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| import os | |
| import torch | |
| import librosa | |
| import gradio as gr | |
| 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_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") | |
| print("Loading FreeVC...") | |
| hps = utils.get_hparams_from_file("configs/freevc.json") | |
| freevc = SynthesizerTrn( | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| **hps.model).to(device) | |
| _ = freevc.eval() | |
| _ = utils.load_checkpoint("checkpoints/freevc.pth", freevc, None) | |
| 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("checkpoints/freevc-24.pth", freevc_24, None) | |
| print("Loading FreeVC-s...") | |
| hps = utils.get_hparams_from_file("configs/freevc-s.json") | |
| freevc_s = SynthesizerTrn( | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| **hps.model).to(device) | |
| _ = freevc_s.eval() | |
| _ = utils.load_checkpoint("checkpoints/freevc-s.pth", freevc_s, 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, | |
| ) | |
| 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")) as demo: | |
| # gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""") | |
| gr.HTML( | |
| """<center><a href="https://huggingface.co/spaces/mikeee/chatglm2-6b-4bit?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>To avoid the queue and for faster inference Duplicate this Space and upgrade to GPU</center>""" | |
| ) | |
| with gr.Accordion("🎈 Info", open=False): | |
| _ = f""" | |
| ## {model_name} | |
| Try to refresh the browser and try again when occasionally an error occurs. | |
| With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters | |
| the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same | |
| question with the same parameters, asked at different times, can result in quite different responses. | |
| * 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. | |
| For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683) | |
| If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin. | |
| The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot! | |
| """ | |
| gr.Markdown(dedent(_)) | |
| chatbot = gr.Chatbot() | |
| 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") | |
| 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") | |
| 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("Example inputs", 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", | |
| ) | |
| # """ | |
| # demo.queue().launch(share=False, inbrowser=True) | |
| # demo.queue().launch(share=True, inbrowser=True, debug=True) | |
| demo.queue().launch(show_error=True, debug=True) | |