add fastspeech2 example inference
This commit is contained in:
TianYuan
parent
47ec051136
commit
3d39385d5e
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# FastSpeech2 with BZNSYP
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------
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## Dataset
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-----
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### Download and Extract the datasaet.
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Download BZNSYP from it's [Official Website](https://test.data-baker.com/data/index/source).
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### Get MFA result of BZNSYP and Extract it.
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we use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
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you can download from here, or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo.
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### Preprocess the dataset.
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Assume the path to the dataset is `~/datasets/BZNSYP`.
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Assume the path to the MFA result of BZNSYP is `./baker_alignment_tone`.
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Run the command below to preprocess the dataset.
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```bash
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./preprocess.sh
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```
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## Train the model
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---
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```bash
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./run.sh
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```
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## Synthesize
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---
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we use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/baker) as the neural vocoder.
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`synthesize.sh` can synthesize waveform for `metadata.jsonl`.
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`synthesize_e2e.sh` can synthesize waveform for text list.
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```bash
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./synthesize.sh
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```
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or
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```bash
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./synthesize_e2e.sh
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```
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you can see the bash files for more datails of input parameter.
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## Pretrained Model
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@ -89,7 +89,7 @@ updater:
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###########################################################
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optimizer:
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optim: adam # optimizer type
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learning_rate: 0.0001 # learning rate
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learning_rate: 0.001 # learning rate
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###########################################################
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# TRAINING SETTING #
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@ -0,0 +1,76 @@
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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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from pypinyin import lazy_pinyin, Style
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import jieba
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class Frontend():
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def __init__(self, vocab_path):
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self.voc_phones = {}
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with open(vocab_path, 'rt') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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for phn, id in phn_id:
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self.voc_phones[phn] = int(id)
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def segment(self, 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(self, sentence):
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segments = self.segment(sentence)
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phones = []
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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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if v:
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phones.append(v)
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# add sp between sentence
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phones.append('sp')
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# replace last sp with <eos>
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phones[-1] = '<eos>'
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return phones
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def p2id(self, phonemes):
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# replace unk phone with sp
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phonemes = [
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phn if phn in self.voc_phones else "sp" for phn in phonemes
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]
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phone_ids = [self.voc_phones[item] for item in phonemes]
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return np.array(phone_ids, np.int64)
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def text_analysis(self, sentence):
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phonemes = self.g2p(sentence)
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phone_ids = self.p2id(phonemes)
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phone_ids = paddle.to_tensor(phone_ids)
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return phone_ids
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@ -45,17 +45,17 @@ def main():
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required=True,
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help="directory to dump normalized feature files.")
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parser.add_argument(
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"--speech_stats",
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"--speech-stats",
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type=str,
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required=True,
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help="speech statistics file.")
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parser.add_argument(
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"--pitch_stats",
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"--pitch-stats",
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type=str,
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required=True,
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help="pitch statistics file.")
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parser.add_argument(
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"--energy_stats",
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"--energy-stats",
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type=str,
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required=True,
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help="energy statistics file.")
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@ -258,7 +258,7 @@ def main():
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type=str,
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help="directory to baker dataset.")
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parser.add_argument(
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"--dur_path",
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"--dur-path",
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default=None,
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type=str,
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help="path to baker durations.txt.")
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@ -275,7 +275,7 @@ def main():
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default=1,
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help="logging level. higher is more logging. (default=1)")
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parser.add_argument(
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"--num_cpu", type=int, default=1, help="number of process.")
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"--num-cpu", type=int, default=1, help="number of process.")
