提交 4d7cd0e0 编写于 作者: 小湉湉's avatar 小湉湉

add streaming synthesize, test=tts

上级 005aa406
......@@ -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}/inference
--am_streaming=True
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}/inference
--am_streaming=True
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}/inference
--output_dir=${train_output_path}/test_e2e_streaming \
--phones_dict=dump/phone_id_map.txt \
--am_streaming=True
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}/inference
--am_streaming=True
fi
# 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()
......@@ -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):
......
......@@ -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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