inference
,
os
.
path
.
join
(
args
.
inference_dir
,
"mb_melgan"
))
mb_melgan_inference
=
paddle
.
jit
.
load
(
os
.
path
.
join
(
args
.
inference_dir
,
"mb_melgan"
))
output_dir
=
Path
(
args
.
output_dir
)
output_dir
.
mkdir
(
parents
=
True
,
exist_ok
=
True
)
for
utt_id
,
sentence
in
sentences
:
input_ids
=
frontend
.
get_input_ids
(
sentence
,
merge_sentences
=
True
)
phone_ids
=
input_ids
[
"phone_ids"
]
flags
=
0
for
part_phone_ids
in
phone_ids
:
with
paddle
.
no_grad
():
mel
=
fastspeech2_inference
(
part_phone_ids
)
temp_wav
=
mb_melgan_inference
(
mel
)
if
flags
==
0
:
wav
=
temp_wav
flags
=
1
else
:
wav
=
paddle
.
concat
([
wav
,
temp_wav
])
sf
.
write
(
str
(
output_dir
/
(
utt_id
+
".wav"
)),
wav
.
numpy
(),
samplerate
=
fastspeech2_config
.
fs
)
print
(
f
"
{
utt_id
}
done!"
)
def
main
():
# parse args and config and redirect to train_sp
parser
=
argparse
.
ArgumentParser
(
description
=
"Synthesize with fastspeech2 & parallel wavegan."
)
parser
.
add_argument
(
"--fastspeech2-config"
,
type
=
str
,
help
=
"fastspeech2 config file."
)
parser
.
add_argument
(
"--fastspeech2-checkpoint"
,
type
=
str
,
help
=
"fastspeech2 checkpoint to load."
)
parser
.
add_argument
(
"--fastspeech2-stat"
,
type
=
str
,
help
=
"mean and standard deviation used to normalize spectrogram when training fastspeech2."
)
parser
.
add_argument
(
"--melgan-config"
,
type
=
str
,
help
=
"parallel wavegan config file."
)
parser
.
add_argument
(
"--melgan-checkpoint"
,
type
=
str
,
help
=
"parallel wavegan generator parameters to load."
)
parser
.
add_argument
(
"--melgan-stat"
,
type
=
str
,
help
=
"mean and standard deviation used to normalize spectrogram when training parallel wavegan."
)
parser
.
add_argument
(
"--phones-dict"
,
type
=
str
,
default
=
"phone_id_map.txt"
,
help
=
"phone vocabulary file."
)
parser
.
add_argument
(
"--text"
,
type
=
str
,
help
=
"text to synthesize, a 'utt_id sentence' pair per line."
)
parser
.
add_argument
(
"--output-dir"
,
type
=
str
,
help
=
"output dir."
)
parser
.
add_argument
(
"--inference-dir"
,
type
=
str
,
help
=
"dir to save inference models"
)
parser
.
add_argument
(
"--device"
,
type
=
str
,
default
=
"gpu"
,
help
=
"device type to use."
)
parser
.
add_argument
(
"--verbose"
,
type
=
int
,
default
=
1
,
help
=
"verbose."
)
args
=
parser
.
parse_args
()
paddle
.
set_device
(
args
.
device
)
with
open
(
args
.
fastspeech2_config
)
as
f
:
fastspeech2_config
=
CfgNode
(
yaml
.
safe_load
(
f
))
with
open
(
args
.
melgan_config
)
as
f
:
melgan_config
=
CfgNode
(
yaml
.
safe_load
(
f
))
print
(
"========Args========"
)
print
(
yaml
.
safe_dump
(
vars
(
args
)))
print
(
"========Config========"
)
print
(
fastspeech2_config
)
print
(
melgan_config
)
evaluate
(
args
,
fastspeech2_config
,
melgan_config
)
if
__name__
==
"__main__"
:
main
()
parakeet/models/melgan/melgan.py
浏览文件 @
ba978fca
...
...
@@ -263,15 +263,13 @@ class MelGANGenerator(nn.Layer):
Tensor
Output tensor (out_channels*T ** prod(upsample_scales), 1).
"""
if
not
isinstance
(
c
,
paddle
.
