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Merge branch 'dev' of https://github.com/mozilla/TTS into dev

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Eren Gölge committed 2021-02-18 17:21:09 +00:00
2 parents 5b70c8ba4f e4f81d6856
commit adaeec57ec
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TTS/.models.json
+10
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@@ -75,6 +75,16 @@
"contact":"erengolge@gmail.com"
}
}
},
"zh":{
"baker":{
"tacotron2-DDC-GST":{
"model_file": "1RR9rZdV_FMm8yvtCHALtUbJf1nxbUiAw",
"config_file": "1daY1JHGXEozJ-MGYLiWEUmzEwEvM5xpz",
"stats_file": "1vl9c-D3dW_E7pdhNpDFQLX-giJc0jOtV",
"commit": ""
}
}
}
},
"vocoder_models":{
TTS/tts/datasets/preprocess.py
+22
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@@ -3,6 +3,7 @@ from glob import glob
import re
import sys
from pathlib import Path
from typing import List, Tuple
from tqdm import tqdm
@@ -368,3 +369,24 @@ def _voxcel_x(root_path, meta_file, voxcel_idx):
with open(str(cache_to), 'r') as f:
return [x.strip().split('|') for x in f.readlines()]
def baker(root_path: str, meta_file: str) -> List[List[str]]:
"""Normalizes the Baker meta data file to TTS format
Args:
root_path (str): path to the baker dataset
meta_file (str): name of the meta dataset containing names of wav to select and the transcript of the sentence
Returns:
List[List[str]]: List of (text, wav_path, speaker_name) associated with each sentences
"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = "baker"
with open(txt_file, 'r') as ttf:
for line in ttf:
wav_name, text = line.rstrip('\n').split("|")
wav_path = os.path.join(root_path, "clips_22", wav_name)
items.append([text, wav_path, speaker_name])
return items
TTS/tts/utils/chinese_mandarin/__init__.py
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Whitespace-only changes.
TTS/tts/utils/chinese_mandarin/numbers.py
+131
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@@ -0,0 +1,131 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Licensed under WTFPL or the Unlicense or CC0.
# This uses Python 3, but it's easy to port to Python 2 by changing
# strings to u'xx'.
import re
import itertools
def _num2chinese(num :str, big=False, simp=True, o=False, twoalt=False) -> str:
"""Convert numerical arabic numbers (0->9) to chinese hanzi numbers (〇 -> 九)
Args:
num (str): arabic number to convert
big (bool, optional): use financial characters. Defaults to False.
simp (bool, optional): use simplified characters instead of tradictional characters. Defaults to True.
o (bool, optional): use 〇 for 'zero'. Defaults to False.
twoalt (bool, optional): use 两/兩 for 'two' when appropriate. Defaults to False.
Raises:
ValueError: if number is more than 1e48
ValueError: if 'e' exposent in number
Returns:
str: converted number as hanzi characters
"""
# check num first
nd = str(num)
if abs(float(nd)) >= 1e48:
raise ValueError('number out of range')
elif 'e' in nd:
raise ValueError('scientific notation is not supported')
c_symbol = '正负点' if simp else '正負點'
if o: # formal
twoalt = False
if big:
c_basic = '零壹贰叁肆伍陆柒捌玖' if simp else '零壹貳參肆伍陸柒捌玖'
c_unit1 = '拾佰仟'
c_twoalt = '贰' if simp else '貳'
else:
c_basic = '〇一二三四五六七八九' if o else '零一二三四五六七八九'
c_unit1 = '十百千'
if twoalt:
c_twoalt = '两' if simp else '兩'
else:
c_twoalt = '二'
c_unit2 = '万亿兆京垓秭穰沟涧正载' if simp else '萬億兆京垓秭穰溝澗正載'
revuniq = lambda l: ''.join(k for k, g in itertools.groupby(reversed(l)))
nd = str(num)
result = []
if nd[0] == '+':
result.append(c_symbol[0])
elif nd[0] == '-':
result.append(c_symbol[1])
if '.' in nd:
integer, remainder = nd.lstrip('+-').split('.')
