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import sys, os | |
if sys.platform == "darwin": | |
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
import logging | |
logging.getLogger("numba").setLevel(logging.WARNING) | |
logging.getLogger("markdown_it").setLevel(logging.WARNING) | |
logging.getLogger("urllib3").setLevel(logging.WARNING) | |
logging.getLogger("matplotlib").setLevel(logging.WARNING) | |
logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") | |
logger = logging.getLogger(__name__) | |
import torch | |
import argparse | |
import commons | |
import utils | |
from models import SynthesizerTrn | |
from text.symbols import symbols | |
from text import cleaned_text_to_sequence, get_bert | |
from text.cleaner import clean_text | |
import gradio as gr | |
import webbrowser | |
import openai | |
# openai.log = "debug" | |
openai.api_base = "https://api.chatanywhere.com.cn/v1" | |
# 非流式响应 | |
def gpt_35_api(gptkey, message): | |
openai.api_key = "sk-" + gptkey | |
completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": message}]) | |
return completion.choices[0].message.content | |
net_g = None | |
def get_text(text, language_str, hps): | |
norm_text, phone, tone, word2ph = clean_text(text, language_str) | |
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
if hps.data.add_blank: | |
phone = commons.intersperse(phone, 0) | |
tone = commons.intersperse(tone, 0) | |
language = commons.intersperse(language, 0) | |
for i in range(len(word2ph)): | |
word2ph[i] = word2ph[i] * 2 | |
word2ph[0] += 1 | |
bert = get_bert(norm_text, word2ph, language_str) | |
del word2ph | |
assert bert.shape[-1] == len(phone) | |
phone = torch.LongTensor(phone) | |
tone = torch.LongTensor(tone) | |
language = torch.LongTensor(language) | |
return bert, phone, tone, language | |
def infer(text, key, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): | |
global net_g | |
message = gpt_35_api(key, text) | |
print(message) | |
bert, phones, tones, lang_ids = get_text(message, "ZH", hps) | |
with torch.no_grad(): | |
x_tst=phones.to(device).unsqueeze(0) | |
tones=tones.to(device).unsqueeze(0) | |
lang_ids=lang_ids.to(device).unsqueeze(0) | |
bert = bert.to(device).unsqueeze(0) | |
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) | |
del phones | |
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) | |
audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio | |
, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() | |
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers | |
return audio | |
def tts_fn(text, key, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): | |
with torch.no_grad(): | |
audio = infer(text, key, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) | |
return "Success", (hps.data.sampling_rate, audio) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--model_dir", default="./logs/Diana/G_4800.pth", help="path of your model") | |
parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") | |
parser.add_argument("--share", default=False, help="make link public") | |
parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") | |
args = parser.parse_args() | |
if args.debug: | |
logger.info("Enable DEBUG-LEVEL log") | |
logging.basicConfig(level=logging.DEBUG) | |
hps = utils.get_hparams_from_file(args.config_dir) | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
''' | |
device = ( | |
"cuda:0" | |
if torch.cuda.is_available() | |
else ( | |
"mps" | |
if sys.platform == "darwin" and torch.backends.mps.is_available() | |
else "cpu" | |
) | |
) | |
''' | |
net_g = SynthesizerTrn( | |
len(symbols), | |
hps.data.filter_length // 2 + 1, | |
hps.train.segment_size // hps.data.hop_length, | |
n_speakers=hps.data.n_speakers, | |
**hps.model).to(device) | |
_ = net_g.eval() | |
_ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) | |
speaker_ids = hps.data.spk2id | |
speakers = list(speaker_ids.keys()) | |
with gr.Blocks() as app: | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown(value=""" | |
【AI嘉然①】在线语音对话版(Bert-Vits2 + gpt)\n | |
音声作者:Xz乔希 https://space.bilibili.com/5859321\n | |
集成作者:碎语碎念 https://space.bilibili.com/4269384\n | |
声音归属:嘉然今天吃什么 https://space.bilibili.com/672328094\n | |
Bert-VITS2项目:https://github.com/Stardust-minus/Bert-VITS2\n | |
GPT_API_free项目:https://github.com/chatanywhere/GPT_API_free\n | |
本项目中的apiKey可以免费从https://github.com/chatanywhere/GPT_API_free获取,参考项目文档即可!\n | |
使用本模型请严格遵守法律法规!\n | |
发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成!\n | |
""") | |
text = gr.TextArea(label="Text", placeholder="Input Text Here", | |
value="虚拟主播是什么?") | |
key = gr.TextArea(label="Key", placeholder="请输入上面提示中获取的gpt key", | |
value="key") | |
speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') | |
sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比') | |
noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节') | |
noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度') | |
length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度') | |
btn = gr.Button("点击生成", variant="primary") | |
btn.click(tts_fn, | |
inputs=[text, key, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], | |
outputs=[text_output, audio_output]) | |
# webbrowser.open("http://127.0.0.1:6006") | |
# app.launch(server_port=6006, show_error=True) | |
app.launch(show_error=True) | |