Tensor
):
c
=
paddle
.
to_tensor
(
c
,
dtype
=
"float32"
)
# pseudo batch
c
=
c
.
transpose
([
1
,
0
]).
unsqueeze
(
0
)
# (B, out_channels, T ** prod(upsample_scales)
out
=
self
.
melgan
(
c
)
if
self
.
pqmf
is
not
None
:
# (B, 1, out_channels * T ** prod(upsample_scales)
out
=
self
.
pqmf
.
synthesis
(
out
)
out
=
self
.
pqmf
(
out
)
out
=
out
.
squeeze
(
0
).
transpose
([
1
,
0
])
return
out
...
...
@@ -551,3 +549,15 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
m
.
weight
.
set_value
(
w
)
self
.
apply
(
_reset_parameters
)
class
MelGANInference
(
nn
.
Layer
):
def
__init__
(
self
,
normalizer
,
melgan_generator
):
super
().
__init__
()
self
.
normalizer
=
normalizer
self
.
melgan_generator
=
melgan_generator
def
forward
(
self
,
logmel
):
normalized_mel
=
self
.
normalizer
(
logmel
)
wav
=
self
.
melgan_generator
.
inference
(
normalized_mel
)
return
wav
parakeet/modules/kaiser.py
0 → 100644
浏览文件 @
ba978fca
# Copyright (c) 2021 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
collections.abc
as
collections_abc
import
paddle
_i0A
=
[
-
4.41534164647933937950E-18
,
3.33079451882223809783E-17
,
-
2.43127984654795469359E-16
,
1.71539128555513303061E-15
,
-
1.16853328779934516808E-14
,
7.67618549860493561688E-14
,
-
4.85644678311192946090E-13
,
2.95505266312963983461E-12
,
-
1.72682629144155570723E-11
,
9.67580903537323691224E-11
,
-
5.18979560163526290666E-10
,
2.65982372468238665035E-9
,
-
1.30002500998624804212E-8
,
6.04699502254191894932E-8
,
-
2.67079385394061173391E-7
,
1.11738753912010371815E-6
,
-
4.41673835845875056359E-6
,
1.64484480707288970893E-5
,
-
5.75419501008210370398E-5
,
1.88502885095841655729E-4
,
-
5.76375574538582365885E-4
,
1.63947561694133579842E-3
,
-
4.32430999505057594430E-3
,
1.05464603945949983183E-2
,
-
2.37374148058994688156E-2
,
4.93052842396707084878E-2
,
-
9.49010970480476444210E-2
,
1.71620901522208775349E-1
,
-
3.04682672343198398683E-1
,
6.76795274409476084995E-1
]
_i0B
=
[
-
7.23318048787475395456E-18
,
-
4.83050448594418207126E-18
,
4.46562142029675999901E-17
,
3.46122286769746109310E-17
,
-
2.82762398051658348494E-16
,
-
3.42548561967721913462E-16
,
1.77256013305652638360E-15
,
3.81168066935262242075E-15
,
-
9.55484669882830764870E-15
,
-
4.15056934728722208663E-14
,
1.54008621752140982691E-14
,
3.85277838274214270114E-13
,
7.18012445138366623367E-13
,
-
1.79417853150680611778E-12
,
-
1.32158118404477131188E-11
,
-
3.14991652796324136454E-11
,
1.18891471078464383424E-11
,
4.94060238822496958910E-10
,
3.39623202570838634515E-9
,
2.26666899049817806459E-8
,
2.04891858946906374183E-7
,
2.89137052083475648297E-6
,
6.88975834691682398426E-5
,
3.36911647825569408990E-3
,
8.04490411014108831608E-1
]
def
piecewise
(
x
,
condlist
,
funclist
,
*
args
,
**
kw
):
n2
=
len
(
funclist
)
# n = len(condlist)
n
=
1
if
n
==
n2
-
1
:
# compute the "otherwise" condition.
condelse
=
~
paddle
.
any
(
condlist
,
axis
=
0
,
keepdim
=
True
)
condlist
=
paddle
.
concat
([
condlist
,
condelse
],
axis
=
0
)
n
+=
1
elif
n
!=
n2
:
raise
ValueError
(
"with {} condition(s), either {} or {} functions are expected"
.
format
(
n
,
n
,
n
+
1
))
y
=
paddle
.
zeros
(
paddle
.
shape
(
x
),
x
.
dtype
)
for
k
in
range
(
n
):
item
=
funclist
[
k
]
if
not
isinstance
(
item
,
collections_abc
.
