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import os, sys |
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import tempfile |
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import gradio as gr |
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from src.gradio_demo import SadTalker |
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from huggingface_hub import snapshot_download |
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import torch |
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import librosa |
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from scipy.io.wavfile import write |
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from transformers import WavLMModel |
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import utils |
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from models import SynthesizerTrn |
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from mel_processing import mel_spectrogram_torch |
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from speaker_encoder.voice_encoder import SpeakerEncoder |
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import time |
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from textwrap import dedent |
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import mdtex2html |
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from loguru import logger |
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from transformers import AutoModel, AutoTokenizer |
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from tts_voice import tts_order_voice |
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import edge_tts |
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import tempfile |
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import anyio |
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def get_source_image(image): |
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return image |
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try: |
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import webui |
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in_webui = True |
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except: |
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in_webui = False |
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def toggle_audio_file(choice): |
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if choice == False: |
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return gr.update(visible=True), gr.update(visible=False) |
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else: |
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return gr.update(visible=False), gr.update(visible=True) |
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def ref_video_fn(path_of_ref_video): |
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if path_of_ref_video is not None: |
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return gr.update(value=True) |
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else: |
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return gr.update(value=False) |
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def download_model(): |
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REPO_ID = 'vinthony/SadTalker-V002rc' |
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snapshot_download(repo_id=REPO_ID, local_dir='./checkpoints', local_dir_use_symlinks=True) |
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def sadtalker_demo(): |
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download_model() |
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sad_talker = SadTalker(lazy_load=True) |
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download_model() |
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sad_talker = SadTalker(lazy_load=True) |
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''' |
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def get_wavlm(): |
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os.system('gdown https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU') |
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shutil.move('WavLM-Large.pt', 'wavlm') |
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''' |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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smodel = SpeakerEncoder('speaker_encoder/ckpt/pretrained_bak_5805000.pt') |
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print("Loading FreeVC(24k)...") |
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hps = utils.get_hparams_from_file("configs/freevc-24.json") |
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freevc_24 = SynthesizerTrn( |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model).to(device) |
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_ = freevc_24.eval() |
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_ = utils.load_checkpoint("checkpoint/freevc-24.pth", freevc_24, None) |
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print("Loading WavLM for content...") |
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cmodel = WavLMModel.from_pretrained("microsoft/wavlm-large").to(device) |
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def convert(model, src, tgt): |
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with torch.no_grad(): |
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wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate) |
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wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20) |
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if model == "FreeVC" or model == "FreeVC (24kHz)": |
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g_tgt = smodel.embed_utterance(wav_tgt) |
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g_tgt = torch.from_numpy(g_tgt).unsqueeze(0).to(device) |
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else: |
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wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(device) |
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mel_tgt = mel_spectrogram_torch( |
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wav_tgt, |
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hps.data.filter_length, |
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hps.data.n_mel_channels, |
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hps.data.sampling_rate, |
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hps.data.hop_length, |
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hps.data.win_length, |
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hps.data.mel_fmin, |
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hps.data.mel_fmax |
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) |
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wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate) |
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wav_src = torch.from_numpy(wav_src).unsqueeze(0).to(device) |
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c = cmodel(wav_src).last_hidden_state.transpose(1, 2).to(device) |
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if model == "FreeVC": |
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audio = freevc.infer(c, g=g_tgt) |
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elif model == "FreeVC-s": |
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audio = freevc_s.infer(c, mel=mel_tgt) |
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else: |
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audio = freevc_24.infer(c, g=g_tgt) |
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audio = audio[0][0].data.cpu().float().numpy() |
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if model == "FreeVC" or model == "FreeVC-s": |
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write("out.wav", hps.data.sampling_rate, audio) |
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else: |
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write("out.wav", 24000, audio) |
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out = "out.wav" |
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return out |
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language_dict = tts_order_voice |
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os.environ["TZ"] = "Asia/Shanghai" |
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try: |
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time.tzset() |
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except Exception: |
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logger.warning("Windows, cant run time.tzset()") |
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model_name = "THUDM/chatglm2-6b-int4" |
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RETRY_FLAG = False |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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has_cuda = torch.cuda.is_available() |
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if has_cuda: |
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model_glm = ( |
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AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half() |
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) |
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else: |
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model_glm = AutoModel.from_pretrained( |
