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4d7cd0e0
编写于
3月 31, 2022
作者:
小湉湉
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差异文件
add streaming synthesize, test=tts
上级
005aa406
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
316 addition
and
24 deletion
+316
-24
examples/csmsc/tts3/local/synthesize_streaming.sh
examples/csmsc/tts3/local/synthesize_streaming.sh
+9
-9
paddlespeech/t2s/exps/synthesize_streaming.py
paddlespeech/t2s/exps/synthesize_streaming.py
+269
-0
paddlespeech/t2s/models/fastspeech2/fastspeech2.py
paddlespeech/t2s/models/fastspeech2/fastspeech2.py
+38
-11
paddlespeech/t2s/modules/transformer/encoder.py
paddlespeech/t2s/modules/transformer/encoder.py
+0
-4
未找到文件。
examples/csmsc/tts3/local/synthesize_streaming.sh
浏览文件 @
4d7cd0e0
...
...
@@ -22,9 +22,9 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--voc_stat
=
pwg_baker_ckpt_0.4/pwg_stats.npy
\
--lang
=
zh
\
--text
=
${
BIN_DIR
}
/../sentences.txt
\
--output_dir
=
${
train_output_path
}
/test_e2e
\
--output_dir
=
${
train_output_path
}
/test_e2e
_streaming
\
--phones_dict
=
dump/phone_id_map.txt
\
--
inference_dir
=
${
train_output_path
}
/inferenc
e
--
am_streaming
=
Tru
e
fi
# for more GAN Vocoders
...
...
@@ -43,9 +43,9 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
--voc_stat
=
mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy
\
--lang
=
zh
\
--text
=
${
BIN_DIR
}
/../sentences.txt
\
--output_dir
=
${
train_output_path
}
/test_e2e
\
--output_dir
=
${
train_output_path
}
/test_e2e
_streaming
\
--phones_dict
=
dump/phone_id_map.txt
\
--
inference_dir
=
${
train_output_path
}
/inferenc
e
--
am_streaming
=
Tru
e
fi
# the pretrained models haven't release now
...
...
@@ -65,9 +65,9 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
--voc_stat
=
style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy
\
--lang
=
zh
\
--text
=
${
BIN_DIR
}
/../sentences.txt
\
--output_dir
=
${
train_output_path
}
/test_e2e
\
--phones_dict
=
dump/phone_id_map.txt
# --inference_dir=${train_output_path}/inferenc
e
--output_dir
=
${
train_output_path
}
/test_e2e
_streaming
\
--phones_dict
=
dump/phone_id_map.txt
\
--am_streaming
=
Tru
e
fi
# hifigan
...
...
@@ -86,7 +86,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
--voc_stat
=
hifigan_csmsc_ckpt_0.1.1/feats_stats.npy
\
--lang
=
zh
\
--text
=
${
BIN_DIR
}
/../sentences.txt
\
--output_dir
=
${
train_output_path
}
/test_e2e
\
--output_dir
=
${
train_output_path
}
/test_e2e
_streaming
\
--phones_dict
=
dump/phone_id_map.txt
\
--
inference_dir
=
${
train_output_path
}
/inferenc
e
--
am_streaming
=
Tru
e
fi
paddlespeech/t2s/exps/synthesize_streaming.py
0 → 100644
浏览文件 @
4d7cd0e0
# 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
math
from
pathlib
import
Path
import
numpy
as
np
import
paddle
import
soundfile
as
sf
import
yaml
from
timer
import
timer
from
yacs.config
import
CfgNode
from
paddlespeech.s2t.utils.dynamic_import
import
dynamic_import
from
paddlespeech.t2s.exps.syn_utils
import
get_frontend
from
paddlespeech.t2s.exps.syn_utils
import
get_sentences
from
paddlespeech.t2s.exps.syn_utils
import
get_voc_inference
from
paddlespeech.t2s.exps.syn_utils
import
model_alias
from
paddlespeech.t2s.utils
import
str2bool
def
denorm
(
data
,
mean
,
std
):
return
data
*
std
+
mean
def
get_chunks
(
data
,
chunk_size
,
pad_size
):
data_len
=
data
.
