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 import asyncio 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 tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] # 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'
'
            else:
                lines[i] = "
" 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] = "
" + 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, voice): communicate = edge_tts.Communicate(text, "-".join(voice.split('-')[:-1])) 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("
" "

📺💕🎶 - ChatGLM2 + Voice Cloning + SadTalker

" "
") gr.Markdown("##
🥳 - Chat with any character you like through ChatGLM2-6B, FreeVC and SadTalker in real time
") gr.Markdown("##
⭐ - If you like the this app, please star my [GitHub repo](https://github.com/KevinWang676/ChatGLM2-Voice-Cloning)
") with gr.Tab("🍻 - ChatGLM2+VC"): with gr.Accordion("📒 Info", open=False): _ = f""" Some parameters of ChatGLM2: * 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 If you would like to chat with a specific character, you can send a prompt like "please act as Elon Musk and chat with me" to ChatGLM2. """ 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="Chat with ChatGLM2 here", placeholder="Enter something here...", ) RETRY_FLAG = gr.Checkbox(value=False, visible=False) with gr.Column(min_width=32, scale=1): with gr.Row(): submitBtn = gr.Button("Chat now", variant="primary") deleteBtn = gr.Button("Delete last turn", variant="secondary") retryBtn = gr.Button("Regenerate", variant="secondary") with gr.Accordion("🔧 Settings", open=False): with gr.Row(): emptyBtn = gr.Button("Clear History") 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="Response from ChatGLM2 (you can edit the content)", lines = 3) with gr.Column(): language = gr.Dropdown(choices=voices, value="en-US-AnaNeural-Female", label="Please select a voice") tts_btn = gr.Button("Generate using Edge-TTS", variant="primary") output_audio = gr.Audio(type="filepath", label="Audio generated by Edge-TTS", 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="Upload reference audio for voice cloning (~5s)", type='filepath') clone_btn = gr.Button("Generate using FreeVC", variant="primary") audio_cloned = gr.Audio(label="Generated audio in a custom voice", 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("📔 Prompts", 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("📺 - SadTalker"): 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="Please upload an image here", source="upload", type="filepath", elem_id="img2img_image").style(width=512) with gr.Tabs(elem_id="sadtalker_driven_audio"): with gr.TabItem('💡You can also download the generated video if you want'): 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='How to handle the input image?', info="crop: resize the image; full: not resize") with gr.Row(): is_still_mode = gr.Checkbox(label="Still mode (fewer head motion)", 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 in generation", step=1, maximum=32, value=2) enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False) submit = gr.Button('Start video chat now', elem_id="sadtalker_generate", variant='primary') with gr.Tabs(elem_id="sadtalker_genearted"): gen_video = gr.Video(label="Generated video", 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("###
❗ Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.
") gr.Markdown("
💡 - How to use this app：After sending your questions to ChatGLM2, click “Chat now”, “Generate using Edge-TTS”, “Generate using FreeVC” and “Start video chat now” in turn.
") demo.queue().launch(show_error=True, debug=True)