mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-29 09:18:21 +00:00
Justin John
700 lines
26 KiB
Python
700 lines
26 KiB
Python
import os
|
||
import sys
|
||
import re
|
||
import logging
|
||
import argparse
|
||
import torch
|
||
import soundfile as sf
|
||
import numpy as np
|
||
import librosa
|
||
from time import time as ttime
|
||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||
|
||
# Add GPT_SoVITS to the path
|
||
sys.path.append(os.path.join(os.getcwd(), "GPT_SoVITS"))
|
||
|
||
# Importing necessary modules
|
||
import LangSegment
|
||
from module.models import SynthesizerTrn
|
||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||
from text import cleaned_text_to_sequence
|
||
from text.cleaner import clean_text
|
||
from module.mel_processing import spectrogram_torch
|
||
from my_utils import load_audio
|
||
from tools.i18n.i18n import I18nAuto
|
||
from text import chinese
|
||
|
||
# Additional Imports and GPU Info
|
||
import pdb
|
||
|
||
# Display environment variables and GPU info
|
||
print("Env vars:")
|
||
print(os.environ)
|
||
|
||
def print_gpu_info():
|
||
print('========================================')
|
||
print('Python interpreter', sys.executable)
|
||
print('PyTorch version', torch.__version__)
|
||
print('CUDA Available?', torch.cuda.is_available())
|
||
print('CUDA Device count', torch.cuda.device_count())
|
||
print('CUDA architectures library was compiled for', torch.cuda.get_arch_list())
|
||
print('========================================', flush=True)
|
||
|
||
print_gpu_info()
|
||
|
||
# Setup logging
|
||
logging.getLogger("markdown_it").setLevel(logging.ERROR)
|
||
logging.getLogger("urllib3").setLevel(logging.ERROR)
|
||
logging.getLogger("httpcore").setLevel(logging.ERROR)
|
||
logging.getLogger("httpx").setLevel(logging.ERROR)
|
||
logging.getLogger("asyncio").setLevel(logging.ERROR)
|
||
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
|
||
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
|
||
|
||
# Define version and pretrained models
|
||
version = os.environ.get("version", "v2")
|
||
pretrained_sovits_name = [
|
||
"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
|
||
"GPT_SoVITS/pretrained_models/s2G488k.pth"
|
||
]
|
||
pretrained_gpt_name = [
|
||
"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
|
||
"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||
]
|
||
|
||
_ = [[], []]
|
||
for i in range(2):
|
||
if os.path.exists(pretrained_gpt_name[i]):
|
||
_[0].append(pretrained_gpt_name[i])
|
||
if os.path.exists(pretrained_sovits_name[i]):
|
||
_[-1].append(pretrained_sovits_name[i])
|
||
pretrained_gpt_name, pretrained_sovits_name = _
|
||
|
||
# Define language dictionaries for v1 and v2
|
||
dict_language_v1 = {
|
||
"chinese": "all_zh",
|
||
"english": "en",
|
||
"japanese": "all_ja",
|
||
"chinese+english": "zh",
|
||
"japanese+english": "ja",
|
||
"automatic": "auto",
|
||
}
|
||
dict_language_v2 = {
|
||
"chinese": "all_zh",
|
||
"english": "en",
|
||
"japanese": "all_ja",
|
||
"chinese+english": "zh",
|
||
"japanese+english": "ja",
|
||
"cantonese": "all_yue",
|
||
"korean": "all_ko",
|
||
"cantonese+english": "yue",
|
||
"korean+english": "ko",
|
||
"automatic": "auto",
|
||
"automatic(cantonese)": "auto_yue",
|
||
}
|
||
|
||
# Define punctuation set
|
||
punctuation = set(['!', '?', '…', ',', '.', '-'," "])
|
||
|
||
# Paths for cnhubert and bert models
|
||
cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
|
||
bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
|
||
|
||
if "_CUDA_VISIBLE_DEVICES" in os.environ:
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
|
||
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
|
||
|
||
# Initialize transformers and utility classes
|
||
i18n = I18nAuto()
|
||
|
||
# Setup device
|
||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
|
||
# Ensure feature_extractor module is imported
|
||
try:
|
||
from feature_extractor import cnhubert
|
||
except ImportError:
|
||
print("Make sure the feature_extractor module is in the GPT_SoVITS directory.")
