mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-08 07:49:59 +08:00
413 lines
15 KiB
Python
413 lines
15 KiB
Python
"""
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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"""
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import random
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import os, sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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import os, re, logging
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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import torch
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
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infer_ttswebui = int(infer_ttswebui)
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is_share = os.environ.get("is_share", "False")
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is_share = eval(is_share)
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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gpt_path = os.environ.get("gpt_path", None)
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sovits_path = os.environ.get("sovits_path", None)
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cnhubert_base_path = os.environ.get("cnhubert_base_path", None)
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bert_path = os.environ.get("bert_path", None)
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import gradio as gr
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from TTS_infer_pack.TTS import TTS, TTS_Config
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from TTS_infer_pack.text_segmentation_method import get_method
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto()
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# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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device = "cuda"
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# elif torch.backends.mps.is_available():
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# device = "mps"
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else:
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device = "cpu"
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dict_language = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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}
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cut_method = {
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i18n("不切"): "cut0",
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i18n("凑四句一切"): "cut1",
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i18n("凑50字一切"): "cut2",
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i18n("按中文句号。切"): "cut3",
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i18n("按英文句号.切"): "cut4",
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i18n("按标点符号切"): "cut5",
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}
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tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml")
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tts_config.device = device
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tts_config.is_half = is_half
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if gpt_path is not None:
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tts_config.t2s_weights_path = gpt_path
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if sovits_path is not None:
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tts_config.vits_weights_path = sovits_path
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if cnhubert_base_path is not None:
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tts_config.cnhuhbert_base_path = cnhubert_base_path
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if bert_path is not None:
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tts_config.bert_base_path = bert_path
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print(tts_config)
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tts_pipline = TTS(tts_config)
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gpt_path = tts_config.t2s_weights_path
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sovits_path = tts_config.vits_weights_path
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def inference(
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text,
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text_lang,
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ref_audio_path,
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prompt_text,
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prompt_lang,
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top_k,
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top_p,
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temperature,
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text_split_method,
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batch_size,
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speed_factor,
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ref_text_free,
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split_bucket,
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fragment_interval,
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seed,
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):
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actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32)
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inputs = {
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"text": text,
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"text_lang": dict_language[text_lang],
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"ref_audio_path": ref_audio_path,
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"prompt_text": prompt_text if not ref_text_free else "",
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"prompt_lang": dict_language[prompt_lang],
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"top_k": top_k,
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"top_p": top_p,
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"temperature": temperature,
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"text_split_method": cut_method[text_split_method],
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"batch_size": int(batch_size),
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"speed_factor": float(speed_factor),
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"split_bucket": split_bucket,
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"return_fragment": False,
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"fragment_interval": fragment_interval,
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"seed": actual_seed,
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}
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for item in tts_pipline.run(inputs):
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yield item, actual_seed
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def custom_sort_key(s):
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# 使用正则表达式提取字符串中的数字部分和非数字部分
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parts = re.split("(\d+)", s)
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# 将数字部分转换为整数，非数字部分保持不变
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parts = [int(part) if part.isdigit() else part for part in parts]
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return parts
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def change_choices():
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SoVITS_names, GPT_names = get_weights_names()
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return {
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"choices": sorted(SoVITS_names, key=custom_sort_key),
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"__type__": "update",
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}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
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pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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pretrained_gpt_name = (
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"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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)
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SoVITS_weight_root = "SoVITS_weights"
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GPT_weight_root = "GPT_weights"
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os.makedirs(SoVITS_weight_root, exist_ok=True)
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os.makedirs(GPT_weight_root, exist_ok=True)
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def get_weights_names():
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SoVITS_names = [pretrained_sovits_name]
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for name in os.listdir(SoVITS_weight_root):
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if name.endswith(".pth"):
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SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
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GPT_names = [pretrained_gpt_name]
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for name in os.listdir(GPT_weight_root):
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if name.endswith(".ckpt"):
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GPT_names.append("%s/%s" % (GPT_weight_root, name))
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return SoVITS_names, GPT_names
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SoVITS_names, GPT_names = get_weights_names()
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if __name__ == "__main__":
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with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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gr.Markdown(
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value=i18n(
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"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. <br>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<b>LICENSE</b>."