shape
[
1
]
chunks
=
[]
n
=
math
.
ceil
(
data_len
/
chunk_size
)
for
i
in
range
(
n
):
start
=
max
(
0
,
i
*
chunk_size
-
pad_size
)
end
=
min
((
i
+
1
)
*
chunk_size
+
pad_size
,
data_len
)
chunks
.
append
(
data
[:,
start
:
end
,
:])
return
chunks
def
evaluate
(
args
):
# Init body.
with
open
(
args
.
am_config
)
as
f
:
am_config
=
CfgNode
(
yaml
.
safe_load
(
f
))
with
open
(
args
.
voc_config
)
as
f
:
voc_config
=
CfgNode
(
yaml
.
safe_load
(
f
))
print
(
"========Args========"
)
print
(
yaml
.
safe_dump
(
vars
(
args
)))
print
(
"========Config========"
)
print
(
am_config
)
print
(
voc_config
)
sentences
=
get_sentences
(
args
)
# frontend
frontend
=
get_frontend
(
args
)
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
)
# acoustic model, only support fastspeech2 here now!
# am_inference, am_name, am_dataset = get_am_inference(args, am_config)
# model: {model_name}_{dataset}
am_name
=
args
.
am
[:
args
.
am
.
rindex
(
'_'
)]
am_dataset
=
args
.
am
[
args
.
am
.
rindex
(
'_'
)
+
1
:]
odim
=
am_config
.
n_mels
am_class
=
dynamic_import
(
am_name
,
model_alias
)
am
=
am_class
(
idim
=
vocab_size
,
odim
=
odim
,
**
am_config
[
"model"
])
am
.
set_state_dict
(
paddle
.
load
(
args
.
am_ckpt
)[
"main_params"
])
am
.
eval
()
am_mu
,
am_std
=
np
.
load
(
args
.
am_stat
)
am_mu
=
paddle
.
to_tensor
(
am_mu
)
am_std
=
paddle
.
to_tensor
(
am_std
)
# vocoder
voc_inference
=
get_voc_inference
(
args
,
voc_config
)
output_dir
=
Path
(
args
.
output_dir
)
output_dir
.
mkdir
(
parents
=
True
,
exist_ok
=
True
)
merge_sentences
=
True
N
=
0
T
=
0
chunk_size
=
42
pad_size
=
12
for
utt_id
,
sentence
in
sentences
:
with
timer
()
as
t
:
get_tone_ids
=
False
if
args
.
lang
==
'zh'
:
input_ids
=
frontend
.
get_input_ids
(
sentence
,
merge_sentences
=
merge_sentences
,
get_tone_ids
=
get_tone_ids
)
phone_ids
=
input_ids
[
"phone_ids"
]
else
:
print
(
"lang should in be 'zh' here!"
)
# merge_sentences=False here, so we only use the first item of phone_ids
phone_ids
=
phone_ids
[
0
]
with
paddle
.
no_grad
():
# acoustic model
orig_hs
,
h_masks
=
am
.
encoder_infer
(
phone_ids
)
if
args
.
am_streaming
:
hss
=
get_chunks
(
orig_hs
,
chunk_size
,
pad_size
)
chunk_num
=
len
(
hss
)
mel_list
=
[]
for
i
,
hs
in
enumerate
(
hss
):
before_outs
,
_
=
am
.
decoder
(
hs
)
after_outs
=
before_outs
+
am
.
postnet
(
before_outs
.
transpose
((
0
,
2
,
1
))).