Callable
):
y
[
condlist
[
k
]]
=
item
else
:
temp
=
condlist
[
k
]
if
paddle
.
shape
(
x
)
==
paddle
.
ones
([
1
]):
vals
=
x
y
=
item
(
vals
,
*
args
,
**
kw
)
else
:
vals
=
x
[
temp
]
y
[
temp
]
=
item
(
vals
,
*
args
,
**
kw
)
return
y
def
_chbevl
(
x
,
vals
):
b0
=
vals
[
0
]
b1
=
0.0
for
i
in
range
(
1
,
len
(
vals
)):
b2
=
b1
b1
=
b0
b0
=
x
*
b1
-
b2
+
vals
[
i
]
return
0.5
*
(
b0
-
b2
)
def
_i0_1
(
x
):
out
=
paddle
.
exp
(
x
)
*
_chbevl
(
x
/
2.0
-
2
,
_i0A
)
return
paddle
.
cast
(
out
,
dtype
=
"float32"
)
def
_i0_2
(
x
):
out
=
paddle
.
exp
(
x
)
*
_chbevl
(
32.0
/
x
-
2.0
,
_i0B
)
/
paddle
.
sqrt
(
x
)
return
paddle
.
cast
(
out
,
dtype
=
"float32"
)
def
_i0_dispatcher
(
x
):
return
(
x
,
)
def
i0
(
x
):
x
=
paddle
.
abs
(
x
)
condlist
=
x
<=
paddle
.
full
([
1
],
8.0
)
condlist
=
condlist
.
unsqueeze
(
0
)
return
piecewise
(
x
,
condlist
,
[
_i0_1
,
_i0_2
])
def
_len_guards
(
M
):
"""Handle small or incorrect window lengths"""
if
int
(
M
)
!=
M
or
M
<
0
:
raise
ValueError
(
'Window length M must be a non-negative integer'
)
return
M
<=
1
def
_extend
(
M
,
sym
):
"""Extend window by 1 sample if needed for DFT-even symmetry"""
if
not
sym
:
return
M
+
1
,
True
else
:
return
M
,
False
def
_truncate
(
w
,
needed
):
"""Truncate window by 1 sample if needed for DFT-even symmetry"""
if
needed
:
return
w
[:
-
1
]
else
:
return
w
def
kaiser
(
M
,
beta
,
sym
=
True
):
if
_len_guards
(
M
):
return
paddle
.
ones
(
M
)
M
,
needs_trunc
=
_extend
(
M
,
sym
)
n
=
paddle
.
arange
(
0
,
M
)
alpha
=
(
M
-
1
)
/
2.0
a
=
i0
(
beta
*
paddle
.
sqrt
(
1
-
((
n
-
alpha
)
/
alpha
)
**
2.0
))
b
=
i0
(
paddle
.
full
([
1
],
beta
))
w
=
a
/
b
return
_truncate
(
w
,
needs_trunc
)
parakeet/modules/pqmf.py
浏览文件 @
ba978fca
...
...
@@ -15,7 +15,8 @@
import
numpy
as
np
import
paddle
import
paddle.nn.functional
as
F
from
scipy.signal
import
kaiser
from
parakeet.modules.kaiser
import
kaiser
def
design_prototype_filter
(
taps
=
62
,
cutoff_ratio
=
0.142
,
beta
=
9.0
):
...
...
@@ -44,15 +45,12 @@ def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
# make initial filter
omega_c
=
np
.
pi
*
cutoff_ratio
with
np
.
errstate
(
invalid
=
"ignore"
):
h_i
=
np
.
sin
(
omega_c
*
(
np
.
arange
(
taps
+
1
)
-
0.5
*
taps
))
/
(
np
.
pi
*
(
np
.
arange
(
taps
+
1
)
-
0.5
*
taps
))
h_i
[
taps
//
2
]
=
np
.
cos
(
0
)
*
cutoff_ratio
# fix nan due to indeterminate form
h_i
=
paddle
.
sin
(
omega_c
*
(
paddle
.
arange
(
taps
+
1
)
-
0.5
*
taps
))
/
(
np
.
pi
*
(
paddle
.
arange
(
taps
+
1
)
-
0.5
*
taps
))
h_i
[
taps
//
2
]
=
1
*
cutoff_ratio
# fix nan due to indeterminate form
# apply kaiser window
w
=
kaiser
(
taps
+
1
,
beta
)
h
=
h_i
*
w
return
h
...
