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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
# ChatGLM2
from transformers import AutoModel, AutoTokenizer, AutoConfig
import gradio as gr
import mdtex2html
import torch
import os
CHECKPOINT_PATH=f'checkpoint-600'
tokenizer = AutoTokenizer.from_pretrained("chatglm2-6b", trust_remote_code=True)
config = AutoConfig.from_pretrained("chatglm2-6b", trust_remote_code=True, pre_seq_len=128)
model = AutoModel.from_pretrained("chatglm2-6b", config=config, trust_remote_code=True)
prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"), map_location=torch.device('cpu'))
new_prefix_state_dict = {}
for k, v in prefix_state_dict.items():
if k.startswith("transformer.prefix_encoder."):
new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
#model = model.half().cuda()
model = model.float()
model.transformer.prefix_encoder.float()
model = model.eval()
"""Override Chatbot.postprocess"""
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is None else mdtex2html.convert(response),
)
return y
gr.Chatbot.postprocess = postprocess
def parse_text(text):
"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split('`')
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", "\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>"+line
text = "".join(lines)
return text
def predict(input, chatbot, max_length, top_p, temperature, history, past_key_values):
chatbot.append((parse_text(input), ""))
for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values,
return_past_key_values=True,
max_length=max_length, top_p=top_p,
temperature=temperature):
chatbot[-1] = (parse_text(input), parse_text(response))
yield chatbot, history, past_key_values, response
def reset_user_input():
return gr.update(value='')
def reset_state():
return [], [], None
# Bert-VITS2
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, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
global net_g
bert, phones, tones, lang_ids = get_text(text, "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, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
with torch.no_grad():
audio = infer(text, 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)
image_markdown = ("""
<h1 align="center"><a href="http://www.talktalkai.com"><img src="https://media.9game.cn/gamebase/2021/7/23/227829877.jpg", alt="talktalkai" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1>
""")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", default="./logs/OUTPUT_MODEL/G_13900.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:
gr.Markdown("# <center>🌊💕🎶 ChatGLM2 神里绫华 + Bert-VITS2</center>")
gr.Markdown("## <center>🌟 - 和绫华 畅所欲言吧:稻妻神里流太刀术皆传,神里绫华,参上! </center>")
gr.Markdown("### <center>🍻 - 更多精彩应用,尽在[滔滔AI](http://www.talktalkai.com);滔滔AI,为爱滔滔!💕</center>")
with gr.Accordion("绫华", open=True):
gr.Markdown(image_markdown)
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(show_label=False, placeholder="和绫华一起叙叙旧吧...", lines=8).style(
container=False)
with gr.Column(min_width=32, scale=1):
submitBtn = gr.Button("开始对话吧!", variant="primary")
with gr.Column(scale=1):
emptyBtn = gr.Button("清空所有聊天记录")
max_length = gr.Slider(0, 32768, value=8192, step=1.0, label="Maximum length", interactive=True)
top_p = gr.Slider(0, 1, value=0.8, step=0.01, label="Top P", interactive=True)
temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
response_lh = gr.Textbox(label="神里绫华的回答", visible=False)
history = gr.State([])
past_key_values = gr.State(None)
submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
[chatbot, history, past_key_values, response_lh], show_progress=True)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[chatbot, history, past_key_values, response_lh], show_progress=True)
with gr.Row():
with gr.Column():
text = response_lh
speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker', visible=False)
with gr.Row():
sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.1, label='语调变化')
noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.1, label='感情变化')
with gr.Row():
noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.1, label='音节发音长度变化')
length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.1, label='语速 (数值越小,语速越快)')
btn = gr.Button("开启AI语音之旅吧!", variant="primary")
with gr.Column():
text_output = gr.Textbox(label="Message", visible=False)
audio_output = gr.Audio(label="神里绫华发来的语音", autoplay=True)
btn.click(tts_fn,
inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
outputs=[text_output, audio_output])
app.launch(show_error=True)
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