From c441baf84cf25c5859f06768c0e1d8349a8ef146 Mon Sep 17 00:00:00 2001 From: XL Date: Wed, 2 Jul 2025 17:30:37 +0800 Subject: [PATCH] tts api --- GPT_SoVITS/inference_webui_api.py | 676 +++++++++++++++++++++++++++++ GPT_SoVITS/inference_webui_fast.py | 17 + 2 files changed, 693 insertions(+) create mode 100644 GPT_SoVITS/inference_webui_api.py diff --git a/GPT_SoVITS/inference_webui_api.py b/GPT_SoVITS/inference_webui_api.py new file mode 100644 index 00000000..bd6a56ea --- /dev/null +++ b/GPT_SoVITS/inference_webui_api.py @@ -0,0 +1,676 @@ +""" +按中英混合识别 +按日英混合识别 +多语种启动切分识别语种 +全部按中文识别 +全部按英文识别 +全部按日文识别 +""" + +import json +import logging +import os +import random +import re +import sys +import time +import io +import traceback +import wave +import torch + +now_dir = os.getcwd() +sys.path.append(now_dir) +sys.path.append("%s/GPT_SoVITS" % (now_dir)) + +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) + + +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 = 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) +version = model_version = os.environ.get("version", "v2") + +import gradio as gr +from TTS_infer_pack.text_segmentation_method import get_method +from TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config + +from tools.assets import css, js, top_html +from tools.i18n.i18n import I18nAuto, scan_language_list +import numpy as np +from fastapi.responses import StreamingResponse + +language = os.environ.get("language", "Auto") +language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language +i18n = I18nAuto(language=language) + + +# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 + +if torch.cuda.is_available(): + device = "cuda" +# elif torch.backends.mps.is_available(): +# device = "mps" +else: + device = "cpu" + +# is_half = False +# device = "cpu" + +dict_language_v1 = { + i18n("中文"): "all_zh", # 全部按中文识别 + i18n("英文"): "en", # 全部按英文识别#######不变 + i18n("日文"): "all_ja", # 全部按日文识别 + i18n("中英混合"): "zh", # 按中英混合识别####不变 + i18n("日英混合"): "ja", # 按日英混合识别####不变 + i18n("多语种混合"): "auto", # 多语种启动切分识别语种 +} +dict_language_v2 = { + i18n("中文"): "all_zh", # 全部按中文识别 + i18n("英文"): "en", # 全部按英文识别#######不变 + i18n("日文"): "all_ja", # 全部按日文识别 + i18n("粤语"): "all_yue", # 全部按中文识别 + i18n("韩文"): "all_ko", # 全部按韩文识别 + i18n("中英混合"): "zh", # 按中英混合识别####不变 + i18n("日英混合"): "ja", # 按日英混合识别####不变 + i18n("粤英混合"): "yue", # 按粤英混合识别####不变 + i18n("韩英混合"): "ko", # 按韩英混合识别####不变 + i18n("多语种混合"): "auto", # 多语种启动切分识别语种 + i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种 +} +dict_language = dict_language_v1 if version == "v1" else dict_language_v2 + +cut_method = { + i18n("不切"): "cut0", + i18n("凑四句一切"): "cut1", + i18n("凑50字一切"): "cut2", + i18n("按中文句号。切"): "cut3", + i18n("按英文句号.切"): "cut4", + i18n("按标点符号切"): "cut5", +} + +from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path + +SoVITS_names, GPT_names = get_weights_names() +from config import pretrained_sovits_name + +path_sovits_v3 = pretrained_sovits_name["v3"] +path_sovits_v4 = pretrained_sovits_name["v4"] +is_exist_s2gv3 = os.path.exists(path_sovits_v3) +is_exist_s2gv4 = os.path.exists(path_sovits_v4) + +tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") +tts_config.device = device +tts_config.is_half = is_half +tts_config.version = version +if gpt_path is not None: + if "！" in gpt_path or "!" in gpt_path: + gpt_path = name2gpt_path[gpt_path] + tts_config.t2s_weights_path = gpt_path +if sovits_path is not None: + if "！" in sovits_path or "!" in sovits_path: + sovits_path = name2sovits_path[sovits_path] + 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 +version = tts_config.version + + +def inference( + text, + text_lang, + ref_audio_path, + aux_ref_audio_paths, + prompt_text, + prompt_lang, + top_k, + top_p, + temperature, + text_split_method, + batch_size, + speed_factor, + ref_text_free, + split_bucket, + fragment_interval, + seed, + keep_random, + parallel_infer, + repetition_penalty, + sample_steps, + super_sampling, +): + seed = -1 if keep_random else seed + actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1) + inputs = { + "text": text, + "text_lang": dict_language[text_lang], + "ref_audio_path": ref_audio_path, + "aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [], + "prompt_text": prompt_text if not ref_text_free else "", + "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, + "sample_steps": int(sample_steps), + "super_sampling": super_sampling, + } + + logging.info( + f"inference_button请求耗时: {inputs}" + ) + + try: + for item in tts_pipeline.run(inputs): + yield item, actual_seed + except NO_PROMPT_ERROR: + gr.Warning(i18n("V3不支持无参考文本模式，请填写参考文本！")) + + +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 + + +if os.path.exists("./weight.json"): + pass +else: + with open("./weight.json", "w", encoding="utf-8") as file: + json.dump({"GPT": {}, "SoVITS": {}}, file) + +with open("./weight.json", "r", encoding="utf-8") as file: + weight_data = file.read() + weight_data = json.loads(weight_data) + gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, GPT_names[-1])) + sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, SoVITS_names[0])) + if isinstance(gpt_path, list): + gpt_path = gpt_path[0] + if isinstance(sovits_path, list): + sovits_path = sovits_path[0] + +from process_ckpt import get_sovits_version_from_path_fast + +v3v4set = {"v3", "v4"} + + +def change_sovits_weights(sovits_path, prompt_language=None, text_language=None): + if "！" in sovits_path or "!" in sovits_path: + sovits_path = name2sovits_path[sovits_path] + global version, model_version, dict_language, if_lora_v3 + version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path) + # print(sovits_path,version, model_version, if_lora_v3) + is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4 + path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4 + if if_lora_v3 == True and is_exist == False: + info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重") + gr.Warning(info) + raise FileExistsError(info) + dict_language = dict_language_v1 if version == "v1" else dict_language_v2 + if prompt_language is not None and text_language is not None: + if prompt_language in list(dict_language.keys()): + prompt_text_update, prompt_language_update = ( + {"__type__": "update"}, + {"__type__": "update", "value": prompt_language}, + ) + else: + prompt_text_update = {"__type__": "update", "value": ""} + prompt_language_update = {"__type__": "update", "value": i18n("中文")} + if text_language in list(dict_language.keys()): + text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language} + else: + text_update = {"__type__": "update", "value": ""} + text_language_update = {"__type__": "update", "value": i18n("中文")} + if model_version in v3v4set: + visible_sample_steps = True + visible_inp_refs = False + else: + visible_sample_steps = False + visible_inp_refs = True + yield ( + {"__type__": "update", "choices": list(dict_language.keys())}, + {"__type__": "update", "choices": list(dict_language.keys())}, + prompt_text_update, + prompt_language_update, + text_update, + text_language_update, + {"__type__": "update", "interactive": visible_sample_steps, "value": 32}, + {"__type__": "update", "visible": visible_inp_refs}, + {"__type__": "update", "interactive": True if model_version not in v3v4set else False}, + {"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False}, + ) + + tts_pipeline.init_vits_weights(sovits_path) + yield ( + {"__type__": "update", "choices": list(dict_language.keys())}, + {"__type__": "update", "choices": list(dict_language.keys())}, + prompt_text_update, + prompt_language_update, + text_update, + text_language_update, + {"__type__": "update", "interactive": visible_sample_steps, "value": 