mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
541 lines
21 KiB
Python
541 lines
21 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 json
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import logging
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import os
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import random
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import re
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import sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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sys.path.append("%s/GPT_SoVITS" % (now_dir))
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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 torch
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try:
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import gradio.analytics as analytics
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analytics.version_check = lambda: None
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except:
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...
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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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version = model_version = os.environ.get("version", "v2")
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import gradio as gr
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from TTS_infer_pack.text_segmentation_method import get_method
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from TTS_infer_pack.TTS import NO_PROMPT_ERROR, TTS, TTS_Config
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from tools.i18n.i18n import I18nAuto, scan_language_list
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language = os.environ.get("language", "Auto")
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language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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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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# is_half = False
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# device = "cpu"
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dict_language_v1 = {
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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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dict_language_v2 = {
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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("粤语"): "all_yue", # 全部按中文识别
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i18n("韩文"): "all_ko", # 全部按韩文识别
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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("粤英混合"): "yue", # 按粤英混合识别####不变
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i18n("韩英混合"): "ko", # 按韩英混合识别####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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i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
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}
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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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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tts_config.version = version
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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_pipeline = 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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version = tts_config.version
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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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aux_ref_audio_paths,
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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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keep_random,
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parallel_infer,
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repetition_penalty,
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sample_steps,
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super_sampling,
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):
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seed = -1 if keep_random else seed
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actual_seed = seed if seed not in [-1, "", None] else random.randint(0, 2**32 - 1)
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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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"aux_ref_audio_paths": [item.name for item in aux_ref_audio_paths] if aux_ref_audio_paths is not None else [],
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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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"parallel_infer": parallel_infer,
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"repetition_penalty": repetition_penalty,
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"sample_steps": int(sample_steps),
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"super_sampling": super_sampling,
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}
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try:
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for item in tts_pipeline.run(inputs):
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yield item, actual_seed
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except NO_PROMPT_ERROR:
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gr.Warning(i18n("V3不支持无参考文本模式，请填写参考文本！"))
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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(GPT_weight_root, SoVITS_weight_root)
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return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
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"choices": sorted(GPT_names, key=custom_sort_key),
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"__type__": "update",
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}
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path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
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pretrained_sovits_name = [
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"GPT_SoVITS/pretrained_models/s2G488k.pth",
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"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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path_sovits_v3,
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]
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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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"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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"GPT_SoVITS/pretrained_models/s1v3.ckpt",
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]
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_ = [[], []]
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for i in range(3):
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if os.path.exists(pretrained_gpt_name[i]):
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_[0].append(pretrained_gpt_name[i])
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if os.path.exists(pretrained_sovits_name[i]):
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_[-1].append(pretrained_sovits_name[i])
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pretrained_gpt_name, pretrained_sovits_name = _
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if os.path.exists("./weight.json"):
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pass
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else:
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with open("./weight.json", "w", encoding="utf-8") as file:
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json.dump({"GPT": {}, "SoVITS": {}}, file)
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with open("./weight.json", "r", encoding="utf-8") as file:
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weight_data = file.read()
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weight_data = json.loads(weight_data)
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gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name))
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sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name))
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if isinstance(gpt_path, list):
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gpt_path = gpt_path[0]
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if isinstance(sovits_path, list):
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sovits_path = sovits_path[0]
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SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3"]
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GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3"]
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for path in SoVITS_weight_root + GPT_weight_root:
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os.makedirs(path, exist_ok=True)
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def get_weights_names(GPT_weight_root, SoVITS_weight_root):
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SoVITS_names = [i for i in pretrained_sovits_name]
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for path in SoVITS_weight_root:
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for name in os.listdir(path):
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if name.endswith(".pth"):
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SoVITS_names.append("%s/%s" % (path, name))
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GPT_names = [i for i in pretrained_gpt_name]
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for path in GPT_weight_root:
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for name in os.listdir(path):
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if name.endswith(".ckpt"):
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GPT_names.append("%s/%s" % (path, name))
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return SoVITS_names, GPT_names
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SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
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from process_ckpt import get_sovits_version_from_path_fast
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def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
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global version, model_version, dict_language, if_lora_v3
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version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
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# print(sovits_path,version, model_version, if_lora_v3)
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if if_lora_v3 and not os.path.exists(path_sovits_v3):
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info = path_sovits_v3 + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
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gr.Warning(info)
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raise FileExistsError(info)
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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if prompt_language is not None and text_language is not None:
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if prompt_language in list(dict_language.keys()):
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prompt_text_update, prompt_language_update = (
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{"__type__": "update"},
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{"__type__": "update", "value": prompt_language},
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)
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else:
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prompt_text_update = {"__type__": "update", "value": ""}
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prompt_language_update = {"__type__": "update", "value": i18n("中文")}
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
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else:
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text_update = {"__type__": "update", "value": ""}
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text_language_update = {"__type__": "update", "value": i18n("中文")}
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if model_version == "v3":
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visible_sample_steps = True
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visible_inp_refs = False
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else:
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visible_sample_steps = False
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visible_inp_refs = True
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# prompt_language,text_language,prompt_text,prompt_language,text,text_language,inp_refs,ref_text_free,
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yield (
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{"__type__": "update", "choices": list(dict_language.keys())},
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{"__type__": "update", "choices": list(dict_language.keys())},
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "interactive": True if model_version != "v3" else False},
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{"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False},
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)
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tts_pipeline.init_vits_weights(sovits_path)
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yield (
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{"__type__": "update", "choices": list(dict_language.keys())},
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{"__type__": "update", "choices": list(dict_language.keys())},
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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{"__type__": "update", "interactive": visible_sample_steps, "value": 32},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "interactive": True if model_version != "v3" else False},
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{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
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)
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with open("./weight.json") as f:
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data = f.read()
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data = json.loads(data)
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data["SoVITS"][version] = sovits_path
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with open("./weight.json", "w") as f:
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f.write(json.dumps(data))
|
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|
||
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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("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>"
|
||
+ 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.")
|
||
)
|
||
|
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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"),
|
||
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")
|
||
+ "<br>"
|
||
+ 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生效)"), value=32, choices=[4, 8, 16, 32], 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(tts_pipeline.init_t2s_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。"))
|
||
|
||
if __name__ == "__main__":
|
||
app.queue().launch( # concurrency_count=511, max_size=1022
|
||
server_name="0.0.0.0",
|
||
inbrowser=True,
|
||
share=is_share,
|
||
server_port=infer_ttswebui,
|
||
quiet=True,
|
||
)
|