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model_path=~/.paddlespeech/models/
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am_model_dir=$model_path/fastspeech2_csmsc-zh/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0/
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voc_model_dir=$model_path/mb_melgan_csmsc-zh/mb_melgan_csmsc_ckpt_0.1.1/
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testdata=../../../../t2s/exps/csmsc_test.txt
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# get am file
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for file in $(ls $am_model_dir)
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do
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if [[ $file == *"yaml"* ]]; then
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am_config_file=$file
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elif [[ $file == *"pdz"* ]]; then
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am_ckpt_file=$file
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elif [[ $file == *"stat"* ]]; then
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am_stat_file=$file
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elif [[ $file == *"phone"* ]]; then
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phones_dict_file=$file
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fi
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done
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# get voc file
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for file in $(ls $voc_model_dir)
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do
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if [[ $file == *"yaml"* ]]; then
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voc_config_file=$file
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elif [[ $file == *"pdz"* ]]; then
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voc_ckpt_file=$file
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elif [[ $file == *"stat"* ]]; then
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voc_stat_file=$file
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fi
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done
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# run test
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# am can choose fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc, where fastspeech2_cnndecoder_csmsc supports streaming inference.
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# voc can choose hifigan_csmsc and mb_melgan_csmsc, They can both support streaming inference.
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# When am is fastspeech2_cnndecoder_csmsc and am_pad is set to 12, there is no diff between streaming and non-streaming inference results.
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# When voc is mb_melgan_csmsc and voc_pad is set to 14, there is no diff between streaming and non-streaming inference results.
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# When voc is hifigan_csmsc and voc_pad is set to 20, there is no diff between streaming and non-streaming inference results.
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python test_online_tts.py --am fastspeech2_cnndecoder_csmsc \
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--am_config $am_model_dir/$am_config_file \
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--am_ckpt $am_model_dir/$am_ckpt_file \
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--am_stat $am_model_dir/$am_stat_file \
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--phones_dict $am_model_dir/$phones_dict_file \
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--voc mb_melgan_csmsc \
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--voc_config $voc_model_dir/$voc_config_file \
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--voc_ckpt $voc_model_dir/$voc_ckpt_file \
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--voc_stat $voc_model_dir/$voc_stat_file \
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--lang zh \
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--device cpu \
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--text $testdata \
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--output_dir ./output \
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--log_file ./result.log \
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--am_streaming True \
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--am_pad 12 \
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--am_block 42 \
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--voc_streaming True \
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--voc_pad 14 \
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--voc_block 14 \
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@ -1,610 +0,0 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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import math
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import threading
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import time
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from pathlib import Path
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.exps.syn_utils import get_am_inference
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.exps.syn_utils import model_alias
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from paddlespeech.t2s.utils import str2bool
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mel_streaming = None
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wav_streaming = None
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streaming_first_time = 0.0
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streaming_voc_st = 0.0
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sample_rate = 0
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def denorm(data, mean, std):
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return data * std + mean
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def get_chunks(data, block_size, pad_size, step):
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if step == "am":
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data_len = data.shape[1]
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elif step == "voc":
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data_len = data.shape[0]
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else:
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print("Please set correct type to get chunks, am or voc")
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chunks = []
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n = math.ceil(data_len / block_size)
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for i in range(n):
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start = max(0, i * block_size - pad_size)
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end = min((i + 1) * block_size + pad_size, data_len)
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if step == "am":
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chunks.append(data[:, start:end, :])
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elif step == "voc":
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chunks.append(data[start:end, :])
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else:
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print("Please set correct type to get chunks, am or voc")
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return chunks
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def get_streaming_am_inference(args, am_config):
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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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am_name = "fastspeech2"
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odim = am_config.n_mels
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am_class = dynamic_import(am_name, model_alias)
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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return am, am_mu, am_std
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def init(args):
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global sample_rate
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# get config
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with open(args.am_config) as f:
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am_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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sample_rate = am_config.fs
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# frontend
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frontend = get_frontend(args)
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# acoustic model
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if args.am == 'fastspeech2_cnndecoder_csmsc':
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am, am_mu, am_std = get_streaming_am_inference(args, am_config)
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am_infer_info = [am, am_mu, am_std, am_config]
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else:
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am_inference, am_name, am_dataset = get_am_inference(args, am_config)
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am_infer_info = [am_inference, am_name, am_dataset, am_config]
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# vocoder
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voc_inference = get_voc_inference(args, voc_config)
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voc_infer_info = [voc_inference, voc_config]
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return frontend, am_infer_info, voc_infer_info
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def get_phone(args, frontend, sentence, merge_sentences, get_tone_ids):
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am_name = args.am[:args.am.rindex('_')]
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tone_ids = None
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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elif args.lang == 'en':
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in {'zh', 'en'}!")