32}, + {"__type__": "update", "visible": visible_inp_refs}, + {"__type__": "update", "interactive": True if model_version not in v3v4set else False}, + {"__type__": "update", "value": i18n("合成语音"), "interactive": True}, + ) + with open("./weight.json") as f: + data = f.read() + data = json.loads(data) + data["SoVITS"][version] = sovits_path + with open("./weight.json", "w") as f: + f.write(json.dumps(data)) + + +def change_gpt_weights(gpt_path): + if "！" in gpt_path or "!" in gpt_path: + gpt_path = name2gpt_path[gpt_path] + tts_pipeline.init_t2s_weights(gpt_path) + + +with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app: + gr.HTML( + top_html.format( + i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.") + + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.") + ), + elem_classes="markdown", + ) + + with gr.Column(): + # with gr.Group(): + gr.Markdown(value=i18n("模型切换")) + with gr.Row(): + GPT_dropdown = gr.Dropdown( + label=i18n("GPT模型列表"), + choices=sorted(GPT_names, key=custom_sort_key), + value=gpt_path, + interactive=True, + ) + SoVITS_dropdown = gr.Dropdown( + label=i18n("SoVITS模型列表"), + choices=sorted(SoVITS_names, key=custom_sort_key), + value=sovits_path, + interactive=True, + ) + refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") + refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) + + with gr.Row(): + with gr.Column(): + gr.Markdown(value=i18n("*请上传并填写参考信息")) + with gr.Row(): + inp_ref = gr.Audio(label=i18n("主参考音频(请上传3~10秒内参考音频，超过会报错！)"), type="filepath") + inp_refs = gr.File( + label=i18n("辅参考音频(可选多个，或不选)"), + file_count="multiple", + visible=True if model_version != "v3" else False, + ) + prompt_text = gr.Textbox(label=i18n("主参考音频的文本"), value="", lines=2) + with gr.Row(): + prompt_language = gr.Dropdown( + label=i18n("主参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文") + ) + with gr.Column(): + ref_text_free = gr.Checkbox( + label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), + value=False, + interactive=True if model_version != "v3" else False, + show_label=True, + ) + gr.Markdown( + i18n("使用无参考文本模式时建议使用微调的GPT") + + "
" + + i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。") + ) + + with gr.Column(): + gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) + text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=20, max_lines=20) + text_language = gr.Dropdown( + label=i18n("需要合成的文本的语种"), choices=list(dict_language.keys()), value=i18n("中文") + ) + + with gr.Group(): + gr.Markdown(value=i18n("推理设置")) + with gr.Row(): + with gr.Column(): + with gr.Row(): + batch_size = gr.Slider( + minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True + ) + sample_steps = gr.Radio( + label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True + ) + with gr.Row(): + fragment_interval = gr.Slider( + minimum=0.01, maximum=1, step=0.01, label=i18n("分段间隔(秒)"), value=0.3, interactive=True + ) + speed_factor = gr.Slider( + minimum=0.6, maximum=1.65, step=0.05, label="语速", value=1.0, interactive=True + ) + with gr.Row(): + top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=5, interactive=True) + top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True) + with gr.Row(): + temperature = gr.Slider( + minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True + ) + repetition_penalty = gr.Slider( + minimum=0, maximum=2, step=0.05, label=i18n("重复惩罚"), value=1.35, interactive=True + ) + + with gr.Column(): + with gr.Row(): + how_to_cut = gr.Dropdown( + label=i18n("怎么切"), + choices=[ + i18n("不切"), + i18n("凑四句一切"), + i18n("凑50字一切"), + i18n("按中文句号。切"), + i18n("按英文句号.