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)
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)
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with gr.Column():
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# with gr.Group():
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gr.Markdown(value=i18n("模型切换"))
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with gr.Row():
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GPT_dropdown = gr.Dropdown(
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label=i18n("GPT模型列表"),
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choices=sorted(GPT_names, key=custom_sort_key),
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value=gpt_path,
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interactive=True,
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)
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SoVITS_dropdown = gr.Dropdown(
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label=i18n("SoVITS模型列表"),
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choices=sorted(SoVITS_names, key=custom_sort_key),
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value=sovits_path,
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interactive=True,
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)
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refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
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refresh_button.click(
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fn=change_choices,
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inputs=[],
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outputs=[SoVITS_dropdown, GPT_dropdown],
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)
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SoVITS_dropdown.change(
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tts_pipline.init_vits_weights, [SoVITS_dropdown], []
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)
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GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], [])
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with gr.Row():
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with gr.Column():
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gr.Markdown(value=i18n("*请上传并填写参考信息"))
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inp_ref = gr.Audio(
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label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath"
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)
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prompt_text = gr.Textbox(
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label=i18n("参考音频的文本"), value="", lines=2
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)
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with gr.Row():
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prompt_language = gr.Dropdown(
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label=i18n("参考音频的语种"),
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choices=[
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i18n("中文"),
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i18n("英文"),
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i18n("日文"),
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i18n("中英混合"),
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i18n("日英混合"),
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i18n("多语种混合"),
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],
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value=i18n("中文"),
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)
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with gr.Column():
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ref_text_free = gr.Checkbox(
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label=i18n(
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"开启无参考文本模式。不填参考文本亦相当于开启。"
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),
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value=False,
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interactive=True,
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show_label=True,
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)
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gr.Markdown(
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i18n(
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"使用无参考文本模式时建议使用微调的GPT，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。"
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)
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)
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with gr.Column():
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gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
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text = gr.Textbox(
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label=i18n("需要合成的文本"), value="", lines=16, max_lines=16
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)
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"),
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choices=[
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i18n("中文"),
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i18n("英文"),
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i18n("日文"),
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i18n("中英混合"),
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i18n("日英混合"),
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i18n("多语种混合"),
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],
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value=i18n("中文"),
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)
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with gr.Group():
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gr.Markdown(value=i18n("推理设置"))
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with gr.Row():
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with gr.Column():
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batch_size = gr.Slider(
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minimum=1,
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maximum=200,
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step=1,
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label=i18n("batch_size"),
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value=20,
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interactive=True,
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)
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fragment_interval = gr.Slider(
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minimum=0.01,
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maximum=1,
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step=0.01,
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label=i18n("分段间隔(秒)"),
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value=0.3,
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interactive=True,
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)
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speed_factor = gr.Slider(
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minimum=0.25,
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maximum=4,
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step=0.05,
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label="speed_factor",
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value=1.0,
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interactive=True,
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)
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top_k = gr.Slider(
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minimum=1,
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maximum=100,
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step=1,
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label=i18n("top_k"),
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value=5,
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interactive=True,
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)
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top_p = gr.Slider(
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minimum=0,
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maximum=1,
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step=0.05,
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label=i18n("top_p"),
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value=1,
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interactive=True,
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)
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temperature = gr.Slider(
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minimum=0,
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maximum=1,
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step=0.05,
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label=i18n("temperature"),
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value=1,
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interactive=True,
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)
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with gr.Column():
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[
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i18n("不切"),
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i18n("凑四句一切"),
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i18n("凑50字一切"),
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i18n("按中文句号。切"),
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i18n("按英文句号.切"),
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i18n("按标点符号切"),
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],
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value=i18n("凑四句一切"),
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interactive=True,
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)
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with gr.Row():
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split_bucket = gr.Checkbox(
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label=i18n("数据分桶(可能会降低一点计算量，选就对了)"),
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value=True,
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interactive=True,
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show_label=True,
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)
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seed = gr.Number(label=i18n("随机种子"), value=-1)
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# with gr.Column():
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output = gr.Audio(label=i18n("输出的语音"))
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with gr.Row():
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inference_button = gr.Button(
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i18n("合成语音"), variant="primary"
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)
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stop_infer = gr.Button(i18n("终止合成"), variant="primary")
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inference_button.click(
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inference,
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[
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text,
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text_language,
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inp_ref,
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prompt_text,
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prompt_language,
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top_k,
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top_p,
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temperature,
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how_to_cut,
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batch_size,
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speed_factor,
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ref_text_free,
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split_bucket,
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fragment_interval,
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seed,
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],
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[output, seed],
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)
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stop_infer.click(tts_pipline.stop, [], [])
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with gr.Group():
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gr.Markdown(
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value=i18n(
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"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。"
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)
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)
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with gr.Row():
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text_inp = gr.Textbox(
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label=i18n("需要合成的切分前文本"), value="", lines=4
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)
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with gr.Column():
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_how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[
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i18n("不切"),
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i18n("凑四句一切"),
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i18n("凑50字一切"),
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i18n("按中文句号。切"),
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i18n("按英文句号.切"),
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i18n("按标点符号切"),
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],
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value=i18n("凑四句一切"),
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interactive=True,
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)
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cut_text = gr.Button(i18n("切分"), variant="primary")
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def to_cut(text_inp, how_to_cut):
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if len(text_inp.strip()) == 0 or text_inp == []:
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return ""
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method = get_method(cut_method[how_to_cut])
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return method(text_inp)
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text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4)
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cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt])
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gr.Markdown(
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value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")
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)
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app.queue(concurrency_count=511, max_size=1022).launch(
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server_name="0.0.0.0",
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inbrowser=True,
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share=is_share,
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server_port=infer_ttswebui,
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quiet=True,
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)
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