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return phone_ids, tone_ids
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@paddle.no_grad()
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# 生成完整的mel
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def gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids):
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# 如果是支持流式的AM模型
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if args.am == 'fastspeech2_cnndecoder_csmsc':
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am, am_mu, am_std, am_config = am_infer_info
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orig_hs, h_masks = am.encoder_infer(part_phone_ids)
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if args.am_streaming:
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am_pad = args.am_pad
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am_block = args.am_block
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hss = get_chunks(orig_hs, am_block, am_pad, "am")
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chunk_num = len(hss)
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mel_list = []
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for i, hs in enumerate(hss):
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before_outs, _ = am.decoder(hs)
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after_outs = before_outs + am.postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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normalized_mel = after_outs[0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-am_pad]
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[am_pad:]
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[am_pad:(am_block + am_pad) -
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sub_mel.shape[0]]
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mel_list.append(sub_mel)
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mel = paddle.concat(mel_list, axis=0)
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else:
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orig_hs, h_masks = am.encoder_infer(part_phone_ids)
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before_outs, _ = am.decoder(orig_hs)
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after_outs = before_outs + am.postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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normalized_mel = after_outs[0]
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mel = denorm(normalized_mel, am_mu, am_std)
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else:
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am_inference, am_name, am_dataset, am_config = am_infer_info
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mel = am_inference(part_phone_ids)
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return mel
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@paddle.no_grad()
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def streaming_voc_infer(args, voc_infer_info, mel_len):
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global mel_streaming
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global streaming_first_time
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global wav_streaming
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voc_inference, voc_config = voc_infer_info
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block = args.voc_block
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pad = args.voc_pad
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upsample = voc_config.n_shift
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wav_list = []
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flag = 1
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valid_start = 0
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valid_end = min(valid_start + block, mel_len)
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actual_start = 0
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actual_end = min(valid_end + pad, mel_len)
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mel_chunk = mel_streaming[actual_start:actual_end, :]
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while valid_end <= mel_len:
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sub_wav = voc_inference(mel_chunk)
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if flag == 1:
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streaming_first_time = time.time()
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flag = 0
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# get valid wav
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start = valid_start - actual_start
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if valid_end == mel_len:
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sub_wav = sub_wav[start * upsample:]
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wav_list.append(sub_wav)
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break
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else:
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end = start + block
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sub_wav = sub_wav[start * upsample:end * upsample]
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wav_list.append(sub_wav)
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# generate new mel chunk
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valid_start = valid_end
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valid_end = min(valid_start + block, mel_len)
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if valid_start - pad < 0:
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actual_start = 0
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else:
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actual_start = valid_start - pad
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actual_end = min(valid_end + pad, mel_len)
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mel_chunk = mel_streaming[actual_start:actual_end, :]
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wav = paddle.concat(wav_list, axis=0)
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wav_streaming = wav
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@paddle.no_grad()
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# 非流式AM / 流式AM + 非流式Voc
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def am_nonstreaming_voc(args, am_infer_info, voc_infer_info, part_phone_ids,
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part_tone_ids):
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mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
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am_infer_time = time.time()
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voc_inference, voc_config = voc_infer_info
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wav = voc_inference(mel)
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first_response_time = time.time()
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final_response_time = first_response_time
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voc_infer_time = first_response_time
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return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
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@paddle.no_grad()
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# 非流式AM + 流式Voc
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def nonstreaming_am_streaming_voc(args, am_infer_info, voc_infer_info,
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part_phone_ids, part_tone_ids):
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global mel_streaming
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global streaming_first_time
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global wav_streaming