切"), + i18n("按标点符号切"), + ], + value=i18n("凑四句一切"), + interactive=True, + scale=1, + ) + super_sampling = gr.Checkbox( + label=i18n("音频超采样(仅对V3生效))"), value=False, interactive=True, show_label=True + ) + + with gr.Row(): + parallel_infer = gr.Checkbox(label=i18n("并行推理"), value=True, interactive=True, show_label=True) + split_bucket = gr.Checkbox( + label=i18n("数据分桶(并行推理时会降低一点计算量)"), + value=True, + interactive=True, + show_label=True, + ) + + with gr.Row(): + seed = gr.Number(label=i18n("随机种子"), value=-1) + keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True) + + output = gr.Audio(label=i18n("输出的语音")) + with gr.Row(): + inference_button = gr.Button(i18n("合成语音"), variant="primary") + stop_infer = gr.Button(i18n("终止合成"), variant="primary") + + inference_button.click( + inference, + [ + text, + text_language, + inp_ref, + inp_refs, + prompt_text, + prompt_language, + top_k, + top_p, + temperature, + how_to_cut, + batch_size, + speed_factor, + ref_text_free, + split_bucket, + fragment_interval, + seed, + keep_random, + parallel_infer, + repetition_penalty, + sample_steps, + super_sampling, + ], + [output, seed], + ) + stop_infer.click(tts_pipeline.stop, [], []) + SoVITS_dropdown.change( + change_sovits_weights, + [SoVITS_dropdown, prompt_language, text_language], + [ + prompt_language, + text_language, + prompt_text, + prompt_language, + text, + text_language, + sample_steps, + inp_refs, + ref_text_free, + inference_button, + ], + ) # + GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) + + with gr.Group(): + gr.Markdown( + value=i18n( + "文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。" + ) + ) + with gr.Row(): + text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4) + with gr.Column(): + _how_to_cut = gr.Radio( + label=i18n("怎么切"), + choices=[ + i18n("不切"), + i18n("凑四句一切"), + i18n("凑50字一切"), + i18n("按中文句号。切"), + i18n("按英文句号.切"), + i18n("按标点符号切"), + ], + value=i18n("凑四句一切"), + interactive=True, + ) + cut_text = gr.Button(i18n("切分"), variant="primary") + + def to_cut(text_inp, how_to_cut): + if len(text_inp.strip()) == 0 or text_inp == []: + return "" + method = get_method(cut_method[how_to_cut]) + return method(text_inp) + + text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4) + cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt]) + gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")) + +from fastapi import FastAPI, UploadFile, File, Form +from fastapi.responses import FileResponse +import tempfile +import shutil +import os +from pydantic import BaseModel + +app = FastAPI() + + +class InferenceRequest(BaseModel): + text: str + text_lang: str = "中文" + ref_audio: str # 这里是base64编码的音频文件内容 + prompt_text: str = "" + prompt_lang: str = "中文" + top_k: int = 5 + top_p: float = 1 + temperature: float = 1 + text_split_method: str = "按标点符号切" + batch_size: int = 20 + speed_factor: float = 1.1 + ref_text_free: bool = True + split_bucket: bool = True + fragment_interval: float = 0.3 + seed: int = -1 + keep_random: bool = True + parallel_infer: bool = True + repetition_penalty: float = 1.35 + sample_steps: int = 32 + super_sampling: bool = False + +@app.post("/tts") +async def api_inference(req: InferenceRequest): + + try: + start_time = time.time() + result = inference( + text=req.text, + text_lang=req.text_lang, + ref_audio_path=req.ref_audio, + aux_ref_audio_paths=None, + prompt_text=req.prompt_text, + prompt_lang=req.prompt_lang, + top_k=req.top_k, + top_p=req.top_p, + temperature=req.temperature, + text_split_method=req.text_split_method, + batch_size=req.batch_size, + speed_factor=req.speed_factor, + ref_text_free=req.ref_text_free, + split_bucket=req.split_bucket, + fragment_interval=req.fragment_interval, + seed=req.seed, + keep_random=req.keep_random, + parallel_infer=req.parallel_infer, + repetition_penalty=req.repetition_penalty, + sample_steps=req.sample_steps, + super_sampling=req.super_sampling, + ) + + logging.info( + f"TTS请求infer