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args = parser.parse_args()
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C = get_cfg_default()
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@ -4,7 +4,7 @@
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python3 gen_duration_from_textgrid.py --inputdir ./baker_alignment_tone --output durations.txt
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# extract features
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python3 preprocess.py --rootdir=~/datasets/BZNSYP/ --dumpdir=dump --dur_path durations.txt --num_cpu 16
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python3 preprocess.py --rootdir=~/datasets/BZNSYP/ --dumpdir=dump --dur-path durations.txt --num-cpu 4
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# # get features' stats(mean and std)
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python3 compute_statistics.py --metadata=dump/train/raw/metadata.jsonl --field-name="speech"
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@ -12,7 +12,7 @@ python3 compute_statistics.py --metadata=dump/train/raw/metadata.jsonl --field-n
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python3 compute_statistics.py --metadata=dump/train/raw/metadata.jsonl --field-name="energy"
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# normalize and covert phone to id, dev and test should use train's stats
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python3 normalize.py --metadata=dump/train/raw/metadata.jsonl --dumpdir=dump/train/norm --speech_stats=dump/train/speech_stats.npy --pitch_stats=dump/train/pitch_stats.npy --energy_stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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python3 normalize.py --metadata=dump/dev/raw/metadata.jsonl --dumpdir=dump/dev/norm --speech_stats=dump/train/speech_stats.npy --pitch_stats=dump/train/pitch_stats.npy --energy_stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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python3 normalize.py --metadata=dump/test/raw/metadata.jsonl --dumpdir=dump/test/norm --speech_stats=dump/train/speech_stats.npy --pitch_stats=dump/train/pitch_stats.npy --energy_stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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python3 normalize.py --metadata=dump/train/raw/metadata.jsonl --dumpdir=dump/train/norm --speech-stats=dump/train/speech_stats.npy --pitch-stats=dump/train/pitch_stats.npy --energy-stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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python3 normalize.py --metadata=dump/dev/raw/metadata.jsonl --dumpdir=dump/dev/norm --speech-stats=dump/train/speech_stats.npy --pitch-stats=dump/train/pitch_stats.npy --energy-stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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python3 normalize.py --metadata=dump/test/raw/metadata.jsonl --dumpdir=dump/test/norm --speech-stats=dump/train/speech_stats.npy --pitch-stats=dump/train/pitch_stats.npy --energy-stats=dump/train/energy_stats.npy --phones dump/phone_id_map.txt
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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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@ -0,0 +1,148 @@
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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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from pathlib import Path
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import jsonlines
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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 parakeet.datasets.data_table import DataTable
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from parakeet.models.fastspeech2 import FastSpeech2, FastSpeech2Inference
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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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def evaluate(args, fastspeech2_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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with jsonlines.open(args.test_metadata, 'r') as reader:
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test_metadata = list(reader)
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test_dataset = DataTable(data=test_metadata, fields=["utt_id", "text"])
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with open(args.phones, "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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odim = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, **fastspeech2_config["model"])
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model.set_state_dict(
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paddle.load(args.fastspeech2_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.fastspeech2_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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fastspeech2_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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fastspeech2_inferencce = FastSpeech2Inference(fastspeech2_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 datum in test_dataset:
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utt_id = datum["utt_id"]
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text = paddle.to_tensor(datum["text"])
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with paddle.no_grad():
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wav = pwg_inference(fastspeech2_inferencce(text))
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav.numpy(),
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samplerate=fastspeech2_config.fs)
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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 fastspeech2 & parallel wavegan.")
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parser.add_argument(
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"--fastspeech2-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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"--fastspeech2-checkpoint",
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type=str,
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help="fastspeech2 checkpoint to load.")
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parser.add_argument(
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"--fastspeech2-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
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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 parallel wavegan."
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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 parallel wavegan."
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)
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parser.add_argument(
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"--phones",
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type=str,
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default="phone_id_map.txt ",
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help="phone vocabulary file.")
