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DeepSpeech
提交 00a6236f 编写于 作者: L lym0302
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remove test code, test=doc

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paddlespeech/server/tests/tts/infer/run.sh 已删除 100644 → 0
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model_path=~/.paddlespeech/models/
am_model_dir=$model_path/fastspeech2_csmsc-zh/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0/
voc_model_dir=$model_path/mb_melgan_csmsc-zh/mb_melgan_csmsc_ckpt_0.1.1/
testdata=../../../../t2s/exps/csmsc_test.txt
# get am file
for file in $(ls $am_model_dir)
do
if [[ $file == *"yaml"* ]]; then
am_config_file=$file
elif [[ $file == *"pdz"* ]]; then
am_ckpt_file=$file
elif [[ $file == *"stat"* ]]; then
am_stat_file=$file
elif [[ $file == *"phone"* ]]; then
phones_dict_file=$file
fi
done
# get voc file
for file in $(ls $voc_model_dir)
do
if [[ $file == *"yaml"* ]]; then
voc_config_file=$file
elif [[ $file == *"pdz"* ]]; then
voc_ckpt_file=$file
elif [[ $file == *"stat"* ]]; then
voc_stat_file=$file
fi
done
# run test
# am can choose fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc, where fastspeech2_cnndecoder_csmsc supports streaming inference.
# voc can choose hifigan_csmsc and mb_melgan_csmsc, They can both support streaming inference.
# When am is fastspeech2_cnndecoder_csmsc and am_pad is set to 12, there is no diff between streaming and non-streaming inference results.
# When voc is mb_melgan_csmsc and voc_pad is set to 14, there is no diff between streaming and non-streaming inference results.
# When voc is hifigan_csmsc and voc_pad is set to 20, there is no diff between streaming and non-streaming inference results.
python test_online_tts.py --am fastspeech2_cnndecoder_csmsc \
--am_config $am_model_dir/$am_config_file \
--am_ckpt $am_model_dir/$am_ckpt_file \
--am_stat $am_model_dir/$am_stat_file \
--phones_dict $am_model_dir/$phones_dict_file \
--voc mb_melgan_csmsc \
--voc_config $voc_model_dir/$voc_config_file \
--voc_ckpt $voc_model_dir/$voc_ckpt_file \
--voc_stat $voc_model_dir/$voc_stat_file \
--lang zh \
--device cpu \
--text $testdata \
--output_dir ./output \
--log_file ./result.log \
--am_streaming True \
--am_pad 12 \
--am_block 42 \
--voc_streaming True \
--voc_pad 14 \
--voc_block 14 \
paddlespeech/server/tests/tts/infer/test_online_tts.py 已删除 100644 → 0
浏览文件 @ 9c0ceaac
# 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 math
import threading
import time
from pathlib import Path
import numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.t2s.exps.syn_utils import get_am_inference
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
mel_streaming = None
wav_streaming = None
streaming_first_time = 0.0
streaming_voc_st = 0.0
sample_rate = 0
def denorm(data, mean, std):
return data * std + mean
def get_chunks(data, block_size, pad_size, step):
if step == "am":
data_len = data.shape[1]
elif step == "voc":
data_len = data.shape[0]
else:
print("Please set correct type to get chunks, am or voc")
chunks = []
n = math.ceil(data_len / block_size)
for i in range(n):
start = max(0, i * block_size - pad_size)
end = min((i + 1) * block_size + pad_size, data_len)
if step == "am":
chunks.append(data[:, start:end, :])
elif step == "voc":
chunks.append(data[start:end, :])
else:
print("Please set correct type to get chunks, am or voc")
return chunks
def get_streaming_am_inference(args, am_config):
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)
am_name = "fastspeech2"
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)
return am, am_mu, am_std
def init(args):
global sample_rate
# get config
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))
sample_rate = am_config.fs
# frontend
frontend = get_frontend(args)
# acoustic model
if args.am == 'fastspeech2_cnndecoder_csmsc':
am, am_mu, am_std = get_streaming_am_inference(args, am_config)
am_infer_info = [am, am_mu, am_std, am_config]
else:
am_inference, am_name, am_dataset = get_am_inference(args, am_config)
am_infer_info = [am_inference, am_name, am_dataset, am_config]
# vocoder
voc_inference = get_voc_inference(args, voc_config)
voc_infer_info = [voc_inference, voc_config]
return frontend, am_infer_info, voc_infer_info
def get_phone(args, frontend, sentence, merge_sentences, get_tone_ids):
am_name = args.am[:args.am.rindex('_')]
tone_ids = None
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"]
if get_tone_ids:
tone_ids = input_ids["tone_ids"]
elif args.lang == 'en':
input_ids = frontend.get_input_ids(
sentence, merge_sentences=merge_sentences)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en'}!")