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model_name, trust_remote_code=True |
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).float() |
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model_glm = model_glm.eval() |
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_ = """Override Chatbot.postprocess""" |
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def postprocess(self, y): |
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if y is None: |
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return [] |
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for i, (message, response) in enumerate(y): |
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y[i] = ( |
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None if message is None else mdtex2html.convert((message)), |
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None if response is None else mdtex2html.convert(response), |
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) |
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return y |
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gr.Chatbot.postprocess = postprocess |
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def parse_text(text): |
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" |
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lines = text.split("\n") |
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lines = [line for line in lines if line != ""] |
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count = 0 |
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for i, line in enumerate(lines): |
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if "```" in line: |
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count += 1 |
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items = line.split("`") |
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if count % 2 == 1: |
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lines[i] = f'<pre><code class="language-{items[-1]}">' |
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else: |
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lines[i] = "<br></code></pre>" |
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else: |
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if i > 0: |
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if count % 2 == 1: |
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line = line.replace("`", r"\`") |
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line = line.replace("<", "<") |
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line = line.replace(">", ">") |
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line = line.replace(" ", " ") |
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line = line.replace("*", "*") |
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line = line.replace("_", "_") |
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line = line.replace("-", "-") |
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line = line.replace(".", ".") |
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line = line.replace("!", "!") |
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line = line.replace("(", "(") |
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line = line.replace(")", ")") |
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line = line.replace("$", "$") |
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lines[i] = "<br>" + line |
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text = "".join(lines) |
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return text |
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def predict( |
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RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values |
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): |
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try: |
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chatbot.append((parse_text(input), "")) |
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except Exception as exc: |
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logger.error(exc) |
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logger.debug(f"{chatbot=}") |
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_ = """ |
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if chatbot: |
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chatbot[-1] = (parse_text(input), str(exc)) |
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yield chatbot, history, past_key_values |
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# """ |
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yield chatbot, history, past_key_values |
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for response, history, past_key_values in model_glm.stream_chat( |
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tokenizer, |
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input, |
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history, |
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past_key_values=past_key_values, |
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return_past_key_values=True, |
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max_length=max_length, |
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top_p=top_p, |
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temperature=temperature, |
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): |
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chatbot[-1] = (parse_text(input), parse_text(response)) |
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yield chatbot, history, past_key_values, parse_text(response) |
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def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2): |
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if max_length < 10: |
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max_length = 4096 |
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if top_p < 0.1 or top_p > 1: |
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top_p = 0.85 |
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if temperature <= 0 or temperature > 1: |
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temperature = 0.01 |
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try: |
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res, _ = model_glm.chat( |
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tokenizer, |
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input, |
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history=[], |
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past_key_values=None, |
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max_length=max_length, |
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top_p=top_p, |
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temperature=temperature, |
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) |
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except Exception as exc: |
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logger.error(f"{exc=}") |
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res = str(exc) |
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return res |
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def reset_user_input(): |
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return gr.update(value="") |
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def reset_state(): |
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return [], [], None, "" |
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def delete_last_turn(chat, history): |
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if chat and history: |
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chat.pop(-1) |
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history.pop(-1) |
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return chat, history |
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def retry_last_answer( |
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user_input, chatbot, max_length, top_p, temperature, history, past_key_values |
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): |
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if chatbot and history: |
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chatbot.pop(-1) |
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RETRY_FLAG = True |
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user_input = history[-1][0] |
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history.pop(-1) |
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yield from predict( |
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RETRY_FLAG, |
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user_input, |
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chatbot, |
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max_length, |
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top_p, |
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temperature, |
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history, |
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past_key_values, |
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) |
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def print(text): |
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return text |
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async def text_to_speech_edge(text, language_code): |
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voice = language_dict[language_code] |
