Spaces:
Sleeping
Sleeping
File size: 7,586 Bytes
bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 b6d7961 bf0a127 82f5cc2 b6d7961 4b5c1de b6d7961 82f5cc2 4b5c1de 82f5cc2 78e14e1 82f5cc2 bf0a127 b6d7961 4b5c1de bf0a127 ed2ee73 f2850f7 1c06304 bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 4b5c1de bf0a127 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import sys, os
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import logging
import openai
# openai.log = "debug"
openai.api_key = "sk-"
openai.api_base = "https://api.chatanywhere.com.cn/v1"
# 非流式响应
# completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world!"}])
# print(completion.choices[0].message.content)
def gpt_35_api_stream(key, messages: list):
openai.api_key = "sk-" + key
"""为提供的对话消息创建新的回答 (流式传输)
Args:
messages (list): 完整的对话消息
api_key (str): OpenAI API 密钥
Returns:
tuple: (results, error_desc)
"""
try:
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=messages,
stream=True,
)
completion = {'role': '', 'content': ''}
for event in response:
if event['choices'][0]['finish_reason'] == 'stop':
print(f'收到的完成数据: {completion}')
break
for delta_k, delta_v in event['choices'][0]['delta'].items():
print(f'流响应数据: {delta_k} = {delta_v}')
completion[delta_k] += delta_v
messages.append(completion) # 直接在传入参数 messages 中追加消息
return True, ''
except Exception as err:
return False, f'OpenAI API 异常: {err}'
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
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_stream(text)
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】在线语音合成(Bert-Vits2)\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="大家好我是嘉然戴安娜,关注嘉然,顿顿解馋,谢谢!")
text = 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)
|