|
||
sys.exit(1)
|
||
|
||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||
|
||
# Define global variables for loaded model paths
|
||
loaded_gpt_path = None
|
||
loaded_sovits_path = None
|
||
|
||
# Function to get BERT feature
|
||
def get_bert_feature(text, word2ph):
|
||
global tokenizer, bert_model
|
||
if not "tokenizer" in globals() or not "bert_model" in globals():
|
||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||
if is_half:
|
||
bert_model = bert_model.half().to(device)
|
||
else:
|
||
bert_model = bert_model.to(device)
|
||
with torch.no_grad():
|
||
inputs = tokenizer(text, return_tensors="pt")
|
||
for i in inputs:
|
||
inputs[i] = inputs[i].to(device)
|
||
res = bert_model(**inputs, output_hidden_states=True)
|
||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||
assert len(word2ph) == len(text)
|
||
phone_level_feature = []
|
||
for i in range(len(word2ph)):
|
||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||
phone_level_feature.append(repeat_feature)
|
||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||
return phone_level_feature.T
|
||
|
||
# Class to convert dictionary to attributes recursively
|
||
class DictToAttrRecursive(dict):
|
||
def __init__(self, input_dict):
|
||
super().__init__(input_dict)
|
||
for key, value in input_dict.items():
|
||
if isinstance(value, dict):
|
||
value = DictToAttrRecursive(value)
|
||
self[key] = value
|
||
setattr(self, key, value)
|
||
|
||
def __getattr__(self, item):
|
||
try:
|
||
return self[item]
|
||
except KeyError:
|
||
raise AttributeError(f"Attribute {item} not found")
|
||
|
||
def __setattr__(self, key, value):
|
||
if isinstance(value, dict):
|
||
value = DictToAttrRecursive(value)
|
||
super(DictToAttrRecursive, self).__setitem__(key, value)
|
||
super().__setattr__(key, value)
|
||
|
||
def __delattr__(self, item):
|
||
try:
|
||
del self[item]
|
||
except KeyError:
|
||
raise AttributeError(f"Attribute {item} not found")
|
||
|
||
# Function to change SoVITS weights
|
||
def change_sovits_weights(sovits_path):
|
||
global vq_model, hps, version, dict_language
|
||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||
hps = dict_s2["config"]
|
||
hps = DictToAttrRecursive(hps)
|
||
hps.model.semantic_frame_rate = "25hz"
|
||
if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
|
||
hps.model.version = "v1"
|
||
else:
|
||
hps.model.version = "v2"
|
||
version = hps.model.version
|
||
vq_model = SynthesizerTrn(
|
||
hps.data.filter_length // 2 + 1,
|
||
hps.train.segment_size // hps.data.hop_length,
|
||
n_speakers=hps.data.n_speakers,
|
||
**hps.model
|
||
)
|
||
if "pretrained" not in sovits_path:
|
||
del vq_model.enc_q
|
||
if is_half:
|
||
vq_model = vq_model.half().to(device)
|
||
else:
|
||
vq_model = vq_model.to(device)
|
||
vq_model.eval()
|
||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
||
f.write(sovits_path)
|
||
dict_language = dict_language_v1 if version == 'v1' else dict_language_v2
|
||
|
||
# Function to change GPT weights
|
||
def change_gpt_weights(gpt_path):
|
||
global hz, max_sec, t2s_model, config
|
||
hz = 50
|
||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||
config = dict_s1["config"]
|
||
max_sec = config["data"]["max_sec"]
|
||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||
t2s_model.load_state_dict(dict_s1["weight"])
|
||
if is_half:
|
||
t2s_model = t2s_model.half()
|
||
t2s_model = t2s_model.to(device)
|
||
t2s_model.eval()
|
||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||
with open("./gweight.txt", "w", encoding="utf-8") as f:
|
||
f.write(gpt_path)
|
||
|
||
# Function to get spectrogram
|
||
def get_spepc(hps, filename):
|
||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||
audio = torch.FloatTensor(audio)
|
||
audio_norm = audio.unsqueeze(0)
|
||
spec = spectrogram_torch(
|
||
audio_norm,
|
||
hps.data.filter_length,
|
||
hps.data.sampling_rate,
|
||
hps.data.hop_length,
|
||
hps.data.win_length,
|
||
center=False,
|
||
)
|
||
return spec
|
||
|
||
# Function to clean text
|
||
def clean_text_inf(text, language, version):
|
||
phones, word2ph, norm_text = clean_text(text, language, version)
|
||
phones = cleaned_text_to_sequence(phones, version)
|
||
return phones, word2ph, norm_text
|
||
|
||
dtype = torch.float16 if is_half else torch.float32
|
||
|
||
# Function to get BERT inference
|
||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||
language = language.replace("all_", "")
|
||
if language == "zh":
|
||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||
else:
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half else torch.float32,
|
||
).to(device)
|
||
|
||
return bert
|
||
|
||
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
|
||
|
||
# Function to get the first segment of text before punctuation
|
||
def get_first(text):
|
||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||
text = re.split(pattern, text)[0].strip()
|
||
return text
|
||
|
||
# Function to get phones and BERT embeddings
|
||
def get_phones_and_bert(text,language,version,final=False):
|
||
if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
|
||
language = language.replace("all_","")
|
||
if language == "en":
|
||
LangSegment.setfilters(["en"])
|
||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||
else:
