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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
Update inference_webui.py
This commit is contained in:
RVC-Boss
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parent
4a9723e571
commit
74409f3570
@ -1,4 +1,5 @@
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import os,re,logging
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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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@ -10,16 +11,16 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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if os.path.exists("./gweight.txt"):
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with open("./gweight.txt", 'r',encoding="utf-8") as file:
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with open("./gweight.txt", 'r', encoding="utf-8") as file:
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gweight_data = file.read()
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gpt_path = os.environ.get(
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"gpt_path", gweight_data)
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"gpt_path", gweight_data)
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else:
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gpt_path = os.environ.get(
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
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if os.path.exists("./sweight.txt"):
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with open("./sweight.txt", 'r',encoding="utf-8") as file:
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with open("./sweight.txt", 'r', encoding="utf-8") as file:
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sweight_data = file.read()
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sovits_path = os.environ.get("sovits_path", sweight_data)
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else:
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@ -37,16 +38,17 @@ bert_path = os.environ.get(
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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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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"))
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import gradio as gr
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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import librosa,torch
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import librosa, torch
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from feature_extractor import cnhubert
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cnhubert.cnhubert_base_path=cnhubert_base_path
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cnhubert.cnhubert_base_path = cnhubert_base_path
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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@ -56,9 +58,10 @@ from time import time as ttime
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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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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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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@ -74,6 +77,7 @@ if is_half == True:
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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@ -89,6 +93,7 @@ def get_bert_feature(text, word2ph):
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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@ -123,10 +128,11 @@ if is_half == True:
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else:
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ssl_model = ssl_model.to(device)
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def change_sovits_weights(sovits_path):
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global vq_model,hps
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dict_s2=torch.load(sovits_path,map_location="cpu")
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hps=dict_s2["config"]
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global vq_model, hps
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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vq_model = SynthesizerTrn(
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@ -135,7 +141,7 @@ def change_sovits_weights(sovits_path):
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n_speakers=hps.data.n_speakers,
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**hps.model
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)
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if("pretrained"not in sovits_path):
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if ("pretrained" not in sovits_path):
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del vq_model.enc_q
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if is_half == True:
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vq_model = vq_model.half().to(device)
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@ -143,11 +149,15 @@ def change_sovits_weights(sovits_path):
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vq_model = vq_model.to(device)
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vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path)
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with open("./sweight.txt", "w", encoding="utf-8") as f:
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f.write(sovits_path)
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change_sovits_weights(sovits_path)
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def change_gpt_weights(gpt_path):
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global hz,max_sec,t2s_model,config
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global hz, max_sec, t2s_model, config
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hz = 50
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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config = dict_s1["config"]
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@ -160,9 +170,12 @@ def change_gpt_weights(gpt_path):
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t2s_model.eval()
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total = sum([param.nelement() for param in t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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with open("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path)
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with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
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change_gpt_weights(gpt_path)
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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@ -179,10 +192,10 @@ def get_spepc(hps, filename):
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return spec
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dict_language={
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i18n("中文"):"zh",
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i18n("英文"):"en",
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i18n("日文"):"ja"
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dict_language = {
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i18n("中文"): "zh",
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i18n("英文"): "en",
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i18n("日文"): "ja"
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}
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@ -262,27 +275,31 @@ def nonen_get_bert_inf(text, language):
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return bert
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splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",}
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splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
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def get_first(text):
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
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text = re.split(pattern, text)[0].strip()
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return text
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_language!="en"else "."
