# Based on GPT-SoVITS-fast-inference by ChasonJiang import random import os import torch if torch.cuda.is_available(): device = "cuda" else: device = "cpu" import re, logging logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) logging.getLogger("httpx").setLevel(logging.ERROR) logging.getLogger("asyncio").setLevel(logging.ERROR) logging.getLogger("charset_normalizer").setLevel(logging.ERROR) logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import json infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) is_share = os.environ.get("is_share", "False") is_share = eval(is_share) if "_CUDA_VISIBLE_DEVICES" in os.environ: os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() gpt_path=None sovits_path=None #gpt_path = os.environ.get("gpt_path", None) #sovits_path = os.environ.get("sovits_path", None) cnhubert_base_path = os.environ.get("cnhubert_base_path", None) bert_path = os.environ.get("bert_path", None) import gradio as gr from TTS_infer_pack.TTS import TTS, TTS_Config from TTS_infer_pack.text_segmentation_method import get_method dict_language = { "ZH": "all_zh",#全部按中文识别 "EN": "en",#全部按英文识别#######不变 "JP": "all_ja",#全部按日文识别 "ZH/EN": "zh",#按中英混合识别####不变 "JP/EN": "ja",#按日英混合识别####不变 "Automatic": "auto",#多语种启动切分识别语种 } cut_method = { "None":"cut0", "4 Sentences": "cut1", "50 Characters": "cut2", "ZH/JP Punctuation": "cut3", "EN Punctuation": "cut4", "All Punctuation": "cut5", } tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") tts_config.device = device tts_config.is_half = is_half if gpt_path is not None: tts_config.t2s_weights_path = gpt_path if sovits_path is not None: tts_config.vits_weights_path = sovits_path if cnhubert_base_path is not None: tts_config.cnhuhbert_base_path = cnhubert_base_path if bert_path is not None: tts_config.bert_base_path = bert_path print(tts_config) tts_pipeline = TTS(tts_config) gpt_path = tts_config.t2s_weights_path sovits_path = tts_config.vits_weights_path clm= "" def inference(name, gptmp, svmp, sty, text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size, speed_factor, split_bucket,fragment_interval, seed, keep_random, parallel_infer, repetition_penalty ): global clm #Live switching if(not ref_audio_path): ref_audio_path=f"referenceaudio/{name}/"+referencedata[name][0][sty] prompt_text=referencedata[name][1][sty] if clm!=name: print(f"Switching to model {name}") clm=name tts_pipeline.init_t2s_weights(gptmp) tts_pipeline.init_vits_weights(svmp) seed = -1 if keep_random else seed actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32) print(f"TMP: {temperature} | SPDFCT: {speed_factor} | STY: {sty} | LANG: {text_lang}") inputs={ "text": text, "text_lang": dict_language[text_lang], "ref_audio_path": ref_audio_path, "prompt_text": prompt_text, "prompt_lang": dict_language[prompt_lang], "top_k": top_k, "top_p": top_p, "temperature": temperature, "text_split_method": cut_method[text_split_method], "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, "return_fragment":False, "fragment_interval":fragment_interval, "seed":actual_seed, "parallel_infer": parallel_infer, "repetition_penalty": repetition_penalty, } for item in tts_pipeline.run(inputs): yield item, actual_seed def custom_sort_key(s): # 使用正则表达式提取字符串中的数字部分和非数字部分 parts = re.split('(\d+)', s) # 将数字部分转换为整数，非数字部分保持不变 parts = [int(part) if part.isdigit() else part for part in parts] return parts def change_choices(): SoVITS_names, GPT_names = get_weights_names() return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" SoVITS_weight_root = "GPT_SoVITS/SoVITS_weights/" GPT_weight_root = "GPT_SoVITS/GPT_weights/" def get_weights_names(): SoVITS_names = [pretrained_sovits_name] for name in os.listdir(SoVITS_weight_root): if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) GPT_names = [pretrained_gpt_name] for name in os.listdir(GPT_weight_root): if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) return SoVITS_names, GPT_names def load_models(): print("Loading models...") voices=[] ustyles={} with open("voicelist.json", "r", encoding="utf-8") as f: voc_info = json.load(f) for name, info in voc_info.items(): if not info['enable']: continue title= info['title'] gptmodelpath= "%s/%s" % (GPT_weight_root, info['gpt_model_path']) sovitsmodelpath= "%s/%s" % (SoVITS_weight_root, info['sovits_model_path']) author= info['modelauthor'] image = info['cover'] styles = info['styles'] #check that all styles properly exist for s in styles.values(): if(not os.path.exists(f"referenceaudio/{name}/{s}")): print(f"WARNING : Some defined preset styles do not exist for model {name}, skipping") styles=None break styletrans = info['styletrans'] st=[styles, styletrans] voices.append((name, title, gptmodelpath, sovitsmodelpath, author, image)) ustyles[name]=st print(f"Indexed model {title}") return voices, ustyles modeldata, referencedata = load_models() #Gradio preload text = gr.TextArea(label="Input Text", value="Hello there! This is test audio of a new text to speech tool.") text_language = gr.Dropdown(label="Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN") how_to_cut = gr.Dropdown(label="Slicing Method", choices=["None", "4 Sentences", "50 Characters", "ZH/JP Punctuation", "EN Punctuation", "All Punctuation" ], value="4 Sentences", interactive=True, ) top_k = gr.Slider(minimum=1,maximum=100,step=1,label="Top_k",value=5,interactive=True) top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="Top_p",value=1,interactive=True) temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="Temperature",value=0.7,interactive=True) batch_size = gr.Slider(minimum=1,maximum=200,step=1,label="Batch Size",value=20,interactive=True) fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label="Fragment Interval",value=0.3,interactive=True) speed_factor = gr.Slider(minimum=0.50,maximum=2,step=0.05,label="Speed Factor",value=1.0,interactive=True) repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label="Repetition Penalty",value=1.35,interactive=True) parallel_infer = gr.Checkbox(label="Parallel Infer", value=True, interactive=True, show_label=True) split_bucket = gr.Checkbox(label="Split Bucket", value=True, interactive=True, show_label=True) seed = gr.Number(label="Random Seed",value=-1, interactive=True, show_label=True) keep_random = gr.Checkbox(label="Use Randomized Seed", value=True, interactive=True, show_label=True) #Main gradio with gr.Blocks(title="Lemonfoot GPT-SoVITS") as app: gr.Markdown( "# Lemonfoot GPT-SoVITS 🚀🍋\n" "### Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds\n" "Based on code originally by RVC_Boss and ChasonJiang\n\n" "Do no evil.\n\n" ) for (name, title, gptmodelpath, sovitsmodelpath, author, image) in modeldata: with gr.TabItem(name): with gr.Row(): with gr.Column(): n = gr.Textbox(value=name, visible=False, interactive=False) gptmp = gr.Textbox(value=gptmodelpath, visible=False, interactive=False) svmp = gr.Textbox(value=sovitsmodelpath, visible=False, interactive=False) gr.Markdown(f"**{title}**\n\n Dataset author: {author}") gr.Image(f"images/{image}", label=None, show_label=False, width=300, show_download_button=False, container=False, show_share_button=False) with gr.Column(): #if there isn't any styles don't bother rendering the style window if(not referencedata[name][0]==None): rd = list(referencedata[name][0].keys()) with gr.TabItem("Style using a preset"): sty = gr.Dropdown( label="Current style", choices=rd, value=rd[0], interactive=True ) else: sty=gr.Textbox(value="none", visible=False, interactive=False) with gr.TabItem("Style using a different audio"): with gr.Column(): ref_audio_path = gr.Audio(label="Reference Audio", type="filepath") prompt_text = gr.Textbox(label="Reference Audio Text", interactive=True, placeholder="Leave blank to use no-text reference mode.") prompt_language = gr.Dropdown(label="Reference Audio Language", choices=["EN", "JP", "ZH", "ZH/EN", "JP/EN", "Automatic"], value="EN") with gr.Column(): inference_button = gr.Button("Synthesize", variant="primary") output = gr.Audio(label="Output") inference_button.click( inference, inputs=[n, gptmp, svmp, sty, text, text_language, ref_audio_path, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor, split_bucket, fragment_interval, seed, keep_random, parallel_infer, repetition_penalty], outputs=[output, seed] ) #bottom info with gr.Row(): with gr.Column(): text.render() text_language.render() how_to_cut.render() with gr.Column(): temperature.render() speed_factor.render() with gr.Accordion("Advanced Inference Parameters", open=False): top_k.render() top_p.render() batch_size.render() fragment_interval.render() repetition_penalty.render() parallel_infer.render() split_bucket.render() seed.render() keep_random.render() app.queue().launch()