|
||
# Since Chinese, Japanese and Korean characters cannot be distinguished, the user input shall prevail.
|
||
formattext = text
|
||
while " " in formattext:
|
||
formattext = formattext.replace(" ", " ")
|
||
if language == "zh":
|
||
if re.search(r'[A-Za-z]', formattext):
|
||
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext,"zh",version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||
elif language == "yue" and re.search(r'[A-Za-z]', formattext):
|
||
formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext,"yue",version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half == True else torch.float32,
|
||
).to(device)
|
||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||
textlist=[]
|
||
langlist=[]
|
||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||
if language == "auto":
|
||
for tmp in LangSegment.getTexts(text):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "auto_yue":
|
||
for tmp in LangSegment.getTexts(text):
|
||
if tmp["lang"] == "zh":
|
||
tmp["lang"] = "yue"
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
else:
|
||
for tmp in LangSegment.getTexts(text):
|
||
if tmp["lang"] == "en":
|
||
langlist.append(tmp["lang"])
|
||
else:
|
||
# 因无法区别中日韩文汉字,以用户输入为准
|
||
langlist.append(language)
|
||
textlist.append(tmp["text"])
|
||
print(textlist)
|
||
print(langlist)
|
||
phones_list = []
|
||
bert_list = []
|
||
norm_text_list = []
|
||
for i in range(len(textlist)):
|
||
lang = langlist[i]
|
||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
|
||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||
phones_list.append(phones)
|
||
norm_text_list.append(norm_text)
|
||
bert_list.append(bert)
|
||
bert = torch.cat(bert_list, dim=1)
|
||
phones = sum(phones_list, [])
|
||
norm_text = ''.join(norm_text_list)
|
||
|
||
if not final and len(phones) < 6:
|
||
return get_phones_and_bert("." + text,language,version,final=True)
|
||
|
||
return phones,bert.to(dtype),norm_text
|
||
|
||
# Function to merge short texts
|
||
def merge_short_text_in_array(texts, threshold):
|
||
if len(texts) < 2:
|
||
return texts
|
||
result = []
|
||
text = ""
|
||
for ele in texts:
|
||
text += ele
|
||
if len(text) >= threshold:
|
||
result.append(text)
|
||
text = ""
|
||
if len(text) > 0:
|
||
if len(result) == 0:
|
||
result.append(text)
|
||
else:
|
||
result[len(result) - 1] += text
|
||
return result
|
||
|
||
ssl_model = cnhubert.get_model()
|
||
if is_half:
|
||
ssl_model = ssl_model.half().to(device)
|
||
else:
|
||
ssl_model = ssl_model.to(device)
|
||
|
||
cache = {}
|
||
|
||
# Main function for TTS synthesis
|
||
def get_tts_wav(ref_wav_paths, prompt_text, prompt_language, text, text_language, how_to_cut="No slice", top_k=20, top_p=0.6, temperature=0.6, ref_free=False, speed=1.0, if_freeze=False):
|
||
global cache
|
||
|
||
if prompt_text is None or len(prompt_text) == 0:
|
||
ref_free = True
|
||
|
||
t0 = ttime()
|
||
prompt_language = dict_language[prompt_language]
|
||
text_language = dict_language[text_language]
|
||
|
||
if not ref_free:
|
||
prompt_text = prompt_text.strip("\n")
|
||
if prompt_text[-1] not in splits:
|
||
prompt_text += "。" if prompt_language != "en" else "."