transpose
(
(
0
,
2
,
1
))
normalized_mel
=
after_outs
[
0
]
sub_mel
=
denorm
(
normalized_mel
,
am_mu
,
am_std
)
# clip output part of pad
if
i
==
0
:
sub_mel
=
sub_mel
[:
-
pad_size
]
elif
i
==
chunk_num
-
1
:
# 最后一块的右侧一定没有 pad 够
sub_mel
=
sub_mel
[
pad_size
:]
else
:
# 倒数几块的右侧也可能没有 pad 够
sub_mel
=
sub_mel
[
pad_size
:(
chunk_size
+
pad_size
)
-
sub_mel
.
shape
[
0
]]
mel_list
.
append
(
sub_mel
)
mel
=
paddle
.
concat
(
mel_list
,
axis
=
0
)
else
:
before_outs
,
_
=
am
.
decoder
(
orig_hs
)
after_outs
=
before_outs
+
am
.
postnet
(
before_outs
.
transpose
((
0
,
2
,
1
))).
transpose
((
0
,
2
,
1
))
normalized_mel
=
after_outs
[
0
]
mel
=
denorm
(
normalized_mel
,
am_mu
,
am_std
)
# vocoder
wav
=
voc_inference
(
mel
)
wav
=
wav
.
numpy
()
N
+=
wav
.
size
T
+=
t
.
elapse
speed
=
wav
.
size
/
t
.
elapse
rtf
=
am_config
.
fs
/
speed
print
(
f
"
{
utt_id
}
, mel:
{
mel
.
shape
}
, wave:
{
wav
.
shape
}
, time:
{
t
.
elapse
}
s, Hz:
{
speed
}
, RTF:
{
rtf
}
."
)
sf
.
write
(
str
(
output_dir
/
(
utt_id
+
".wav"
)),
wav
,
samplerate
=
am_config
.
fs
)
print
(
f
"
{
utt_id
}
done!"
)
print
(
f
"generation speed:
{
N
/
T
}
Hz, RTF:
{
am_config
.
fs
/
(
N
/
T
)
}
"
)
def
parse_args
():
# parse args and config and redirect to train_sp
parser
=
argparse
.
ArgumentParser
(
description
=
"Synthesize with acoustic model & vocoder"
)
# acoustic model
parser
.
add_argument
(
'--am'
,
type
=
str
,
default
=
'fastspeech2_csmsc'
,
choices
=
[
'fastspeech2_csmsc'
],
help
=
'Choose acoustic model type of tts task.'
)
parser
.
add_argument
(
'--am_config'
,
type
=
str
,
default
=
None
,
help
=
'Config of acoustic model. Use deault config when it is None.'
)
parser
.
add_argument
(
'--am_ckpt'
,
type
=
str
,
default
=
None
,
help
=
'Checkpoint file of acoustic model.'
)
parser
.
add_argument
(
"--am_stat"
,
type
=
str
,
default
=
None
,
help
=
"mean and standard deviation used to normalize spectrogram when training acoustic model."
)
parser
.
add_argument
(
"--phones_dict"
,
type
=
str
,
default
=
None
,
help
=
"phone vocabulary file."
)
parser
.
add_argument
(
"--tones_dict"
,
type
=
str
,
default
=
None
,
help
=
"tone vocabulary file."
)
# vocoder
parser
.
add_argument
(
'--voc'
,
type
=
str
,
default
=
'pwgan_csmsc'
,
choices
=
[
'pwgan_csmsc'
,
'pwgan_ljspeech'
,
'pwgan_aishell3'
,
'pwgan_vctk'
,
'mb_melgan_csmsc'
,
'style_melgan_csmsc'
,
'hifigan_csmsc'
,
'hifigan_ljspeech'
,
'hifigan_aishell3'
,
'hifigan_vctk'
,
'wavernn_csmsc'
,
],
help
=
'Choose vocoder type of tts task.'
)
parser
.
add_argument
(
'--voc_config'
,
type
=
str
,
default
=
None
,
help
=
'Config of voc. Use deault config when it is None.'