else:
integer, remainder = nd.lstrip('+-'), None
if int(integer):
splitted = [integer[max(i - 4, 0):i]
for i in range(len(integer), 0, -4)]
intresult = []
for nu, unit in enumerate(splitted):
# special cases
if int(unit) == 0: # 0000
intresult.append(c_basic[0])
continue
elif nu > 0 and int(unit) == 2: # 0002
intresult.append(c_twoalt + c_unit2[nu - 1])
continue
ulist = []
unit = unit.zfill(4)
for nc, ch in enumerate(reversed(unit)):
if ch == '0':
if ulist: # ???0
ulist.append(c_basic[0])
elif nc == 0:
ulist.append(c_basic[int(ch)])
elif nc == 1 and ch == '1' and unit[1] == '0':
# special case for tens
# edit the 'elif' if you don't like
# 十四, 三千零十四, 三千三百一十四
ulist.append(c_unit1[0])
elif nc > 1 and ch == '2':
ulist.append(c_twoalt + c_unit1[nc - 1])
else:
ulist.append(c_basic[int(ch)] + c_unit1[nc - 1])
ustr = revuniq(ulist)
if nu == 0:
intresult.append(ustr)
else:
intresult.append(ustr + c_unit2[nu - 1])
result.append(revuniq(intresult).strip(c_basic[0]))
else:
result.append(c_basic[0])
if remainder:
result.append(c_symbol[2])
result.append(''.join(c_basic[int(ch)] for ch in remainder))
return ''.join(result)
def _number_replace(match) -> str:
"""function to apply in a match, transform all numbers in a match by chinese characters
Args:
match (re.Match): numbers regex matches
Returns:
str: replaced characters for the numbers
"""
match_str: str = match.group()
return _num2chinese(match_str)
def replace_numbers_to_characters_in_text(text: str) -> str:
"""Replace all arabic numbers in a text by their equivalent in chinese characters (simplified)
Args:
text (str): input text to transform
Returns:
str: output text
"""
text = re.sub(r'[0-9]+', _number_replace, text)
return text
TTS/tts/utils/chinese_mandarin/phonemizer.py
+41
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@@ -0,0 +1,41 @@
from typing import List
import pypinyin
from .pinyinToPhonemes import PINYIN_DICT
import jieba
def _chinese_character_to_pinyin(text: str) -> List[str]:
pinyins = pypinyin.pinyin(
text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True
)
pinyins_flat_list = [item for sublist in pinyins for item in sublist]
return pinyins_flat_list
def _chinese_pinyin_to_phoneme(pinyin: str) -> str:
segment = pinyin[:-1]
tone = pinyin[-1]
phoneme = PINYIN_DICT.get(segment, [""])[0]
return phoneme + tone
def chinese_text_to_phonemes(text: str) -> str:
tokenized_text = jieba.cut(text, HMM=False)
tokenized_text = " ".join(tokenized_text)
pinyined_text: List[str] = _chinese_character_to_pinyin(tokenized_text)
results: List[str] = []
for token in pinyined_text:
