""" 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 import numpy as np from fastapi.responses import StreamingResponse 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 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: 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！")) 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 import soundfile as sf app = FastAPI() class InferenceRequest(BaseModel): text: str text_lang: str = i18n("中文") ref_audio: str # 这里是base64编码的音频文件内容 prompt_text: str prompt_lang: str = i18n("中文") top_k: int = 6 top_p: float = 0.9 temperature: float = 0.95 text_split_method: str = i18n("按标点符号切") batch_size: int = 20 speed_factor: float = 1.1 ref_text_free: bool = False split_bucket: bool = True fragment_interval: float = 0.3 seed: int = -1 keep_random: bool = True parallel_infer: bool = True repetition_penalty: float = 1.45 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 audio, _ in infer_gen: audio_data = audio[0] if isinstance(audio[0], np.ndarray) else audio[1] yield encode_wav_chunk(32000, audio_data) # 每段 WAV 数据 @app.post("/tts_stream") async def api_inference(req: InferenceRequest): try: infer_gen = inference( text=req.text, text_lang=i18n(req.text_lang), ref_audio_path=req.ref_audio, aux_ref_audio_paths=[], prompt_text=req.prompt_text, prompt_lang=i18n(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 port = int(os.environ.get("PORT", 8001)) # 默认端口8001 uvicorn.run(app, host="0.0.0.0", port=port)