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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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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.fastspeech2_config) as f:
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fastspeech2_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(fastspeech2_config)
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print(pwg_config)
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evaluate(args, fastspeech2_config, pwg_config)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,14 @@
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#!/bin/bash
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python3 synthesize.py \
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--fastspeech2-config=conf/default.yaml \
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--fastspeech2-checkpoint=exp/default/checkpoints/snapshot_iter_62577.pdz \
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--fastspeech2-stat=dump/train/speech_stats.npy \
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--pwg-config=pwg_default.yaml \
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--pwg-params=pwg_generator.pdparams \
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--pwg-stat=pwg_stats.npy \
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--test-metadata=dump/test/norm/metadata.jsonl \
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--output-dir=exp/debug/test \
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--device="gpu" \
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--phones=dump/phone_id_map.txt
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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
|
||||
#
|
||||
# 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
|
||||
# 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.
|
||||
# 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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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 parakeet.models.fastspeech2 import FastSpeech2, FastSpeech2Inference
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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 Frontend
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def evaluate(args, fastspeech2_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:
|
||||
for line in f:
|
||||
utt_id, sentence = line.strip().split()
|
||||
sentences.append((utt_id, sentence))
|
||||
|
||||
with open(args.phones, "r") as f:
|
||||
phn_id = [line.strip().split() for line in f.readlines()]
|
||||
vocab_size = len(phn_id)
|
||||
print("vocab_size:", vocab_size)
|
||||
odim = fastspeech2_config.n_mels
|
||||
model = FastSpeech2(
|
||||
idim=vocab_size, odim=odim, **fastspeech2_config["model"])
|
||||
|
||||
model.set_state_dict(
|
||||
paddle.load(args.fastspeech2_checkpoint)["main_params"])
|
||||
model.eval()
|
||||
|
||||
vocoder = PWGGenerator(**pwg_config["generator_params"])
|
||||
vocoder.set_state_dict(paddle.load(args.pwg_params))
|
||||
vocoder.remove_weight_norm()
|
||||
vocoder.eval()
|
||||
print("model done!")
|
||||
|
||||
frontend = Frontend(args.phones)
|
||||
print("frontend done!")
|
||||
|
||||
stat = np.load(args.fastspeech2_stat)
|
||||
mu, std = stat
|
||||
mu = paddle.to_tensor(mu)
|
||||
std = paddle.to_tensor(std)
|
||||
fastspeech2_normalizer = ZScore(mu, std)
|
||||
|
||||
stat = np.load(args.pwg_stat)
|
||||
mu, std = stat
|
||||
mu = paddle.to_tensor(mu)
|
||||
std = paddle.to_tensor(std)
|
||||
pwg_normalizer = ZScore(mu, std)
|
||||
|
||||
fastspeech2_inferencce = FastSpeech2Inference(fastspeech2_normalizer,
|
||||
model)
|
||||
pwg_inference = PWGInference(pwg_normalizer, vocoder)
|
||||
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
for utt_id, sentence in sentences:
|
||||
phone_ids = frontend.text_analysis(sentence)
|
||||
with paddle.no_grad():
|
||||
wav = pwg_inference(fastspeech2_inferencce(phone_ids))
|
||||
sf.write(
|
||||
str(output_dir / (utt_id + ".wav")),
|
||||
wav.numpy(),
|
||||
samplerate=fastspeech2_config.fs)
|
||||
print(f"{utt_id} done!")
|
||||
|
||||
|
||||
def main():
|
||||
# parse args and config and redirect to train_sp
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Synthesize with fastspeech2 & parallel wavegan.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-config",
|
||||
type=str,
|
||||
help="config file to overwrite default config")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-checkpoint",
|
||||
type=str,
|
||||
help="fastspeech2 checkpoint to load.")
|
||||
parser.add_argument(
|
||||
"--fastspeech2-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pwg-config",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pwg-params",
|
||||
type=str,
|
||||
help="parallel wavegan generator parameters to load.")