|
||
print("Actual reference text:", prompt_text)
|
||
|
||
text = text.strip("\n")
|
||
if text[0] not in splits and len(get_first(text)) < 4:
|
||
text = "。" + text if text_language != "en" else "." + text
|
||
|
||
print("Actual target text:", text)
|
||
zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half else np.float32)
|
||
|
||
t1 = ttime()
|
||
|
||
if not ref_free:
|
||
refers = []
|
||
with torch.no_grad():
|
||
for ref_wav_path in ref_wav_paths:
|
||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||
if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
|
||
raise OSError("Each reference audio must be within 3~10 seconds.")
|
||
wav16k = torch.from_numpy(wav16k)
|
||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||
wav16k = wav16k.half().to(device) if is_half else wav16k.to(device)
|
||
zero_wav_torch = zero_wav_torch.half().to(device) if is_half else zero_wav_torch.to(device)
|
||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)
|
||
codes = vq_model.extract_latent(ssl_content)
|
||
prompt_semantic = codes[0, 0]
|
||
refers.append(prompt_semantic.unsqueeze(0).to(device))
|
||
|
||
prompt = torch.mean(torch.stack(refers), dim=0)
|
||
|
||
t2 = ttime()
|
||
|
||
# Slice the text based on the provided method
|
||
if how_to_cut == "Slice once every 4 sentences":
|
||
text = cut1(text)
|
||
elif how_to_cut == "Cut per 50 characters":
|
||
text = cut2(text)
|
||
elif how_to_cut == "Slice by Chinese punct":
|
||
text = cut3(text)
|
||
elif how_to_cut == "Slice by English punct":
|
||
text = cut4(text)
|
||
elif how_to_cut == "Slice by every punct":
|
||
text = cut5(text)
|
||
|
||
while "\n\n" in text:
|
||
text = text.replace("\n\n", "\n")
|
||
|
||
print("Actual target text (after slicing):", text)
|
||
texts = text.split("\n")
|
||
texts = process_text(texts) # Filter out invalid text entries
|
||
texts = merge_short_text_in_array(texts, 5)
|
||
audio_opt = []
|
||
|
||
if not ref_free:
|
||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
|
||
|
||
t3_start = ttime()
|
||
|
||
for i_text, text in enumerate(texts):
|
||
if len(text.strip()) == 0:
|
||
continue
|
||
if text[-1] not in splits:
|
||
text += "。" if text_language != "en" else "."
|
||
print("Actual target text (per sentence):", text)
|
||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
|
||
print("Processed text (per sentence):", norm_text2)
|
||
|
||
if not ref_free:
|
||
bert = torch.cat([bert1, bert2], 1)
|
||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||
else:
|
||
bert = bert2
|
||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||
|
||
bert = bert.to(device).unsqueeze(0)
|
||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||
|
||
if i_text in cache and if_freeze:
|
||
pred_semantic = cache[i_text]
|
||
else:
|
||
with torch.no_grad():
|
||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||
all_phoneme_ids,
|
||
all_phoneme_len,
|
||
None if ref_free else prompt,
|
||
bert,
|
||
top_k=top_k,
|
||
top_p=top_p,
|
||
temperature=temperature,
|
||
early_stop_num=hz * max_sec,
|
||
)
|
||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
|
||
cache[i_text] = pred_semantic
|
||
|
||
refer_spectrograms = [get_spepc(hps, ref_wav_path).to(dtype).to(device) for ref_wav_path in ref_wav_paths]
|
||
audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer_spectrograms, speed=speed).detach().cpu().numpy()[0, 0]
|
||
max_audio = np.abs(audio).max()
|
||
if max_audio > 1:
|
||
audio /= max_audio
|
||
audio_opt.append(audio)
|
||
audio_opt.append(zero_wav)
|
||
|
||
t3_end = ttime()
|
||
|
||
t4_start = ttime()
|
||
|
||
# Concatenate audio options
|
||