)
parser
.
add_argument
(
'--voc_ckpt'
,
type
=
str
,
default
=
None
,
help
=
'Checkpoint file of voc.'
)
parser
.
add_argument
(
"--voc_stat"
,
type
=
str
,
default
=
None
,
help
=
"mean and standard deviation used to normalize spectrogram when training voc."
)
# other
parser
.
add_argument
(
'--lang'
,
type
=
str
,
default
=
'zh'
,
help
=
'Choose model language. zh or en'
)
parser
.
add_argument
(
"--ngpu"
,
type
=
int
,
default
=
1
,
help
=
"if ngpu == 0, use cpu."
)
parser
.
add_argument
(
"--text"
,
type
=
str
,
help
=
"text to synthesize, a 'utt_id sentence' pair per line."
)
parser
.
add_argument
(
"--am_streaming"
,
type
=
str2bool
,
default
=
False
,
help
=
"whether use streaming acoustic model"
)
parser
.
add_argument
(
"--output_dir"
,
type
=
str
,
help
=
"output dir."
)
args
=
parser
.
parse_args
()
return
args
def
main
():
args
=
parse_args
()
if
args
.
ngpu
==
0
:
paddle
.
set_device
(
"cpu"
)
elif
args
.
ngpu
>
0
:
paddle
.
set_device
(
"gpu"
)
else
:
print
(
"ngpu should >= 0 !"
)
evaluate
(
args
)
if
__name__
==
"__main__"
:
main
()
paddlespeech/t2s/models/fastspeech2/fastspeech2.py
浏览文件 @
4d7cd0e0
...
...
@@ -509,6 +509,7 @@ class FastSpeech2(nn.Layer):
ps
:
paddle
.
Tensor
=
None
,
es
:
paddle
.
Tensor
=
None
,
is_inference
:
bool
=
False
,
return_after_enc
=
False
,
alpha
:
float
=
1.0
,
spk_emb
=
None
,
spk_id
=
None
,
...
...
@@ -589,8 +590,10 @@ class FastSpeech2(nn.Layer):
h_masks
=
self
.
_source_mask
(
olens_in
)
else
:
h_masks
=
None
# (B, Lmax, adim)
if
return_after_enc
:
return
hs
,
h_masks
# (B, Lmax, adim)
zs
,
_
=
self
.
decoder
(
hs
,
h_masks
)
# (B, Lmax, odim)
if
self
.
decoder_type
==
'cnndecoder'
:
...
...
@@ -608,10 +611,42 @@ class FastSpeech2(nn.Layer):
return
before_outs
,
after_outs
,
d_outs
,
p_outs
,
e_outs
def
encoder_infer
(
self
,
text
:
paddle
.
Tensor
,
alpha
:
float
=
1.0
,
spk_emb
=
None
,
spk_id
=
None
,
tone_id
=
None
,
)
->
Tuple
[
paddle
.
Tensor
,
paddle
.
Tensor
,
paddle
.
Tensor
]:
# input of embedding must be int64
x
=
paddle
.
cast
(
text
,
'int64'
)
# setup batch axis
ilens
=
paddle
.
shape
(
x
)[
0
]
xs
=
x
.
unsqueeze
(
0
)
if
spk_emb
is
not
None
:
spk_emb
=
spk_emb
.
unsqueeze
(
0
)
if
tone_id
is
not
None
:
tone_id
=
tone_id
.
unsqueeze
(
0
)
# (1, L, odim)
hs
,
h_masks
=
self
.
_forward
(
xs
,
ilens
,
is_inference
=
True
,
return_after_enc
=
True
,
alpha
=
alpha
,
spk_emb
=
spk_emb
,
spk_id
=
spk_id
,
tone_id
=
tone_id
)
return
hs
,
h_masks
def
inference
(
self
,
text
:
paddle
.
Tensor
,
speech
:
paddle
.
Tensor
=
None
,
durations
:
paddle
.
