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import os, sys
import tempfile
import gradio as gr
from src.gradio_demo import SadTalker
# from src.utils.text2speech import TTSTalker
from huggingface_hub import snapshot_download
import torch
import librosa
from scipy.io.wavfile import write
from transformers import WavLMModel
import utils
from models import SynthesizerTrn
from mel_processing import mel_spectrogram_torch
from speaker_encoder.voice_encoder import SpeakerEncoder
import time
from textwrap import dedent
import mdtex2html
from loguru import logger
from transformers import AutoModel, AutoTokenizer
from tts_voice import tts_order_voice
import edge_tts
import tempfile
import anyio
def get_source_image(image):
return image
try:
import webui # in webui
in_webui = True
except:
in_webui = False
def toggle_audio_file(choice):
if choice == False:
return gr.update(visible=True), gr.update(visible=False)
else:
return gr.update(visible=False), gr.update(visible=True)
def ref_video_fn(path_of_ref_video):
if path_of_ref_video is not None:
return gr.update(value=True)
else:
return gr.update(value=False)
def download_model():
REPO_ID = 'vinthony/SadTalker-V002rc'
snapshot_download(repo_id=REPO_ID, local_dir='./checkpoints', local_dir_use_symlinks=True)
def sadtalker_demo():
download_model()
sad_talker = SadTalker(lazy_load=True)
# tts_talker = TTSTalker()
download_model()
sad_talker = SadTalker(lazy_load=True)
# ChatGLM2 & FreeVC
'''
def get_wavlm():
os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU')
shutil.move('WavLM-Large.pt', 'wavlm')
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt')
print("Loading FreeVC(24k)...")
hps = utils.get_hparams_from_file("configs/freevc-24.json")
freevc_24 = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model).to(device)
_ = freevc_24.eval()
_ = utils.load_checkpoint("checkpoint/freevc-24.pth", freevc_24, None)
print("Loading WavLM for content...")
cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device)
def convert(model, src, tgt):
with torch.no_grad():
# tgt
wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
if model == "FreeVC" or model == "FreeVC (24kHz)":
g_tgt = smodel.embed_utterance(wav_tgt)
g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device)
else:
wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device)
mel_tgt = mel_spectrogram_torch(
wav_tgt,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
# src
wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device)
c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device)
# infer
if model == "FreeVC":
audio = freevc.infer(c, g=g_tgt)
elif model == "FreeVC-s":
audio = freevc_s.infer(c, mel=mel_tgt)
else:
audio = freevc_24.infer(c, g=g_tgt)
audio = audio[0][0].data.cpu().float().numpy()
if model == "FreeVC" or model == "FreeVC-s":
write("out.wav", hps.data.sampling_rate, audio)
else:
write("out.wav", 24000, audio)
out = "out.wav"
return out
# GLM2
language_dict = tts_order_voice
# fix timezone in Linux
os.environ["TZ"] = "Asia/Shanghai"
try:
time.tzset() # type: ignore # pylint: disable=no-member
except Exception:
# Windows
logger.warning("Windows, cant run time.tzset()")
# model_name = "THUDM/chatglm2-6b"
model_name = "THUDM/chatglm2-6b-int4"
RETRY_FLAG = False
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
# 4/8 bit
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
has_cuda = torch.cuda.is_available()
# has_cuda = False # force cpu
if has_cuda:
model_glm = (
AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()
) # 3.92G
else:
model_glm = AutoModel.from_pretrained(
model_name, trust_remote_code=True
).float() # .float() .half().float()
model_glm = model_glm.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] = "<br></code></pre>"
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", r"\`")
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(
RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values
):
try:
chatbot.append((parse_text(input), ""))
except Exception as exc:
logger.error(exc)
logger.debug(f"{chatbot=}")
_ = """
if chatbot:
chatbot[-1] = (parse_text(input), str(exc))
yield chatbot, history, past_key_values
# """
yield chatbot, history, past_key_values
for response, history, past_key_values in model_glm.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))
# chatbot[-1][-1] = parse_text(response)
yield chatbot, history, past_key_values, parse_text(response)
def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
if max_length < 10:
max_length = 4096
if top_p < 0.1 or top_p > 1:
top_p = 0.85
if temperature <= 0 or temperature > 1:
