mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
363 lines
16 KiB
Python
363 lines
16 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_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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def inference(text, text_lang,
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ref_audio_path, prompt_text,
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prompt_lang, top_k,
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top_p, temperature,
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text_split_method, batch_size,
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speed_factor, ref_text_free,
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split_bucket,fragment_interval,
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seed, keep_random, parallel_infer,
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repetition_penalty
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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.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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"parallel_infer": parallel_infer,
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"repetition_penalty": repetition_penalty,
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}
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for item in tts_pipeline.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 {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"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 = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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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"): 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"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
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return SoVITS_names, GPT_names
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#region 输出音频历史记录相关
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output_history =[]
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history_max_num = 20
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def sync_output_history_to_checkbox_audio():
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checkbox_result = []
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audio_result = []
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for item in output_history:
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label = item['label']
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if len(label)>15:
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label=label[:15]+'...'
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checkbox_result.append(gr.update(label=label,value=False))
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audio_result.append(gr.Audio.update(value=item['value']))
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for _ in range(len(audio_result),history_max_num):
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checkbox_result.append(gr.Checkbox.update(label="",value=False))
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audio_result.append(gr.Audio.update(value = None))
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return [*checkbox_result,*audio_result]
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def add_to_history(audio,input_text):
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if(audio is None or audio[1] is not None):
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if len(output_history) == history_max_num:
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output_history.pop()
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output_history.insert(0,{'value':audio,'label':input_text})
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return [*sync_output_history_to_checkbox_audio()]
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def clear_history():
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output_history = []
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checkbox_result = []
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audio_result = []
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for _ in range(history_max_num):
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checkbox_result.append(gr.Checkbox.update(label="",value=False))
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audio_result.append(gr.Audio.update(value = None))
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return [*checkbox_result,*audio_result]
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def shown_audio_num_change(audio_num):
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audio_num = int(audio_num)
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audio_result = []
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checkbox_result = []
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for _ in range(audio_num):
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audio_result.append(gr.Audio.update(visible=True))
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checkbox_result.append(gr.update(visible=True))
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for _ in range(audio_num,history_max_num):
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audio_result.append(gr.Audio.update(visible=False))
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checkbox_result.append(gr.update(visible=False))
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return [*checkbox_result,*audio_result]
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def delete_selected_history(*selected_list):
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for i in reversed(range(len(output_history))):
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if(selected_list[i]):
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output_history.pop(i)
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return [*sync_output_history_to_checkbox_audio()]
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def merge_selected_history(*selected_list):
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m_list = []
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labels = ""
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for i in reversed(range(len(output_history))):
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if(selected_list[i]):
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labels+=" "+output_history[i]["label"]
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m_list.append(output_history[i]["value"][1])
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if(m_list):
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combined = np.hstack(m_list)
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delete_selected_history(*selected_list)
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return add_to_history((32000, combined),labels)
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return [*sync_output_history_to_checkbox_audio()]
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#endregion
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SoVITS_names, GPT_names = get_weights_names()
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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>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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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(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
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SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
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refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
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refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
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SoVITS_dropdown.change(tts_pipeline.init_vits_weights, [SoVITS_dropdown], [])
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GPT_dropdown.change(tts_pipeline.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(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
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prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=2)
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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"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
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)
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with gr.Column():
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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, show_label=True)
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gr.Markdown(i18n("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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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(label=i18n("需要合成的文本"), value="", lines=16, max_lines=16)
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], 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(minimum=1,maximum=200,step=1,label=i18n("batch_size"),value=20,interactive=True)
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fragment_interval = gr.Slider(minimum=0.01,maximum=1,step=0.01,label=i18n("分段间隔(秒)"),value=0.3,interactive=True)
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speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="speed_factor",value=1.0,interactive=True)
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top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
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top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
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temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
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repetition_penalty = gr.Slider(minimum=0,maximum=2,step=0.05,label=i18n("重复惩罚"),value=1.35,interactive=True)
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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=[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"), ],
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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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parallel_infer = gr.Checkbox(label=i18n("并行推理(速度更快，但可能增大复读概率)"), value=True, interactive=True, show_label=True)
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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)
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seed = gr.Number(label=i18n("随机种子"),value=-1)
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keep_random = gr.Checkbox(label=i18n("保持随机"), value=True, interactive=True, show_label=True)
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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(i18n("合成语音"), variant="primary")
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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,text_language, inp_ref,
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prompt_text, prompt_language,
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top_k, top_p, temperature,
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how_to_cut, batch_size,
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speed_factor, ref_text_free,
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split_bucket,fragment_interval,
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seed, keep_random, parallel_infer,
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repetition_penalty
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],
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[output, seed],
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)
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stop_infer.click(tts_pipeline.stop, [], [])
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with gr.Group():
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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。"))
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with gr.Row():
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text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4)
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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=[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"), ],
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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(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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history_audio = []
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history_checkbox = []
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with gr.Accordion("生成历史"):
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with gr.Row():
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shown_audio_num = gr.Slider(1,20,history_max_num,step=1,interactive=True,label="记录显示数量")
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add_history_button = gr.Button("添加当前音频记录",variant="primary")
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merge_audio_button = gr.Button("合并选中音频",variant="primary")
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delete_select_history_button = gr.Button("删除选择的记录")
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clear_history_button = gr.Button("清空记录")
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index=0
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while(index<history_max_num):
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index+=5
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with gr.Row():
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for _ in range(5):
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with gr.Group():
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history_checkbox.append(gr.Checkbox(interactive=True,show_label=False,label=""))
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history_audio.append(gr.Audio(label=""))
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shown_audio_num.change(shown_audio_num_change,[shown_audio_num],[*history_checkbox,*history_audio])
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add_history_button.click(add_to_history,[output,text],[*history_checkbox,*history_audio])
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merge_audio_button.click(merge_selected_history,[*history_checkbox],[*history_checkbox,*history_audio])
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delete_select_history_button.click(delete_selected_history,[*history_checkbox],[*history_checkbox,*history_audio])
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clear_history_button.click(clear_history,outputs=[*history_checkbox,*history_audio])
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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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