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mel = gen_mel(args, am_infer_info, part_phone_ids, part_tone_ids)
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am_infer_time = time.time()
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# voc streaming
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mel_streaming = mel
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mel_len = mel.shape[0]
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streaming_voc_infer(args, voc_infer_info, mel_len)
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first_response_time = streaming_first_time
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wav = wav_streaming
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final_response_time = time.time()
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voc_infer_time = final_response_time
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return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
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@paddle.no_grad()
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# 流式AM + 流式 Voc
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def streaming_am_streaming_voc(args, am_infer_info, voc_infer_info,
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part_phone_ids, part_tone_ids):
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global mel_streaming
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global streaming_first_time
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global wav_streaming
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global streaming_voc_st
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mel_streaming = None
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#用来表示开启流式voc的线程
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flag = 1
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am, am_mu, am_std, am_config = am_infer_info
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orig_hs, h_masks = am.encoder_infer(part_phone_ids)
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mel_len = orig_hs.shape[1]
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am_block = args.am_block
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am_pad = args.am_pad
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hss = get_chunks(orig_hs, am_block, am_pad, "am")
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chunk_num = len(hss)
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for i, hs in enumerate(hss):
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before_outs, _ = am.decoder(hs)
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after_outs = before_outs + am.postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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normalized_mel = after_outs[0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-am_pad]
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mel_streaming = sub_mel
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[am_pad:]
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mel_streaming = paddle.concat([mel_streaming, sub_mel])
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am_infer_time = time.time()
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[am_pad:(am_block + am_pad) - sub_mel.shape[0]]
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mel_streaming = paddle.concat([mel_streaming, sub_mel])
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if flag and mel_streaming.shape[0] > args.voc_block + args.voc_pad:
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t = threading.Thread(
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target=streaming_voc_infer,
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args=(args, voc_infer_info, mel_len, ))
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t.start()
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streaming_voc_st = time.time()
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flag = 0
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t.join()
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final_response_time = time.time()
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voc_infer_time = final_response_time
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first_response_time = streaming_first_time
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wav = wav_streaming
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return am_infer_time, voc_infer_time, first_response_time, final_response_time, wav
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def warm_up(args, logger, frontend, am_infer_info, voc_infer_info):
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global sample_rate
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logger.info(
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"Before the formal test, we test a few texts to make the inference speed more stable."
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)
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if args.lang == 'zh':
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sentence = "您好,欢迎使用语音合成服务。"
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if args.lang == 'en':
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sentence = "Hello and welcome to the speech synthesis service."
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if args.voc_streaming:
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if args.am_streaming:
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infer_func = streaming_am_streaming_voc
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else:
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infer_func = nonstreaming_am_streaming_voc
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else:
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infer_func = am_nonstreaming_voc
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merge_sentences = True
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get_tone_ids = False
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for i in range(5): # 推理5次
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st = time.time()
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phone_ids, tone_ids = get_phone(args, frontend, sentence,
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merge_sentences, get_tone_ids)
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part_phone_ids = phone_ids[0]
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if tone_ids:
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part_tone_ids = tone_ids[0]
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else:
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part_tone_ids = None
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am_infer_time, voc_infer_time, first_response_time, final_response_time, wav = infer_func(
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args, am_infer_info, voc_infer_info, part_phone_ids, part_tone_ids)
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wav = wav.numpy()
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duration = wav.size / sample_rate
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logger.info(
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f"sentence: {sentence}; duration: {duration} s; first response time: {first_response_time - st} s; final response time: {final_response_time - st} s"
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)
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def evaluate(args, logger, frontend, am_infer_info, voc_infer_info):
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global sample_rate
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sentences = get_sentences(args)
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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()
|
Loading…
Reference in new issue