if token[-1] in "12345": # TODO transform to is_pinyin()
pinyin_phonemes = _chinese_pinyin_to_phoneme(token)
results += list(pinyin_phonemes)
else: # is ponctuation or other
results += list(token)
return "|".join(results)
TTS/tts/utils/chinese_mandarin/pinyinToPhonemes.py
+420
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@@ -0,0 +1,420 @@
PINYIN_DICT = {
"a": ["a"],
"ai": ["ai"],
"an": ["an"],
"ang": ["ɑŋ"],
"ao": ["aʌ"],
"ba": ["ba"],
"bai": ["bai"],
"ban": ["ban"],
"bang": ["bɑŋ"],
"bao": ["baʌ"],
# "be": ["be"], doesnt exist
"bei": ["bɛi"],
"ben": ["bœn"],
"beng": ["bɵŋ"],
"bi": ["bi"],
"bian": ["biɛn"],
"biao": ["biaʌ"],
"bie": ["bie"],
"bin": ["bin"],
"bing": ["bɨŋ"],
"bo": ["bo"],
"bu": ["bu"],
"ca": ["tsa"],
"cai": ["tsai"],
"can": ["tsan"],
"cang": ["tsɑŋ"],
"cao": ["tsaʌ"],
"ce": ["tsø"],
"cen": ["tsœn"],
"ceng": ["tsɵŋ"],
"cha": ["ʈʂa"],
"chai": ["ʈʂai"],
"chan": ["ʈʂan"],
"chang": ["ʈʂɑŋ"],
"chao": ["ʈʂaʌ"],
"che": ["ʈʂø"],
"chen": ["ʈʂœn"],
"cheng": ["ʈʂɵŋ"],
"chi": ["ʈʂʏ"],
"chong": ["ʈʂoŋ"],
"chou": ["ʈʂou"],
"chu": ["ʈʂu"],
"chua": ["ʈʂua"],
"chuai": ["ʈʂuai"],
"chuan": ["ʈʂuan"],
"chuang": ["ʈʂuɑŋ"],
"chui": ["ʈʂuei"],
"chun": ["ʈʂun"],
"chuo": ["ʈʂuo"],
"ci": ["tsɪ"],
"cong": ["tsoŋ"],
"cou": ["tsou"],
"cu": ["tsu"],
"cuan": ["tsuan"],
"cui": ["tsuei"],
"cun": ["tsun"],
"cuo": ["tsuo"],
"da": ["da"],
"dai": ["dai"],
"dan": ["dan"],
"dang": ["dɑŋ"],
"dao": ["daʌ"],
"de": ["dø"],
"dei": ["dei"],
# "den": ["dœn"],
"deng": ["dɵŋ"],
"di": ["di"],
"dia": ["dia"],
"dian": ["diɛn"],
"diao": ["diaʌ"],
"die": ["die"],
"ding": ["dɨŋ"],
"diu": ["dio"],
"dong": ["doŋ"],
"dou": ["dou"],
"du": ["du"],
"duan": ["duan"],
"dui": ["duei"],
"dun": ["dun"],
"duo": ["duo"],
"e": ["ø"],
"ei": ["ei"],
"en": ["œn"],
# "ng": ["œn"],
# "eng": ["ɵŋ"],
"er": ["er"],
"fa": ["fa"],
"fan": ["fan"],
"fang": ["fɑŋ"],
"fei": ["fei"],
"fen": ["fœn"],
"feng": ["fɵŋ"],
"fo": ["fo"],
"fou": ["fou"],
"fu": ["fu"],
"ga": ["ga"],
"gai": ["gai"],
"gan": ["gan"],
"gang": ["gɑŋ"],
"gao": ["gaʌ"],
"ge": ["gø"],
"gei": ["gei"],
"gen": ["gœn"],
"geng": ["gɵŋ"],
"gong": ["goŋ"],
"gou": ["gou"],
"gu": ["gu"],
"gua": ["gua"],
"guai": ["guai"],
"guan": ["guan"],
"guang": ["guɑŋ"],
"gui": ["guei"],
"gun": ["gun"],
"guo": ["guo"],
"ha": ["xa"],
"hai": ["xai"],
"han": ["xan"],
"hang": ["xɑŋ"],
"hao": ["xaʌ"],
"he": ["xø"],
"hei": ["xei"],
"hen": ["xœn"],
"heng": ["xɵŋ"],
"hong": ["xoŋ"],
"hou": ["xou"],
"hu": ["xu"],
"hua": ["xua"],
"huai": ["xuai"],
"huan": ["xuan"],
"huang": ["xuɑŋ"],
"hui": ["xuei"],
"hun": ["xun"],
"huo": ["xuo"],
"ji": ["dʑi"],
"jia": ["dʑia"],
