[TTS]Cantonese FastSpeech2 e2e infer, test=tts (#2927)
HuangLiangJie
2 years ago
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GitHub
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004a4d6096
commit
1af9bd47d9
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_canton \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_aishell3 \
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--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
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--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
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--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
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--lang=canton \
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--text=${BIN_DIR}/../sentences_canton.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0 \
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--inference_dir=${train_output_path}/inference
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fi
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "in hifigan syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_canton \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_aishell3 \
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--voc_config=hifigan_aishell3_ckpt_0.2.0/default.yaml \
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--voc_ckpt=hifigan_aishell3_ckpt_0.2.0/snapshot_iter_2500000.pdz \
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--voc_stat=hifigan_aishell3_ckpt_0.2.0/feats_stats.npy \
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--lang=canton \
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--text=${BIN_DIR}/../sentences_canton.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0 \
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--inference_dir=${train_output_path}/inference
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fi
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@ -0,0 +1,7 @@
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001 白云山爬过一次嘅，好远啊，爬上去都成两个钟
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002 睇书咯，番屋企，而家好多人好少睇书噶喎
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003 因为如果唔考试嘅话，工资好低噶
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004 冇固定噶，你中意休边日就边日噶
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005 即系太迟嘅话咧，落班太迟嘅话就喺出边食啲咯
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006 是非有公理，慎言莫冒犯别人
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007 遇上冷风雨，休太认真
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@ -0,0 +1,106 @@
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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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from typing import Dict
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from typing import List
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import numpy as np
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import paddle
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import ToJyutping
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from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
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INITIALS = [
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'p', 'b', 't', 'd', 'ts', 'dz', 'k', 'g', 'kw', 'gw', 'f', 'h', 'l', 'm',
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'ng', 'n', 's', 'y', 'w', 'c', 'z', 'j'
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]
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INITIALS += ['sp', 'spl', 'spn', 'sil']
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def get_lines(cantons: List[str]):
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phones = []
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for canton in cantons:
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for consonant in INITIALS:
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if canton.startswith(consonant):
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c, v = canton[:len(consonant)], canton[len(consonant):]
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phones = phones + [c, v]
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return phones
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class CantonFrontend():
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def __init__(self, phone_vocab_path: str):
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self.text_normalizer = TextNormalizer()
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self.punc = "：，；。？！“”‘’':,;.?!"
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self.vocab_phones = {}
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if phone_vocab_path:
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with open(phone_vocab_path, 'rt', encoding='utf-8') 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.vocab_phones[phn] = int(id)
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# if merge_sentences, merge all sentences into one phone sequence
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def _g2p(self, sentences: List[str],
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merge_sentences: bool=True) -> List[List[str]]:
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phones_list = []
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for sentence in sentences:
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phones_str = ToJyutping.get_jyutping_text(sentence)
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phones_split = get_lines(phones_str.split(' '))
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phones_list.append(phones_split)
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return phones_list
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def _p2id(self, phonemes: List[str]) -> np.ndarray:
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# replace unk phone with sp
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phonemes = [
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phn if phn in self.vocab_phones else "sp" for phn in phonemes
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]
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phone_ids = [self.vocab_phones[item] for item in phonemes]
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return np.array(phone_ids, np.int64)
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def get_phonemes(self,
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sentence: str,
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merge_sentences: bool=True,
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print_info: bool=False) -> List[List[str]]:
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sentences = self.text_normalizer.normalize(sentence)
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phonemes = self._g2p(sentences, merge_sentences=merge_sentences)
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if print_info:
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print("----------------------------")
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print("text norm results:")
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print(sentences)
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print("----------------------------")
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print("g2p results:")
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print(phonemes)
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print("----------------------------")
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return phonemes
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def get_input_ids(self,
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sentence: str,
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merge_sentences: bool=True,
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print_info: bool=False,
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to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
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phonemes = self.get_phonemes(
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sentence, merge_sentences=merge_sentences, print_info=print_info)
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result = {}
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temp_phone_ids = []
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for phones in phonemes:
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if phones:
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phone_ids = self._p2id(phones)
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# if use paddle.to_tensor() in onnxruntime, the first time will be too low
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if to_tensor:
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phone_ids = paddle.to_tensor(phone_ids)
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temp_phone_ids.append(phone_ids)
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if temp_phone_ids:
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result["phone_ids"] = temp_phone_ids
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return result
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