|
||||
parser.add_argument(
|
||||
"--pwg-stat",
|
||||
type=str,
|
||||
help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones",
|
||||
type=str,
|
||||
default="phone_id_map.txt ",
|
||||
help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
help="text to synthesize, a 'utt_id sentence' pair per line")
|
||||
parser.add_argument("--output-dir", type=str, help="output dir")
|
||||
parser.add_argument(
|
||||
"--device", type=str, default="gpu", help="device type to use")
|
||||
parser.add_argument("--verbose", type=int, default=1, help="verbose")
|
||||
|
||||
args = parser.parse_args()
|
||||
with open(args.fastspeech2_config) as f:
|
||||
fastspeech2_config = CfgNode(yaml.safe_load(f))
|
||||
with open(args.pwg_config) as f:
|
||||
pwg_config = CfgNode(yaml.safe_load(f))
|
||||
|
||||
print("========Args========")
|
||||
print(yaml.safe_dump(vars(args)))
|
||||
print("========Config========")
|
||||
print(fastspeech2_config)
|
||||
print(pwg_config)
|
||||
|
||||
evaluate(args, fastspeech2_config, pwg_config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
|
||||
#!/bin/bash
|
||||
|
||||
python3 synthesize_e2e.py \
|
||||
--fastspeech2-config=conf/default.yaml \
|
||||
--fastspeech2-checkpoint=exp/default/checkpoints/snapshot_iter_136017.pdz \
|
||||
--fastspeech2-stat=dump/train/speech_stats.npy \
|
||||
--pwg-config=pwg_default.yaml \
|
||||
--pwg-params=pwg_generator.pdparams \
|
||||
--pwg-stat=pwg_stats.npy \
|
||||
--text=sentences.txt \
|
||||
--output-dir=exp/debug/test_e2e \
|
||||
--device="gpu" \
|
||||
--phones=dump/phone_id_map.txt
|
||||
|
|
@ -397,11 +397,11 @@ class FastSpeech2(nn.Layer):
|
|||
speech : Tensor, optional
|
||||
Feature sequence to extract style (N, idim).
|
||||
durations : LongTensor, optional
|
||||
Groundtruth of duration (T + 1,).
|
||||
Groundtruth of duration (T,).
|
||||
pitch : Tensor, optional
|
||||
Groundtruth of token-averaged pitch (T + 1, 1).
|
||||
Groundtruth of token-averaged pitch (T, 1).
|
||||
energy : Tensor, optional
|
||||
Groundtruth of token-averaged energy (T + 1, 1).
|
||||
Groundtruth of token-averaged energy (T, 1).
|
||||
alpha : float, optional
|
||||
Alpha to control the speed.
|
||||
use_teacher_forcing : bool, optional
|
||||
|
|
@ -412,9 +412,6 @@ class FastSpeech2(nn.Layer):
|
|||
----------
|
||||
Tensor
|
||||
Output sequence of features (L, odim).
|
||||
None
|
||||
Dummy for compatibility.
|
||||
|
||||
"""
|
||||
x, y = text, speech
|
||||
d, p, e = durations, pitch, energy
|
||||
|
|
@ -455,7 +452,7 @@ class FastSpeech2(nn.Layer):
|
|||
is_inference=True,
|
||||
alpha=alpha, )
|
||||
|
||||
return outs[0], None, None
|
||||
return outs[0]
|
||||
|
||||
def _source_mask(self, ilens: paddle.Tensor) -> paddle.Tensor:
|
||||
"""Make masks for self-attention.
|
||||
|
|
@ -501,6 +498,18 @@ class FastSpeech2(nn.Layer):
|
|||
init_dec_alpha))
|
||||
|
||||
|
||||
class FastSpeech2Inference(nn.Layer):
|
||||
def __init__(self, normalizer, model):
|
||||
super().__init__()
|
||||
self.normalizer = normalizer
|
||||
self.acoustic_model = model
|
||||
|
||||
def forward(self, text):
|
||||
normalized_mel = self.acoustic_model.inference(text)
|
||||
logmel = self.normalizer.inverse(normalized_mel)
|
||||
return logmel
|
||||
|
||||
|
||||
class FastSpeech2Loss(nn.Layer):
|
||||
"""Loss function module for FastSpeech2."""
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue