* SYNC CHANGE TO EMO BRANCH (#162)

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* Fix: change /voice api to post (#160)

* Fix: change /voice api to post

* Fix: support /voice api get

* Fix: Add missing torch.cuda.empty_cache() (#161)

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* sync  (#163)

* Update README.md

* 更新 bert_models.json

* fix

* Update data_utils.py

* Update infer.py

* performance improve

* Feat: support auto split in webui (#158)

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* Fix: change /voice api to post (#160)

* Fix: change /voice api to post

* Fix: support /voice api get

* Fix: Add missing torch.cuda.empty_cache() (#161)

* del emo

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* Add files via upload

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* Fix slice segments GPU perf (#165)

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* Update infer.py

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* Update train_ms.py

* Revert "Fix slice segments GPU perf (#165)" (#169)

This reverts commit 28430fc76bc628297bb59d8f8d25100dbe46ab59.

* Update train_ms.py

* Update train_ms.py

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* Fix english bert gen (#175)

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* 复用相同逻辑,修正静音添加错误 (#181)

* Refactor: reuse the same part of voice api.

* Fix: server_fastapi.py

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* Update data_utils.py

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* Update train_ms.py

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* Update default_config.yml

* Update train_ms.py

* Update train_ms.py

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* feat: add voice mix and tone mix (#187)

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This commit is contained in:
Stardust·减
2023-11-25 13:08:51 +08:00
committed by GitHub
parent ec9145cb59
commit b186499ae9
38 changed files with 156708 additions and 875 deletions

View File

@@ -2,6 +2,7 @@ import pickle
import os
import re
from g2p_en import G2p
from transformers import DebertaV2Tokenizer
from text import symbols
@@ -9,6 +10,8 @@ current_file_path = os.path.dirname(__file__)
CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle")
_g2p = G2p()
LOCAL_PATH = "./bert/deberta-v3-large"
tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
arpa = {
"AH0",
@@ -96,7 +99,9 @@ def post_replace_ph(ph):
"\n": ".",
"·": ",",
"": ",",
"...": "",
"": "...",
"···": "...",
"・・・": "...",
"v": "V",
}
if ph in rep_map.keys():
@@ -108,6 +113,62 @@ def post_replace_ph(ph):
return ph
rep_map = {
"": ",",
"": ",",
"": ",",
"": ".",
"": "!",
"": "?",
"\n": ".",
"": ".",
"": "...",
"···": "...",
"・・・": "...",
"·": ",",
"": ",",
"": ",",
"$": ".",
"": "'",
"": "'",
'"': "'",
"": "'",
"": "'",
"": "'",
"": "'",
"(": "'",
")": "'",
"": "'",
"": "'",
"": "'",
"": "'",
"[": "'",
"]": "'",
"": "-",
"": "-",
"": "-",
"~": "-",
"": "'",
"": "'",
}
def replace_punctuation(text):
pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
# replaced_text = re.sub(
# r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
# + "".join(punctuation)
# + r"]+",
# "",
# replaced_text,
# )
return replaced_text
def read_dict():
g2p_dict = {}
start_line = 49
@@ -308,38 +369,68 @@ def normalize_numbers(text):
def text_normalize(text):
text = normalize_numbers(text)
text = replace_punctuation(text)
text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
return text
def distribute_phone(n_phone, n_word):
phones_per_word = [0] * n_word
for task in range(n_phone):
min_tasks = min(phones_per_word)
min_index = phones_per_word.index(min_tasks)
phones_per_word[min_index] += 1
return phones_per_word
def sep_text(text):
words = re.split(r"([,;.\?\!\s+])", text)
words = [word for word in words if word.strip() != ""]
return words
def g2p(text):
phones = []
tones = []
word2ph = []
words = re.split(r"([,;.\-\?\!\s+])", text)
words = [word for word in words if word.strip() != ""]
# word2ph = []
words = sep_text(text)
tokens = [tokenizer.tokenize(i) for i in words]
for word in words:
if word.upper() in eng_dict:
phns, tns = refine_syllables(eng_dict[word.upper()])
phones += phns
tones += tns
word2ph.append(len(phns))
phones.append([post_replace_ph(i) for i in phns])
tones.append(tns)
# word2ph.append(len(phns))
else:
phone_list = list(filter(lambda p: p != " ", _g2p(word)))
phns = []
tns = []
for ph in phone_list:
if ph in arpa:
ph, tn = refine_ph(ph)
phones.append(ph)
tones.append(tn)
phns.append(ph)
tns.append(tn)
else:
phones.append(ph)
tones.append(0)
word2ph.append(len(phone_list))
phns.append(ph)
tns.append(0)
phones.append([post_replace_ph(i) for i in phns])
tones.append(tns)
# word2ph.append(len(phns))
# phones = [post_replace_ph(i) for i in phones]
phones = [post_replace_ph(i) for i in phones]
word2ph = []
for token, phoneme in zip(tokens, phones):
phone_len = len(phoneme)
word_len = len(token)
phones = ["_"] + phones + ["_"]
tones = [0] + tones + [0]
aaa = distribute_phone(phone_len, word_len)
word2ph += aaa
phones = ["_"] + [j for i in phones for j in i] + ["_"]
tones = [0] + [j for i in tones for j in i] + [0]
word2ph = [1] + word2ph + [1]
assert len(phones) == len(tones), text
assert len(phones) == sum(word2ph), text
return phones, tones, word2ph