temperature = 0.01
try:
res, _ = model_glm.chat(
tokenizer,
input,
history=[],
past_key_values=None,
max_length=max_length,
top_p=top_p,
temperature=temperature,
)
# logger.debug(f"{res=} \n{_=}")
except Exception as exc:
logger.error(f"{exc=}")
res = str(exc)
return res
def reset_user_input():
return gr.update(value="")
def reset_state():
return [], [], None, ""
# Delete last turn
def delete_last_turn(chat, history):
if chat and history:
chat.pop(-1)
history.pop(-1)
return chat, history
# Regenerate response
def retry_last_answer(
user_input, chatbot, max_length, top_p, temperature, history, past_key_values
):
if chatbot and history:
# Removing the previous conversation from chat
chatbot.pop(-1)
# Setting up a flag to capture a retry
RETRY_FLAG = True
# Getting last message from user
user_input = history[-1][0]
# Removing bot response from the history
history.pop(-1)
yield from predict(
RETRY_FLAG, # type: ignore
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
)
# print
def print(text):
return text
# TTS
async def text_to_speech_edge(text, language_code):
voice = language_dict[language_code]
communicate = edge_tts.Communicate(text, voice)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
return tmp_path
with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm"), analytics_enabled=False) as demo:
gr.HTML("<center>"
"<h1>🥳💕🎶 - ChatGLM2+声音克隆+视频对话:和喜欢的角色畅所欲言吧!</h1>"
"</center>")
gr.Markdown("## <center>💡 - ChatGLM2+FreeVC+SadTalker,为您打造独一无二的沉浸式对话体验,支持中英双语</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>")
gr.Markdown("### <center>⭐ - 如果您喜欢这个程序,欢迎给我的[Github项目](https://github.com/KevinWang676/ChatGLM2-Voice-Cloning)点赞支持!</center>")
with gr.Tab("🍻 - ChatGLM2聊天区"):
with gr.Accordion("📒 相关信息", open=False):
_ = f""" ChatGLM2的可选参数信息:
* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
* Top P controls dynamic vocabulary selection based on context.\n
如果您想让ChatGLM2进行角色扮演并与之对话,请先输入恰当的提示词,如“请你扮演成动漫角色蜡笔小新并和我进行对话”;您也可以为ChatGLM2提供自定义的角色设定\n
当您使用声音克隆功能时,请先在此程序的对应位置上传一段您喜欢的音频
"""
gr.Markdown(dedent(_))
chatbot = gr.Chatbot(height=300)
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(
label="请在此处和GLM2聊天 (按回车键即可发送)",
placeholder="聊点什么吧",
)
RETRY_FLAG = gr.Checkbox(value=False, visible=False)
with gr.Column(min_width=32, scale=1):
with gr.Row():
submitBtn = gr.Button("开始和GLM2交流吧", variant="primary")
deleteBtn = gr.Button("删除最新一轮对话", variant="secondary")
retryBtn = gr.Button("重新生成最新一轮对话", variant="secondary")
with gr.Accordion("🔧 更多设置", open=False):
with gr.Row():
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=0.85, step=0.01, label="Top P", interactive=True
)
temperature = gr.Slider(
0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True
)
with gr.Row():
test1 = gr.Textbox(label="GLM2的最新回答 (可编辑)", lines = 3)
with gr.Column():
language = gr.Dropdown(choices=list(language_dict.keys()), value="普通话 (中国大陆)-Xiaoxiao-女", label="请选择文本对应的语言及您喜欢的说话人")
tts_btn = gr.Button("生成对应的音频吧", variant="primary")
output_audio = gr.Audio(type="filepath", label="为您生成的音频", interactive=False)
tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio])
with gr.Row():
model_choice = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC (24kHz)", label="Model", visible=False)
audio1 = output_audio
audio2 = gr.Audio(label="请上传您喜欢的声音进行声音克隆", type='filepath')
clone_btn = gr.Button("开始AI声音克隆吧", variant="primary")
audio_cloned = gr.Audio(label="为您生成的专属声音克隆音频", type='filepath')
clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned])
history = gr.State([])
past_key_values = gr.State(None)
user_input.submit(
predict,
[
RETRY_FLAG,
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
[chatbot, history, past_key_values, test1],
show_progress="full",
)
submitBtn.click(
predict,
[
RETRY_FLAG,
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
[chatbot, history, past_key_values, test1],
show_progress="full",
api_name="predict",
)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(
reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress="full"
)
retryBtn.click(
retry_last_answer,
inputs=[
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
# outputs = [chatbot, history, last_user_message, user_message]
outputs=[chatbot, history, past_key_values, test1],
)
deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])
with gr.Accordion("📔 提示词示例", open=False):
etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
examples = gr.Examples(
examples=[
["Explain the plot of Cinderella in a sentence."],
[
"How long does it take to become proficient in French, and what are the best methods for retaining information?"