return phone_ids, tone_ids
@paddle.no_grad()
# 生成完整的mel
def gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids):
# 如果是支持流式的AM模型
if args.am == 'fastspeech2_cnndecoder_csmsc':
am, am_mu, am_std, am_config = am_infer_info
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
if args.am_streaming:
am_pad = args.am_pad
am_block = args.am_block
hss = get_chunks(orig_hs, am_block, am_pad, "am")
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[:-am_pad]
elif i == chunk_num - 1:
# 最后一块的右侧一定没有 pad 够
sub_mel = sub_mel[am_pad:]
else:
# 倒数几块的右侧也可能没有 pad 够
sub_mel = sub_mel[am_pad:(am_block + am_pad) -
sub_mel.shape[0]]
mel_list.append(sub_mel)
mel = paddle.concat(mel_list, axis=0)
else:
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
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)
else:
am_inference, am_name, am_dataset, am_config = am_infer_info
mel = am_inference(part_phone_ids)
return mel
@paddle.no_grad()
def streaming_voc_infer(args, voc_infer_info, mel_len):
global mel_streaming
global streaming_first_time
global wav_streaming
voc_inference, voc_config = voc_infer_info
block = args.voc_block
pad = args.voc_pad
upsample = voc_config.n_shift
wav_list = []
flag = 1
valid_start = 0
valid_end = min(valid_start + block, mel_len)
actual_start = 0
actual_end = min(valid_end + pad, mel_len)
mel_chunk = mel_streaming[actual_start:actual_end, :]
while valid_end <= mel_len:
sub_wav = voc_inference(mel_chunk)
if flag == 1:
streaming_first_time = time.time()
flag = 0
# get valid wav
start = valid_start - actual_start
if valid_end == mel_len:
sub_wav = sub_wav[start * upsample:]
wav_list.append(sub_wav)
break
else:
end = start + block
sub_wav = sub_wav[start * upsample:end * upsample]
wav_list.append(sub_wav)
# generate new mel chunk
valid_start = valid_end
valid_end = min(valid_start + block, mel_len)
if valid_start - pad < 0:
actual_start = 0
else:
actual_start = valid_start - pad
actual_end = min(valid_end + pad, mel_len)
mel_chunk = mel_streaming[actual_start:actual_end, :]
wav = paddle.concat(wav_list, axis=0)
wav_streaming = wav
@paddle.no_grad()
# 非流式AM / 流式AM + 非流式Voc
def am_nonstreaming_voc(args, am_infer_info, voc_infer_info, part_phone_ids,
part_tone_ids):
mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
am_infer_time = time.time()
voc_inference, voc_config = voc_infer_info
wav = voc_inference(mel)
first_response_time = time.time()
final_response_time = first_response_time
voc_infer_time = first_response_time
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
@paddle.no_grad()
# 非流式AM + 流式Voc
def nonstreaming_am_streaming_voc(args, am_infer_info, voc_infer_info,
part_phone_ids, part_tone_ids):
global mel_streaming
global streaming_first_time
global wav_streaming
mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
am_infer_time = time.time()
# voc streaming
mel_streaming = mel
mel_len = mel.shape[0]
streaming_voc_infer(args, voc_infer_info, mel_len)
first_response_time = streaming_first_time
wav = wav_streaming
final_response_time = time.time()
voc_infer_time = final_response_time
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
@paddle.no_grad()
# 流式AM + 流式 Voc
def streaming_am_streaming_voc(args, am_infer_info, voc_infer_info,
part_phone_ids, part_tone_ids):
global mel_streaming
global streaming_first_time
global wav_streaming
global streaming_voc_st
mel_streaming = None
#用来表示开启流式voc的线程
flag = 1
am, am_mu, am_std, am_config = am_infer_info
orig_hs, h_masks = am.encoder_infer(part_phone_ids)
mel_len = orig_hs.shape[1]
am_block = args.am_block
am_pad = args.am_pad
hss = get_chunks(orig_hs, am_block, am_pad, "am")
chunk_num = len(hss)
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[:-am_pad]
mel_streaming = sub_mel
elif i == chunk_num - 1:
# 最后一块的右侧一定没有 pad 够
sub_mel = sub_mel[am_pad:]
mel_streaming = paddle.concat([mel_streaming, sub_mel])
am_infer_time = time.time()
else:
# 倒数几块的右侧也可能没有 pad 够
sub_mel = sub_mel[am_pad:(am_block + am_pad) - sub_mel.shape[0]]
mel_streaming = paddle.concat([mel_streaming, sub_mel])
if flag and mel_streaming.shape[0] > args.voc_block + args.voc_pad:
t = threading.Thread(
target=streaming_voc_infer,
args=(args, voc_infer_info, mel_len, ))
t.start()
streaming_voc_st = time.time()
flag = 0
t.join()
final_response_time = time.time()
voc_infer_time = final_response_time
first_response_time = streaming_first_time
wav = wav_streaming
return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
def warm_up(args, logger, frontend, am_infer_info, voc_infer_info):
global sample_rate
logger.info(
"Before the formal test, we test a few texts to make the inference speed more stable."