"jian": ["dʑiɛn"],
"jiang": ["dʑiɑŋ"],
"jiao": ["dʑiaʌ"],
"jie": ["dʑie"],
"jin": ["dʑin"],
"jing": ["dʑɨŋ"],
"jiong": ["dʑioŋ"],
"jiu": ["dʑio"],
"ju": ["dʑy"],
"juan": ["dʑyɛn"],
"jue": ["dʑye"],
"jun": ["dʑyn"],
"ka": ["ka"],
"kai": ["kai"],
"kan": ["kan"],
"kang": ["kɑŋ"],
"kao": ["kaʌ"],
"ke": ["kø"],
"kei": ["kei"],
"ken": ["kœn"],
"keng": ["kɵŋ"],
"kong": ["koŋ"],
"kou": ["kou"],
"ku": ["ku"],
"kua": ["kua"],
"kuai": ["kuai"],
"kuan": ["kuan"],
"kuang": ["kuɑŋ"],
"kui": ["kuei"],
"kun": ["kun"],
"kuo": ["kuo"],
"la": ["la"],
"lai": ["lai"],
"lan": ["lan"],
"lang": ["lɑŋ"],
"lao": ["laʌ"],
"le": ["lø"],
"lei": ["lei"],
"leng": ["lɵŋ"],
"li": ["li"],
"lia": ["lia"],
"lian": ["liɛn"],
"liang": ["liɑŋ"],
"liao": ["liaʌ"],
"lie": ["lie"],
"lin": ["lin"],
"ling": ["lɨŋ"],
"liu": ["lio"],
"lo": ["lo"],
"long": ["loŋ"],
"lou": ["lou"],
"lu": ["lu"],
"lv": ["ly"],
"luan": ["luan"],
"lve": ["lye"],
"lue": ["lue"],
"lun": ["lun"],
"luo": ["luo"],
"ma": ["ma"],
"mai": ["mai"],
"man": ["man"],
"mang": ["mɑŋ"],
"mao": ["maʌ"],
"me": ["mø"],
"mei": ["mei"],
"men": ["mœn"],
"meng": ["mɵŋ"],
"mi": ["mi"],
"mian": ["miɛn"],
"miao": ["miaʌ"],
"mie": ["mie"],
"min": ["min"],
"ming": ["mɨŋ"],
"miu": ["mio"],
"mo": ["mo"],
"mou": ["mou"],
"mu": ["mu"],
"na": ["na"],
"nai": ["nai"],
"nan": ["nan"],
"nang": ["nɑŋ"],
"nao": ["naʌ"],
"ne": ["nø"],
"nei": ["nei"],
"nen": ["nœn"],
"neng": ["nɵŋ"],
"ni": ["ni"],
"nia": ["nia"],
"nian": ["niɛn"],
"niang": ["niɑŋ"],
"niao": ["niaʌ"],
"nie": ["nie"],
"nin": ["nin"],
"ning": ["nɨŋ"],
"niu": ["nio"],
"nong": ["noŋ"],
"nou": ["nou"],
"nu": ["nu"],
"nv": ["ny"],
"nuan": ["nuan"],
"nve": ["nye"],
"nue": ["nye"],
"nuo": ["nuo"],
"o": ["o"],
"ou": ["ou"],
"pa": ["pa"],
"pai": ["pai"],
"pan": ["pan"],
"pang": ["pɑŋ"],
"pao": ["paʌ"],
"pe": ["pø"],
"pei": ["pei"],
"pen": ["pœn"],
"peng": ["pɵŋ"],
"pi": ["pi"],
"pian": ["piɛn"],
"piao": ["piaʌ"],
"pie": ["pie"],
"pin": ["pin"],
"ping": ["pɨŋ"],
"po": ["po"],
"pou": ["pou"],
"pu": ["pu"],
"qi": ["tɕi"],
"qia": ["tɕia"],
"qian": ["tɕiɛn"],
"qiang": ["tɕiɑŋ"],
"qiao": ["tɕiaʌ"],
"qie": ["tɕie"],
"qin": ["tɕin"],
"qing": ["tɕɨŋ"],
"qiong": ["tɕioŋ"],
"qiu": ["tɕio"],
"qu": ["tɕy"],
"quan": ["tɕyɛn"],
"que": ["tɕye"],
"qun": ["tɕyn"],
"ran": ["ʐan"],
"rang": ["ʐɑŋ"],
"rao": ["ʐaʌ"],
"re": ["ʐø"],
"ren": ["ʐœn"],
"reng": ["ʐɵŋ"],
"ri": ["ʐʏ"],
"rong": ["ʐoŋ"],
"rou": ["ʐou"],
"ru": ["ʐu"],
"rua": ["ʐua"],
"ruan": ["ʐuan"],
"rui": ["ʐuei"],
"run": ["ʐun"],
"ruo": ["ʐuo"],
"sa": ["sa"],
"sai": ["sai"],
"san": ["san"],
"sang": ["sɑŋ"],
"sao": ["saʌ"],
"se": ["sø"],
"sen": ["sœn"],