Tensor
=
None
,
pitch
:
paddle
.
Tensor
=
None
,
energy
:
paddle
.
Tensor
=
None
,
...
...
@@ -625,7 +660,6 @@ class FastSpeech2(nn.Layer):
Args:
text(Tensor(int64)): Input sequence of characters (T,).
speech(Tensor, optional): Feature sequence to extract style (N, idim).
durations(Tensor, optional (int64)): Groundtruth of duration (T,).
pitch(Tensor, optional): Groundtruth of token-averaged pitch (T, 1).
energy(Tensor, optional): Groundtruth of token-averaged energy (T, 1).
...
...
@@ -642,15 +676,11 @@ class FastSpeech2(nn.Layer):
"""
# input of embedding must be int64
x
=
paddle
.
cast
(
text
,
'int64'
)
y
=
speech
d
,
p
,
e
=
durations
,
pitch
,
energy
# setup batch axis
ilens
=
paddle
.
shape
(
x
)[
0
]
xs
,
ys
=
x
.
unsqueeze
(
0
),
None
if
y
is
not
None
:
ys
=
y
.
unsqueeze
(
0
)
xs
=
x
.
unsqueeze
(
0
)
if
spk_emb
is
not
None
:
spk_emb
=
spk_emb
.
unsqueeze
(
0
)
...
...
@@ -668,7 +698,6 @@ class FastSpeech2(nn.Layer):
_
,
outs
,
d_outs
,
p_outs
,
e_outs
=
self
.
_forward
(
xs
,
ilens
,
ys
,
ds
=
ds
,
ps
=
ps
,
es
=
es
,
...
...
@@ -681,7 +710,6 @@ class FastSpeech2(nn.Layer):
_
,
outs
,
d_outs
,
p_outs
,
e_outs
=
self
.
_forward
(
xs
,
ilens
,
ys
,
is_inference
=
True
,
alpha
=
alpha
,
spk_emb
=
spk_emb
,
...
...
@@ -829,7 +857,6 @@ class StyleFastSpeech2Inference(FastSpeech2Inference):
Args:
text(Tensor(int64)): Input sequence of characters (T,).
speech(Tensor, optional): Feature sequence to extract style (N, idim).
durations(paddle.Tensor/np.ndarray, optional (int64)): Groundtruth of duration (T,), this will overwrite the set of durations_scale and durations_bias
durations_scale(int/float, optional):
durations_bias(int/float, optional):
...
...
paddlespeech/t2s/modules/transformer/encoder.py
浏览文件 @
4d7cd0e0
...
...
@@ -587,7 +587,6 @@ class CNNDecoder(nn.Layer):
Returns:
Tensor: Output tensor (#batch, time, odim).
"""
# print("input.shape in CNNDecoder:",xs.shape)
# exchange the temporal dimension and the feature dimension
xs
=
xs
.
transpose
([
0
,
2
,
1
])
if
masks
is
not
None
:
...
...
@@ -603,7 +602,6 @@ class CNNDecoder(nn.Layer):
if
masks
is
not
None
:
outputs
=
outputs
*
masks
outputs
=
outputs
.
transpose
([
0
,
2
,
1
])
# print("outputs.shape in CNNDecoder:",outputs.shape)
return
outputs
,
masks
...
...
@@ -636,7 +634,6 @@ class CNNPostnet(nn.Layer):
Returns:
Tensor: Output tensor (#batch, odim, time).
"""
# print("xs.shape in CNNPostnet:",xs.shape)
for
layer
in
self
.
residual_blocks
:
outputs
=
layer
(
xs
)
if
masks
is
not
None
:
...
...
@@ -646,5 +643,4 @@ class CNNPostnet(nn.Layer):
outputs
=
self
.
conv1d
(
outputs
)
if
masks
is
not
None
:
outputs
=
outputs
*
masks
# print("outputs.shape in CNNPostnet:",outputs.shape)
return
outputs
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