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 import openai # openai.log = "debug" openai.api_base = "https://api.chatanywhere.com.cn/v1" # 非流式响应 def gpt_35_api(gptkey, message): openai.api_key = "sk-" + gptkey completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": message}]) return completion.choices[0].message.content 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 print(text) 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, key, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): message = gpt_35_api(key, text) with torch.no_grad(): audio = infer(message, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) return "Success", message, (hps.data.sampling_rate, audio) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model_dir", default="./logs/Diana/G_4800.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: with gr.Row(): with gr.Column(): gr.Markdown(value=""" # AI嘉然——在线gpt对话版（Bert-Vits2 + gpt）\n （注：转发生成可能较慢，请等待大约2分钟哦，目前只支持中文语言的答案，其他语言的支持在开发中捏！）\n 作者：[Xz乔希](https://space.bilibili.com/5859321) & [碎语碎念](https://space.bilibili.com/4269384) 声音归属：[嘉然今天吃什么](https://space.bilibili.com/672328094) \n Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n GPT_API_free项目：https://github.com/chatanywhere/GPT_API_free\n 本项目中的apiKey可以从https://github.com/chatanywhere/GPT_API_free\n 免费获取（本项目默认提供了一个，如果没法用了去仓库申请替换就好啦）！\n 使用本模型请严格遵守法律法规！\n 发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n """) with gr.Column(): gr.Markdown(value=""" ![avatar](https://img0.baidu.com/it/u=3803234914,1472318531&fm=253&fmt=auto&app=120&f=PNG?w=500&h=500) \n """) with gr.Row(): with gr.Column(): text = gr.TextArea(label="请输入要提问然然的问题", placeholder="Input Text Here", value="虚拟主播是什么？") key = gr.Text(label="GPT Key(过期请更换)", placeholder="请输入上面提示中获取的gpt key", value="izlrijShDu7tp2rIgvYfibcC2J0Eh3uWfdm9ndrxN5nWrL96") speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='说话人') sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比') noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节') noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度') length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度') with gr.Column(): btn = gr.Button("点击生成", variant="primary") text_output = gr.Textbox(label="获取状态") gpt_output = gr.TextArea(label="然然老师的文字回答") audio_output = gr.Audio(label="然然老师的语音回答") btn.click(tts_fn, inputs=[text, key, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], outputs=[text_output, gpt_output, audio_output]) # webbrowser.open("http://127.0.0.1:6006") # app.launch(server_port=6006, show_error=True) app.launch(show_error=True)