"seng": ["sɵŋ"],
"sha": ["ʂa"],
"shai": ["ʂai"],
"shan": ["ʂan"],
"shang": ["ʂɑŋ"],
"shao": ["ʂaʌ"],
"she": ["ʂø"],
"shei": ["ʂei"],
"shen": ["ʂœn"],
"sheng": ["ʂɵŋ"],
"shi": ["ʂʏ"],
"shou": ["ʂou"],
"shu": ["ʂu"],
"shua": ["ʂua"],
"shuai": ["ʂuai"],
"shuan": ["ʂuan"],
"shuang": ["ʂuɑŋ"],
"shui": ["ʂuei"],
"shun": ["ʂun"],
"shuo": ["ʂuo"],
"si": ["sɪ"],
"song": ["soŋ"],
"sou": ["sou"],
"su": ["su"],
"suan": ["suan"],
"sui": ["suei"],
"sun": ["sun"],
"suo": ["suo"],
"ta": ["ta"],
"tai": ["tai"],
"tan": ["tan"],
"tang": ["tɑŋ"],
"tao": ["taʌ"],
"te": ["tø"],
"tei": ["tei"],
"teng": ["tɵŋ"],
"ti": ["ti"],
"tian": ["tiɛn"],
"tiao": ["tiaʌ"],
"tie": ["tie"],
"ting": ["tɨŋ"],
"tong": ["toŋ"],
"tou": ["tou"],
"tu": ["tu"],
"tuan": ["tuan"],
"tui": ["tuei"],
"tun": ["tun"],
"tuo": ["tuo"],
"wa": ["wa"],
"wai": ["wai"],
"wan": ["wan"],
"wang": ["wɑŋ"],
"wei": ["wei"],
"wen": ["wœn"],
"weng": ["wɵŋ"],
"wo": ["wo"],
"wu": ["wu"],
"xi": ["ɕi"],
"xia": ["ɕia"],
"xian": ["ɕiɛn"],
"xiang": ["ɕiɑŋ"],
"xiao": ["ɕiaʌ"],
"xie": ["ɕie"],
"xin": ["ɕin"],
"xing": ["ɕɨŋ"],
"xiong": ["ɕioŋ"],
"xiu": ["ɕio"],
"xu": ["ɕy"],
"xuan": ["ɕyɛn"],
"xue": ["ɕye"],
"xun": ["ɕyn"],
"ya": ["ia"],
"yan": ["iɛn"],
"yang": ["iɑŋ"],
"yao": ["iaʌ"],
"ye": ["ie"],
"yi": ["i"],
"yin": ["in"],
"ying": ["ɨŋ"],
"yo": ["io"],
"yong": ["ioŋ"],
"you": ["io"],
"yu": ["y"],
"yuan": ["yɛn"],
"yue": ["ye"],
"yun": ["yn"],
"za": ["dza"],
"zai": ["dzai"],
"zan": ["dzan"],
"zang": ["dzɑŋ"],
"zao": ["dzaʌ"],
"ze": ["dzø"],
"zei": ["dzei"],
"zen": ["dzœn"],
"zeng": ["dzɵŋ"],
"zha": ["dʒa"],
"zhai": ["dʒai"],
"zhan": ["dʒan"],
"zhang": ["dʒɑŋ"],
"zhao": ["dʒaʌ"],
"zhe": ["dʒø"],
# "zhei": ["dʒei"], it doesn't exist
"zhen": ["dʒœn"],
"zheng": ["dʒɵŋ"],
"zhi": ["dʒʏ"],
"zhong": ["dʒoŋ"],
"zhou": ["dʒou"],
"zhu": ["dʒu"],
"zhua": ["dʒua"],
"zhuai": ["dʒuai"],
"zhuan": ["dʒuan"],
"zhuang": ["dʒuɑŋ"],
"zhui": ["dʒuei"],
"zhun": ["dʒun"],
"zhuo": ["dʒuo"],
"zi": ["dzɪ"],
"zong": ["dzoŋ"],
"zou": ["dzou"],
"zu": ["dzu"],
"zuan": ["dzuan"],
"zui": ["dzuei"],
"zun": ["dzun"],
"zuo": ["dzuo"],
}
TTS/tts/utils/synthesis.py
+1 -1
View File
@@ -219,7 +219,7 @@ def synthesis(model,
ap (TTS.tts.utils.audio.AudioProcessor): audio processor to process
model outputs.
speaker_id (int): id of speaker
style_wav (str): Uses for style embedding of GST.
style_wav (str | Dict[str, float]): Uses for style embedding of GST.
truncated (bool): keep model states after inference. It can be used
for continuous inference at long texts.
enable_eos_bos_chars (bool): enable special chars for end of sentence and start of sentence.