concatenated_audio = np.concatenate(audio_opt, 0) * 32768
|
||
|
||
t4_end = ttime()
|
||
|
||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3_end - t3_start, t4_end - t4_start))
|
||
return (t1 - t0, t2 - t1, t3_end - t3_start, t4_end - t4_start, t4_end - t4_start), hps.data.sampling_rate, concatenated_audio.astype(np.int16)
|
||
|
||
# Function to split text based on punctuation
|
||
def split(todo_text):
|
||
todo_text = todo_text.replace("……", "。").replace("——", "，")
|
||
if todo_text[-1] not in splits:
|
||
todo_text += "。"
|
||
i_split_head = i_split_tail = 0
|
||
len_text = len(todo_text)
|
||
todo_texts = []
|
||
while 1:
|
||
if i_split_head >= len_text:
|
||
break # There will always be punctuation at the end, so just exit, the last segment was already added last time
|
||
if todo_text[i_split_head] in splits:
|
||
i_split_head += 1
|
||
todo_texts.append(todo_text[i_split_tail:i_split_head])
|
||
i_split_tail = i_split_head
|
||
else:
|
||
i_split_head += 1
|
||
return todo_texts
|
||
|
||
# Functions to cut text in various ways
|
||
def cut1(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
split_idx = list(range(0, len(inps), 4))
|
||
split_idx[-1] = None
|
||
if len(split_idx) > 1:
|
||
opts = []
|
||
for idx in range(len(split_idx) - 1):
|
||
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
|
||
else:
|
||
opts = [inp]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
def cut2(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
if len(inps) < 2:
|
||
return inp
|
||
opts = []
|
||
summ = 0
|
||
tmp_str = ""
|
||
for i in range(len(inps)):
|
||
summ += len(inps[i])
|
||
tmp_str += inps[i]
|
||
if summ > 50:
|
||
summ = 0
|
||
opts.append(tmp_str)
|
||
tmp_str = ""
|
||
if tmp_str != "":
|
||
opts.append(tmp_str)
|
||
if len(opts) > 1 and len(opts[-1]) < 50:
|
||
opts[-2] = opts[-2] + opts[-1]
|
||
opts = opts[:-1]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
def cut3(inp):
|
||
inp = inp.strip("\n")
|
||
opts = ["%s" % item for item in inp.strip("。").split("。")]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
def cut4(inp):
|
||
inp = inp.strip("\n")
|
||
opts = ["%s" % item for item in inp.strip(".").split(".")]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
# Contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||
def cut5(inp):
|
||
inp = inp.strip("\n")
|
||
punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
|
||
mergeitems = []
|
||
items = []
|
||
|
||
for i, char in enumerate(inp):
|
||
if char in punds:
|
||
if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
|
||
items.append(char)
|
||
else:
|
||
items.append(char)
|
||
mergeitems.append("".join(items))
|
||
items = []
|
||
else:
|
||
items.append(char)
|
||
|
||
if items:
|
||
mergeitems.append("".join(items))
|
||
|
||
opt = [item for item in mergeitems if not set(item).issubset(punds)]
|
||
return "\n".join(opt)
|
||
|
||
# Function to process and filter text entries
|
||
def process_text(texts):
|
||
_text = []
|
||
if all(text in [None, " ", "\n", ""] for text in texts):
|
||
raise ValueError(i18n("Please enter valid text"))
|
||
for text in texts:
|
||
if text not in [None, " ", "", "\n"]:
|
||
_text.append(text)
|
||
return _text
|
||
|
||
# Main function for inference
|
||
def gptsovits_inference(gpt_model_path, sovits_model_path, ref_audio_paths, prompt_text, prompt_language, text, text_language, how_to_cut="No slice", top_k=20, top_p=0.6, temperature=0.6, ref_free=False, speed=1.0, output_file_path=None):
|
||
global loaded_gpt_path, loaded_sovits_path
|
||
if not ref_audio_paths:
|
||
print("You must input at least one reference audio path!")