)
if args.lang == 'zh':
sentence = "您好，欢迎使用语音合成服务。"
if args.lang == 'en':
sentence = "Hello and welcome to the speech synthesis service."
if args.voc_streaming:
if args.am_streaming:
infer_func = streaming_am_streaming_voc
else:
infer_func = nonstreaming_am_streaming_voc
else:
infer_func = am_nonstreaming_voc
merge_sentences = True
get_tone_ids = False
for i in range(5): # 推理5次
st = time.time()
phone_ids, tone_ids = get_phone(args, frontend, sentence,
merge_sentences, get_tone_ids)
part_phone_ids = phone_ids[0]
if tone_ids:
part_tone_ids = tone_ids[0]
else:
part_tone_ids = None
am_infer_time, voc_infer_time, first_response_time, final_response_time, wav = infer_func(
args, am_infer_info, voc_infer_info, part_phone_ids, part_tone_ids)
wav = wav.numpy()
duration = wav.size / sample_rate
logger.info(
f"sentence: {sentence}; duration: {duration} s; first response time: {first_response_time - st} s; final response time: {final_response_time - st} s"
)
def evaluate(args, logger, frontend, am_infer_info, voc_infer_info):
global sample_rate
sentences = get_sentences(args)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
get_tone_ids = False
merge_sentences = True
# choose infer function
if args.voc_streaming:
if args.am_streaming:
infer_func = streaming_am_streaming_voc
else:
infer_func = nonstreaming_am_streaming_voc
else:
infer_func = am_nonstreaming_voc
final_up_duration = 0.0
sentence_count = 0
front_time_list = []
am_time_list = []
voc_time_list = []
first_response_list = []
final_response_list = []
sentence_length_list = []
duration_list = []
for utt_id, sentence in sentences:
# front
front_st = time.time()
phone_ids, tone_ids = get_phone(args, frontend, sentence,
merge_sentences, get_tone_ids)
part_phone_ids = phone_ids[0]
if tone_ids:
part_tone_ids = tone_ids[0]
else:
part_tone_ids = None
front_et = time.time()
front_time = front_et - front_st
am_st = time.time()
am_infer_time, voc_infer_time, first_response_time, final_response_time, wav = infer_func(
args, am_infer_info, voc_infer_info, part_phone_ids, part_tone_ids)
am_time = am_infer_time - am_st
if args.voc_streaming and args.am_streaming:
voc_time = voc_infer_time - streaming_voc_st
else:
voc_time = voc_infer_time - am_infer_time
first_response = first_response_time - front_st
final_response = final_response_time - front_st
wav = wav.numpy()
duration = wav.size / sample_rate
sf.write(
str(output_dir / (utt_id + ".wav")), wav, samplerate=sample_rate)
print(f"{utt_id} done!")