TTS/tts/utils/text/__init__.py
+17 -2
View File
@@ -8,6 +8,7 @@ from phonemizer.phonemize import phonemize
from TTS.tts.utils.text import cleaners
from TTS.tts.utils.text.symbols import (_bos, _eos, _punctuations,
make_symbols, phonemes, symbols)
from TTS.tts.utils.chinese_mandarin.phonemizer import chinese_text_to_phonemes
# pylint: disable=unnecessary-comprehension
@@ -28,9 +29,23 @@ PHONEME_PUNCTUATION_PATTERN = r'['+_punctuations.replace(' ', '')+']+'
def text2phone(text, language):
'''Convert graphemes to phonemes. For most of the languages, it calls
the phonemizer python library that calls espeak/espeak-ng. For chinese
mandarin, it calls pypinyin + custom function for phonemizing
Parameters:
text (str): text to phonemize
language (str): language of the text
Returns:
ph (str): phonemes as a string seperated by "|"
ph = "ɪ|g|ˈ|z|æ|m|p|ə|l"
'''
Convert graphemes to phonemes.
'''
# TO REVIEW : How to have a good implementation for this?
if language == "chinese-mandarin":
ph = chinese_text_to_phonemes(text)
return ph
seperator = phonemizer.separator.Separator(' |', '', '|')
#try:
punctuations = re.findall(PHONEME_PUNCTUATION_PATTERN, text)
TTS/tts/utils/text/cleaners.py
+9
View File
@@ -15,6 +15,8 @@ from unidecode import unidecode
from .number_norm import normalize_numbers
from .abbreviations import abbreviations_en, abbreviations_fr
from .time import expand_time_english
from TTS.tts.utils.chinese_mandarin.numbers import replace_numbers_to_characters_in_text
# Regular expression matching whitespace:
_whitespace_re = re.compile(r'\s+')
@@ -122,6 +124,13 @@ def portuguese_cleaners(text):
text = collapse_whitespace(text)
return text
def chinese_mandarin_cleaners(text: str) -> str:
'''Basic pipeline for chinese'''
text = replace_numbers_to_characters_in_text(text)
return text
def phoneme_cleaners(text):
'''Pipeline for phonemes mode, including number and abbreviation expansion.'''
text = expand_numbers(text)
notebooks/Chinese_Mandarin_DDC_GST_Tacotron2_TTS_and_MultiBand_MelGAN_Example.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6LWsNd3_M3MP"
},
"source": [
"# Mozilla TTS on CPU Real-Time Speech Synthesis "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FAqrSIWgLyP0"
},
"source": [
"We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
"\n",
"Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n",
"\n",
"MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n",
"\n",
"Note that both model performances can be improved with more training."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ku-dA4DKoeXk"
},
"source": [
"### Download Models"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 162
},
"colab_type": "code",
"id": "jGIgnWhGsxU1",
"outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mkdir: cannot create directory 'data/': File exists\n",
"Downloading...\n",
"From: https://drive.google.com/uc?id=1vl9c-D3dW_E7pdhNpDFQLX-giJc0jOtV\n",
"To: /root/projects/speech/mozilla-TTS_dev/notebooks/data/tts_scale_stats.npy\n",
"100%|██████████████████████████████████████| 10.5k/10.5k [00:00<00:00, 18.1MB/s]\n"
]
}
],
"source": [
"! mkdir data/\n",
"! gdown --id 1RR9rZdV_FMm8yvtCHALtUbJf1nxbUiAw -O data/tts_model.pth.tar\n",
"! gdown --id 1daY1JHGXEozJ-MGYLiWEUmzEwEvM5xpz -O data/tts_config.json\n",
"! gdown --id 1vl9c-D3dW_E7pdhNpDFQLX-giJc0jOtV -O data/tts_scale_stats.npy"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"colab_type": "code",
"id": "4dnpE0-kvTsu",
"outputId": "76377c6d-789c-4995-ba00-a21a6e1c401e",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU\n",
"To: /root/projects/speech/mozilla-TTS_dev/notebooks/data/vocoder_scale_stats.npy\n",
"100%|██████████████████████████████████████| 10.5k/10.5k [00:00<00:00, 16.7MB/s]\n"
]
}
],
"source": [
"! gdown --id 1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K -O data/vocoder_model.pth.tar\n",
"! gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/vocoder_config.json\n",
"! gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/vocoder_scale_stats.npy"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Zlgi8fPdpRF0"
},
"source": [
"### Define TTS function"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "f-Yc42nQZG5A"