...
@@ -78,26 +76,25 @@ class PQMF(paddle.nn.Layer):
beta : float
Beta coefficient for kaiser window.
"""
super
(
PQMF
,
self
).
__init__
()
# build analysis & synthesis filter coefficients
super
().
__init__
()
h_proto
=
design_prototype_filter
(
taps
,
cutoff_ratio
,
beta
)
h_analysis
=
np
.
zeros
((
subbands
,
len
(
h_proto
)))
h_synthesis
=
np
.
zeros
((
subbands
,
len
(
h_proto
)))
h_proto_len
=
paddle
.
shape
(
h_proto
)[
0
]
h_analysis
=
paddle
.
zeros
((
subbands
,
h_proto_len
))
h_synthesis
=
paddle
.
zeros
((
subbands
,
h_proto_len
))
for
k
in
range
(
subbands
):
h_analysis
[
k
]
=
(
2
*
h_proto
*
np
.
cos
((
2
*
k
+
1
)
*
(
np
.
pi
/
(
2
*
subbands
))
*
(
np
.
arange
(
taps
+
1
)
-
(
taps
/
2
))
+
(
-
1
)
**
k
*
np
.
pi
/
4
))
2
*
h_proto
*
paddle
.
cos
((
2
*
k
+
1
)
*
(
np
.
pi
/
(
2
*
subbands
))
*
(
paddle
.
arange
(
taps
+
1
)
-
(
taps
/
2
))
+
(
-
1
)
**
k
*
np
.
pi
/
4
)
)
h_synthesis
[
k
]
=
(
2
*
h_proto
*
np
.
cos
((
2
*
k
+
1
)
*
(
np
.
pi
/
(
2
*
subbands
))
*
(
np
.
arange
(
taps
+
1
)
-
(
taps
/
2
))
-
(
-
1
)
**
k
*
np
.
pi
/
4
))
# convert to tensor
self
.
analysis_filter
=
paddle
.
to_tensor
(
h_analysis
,
dtype
=
"float32"
).
unsqueeze
(
1
)
self
.
synthesis_filter
=
paddle
.
to_tensor
(
h_synthesis
,
dtype
=
"float32"
).
unsqueeze
(
0
)
2
*
h_proto
*
paddle
.
cos
((
2
*
k
+
1
)
*
(
np
.
pi
/
(
2
*
subbands
))
*
(
paddle
.
arange
(
taps
+
1
)
-
(
taps
/
2
))
-
(
-
1
)
**
k
*
np
.
pi
/
4
)
)
self
.
analysis_filter
=
h_analysis
.
unsqueeze
(
1
)
self
.
synthesis_filter
=
h_synthesis
.
unsqueeze
(
0
)
# filter for downsampling & upsampling
updown_filter
=
paddle
.
zeros
(
(
subbands
,
subbands
,
subbands
),
dtype
=
"float32"
)
...
...
@@ -105,7 +102,6 @@ class PQMF(paddle.nn.Layer):
updown_filter
[
k
,
k
,
0
]
=
1.0
self
.
updown_filter
=
updown_filter
self
.
subbands
=
subbands
# keep padding info
self
.
pad_fn
=
paddle
.
nn
.
Pad1D
(
taps
//
2
,
mode
=
'constant'
,
value
=
0.0
)
...
...
@@ -134,7 +130,11 @@ class PQMF(paddle.nn.Layer):
Tensor
Output tensor (B, 1, T).
"""
x
=
F
.
conv1d_transpose
(
x
,
self
.
updown_filter
*
self
.
subbands
,
stride
=
self
.
subbands
)
return
F
.
conv1d
(
self
.
pad_fn
(
x
),
self
.
synthesis_filter
)
# when converting dygraph to static graph, can not use self.pqmf.synthesis directly
def
forward
(
self
,
x
):
return
self
.
synthesis
(
x
)
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