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communicate = edge_tts.Communicate(text, voice) |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file: |
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tmp_path = tmp_file.name |
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await communicate.save(tmp_path) |
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return tmp_path |
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with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm"), analytics_enabled=False) as demo: |
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gr.HTML("<center>" |
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"<h1>📺💕🎶 - ChatGLM2+声音克隆+视频对话：和喜欢的角色畅所欲言吧！</h1>" |
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"</center>") |
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gr.Markdown("## <center>🥳 - ChatGLM2+FreeVC+SadTalker，为您打造沉浸式的视频对话体验，支持中英双语</center>") |
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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>") |
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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>") |
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with gr.Tab("🍻 - ChatGLM2聊天区"): |
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with gr.Accordion("📒 相关信息", open=False): |
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_ = f""" ChatGLM2的可选参数信息： |
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* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative. |
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* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4 |
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* Top P controls dynamic vocabulary selection based on context.\n |
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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 |
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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 |
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""" |
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gr.Markdown(dedent(_)) |
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chatbot = gr.Chatbot(height=300) |
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with gr.Row(): |
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with gr.Column(scale=4): |
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with gr.Column(scale=12): |
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user_input = gr.Textbox( |
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label="请在此处和GLM2聊天 (按回车键即可发送)", |
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placeholder="聊点什么吧", |
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) |
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RETRY_FLAG = gr.Checkbox(value=False, visible=False) |
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with gr.Column(min_width=32, scale=1): |
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with gr.Row(): |
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submitBtn = gr.Button("开始和GLM2交流吧", variant="primary") |
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deleteBtn = gr.Button("删除最新一轮对话", variant="secondary") |
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retryBtn = gr.Button("重新生成最新一轮对话", variant="secondary") |
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with gr.Accordion("🔧 更多设置", open=False): |
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with gr.Row(): |
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emptyBtn = gr.Button("清空所有聊天记录") |
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max_length = gr.Slider( |
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0, |
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32768, |
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value=8192, |
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step=1.0, |
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label="Maximum length", |
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interactive=True, |
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) |
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top_p = gr.Slider( |
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0, 1, value=0.85, step=0.01, label="Top P", interactive=True |
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) |
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temperature = gr.Slider( |
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0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True |
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) |
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with gr.Row(): |
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test1 = gr.Textbox(label="GLM2的最新回答 (可编辑)", lines = 3) |
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with gr.Column(): |
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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") |
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tts_btn = gr.Button("生成对应的音频吧", variant="primary") |
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output_audio = gr.Audio(type="filepath", label="为您生成的音频", interactive=False) |
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tts_btn.click(text_to_speech_edge, inputs=[test1, language], outputs=[output_audio]) |
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with gr.Row(): |
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model_choice = gr.Dropdown(choices=["FreeVC", "FreeVC-s", "FreeVC (24kHz)"], value="FreeVC (24kHz)", label="Model", visible=False) |
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audio1 = output_audio |
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audio2 = gr.Audio(label="请上传您喜欢的声音进行声音克隆", type='filepath') |
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clone_btn = gr.Button("开始AI声音克隆吧", variant="primary") |
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audio_cloned = gr.Audio(label="为您生成的专属声音克隆音频", type='filepath') |
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clone_btn.click(convert, inputs=[model_choice, audio1, audio2], outputs=[audio_cloned]) |
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history = gr.State([]) |
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past_key_values = gr.State(None) |
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|
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user_input.submit( |
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predict, |
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[ |
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RETRY_FLAG, |
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user_input, |
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chatbot, |
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max_length, |
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top_p, |
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temperature, |
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history, |
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past_key_values, |
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], |
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[chatbot, history, past_key_values, test1], |
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show_progress="full", |
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) |
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submitBtn.click( |
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predict, |
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[ |
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RETRY_FLAG, |
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user_input, |
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chatbot, |
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max_length, |
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top_p, |
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temperature, |
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history, |
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past_key_values, |
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], |
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[chatbot, history, past_key_values, test1], |
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show_progress="full", |
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api_name="predict", |
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) |
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submitBtn.click(reset_user_input, [], [user_input]) |
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|
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emptyBtn.click( |
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reset_state, outputs=[chatbot, history, past_key_values, test1], show_progress="full" |
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) |
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retryBtn.click( |
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retry_last_answer, |
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inputs=[ |
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user_input, |
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chatbot, |
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max_length, |
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top_p, |
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temperature, |
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history, |
