add e2e inference script
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chenfeiyu
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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 re
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import numpy as np
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import paddle
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import pypinyin
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from pypinyin import lazy_pinyin, Style
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import jieba
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import phkit
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phkit.initialize()
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from parakeet.frontend.vocab import Vocab
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with open("phones.txt", 'rt') as f:
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phones = [line.strip() for line in f.readlines()]
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with open("tones.txt", 'rt') as f:
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tones = [line.strip() for line in f.readlines()]
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voc_phones = Vocab(phones, start_symbol=None, end_symbol=None)
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voc_tones = Vocab(tones, start_symbol=None, end_symbol=None)
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def segment(sentence):
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segments = re.split(r'[：，；。？！]', sentence)
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segments = [seg for seg in segments if len(seg)]
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return segments
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def g2p(sentence):
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segments = segment(sentence)
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phones = []
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phones.append('sil')
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tones = []
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tones.append('0')
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for seg in segments:
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seg = jieba.lcut(seg)
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initials = lazy_pinyin(
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seg, neutral_tone_with_five=True, style=Style.INITIALS)
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finals = lazy_pinyin(
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seg, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
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for c, v in zip(initials, finals):
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# NOTE: post process for pypinyin outputs
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# we discriminate i, ii and iii
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if re.match(r'i\d', v):
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if c in ['z', 'c', 's']:
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v = re.sub('i', 'ii', v)
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elif c in ['zh', 'ch', 'sh', 'r']:
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v = re.sub('i', 'iii', v)
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if c:
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phones.append(c)
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tones.append('0')
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if v:
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phones.append(v[:-1])
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tones.append(v[-1])
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phones.append('sp')
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tones.append('0')
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phones[-1] = 'sil'
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tones[-1] = '0'
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return (phones, tones)
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def p2id(voc, phonemes):
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phone_ids = [voc.lookup(item) for item in phonemes]
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return np.array(phone_ids, np.int64)
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def t2id(voc, tones):
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tone_ids = [voc.lookup(item) for item in tones]
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return np.array(tone_ids, np.int64)
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def text_analysis(sentence):
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phonemes, tones = g2p(sentence)
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print(sentence)
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print([p + t if t != '0' else p for p, t in zip(phonemes, tones)])
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phone_ids = p2id(voc_phones, phonemes)
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tone_ids = t2id(voc_tones, tones)
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phones = paddle.to_tensor(phone_ids)
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tones = paddle.to_tensor(tone_ids)
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return phones, tones
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@ -0,0 +1,16 @@
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001 凯莫瑞安联合体的经济崩溃，迫在眉睫。
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002 对于所有想要离开那片废土，去寻找更美好生活的人来说。
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003 克哈，是你们所有人安全的港湾。
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004 为了保护尤摩扬人民不受异虫的残害，我所做的，比他们自己的领导委员会都多。
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005 无论他们如何诽谤我，我将继续为所有泰伦人的最大利益，而努力奋斗。
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006 身为你们的元首，我带领泰伦人实现了人类统治领地和经济的扩张。
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007 我们将继续成长，用行动回击那些只会说风凉话，不愿意和我们相向而行的害群之马。
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008 帝国武装力量，无数的优秀儿女，正时刻守卫着我们的家园大门，但是他们孤木难支。
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009 凡是今天应征入伍者，所获的所有刑罚罪责，减半。
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010 激进分子和异见者希望你们一听见枪声，就背弃多年的和平与繁荣。
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011 他们没有勇气和能力，带领人类穿越一个充满危险的星系。
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012 法治是我们的命脉，然而它却受到前所未有的挑战。
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013 我将恢复我们帝国的荣光，绝不会向任何外星势力低头。
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014 我已经驯服了异虫，荡平了星灵。如今它们的创造者，想要夺走我们拥有的一切。
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015 永远记住，谁才是最能保护你们的人。
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016 不要听信别人的谗言，我不是什么克隆人。
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@ -121,7 +121,7 @@ def main():
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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parser.add_argument("--test-metadata", type=str, help="training data")
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parser.add_argument("--test-metadata", type=str, help="test metadata")
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parser.add_argument("--output-dir", type=str, help="output dir")
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parser.add_argument(
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"--device", type=str, default="gpu", help="device type to use")
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@ -0,0 +1,150 @@
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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 os
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import sys
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import logging
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import argparse
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import dataclasses
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from pathlib import Path
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import yaml
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import jsonlines
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import paddle
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import numpy as np
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import soundfile as sf
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle import distributed as dist
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from yacs.config import CfgNode
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from parakeet.datasets.data_table import DataTable
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from parakeet.models.speedyspeech import SpeedySpeech, SpeedySpeechInference
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from parakeet.models.parallel_wavegan import PWGGenerator, PWGInference
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from parakeet.modules.normalizer import ZScore
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from frontend import text_analysis
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def evaluate(args, speedyspeech_config, pwg_config):
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for evaluation
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sentences = []
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with open(args.text, 'rt') as f:
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for line in f:
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utt_id, sentence = line.strip().split()
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sentences.append((utt_id, sentence))
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model = SpeedySpeech(**speedyspeech_config["model"])
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model.set_state_dict(
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paddle.load(args.speedyspeech_checkpoint)["main_params"])
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model.eval()
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vocoder = PWGGenerator(**pwg_config["generator_params"])
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vocoder.set_state_dict(paddle.load(args.pwg_params))
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vocoder.remove_weight_norm()
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vocoder.eval()
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print("model done!")
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stat = np.load(args.speedyspeech_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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speedyspeech_normalizer = ZScore(mu, std)
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stat = np.load(args.pwg_stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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pwg_normalizer = ZScore(mu, std)
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speedyspeech_inferencce = SpeedySpeechInference(speedyspeech_normalizer,
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model)
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pwg_inference = PWGInference(pwg_normalizer, vocoder)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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for utt_id, sentence in sentences:
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phones, tones = text_analysis(sentence)
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with paddle.no_grad():
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wav = pwg_inference(speedyspeech_inferencce(phones, tones))
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sf.write(
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output_dir / (utt_id + ".wav"),
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wav.numpy(),
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samplerate=speedyspeech_config.sr)
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print(f"{utt_id} done!")
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Synthesize with speedyspeech & parallel wavegan.")
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parser.add_argument(
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"--speedyspeech-config",
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type=str,
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help="config file to overwrite default config")
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parser.add_argument(
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"--speedyspeech-checkpoint",
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type=str,
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help="speedyspeech checkpoint to load.")
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parser.add_argument(
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"--speedyspeech-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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parser.add_argument(
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"--pwg-config",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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parser.add_argument(
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"--pwg-params",
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type=str,
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help="parallel wavegan generator parameters to load.")
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parser.add_argument(
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"--pwg-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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parser.add_argument("--output-dir", type=str, help="output dir")
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parser.add_argument(
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"--device", type=str, default="gpu", help="device type to use")
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parser.add_argument("--verbose", type=int, default=1, help="verbose")
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args = parser.parse_args()
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with open(args.speedyspeech_config) as f:
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speedyspeech_config = CfgNode(yaml.safe_load(f))
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with open(args.pwg_config) as f:
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pwg_config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(speedyspeech_config)
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print(pwg_config)
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evaluate(args, speedyspeech_config, pwg_config)
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if __name__ == "__main__":
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main()
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