ence耗时: {time.time() - start_time:.3f}s | 文本: {req.text}" + ) + for wav_data, _ in result: + sr, audio = wav_data + # 确保音频数据为16位整数格式 + if not isinstance(audio, np.ndarray): + audio = np.array(audio) + if audio.dtype != np.int16: + audio = (audio * 32768).astype(np.int16) + + # 创建临时WAV文件 + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav: + temp_path = temp_wav.name + # 写入WAV格式 + import wave + import struct + with wave.open(temp_path, "wb") as wav_file: + wav_file.setnchannels(1) # 单声道 + wav_file.setsampwidth(2) # 16位 + wav_file.setframerate(sr) + wav_file.writeframes(audio.tobytes()) + logging.info( + f"TTS请求耗时: {time.time() - start_time:.3f}s | 文本: {req.text}" + ) + # 返回WAV文件 + return FileResponse( + temp_path, + media_type="audio/wav", + headers={ + "Content-Disposition": "attachment;filename=output.wav" + } + ) + + except Exception as e: + + traceback.print_exc() + logging.error(f"Error during inference: {e}") + # 返回错误信息 + return {"error": "未能生成音频"} + + +def wav_chunk_streamer(infer_gen): + def encode_wav_chunk(sr, audio): + buffer = io.BytesIO() + with wave.open(buffer, 'wb') as wav_file: + wav_file.setnchannels(1) + wav_file.setsampwidth(2) + wav_file.setframerate(sr) + wav_file.writeframes(audio.tobytes()) + return buffer.getvalue() + + for wav_data, _ in infer_gen: + sr, audio = wav_data + if not isinstance(audio, np.ndarray): + audio = np.array(audio) + if audio.dtype != np.int16: + audio = (audio * 32768).astype(np.int16) + yield encode_wav_chunk(sr, audio) # 每段 WAV 数据 + +@app.post("/tts_stream") +async def api_inference(req: InferenceRequest): + try: + infer_gen = inference( + text=req.text, + text_lang=req.text_lang, + ref_audio_path=req.ref_audio, + aux_ref_audio_paths=[], + prompt_text=req.prompt_text, + prompt_lang=req.prompt_lang, + top_k=req.top_k, + top_p=req.top_p, + temperature=req.temperature, + text_split_method=req.text_split_method, + batch_size=req.batch_size, + speed_factor=req.speed_factor, + ref_text_free=req.ref_text_free, + split_bucket=req.split_bucket, + fragment_interval=req.fragment_interval, + seed=req.seed, + keep_random=req.keep_random, + parallel_infer=req.parallel_infer, + repetition_penalty=req.repetition_penalty, + sample_steps=req.sample_steps, + super_sampling=req.super_sampling, + ) + + return StreamingResponse( + wav_chunk_streamer(infer_gen), + media_type="audio/wav", + headers={ + "Content-Disposition": "inline; filename=output.wav" + } + ) + + except Exception as e: + import traceback + traceback.print_exc() + return {"error": f"生成失败: {str(e)}"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8001) \ No newline at end of file diff --git a/GPT_SoVITS/inference_webui_fast.py b/GPT_SoVITS/inference_webui_fast.py index 470b7bbd..879717e6 100644 --- a/GPT_SoVITS/inference_webui_fast.py +++ b/GPT_SoVITS/inference_webui_fast.py @@ -16,6 +16,9 @@ import sys import torch +import logging +import time + now_dir = os.getcwd() sys.path.append(now_dir) sys.path.append("%s/GPT_SoVITS" % (now_dir)) @@ -156,6 +159,7 @@ def inference( sample_steps, super_sampling, ): + seed = -1 if keep_random else seed actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1) inputs = { @@ -180,9 +184,21 @@ def inference( "sample_steps": int(sample_steps), "super_sampling": super_sampling, } + + + logging.info( + f"inference_button请求耗时: {inputs}" + ) try: + + start_time = time.time() + for item in tts_pipeline.run(inputs): yield item, actual_seed + + logging.info( + f"TTS请求耗时: {time.time() - start_time:.3f}s | 文本: {text}" + ) except NO_PROMPT_ERROR: gr.Warning(i18n("V3不支持无参考文本模式，请填写参考文本！")) @@ -419,6 +435,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css inference_button = gr.Button(i18n("合成语音"), variant="primary") stop_infer = gr.Button(i18n("终止合成"), variant="primary") + inference_button.click( inference, [