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past_key_values, |
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], |
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|
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outputs=[chatbot, history, past_key_values, test1], |
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) |
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deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history]) |
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|
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with gr.Accordion("📔 提示词示例", open=False): |
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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. """ |
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examples = gr.Examples( |
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examples=[ |
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["Explain the plot of Cinderella in a sentence."], |
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[ |
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"How long does it take to become proficient in French, and what are the best methods for retaining information?" |
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], |
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["What are some common mistakes to avoid when writing code?"], |
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["Build a prompt to generate a beautiful portrait of a horse"], |
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["Suggest four metaphors to describe the benefits of AI"], |
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["Write a pop song about leaving home for the sandy beaches."], |
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["Write a summary demonstrating my ability to tame lions"], |
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["鲁迅和周树人什么关系"], |
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["从前有一头牛，这头牛后面有什么？"], |
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["正无穷大加一大于正无穷大吗？"], |
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["正无穷大加正无穷大大于正无穷大吗？"], |
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["-2的平方根等于什么"], |
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["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"], |
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["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。"], |
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["鲁迅和周树人什么关系 用英文回答"], |
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["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"], |
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[f"{etext} 翻成中文，列出3个版本"], |
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[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"], |
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["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)]"], |
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["假定 1 + 2 = 4, 试求 7 + 8"], |
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["Erkläre die Handlung von Cinderella in einem Satz."], |
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["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"], |
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], |
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inputs=[user_input], |
|
examples_per_page=30, |
|
) |
|
|
|
with gr.Accordion("For Chat/Translation API", open=False, visible=False): |
|
input_text = gr.Text() |
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tr_btn = gr.Button("Go", variant="primary") |
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out_text = gr.Text() |
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tr_btn.click( |
|
trans_api, |
|
[input_text, max_length, top_p, temperature], |
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out_text, |
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|
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api_name="tr", |
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) |
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_ = """ |
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input_text.submit( |
|
trans_api, |
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[input_text, max_length, top_p, temperature], |
|
out_text, |
|
show_progress="full", |
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api_name="tr1", |
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) |
|
# """ |
|
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('图片上传'): |
|
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]) |
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|
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with gr.Row(): |
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ref_video = gr.Video(label="Reference Video", source="upload", type="filepath", elem_id="vidref", visible=False).style(width=512) |
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|
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with gr.Column(): |
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use_ref_video = gr.Checkbox(label="Use Reference Video", visible=False) |
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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) |
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|
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ref_video.change(ref_video_fn, inputs=ref_video, outputs=[use_ref_video]) |
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|
|
|
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with gr.Column(variant='panel'): |
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with gr.Tabs(elem_id="sadtalker_checkbox"): |
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with gr.TabItem('视频设置'): |
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with gr.Column(variant='panel'): |
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|
|
|
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with gr.Row(): |
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pose_style = gr.Slider(minimum=0, maximum=45, step=1, label="Pose style", value=0, visible=False) |
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exp_weight = gr.Slider(minimum=0, maximum=3, step=0.1, label="expression scale", value=1, visible=False) |
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blink_every = gr.Checkbox(label="use eye blink", value=True, visible=False) |
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|
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with gr.Row(): |
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size_of_image = gr.Radio([256, 512], value=256, label='face model resolution', info="use 256/512 model?", visible=False) |
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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") |
|
|
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with gr.Row(): |
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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) |
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facerender = gr.Radio(['facevid2vid','pirender'], value='facevid2vid', label='facerender', info="which face render?", visible=False) |
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|
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with gr.Row(): |
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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) |
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enhancer = gr.Checkbox(label="GFPGAN as Face enhancer", value=True, visible=False) |
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|
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submit = gr.Button('开始视频聊天吧', elem_id="sadtalker_generate", variant='primary') |
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|
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with gr.Tabs(elem_id="sadtalker_genearted"): |
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gen_video = gr.Video(label="为您生成的专属视频", format="mp4").style(width=256) |
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|
|
|
|
|
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submit.click( |
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fn=sad_talker.test, |
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inputs=[source_image, |
|
driven_audio, |
|
preprocess_type, |
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is_still_mode, |
|
enhancer, |
|
batch_size, |
|
size_of_image, |
|
pose_style, |
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facerender, |
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exp_weight, |
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use_ref_video, |
|
ref_video, |
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ref_info, |
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use_idle_mode, |
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length_of_audio, |
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blink_every |
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], |
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outputs=[gen_video] |
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) |
|
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”四个按键即可；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 |
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</p> |
|
</div> |
|
''') |
|
|
|
|
|
demo.queue().launch(show_error=True, debug=True) |
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|