},
"outputs": [],
"source": [
"def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True, style_wav=None):\n",
" t_1 = time.time()\n",
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=style_wav,\n",
" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n",
" # mel_postnet_spec = ap.denormalize(mel_postnet_spec.T)\n",
" if not use_gl:\n",
" waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
" waveform = waveform.flatten()\n",
" if use_cuda:\n",
" waveform = waveform.cpu()\n",
" waveform = waveform.numpy()\n",
" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
" tps = (time.time() - t_1) / len(waveform)\n",
" print(waveform.shape)\n",
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
" print(\" > Real-time factor: {}\".format(rtf))\n",
" print(\" > Time per step: {}\".format(tps))\n",
" IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
" return alignment, mel_postnet_spec, stop_tokens, waveform"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZksegYQepkFg"
},
"source": [
"### Load Models"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "oVa0kOamprgj"
},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import time\n",
"import IPython\n",
"\n",
"from TTS.tts.utils.generic_utils import setup_model\n",
"from TTS.utils.io import load_config\n",
"from TTS.tts.utils.text.symbols import symbols, phonemes, make_symbols\n",
"from TTS.utils.audio import AudioProcessor\n",
"from TTS.tts.utils.synthesis import synthesis"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "EY-sHVO8IFSH"
},
"outputs": [],
"source": [
"# runtime settings\n",
"use_cuda = False"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "_1aIUp2FpxOQ"
},
"outputs": [],
"source": [
"# model paths\n",
"TTS_MODEL = \"/tank/models/tts/mozilla-TTS/tacotron2-DCC/chinese_mandarin/mandarin_dca_attn_gst_dcc-February-12-2021_03+13PM-5dbb48d/checkpoint_17000.pth.tar\"\n",
"TTS_CONFIG = \"/tank/models/tts/mozilla-TTS/tacotron2-DCC/chinese_mandarin/mandarin_dca_attn_gst_dcc-February-12-2021_03+13PM-5dbb48d/config.json\"\n",
"\n",
"TTS_MODEL = \"data/tts_model.pth.tar\"\n",
"TTS_CONFIG = \"data/tts_config.json\"\n",
"\n",
"VOCODER_MODEL = \"/root/.local/share/tts/vocoder_models--en--ljspeech--mulitband-melgan/model_file.pth.tar\"\n",
"VOCODER_CONFIG = \"/root/.local/share/tts/vocoder_models--en--ljspeech--mulitband-melgan/config.json\"\n",
"\n",
"VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n",
"VOCODER_CONFIG = \"data/vocoder_config.json\""
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CpgmdBVQplbv"
},
"outputs": [],
"source": [
"# load configs\n",
"TTS_CONFIG = load_config(TTS_CONFIG)\n",
"VOCODER_CONFIG = load_config(VOCODER_CONFIG)\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 471
},
"colab_type": "code",
"id": "zmrQxiozIUVE",
"outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Setting up Audio Processor...\n",
" | > sample_rate:22050\n",
" | > resample:False\n",
" | > num_mels:80\n",
" | > min_level_db:-100\n",
" | > frame_shift_ms:None\n",
" | > frame_length_ms:None\n",
" | > ref_level_db:0\n",
" | > fft_size:1024\n",
" | > power:1.5\n",
" | > preemphasis:0.0\n",
" | > griffin_lim_iters:60\n",
" | > signal_norm:True\n",
" | > symmetric_norm:True\n",
" | > mel_fmin:50.0\n",
" | > mel_fmax:7600.0\n",
" | > spec_gain:1.0\n",
" | > stft_pad_mode:reflect\n",
" | > max_norm:4.0\n",
" | > clip_norm:True\n",
" | > do_trim_silence:True\n",
" | > trim_db:60\n",
" | > do_sound_norm:False\n",
" | > stats_path:data/tts_scale_stats.npy\n",
" | > hop_length:256\n",
" | > win_length:1024\n"
]
}
],
"source": [
"# load the audio processor\n",
"TTS_CONFIG.audio['stats_path'] = 'data/tts_scale_stats.npy'\n",
"ap = AudioProcessor(**TTS_CONFIG.audio) "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "8fLoI4ipqMeS",
"outputId": "b789066e-e305-42ad-b3ca-eba8d9267382",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Using model: tacotron2\n"
]
}
],
"source": [
"# LOAD TTS MODEL\n",
"# multi speaker \n",
"speaker_id = None\n",
"speakers = []\n",
"\n",
"# load the model (chinese_mandarin special characters/punctuations are in the tts_config.json)\n",