],
["What are some common mistakes to avoid when writing code?"],
["Build a prompt to generate a beautiful portrait of a horse"],
["Suggest four metaphors to describe the benefits of AI"],
["Write a pop song about leaving home for the sandy beaches."],
["Write a summary demonstrating my ability to tame lions"],
["鲁迅和周树人什么关系"],
["从前有一头牛,这头牛后面有什么?"],
["正无穷大加一大于正无穷大吗?"],
["正无穷大加正无穷大大于正无穷大吗?"],
["-2的平方根等于什么"],
["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"],
["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"],
["鲁迅和周树人什么关系 用英文回答"],
["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
[f"{etext} 翻成中文,列出3个版本"],
[f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本"],
["js 判断一个数是不是质数"],
["js 实现python 的 range(10)"],
["js 实现python 的 [*(range(10)]"],
["假定 1 + 2 = 4, 试求 7 + 8"],
["Erkläre die Handlung von Cinderella in einem Satz."],
["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
],
inputs=[user_input],
examples_per_page=30,
)
with gr.Accordion("For Chat/Translation API", open=False, visible=False):
input_text = gr.Text()
tr_btn = gr.Button("Go", variant="primary")
out_text = gr.Text()
tr_btn.click(
trans_api,
[input_text, max_length, top_p, temperature],
out_text,
# show_progress="full",
api_name="tr",
)
_ = """
input_text.submit(
trans_api,
[input_text, max_length, top_p, temperature],
out_text,
show_progress="full",
api_name="tr1",
)
# """
with gr.Tab("📺 - 视频聊天区"):
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="sadtalker_source_image"):
with gr.TabItem('Source image'):
with gr.Row():
source_image = gr.Image(label="请上传一张您喜欢角色的图片", source="upload", type="filepath", elem_id="img2img_image").style(width=512)
with gr.Tabs(elem_id="sadtalker_driven_audio"):
with gr.TabItem('💡您还可以将视频下载到本地'):
with gr.Row():
driven_audio = audio_cloned
driven_audio_no = gr.Audio(label="Use IDLE mode, no audio is required", source="upload", type="filepath", visible=False)
with gr.Column():
use_idle_mode = gr.Checkbox(label="Use Idle Animation", visible=False)
length_of_audio = gr.Number(value=5, label="The length(seconds) of the generated video.", visible=False)
use_idle_mode.change(toggle_audio_file, inputs=use_idle_mode, outputs=[driven_audio, driven_audio_no]) # todo
with gr.Row():
ref_video = gr.Video(label="Reference Video", source="upload", type="filepath", elem_id="vidref", visible=False).style(width=512)
with gr.Column():
use_ref_video = gr.Checkbox(label="Use Reference Video", visible=False)
ref_info = gr.Radio(['pose', 'blink','pose+blink', 'all'], value='pose', label='Reference Video',info="How to borrow from reference Video?((fully transfer, aka, video driving mode))", visible=False)
ref_video.change(ref_video_fn, inputs=ref_video, outputs=[use_ref_video]) # todo
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="sadtalker_checkbox"):
with gr.TabItem('Settings'):
with gr.Column(variant='panel'):
# width = gr.Slider(minimum=64, elem_id="img2img_width", maximum=2048, step=8, label="Manually Crop Width", value=512) # img2img_width
# height = gr.Slider(minimum=64, elem_id="img2img_height", maximum=2048, step=8, label="Manually Crop Height", value=512) # img2img_width
with gr.Row():
pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="Pose style", value=0, visible=False) #
exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label="expression scale", value=1, visible=False) #
blink_every = gr.Checkbox(label="use eye blink", value=True, visible=False)
with gr.Row():
size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?", visible=False) #
preprocess_type = gr.Radio(['crop', 'full'], value='crop', label='是否聚焦角色面部', info="crop:视频会聚焦角色面部;full:视频会显示图片全貌")
with gr.Row():
is_still_mode = gr.Checkbox(label="静态模式 (开启静态模式,角色的面部动作会减少;默认开启)", value=True)
facerender = gr.Radio(['facevid2vid','pirender'], value='facevid2vid', label='facerender', info="which face render?", visible=False)
with gr.Row():
batch_size = gr.Slider(label="Batch size (数值越大,生成速度越快;若显卡性能好,可增大数值)", step=1, maximum=32, value=2)
enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False)
submit = gr.Button('开始视频聊天吧', elem_id="sadtalker_generate", variant='primary')
with gr.Tabs(elem_id="sadtalker_genearted"):
gen_video = gr.Video(label="为您生成的专属视频", format="mp4").style(width=256)
submit.click(
fn=sad_talker.test,
inputs=[source_image,
driven_audio,
preprocess_type,
is_still_mode,
enhancer,
batch_size,
size_of_image,
pose_style,
facerender,
exp_weight,
use_ref_video,
ref_video,
ref_info,
use_idle_mode,
length_of_audio,
blink_every
],
outputs=[gen_video]
)
gr.Markdown("### <center>注意❗:请不要生成会对个人以及组织造成侵害的内容,此程序仅供科研、学习及个人娱乐使用。</center>")
gr.Markdown("<center>💡 - 如何使用此程序:输入您对ChatGLM的提问后,依次点击“开始和GLM2交流吧”、“生成对应的音频吧”、“开始AI声音克隆吧”、“开始视频聊天吧”三个按键即可;使用声音克隆功能时,请先上传一段您喜欢的音频</center>")
gr.HTML('''
<div class="footer">
<p>🌊🏞️🎶 - 江水东流急,滔滔无尽声。 明·顾璘
</p>
</div>
''')
demo.queue().launch(show_error=True, debug=True)
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