sentence_count += 1
front_time_list.append(front_time)
am_time_list.append(am_time)
voc_time_list.append(voc_time)
first_response_list.append(first_response)
final_response_list.append(final_response)
sentence_length_list.append(len(sentence))
duration_list.append(duration)
logger.info(
f"uttid: {utt_id}; sentence: '{sentence}'; front time: {front_time} s; am time: {am_time} s; voc time: {voc_time} s; \
first response time: {first_response} s; final response time: {final_response} s; audio duration: {duration} s;"
)
if final_response > duration:
final_up_duration += 1
all_time_sum = sum(final_response_list)
front_rate = sum(front_time_list) / all_time_sum
am_rate = sum(am_time_list) / all_time_sum
voc_rate = sum(voc_time_list) / all_time_sum
rtf = all_time_sum / sum(duration_list)
logger.info(
f"The length of test text information, test num: {sentence_count}; text num: {sum(sentence_length_list)}; min: {min(sentence_length_list)}; max: {max(sentence_length_list)}; avg: {sum(sentence_length_list)/len(sentence_length_list)}"
)
logger.info(
f"duration information, min: {min(duration_list)}; max: {max(duration_list)}; avg: {sum(duration_list) / len(duration_list)}; sum: {sum(duration_list)}"
)
logger.info(
f"Front time information: min: {min(front_time_list)} s; max: {max(front_time_list)} s; avg: {sum(front_time_list)/len(front_time_list)} s; ratio: {front_rate * 100}%"
)
logger.info(
f"AM time information: min: {min(am_time_list)} s; max: {max(am_time_list)} s; avg: {sum(am_time_list)/len(am_time_list)} s; ratio: {am_rate * 100}%"
)
logger.info(
f"Vocoder time information: min: {min(voc_time_list)} s, max: {max(voc_time_list)} s; avg: {sum(voc_time_list)/len(voc_time_list)} s; ratio: {voc_rate * 100}%"
)
logger.info(
f"first response time information: min: {min(first_response_list)} s; max: {max(first_response_list)} s; avg: {sum(first_response_list)/len(first_response_list)} s"
)
logger.info(
f"final response time information: min: {min(final_response_list)} s; max: {max(final_response_list)} s; avg: {sum(final_response_list)/len(final_response_list)} s"
)
logger.info(f"RTF is: {rtf}")
logger.info(
f"The number of final_response is greater than duration is {final_up_duration}, ratio: {final_up_duration / sentence_count}%"
)
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', 'fastspeech2_cnndecoder_csmsc'],
help='Choose acoustic model type of tts task. where fastspeech2_cnndecoder_csmsc supports streaming inference'
)
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='mb_melgan_csmsc',
choices=['mb_melgan_csmsc', 'hifigan_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',
choices=['zh', 'en'],
help='Choose model language. zh or en')
parser.add_argument(
"--device", type=str, default='cpu', help="set cpu or gpu:id")
parser.add_argument(
"--text",
type=str,
default="./csmsc_test.txt",
help="text to synthesize, a 'utt_id sentence' pair per line.")
parser.add_argument("--output_dir", type=str, help="output dir.")
parser.add_argument(
"--log_file", type=str, default="result.log", help="log file.")
parser.add_argument(
"--am_streaming",
type=str2bool,
default=False,
help="whether use streaming acoustic model")
parser.add_argument("--am_pad", type=int, default=12, help="am pad size.")
parser.add_argument(
"--am_block", type=int, default=42, help="am block size.")
parser.add_argument(
"--voc_streaming",
type=str2bool,
default=False,
help="whether use streaming vocoder model")
parser.add_argument("--voc_pad", type=int, default=14, help="voc pad size.")
parser.add_argument(
"--voc_block", type=int, default=14, help="voc block size.")
args = parser.parse_args()
return args
def main():
args = parse_args()
paddle.set_device(args.device)
if args.am_streaming:
assert (args.am == 'fastspeech2_cnndecoder_csmsc')
logger = logging.getLogger()
fhandler = logging.FileHandler(filename=args.log_file, mode='w')
formatter = logging.Formatter(
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.setLevel(logging.DEBUG)
# set basic information
logger.info(
f"AM: {args.am}; Vocoder: {args.voc}; device: {args.device}; am streaming: {args.am_streaming}; voc streaming: {args.voc_streaming}"
)
logger.info(
f"am pad size: {args.am_pad}; am block size: {args.am_block}; voc pad size: {args.voc_pad}; voc block size: {args.voc_block};"
)
# get information about model
frontend, am_infer_info, voc_infer_info = init(args)
logger.info(
"************************ warm up *********************************")
warm_up(args, logger, frontend, am_infer_info, voc_infer_info)
logger.info(
"************************ normal test *******************************")
evaluate(args, logger, frontend, am_infer_info, voc_infer_info)
if __name__ == "__main__":
main()
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