|
||
return None
|
||
gpt_path = gpt_model_path if gpt_model_path else pretrained_gpt_name[0]
|
||
sovits_path = sovits_model_path if sovits_model_path else pretrained_sovits_name[0]
|
||
|
||
if loaded_gpt_path != gpt_path:
|
||
change_gpt_weights(gpt_path)
|
||
loaded_gpt_path = gpt_path
|
||
if loaded_sovits_path != sovits_path:
|
||
change_sovits_weights(sovits_path)
|
||
loaded_sovits_path = sovits_path
|
||
|
||
current_output_path = output_file_path
|
||
|
||
if not current_output_path:
|
||
opt_root = os.path.join(os.getcwd(), "output")
|
||
os.makedirs(opt_root, exist_ok=True)
|
||
output_count = 1
|
||
|
||
while True:
|
||
opt_filename = f"{output_count}_GPTSoVITS.wav"
|
||
current_output_path = os.path.join(opt_root, opt_filename)
|
||
if not os.path.exists(current_output_path):
|
||
break
|
||
output_count += 1
|
||
|
||
times, sr, audio_data = get_tts_wav(ref_audio_paths, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_free, speed)
|
||
|
||
sf.write(current_output_path, audio_data, sr, format="wav")
|
||
print("Times:\nref_audio: {:.2f}s text: {:.2f}s phonemes & bert: {:.2f}s gpt: {:.2f}s sovits: {:.2f}s".format(*times))
|
||
|
||
return current_output_path
|
||
|
||
# Command-line interface
|
||
def main():
|
||
parser = argparse.ArgumentParser(description="GPT-SoVITS TTS Inference CLI")
|
||
parser.add_argument("--use_pretrained_gpt", action="store_true", help="Use pretrained GPT model")
|
||
parser.add_argument("--use_pretrained_sovits", action="store_true", help="Use pretrained SoVITS model")
|
||
parser.add_argument("--gpt_model", type=str, help="Path to GPT model checkpoint")
|
||
parser.add_argument("--sovits_model", type=str, help="Path to SoVITS model checkpoint")
|
||
parser.add_argument("--ref_audio", type=str, nargs='+', help="Paths to reference wav files (multiple files allowed)")
|
||
parser.add_argument("--ref_text", type=str, help="Path to reference text file (optional)")
|
||
parser.add_argument("--target_language", type=str, choices=dict_language_v1.keys() | dict_language_v2.keys(), help="Language of the target text")
|
||
parser.add_argument("--ref_language", type=str, choices=dict_language_v1.keys() | dict_language_v2.keys(), help="Language of the reference text")
|
||
parser.add_argument("--target_text", type=str, help="Path to the target text file")
|
||
parser.add_argument("--how_to_cut", type=str, default="No slice", help="How to cut the text for synthesis")
|
||
parser.add_argument("--top_k", type=int, default=20, help="Top K sampling")
|
||
parser.add_argument("--top_p", type=float, default=0.6, help="Top P sampling")
|
||
parser.add_argument("--temperature", type=float, default=0.6, help="Sampling temperature")
|
||
parser.add_argument("--speed", type=float, default=1.0, help="Speed of the synthesized speech")
|
||
parser.add_argument("--output_path", type=str, default="output", help="Path to save the output wav file")
|
||
|
||
args = parser.parse_args()
|
||
|
||
gpt_model_path = args.gpt_model if args.gpt_model else (pretrained_gpt_name[0] if args.use_pretrained_gpt else None)
|
||
sovits_model_path = args.sovits_model if args.sovits_model else (pretrained_sovits_name[0] if args.use_pretrained_sovits else None)
|
||
|
||
if not gpt_model_path or not sovits_model_path:
|
||
parser.error("--gpt_model and --sovits_model are required when not using pretrained models")
|
||
|
||
if not args.ref_audio or not args.target_text or not args.target_language:
|
||
parser.error("--ref_audio, --target_text, and --target_language are required")
|
||
|
||
prompt_text = None
|
||
if args.ref_text and os.path.exists(args.ref_text):
|
||
with open(args.ref_text, 'r', encoding='utf-8') as f:
|
||
prompt_text = f.read().strip()
|
||
|
||
with open(args.target_text, 'r', encoding='utf-8') as f:
|
||
target_text = f.read().strip()
|
||
|
||
gptsovits_output_audio_path = gptsovits_inference(
|
||
gpt_model_path=gpt_model_path,
|
||
sovits_model_path=sovits_model_path,
|
||
ref_audio_paths=args.ref_audio,
|
||
prompt_text=prompt_text,
|
||
prompt_language=args.ref_language,
|
||
text=target_text,
|
||
text_language=args.target_language,
|
||
how_to_cut=args.how_to_cut,
|
||
top_k=args.top_k,
|
||
top_p=args.top_p,
|
||
temperature=args.temperature,
|
||
ref_free=False,
|
||
speed=args.speed,
|
||
output_file_path=args.output_path,
|
||
)
|
||
|
||
if gptsovits_output_audio_path:
|
||
t1 = ttime()
|
||
print(f"Total time: {t1-t0:.2f}s")
|
||
print(f"Generated audio saved at \033[1m{gptsovits_output_audio_path}\033[0m")
|
||
|
||
if __name__ == "__main__":
|
||
t0 = ttime()
|
||
main() |