"if TTS_CONFIG.get(\"characters\"):\n",
" _characters = TTS_CONFIG[\"characters\"][\"characters\"]\n",
" _phonemes = TTS_CONFIG[\"characters\"][\"phonemes\"]\n",
" _punctuations = TTS_CONFIG[\"characters\"][\"punctuations\"]\n",
" _pad = TTS_CONFIG[\"characters\"][\"pad\"]\n",
" _eos = TTS_CONFIG[\"characters\"][\"eos\"]\n",
" _bos = TTS_CONFIG[\"characters\"][\"bos\"]\n",
" \n",
" symbols, phonemes = make_symbols(_characters, _phonemes, punctuations= _punctuations, pad=_pad, eos=_eos, bos=_bos )\n",
"\n",
"num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
"model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
"\n",
"# load model state\n",
"cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))\n",
"\n",
"# load the model\n",
"model.load_state_dict(cp['model'])\n",
"if use_cuda:\n",
" model.cuda()\n",
"model.eval()\n",
"\n",
"# set model stepsize\n",
"if 'r' in cp:\n",
" model.decoder.set_r(cp['r'])"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "zKoq0GgzqzhQ",
"outputId": "234efc61-f37a-40bc-95a3-b51896018ccb",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Generator Model: multiband_melgan_generator\n",
" > Setting up Audio Processor...\n",
" | > sample_rate:22050\n",
" | > resample:False\n",
" | > num_mels:80\n",
" | > min_level_db:-100\n",
" | > frame_shift_ms:None\n",
" | > frame_length_ms:None\n",
" | > ref_level_db:0\n",
" | > fft_size:1024\n",
" | > power:None\n",
" | > preemphasis:0.0\n",
" | > griffin_lim_iters:None\n",
" | > signal_norm:True\n",
" | > symmetric_norm:True\n",
" | > mel_fmin:50.0\n",
" | > mel_fmax:7600.0\n",
" | > spec_gain:1.0\n",
" | > stft_pad_mode:reflect\n",
" | > max_norm:4.0\n",
" | > clip_norm:True\n",
" | > do_trim_silence:True\n",
" | > trim_db:60\n",
" | > do_sound_norm:False\n",
" | > stats_path:data/vocoder_scale_stats.npy\n",
" | > hop_length:256\n",
" | > win_length:1024\n",
"\n",
"Vocoder loaded\n"
]
}
],
"source": [
"from TTS.vocoder.utils.generic_utils import setup_generator\n",
"\n",
"# LOAD VOCODER MODEL\n",
"vocoder_model = setup_generator(VOCODER_CONFIG)\n",
"vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n",
"vocoder_model.remove_weight_norm()\n",
"vocoder_model.inference_padding = 0\n",
"\n",
"\n",
"VOCODER_CONFIG.audio['stats_path'] = 'data/vocoder_scale_stats.npy'\n",
"ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) \n",
"if use_cuda:\n",
" vocoder_model.cuda()\n",
"vocoder_model.eval()\n",
"print(\"\\nVocoder loaded\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ws_YkPKsLgo-"
},
"source": [
"## Run Inference"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"# Here some test sentences for you to play with :\n",
"sentence = \"我从来不会说很标准的中文。\"\n",
"sentence = \"我喜欢听人工智能的博客。\"\n",
"sentence = \"我来自一个法国郊区的地方。\"\n",
"sentence = \"不比不知道，一比吓一跳！\"\n",
"sentence = \"台湾是一个真的很好玩的地方！\"\n",
"sentence = \"干一行，行一行，行行都行。\"\n",
"sentence = \"我要盖被子，好尴尬！\""
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"# You can also play with the style_wav global style token. However, the lady speaking in the baker dataset\n",
"# has no emotion through all the sentences. It's hard to get some nice GST with this.\n",
"# That's also why adding \"!\" or \"?\" at the end of sentence change nothing. The dataset has no such prosody.\n",
"style_wav = {\"2\": 0.3, \"1\": -0.1}\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
},
"colab_type": "code",
"id": "FuWxZ9Ey5Puj",
"outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50688,)\n",
" > Run-time: 1.5945854187011719\n",
" > Real-time factor: 0.6935317513786934\n",
" > Time per step: 3.145291761617468e-05\n"
]
},
{
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sentence = \"我喜欢听人工智能的博客。\"\n",
"style_wav = {\"2\": 0.2, \"7\": -0.1}\n",
"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True, style_wav= style_wav)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "DDC-TTS_and_MultiBand-MelGAN_Example.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
requirements.txt
+2
View File
@@ -6,6 +6,8 @@ numba==0.48
librosa==0.7.2
phonemizer>=2.2.0
unidecode==0.4.20
pypinyin
jieba
tensorboardX
matplotlib
Pillow