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
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text = text.strip("\n")
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if(text[0]not in splits and len(get_first(text))<4):text="。"+text if text_language!="en"else "."+text
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print(i18n("实际输入的参考文本:"),prompt_text)
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print(i18n("实际输入的目标文本:"),text)
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if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
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print(i18n("实际输入的参考文本:"), prompt_text)
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print(i18n("实际输入的目标文本:"), text)
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zero_wav = np.zeros(
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int(hps.data.sampling_rate * 0.3),
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dtype=np.float16 if is_half == True else np.float32,
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)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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if(wav16k.shape[0]>160000 or wav16k.shape[0]<48000):
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if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
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raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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@ -292,7 +309,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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else:
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wav16k = wav16k.to(device)
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zero_wav_torch = zero_wav_torch.to(device)
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wav16k=torch.cat([wav16k,zero_wav_torch])
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wav16k = torch.cat([wav16k, zero_wav_torch])
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
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"last_hidden_state"
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].transpose(
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@ -307,13 +324,19 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
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else:
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phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
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if(how_to_cut==i18n("凑四句一切")):text=cut1(text)
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elif(how_to_cut==i18n("凑50字一切")):text=cut2(text)
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elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text)
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elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text)
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text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
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print(i18n("实际输入的目标文本(切句后):"),text)
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texts=text.split("\n")
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if (how_to_cut == i18n("凑四句一切")):
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text = cut1(text)
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elif (how_to_cut == i18n("凑50字一切")):
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text = cut2(text)
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elif (how_to_cut == i18n("按中文句号。切")):
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text = cut3(text)
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elif (how_to_cut == i18n("按英文句号.切")):
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text = cut4(text)
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elif (how_to_cut == i18n("按标点符号切")):
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text = cut5(text)
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text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n")
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print(i18n("实际输入的目标文本(切句后):"), text)
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texts = text.split("\n")
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audio_opt = []
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if prompt_language == "en":
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bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
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@ -368,9 +391,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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vq_model.decode(
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pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
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)
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.detach()
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.cpu()
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.numpy()[0, 0]
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.detach()
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.cpu()
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.numpy()[0, 0]
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) ###试试重建不带上prompt部分
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audio_opt.append(audio)
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audio_opt.append(zero_wav)
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@ -380,6 +403,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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np.int16
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)
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def split(todo_text):
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in splits:
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@ -407,7 +431,7 @@ def cut1(inp):
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if len(split_idx) > 1:
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opts = []
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for idx in range(len(split_idx) - 1):
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opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
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opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
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else:
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opts = [inp]
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return "\n".join(opts)
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@ -431,7 +455,7 @@ def cut2(inp):
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if tmp_str != "":
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opts.append(tmp_str)
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# print(opts)
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if len(opts)>1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
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if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
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opts[-2] = opts[-2] + opts[-1]
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opts = opts[:-1]
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return "\n".join(opts)
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@ -440,10 +464,25 @@ def cut2(inp):
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def cut3(inp):
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inp = inp.strip("\n")
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return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
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def cut4(inp):
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inp = inp.strip("\n")
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return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
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# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
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def cut5(inp):
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# if not re.search(r'[^\w\s]', inp[-1]):
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# inp += '。'
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inp = inp.strip("\n")
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punds = r'[,.;?!、，。？！;：]'
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items = re.split(f'({punds})', inp)
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items = ["".join(group) for group in zip(items[::2], items[1::2])]
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opt = "\n".join(items)
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return opt
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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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@ -451,25 +490,31 @@ def custom_sort_key(s):
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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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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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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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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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SoVITS_names,GPT_names = get_weights_names()
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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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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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@ -478,29 +523,29 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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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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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(change_sovits_weights,[SoVITS_dropdown],[])
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GPT_dropdown.change(change_gpt_weights,[GPT_dropdown],[])
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SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
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GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
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gr.Markdown(value=i18n("*请上传并填写参考信息"))
|
||||
with gr.Row():
|
||||
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
|
||||
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
|
||||
prompt_language = gr.Dropdown(
|
||||
label=i18n("参考音频的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文")
|
||||
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
|
||||
)
|
||||
gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
|
||||
with gr.Row():
|
||||
text = gr.Textbox(label=i18n("需要合成的文本"), value="")
|
||||
text_language = gr.Dropdown(
|
||||
label=i18n("需要合成的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文")
|
||||
label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
|
||||
)
|
||||
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"),],
|
||||
value=i18n("凑50字一切"),
|
||||
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,
|
||||
)
|
||||
inference_button = gr.Button(i18n("合成语音"), variant="primary")
|
||||
@ -508,22 +553,24 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
|
||||
inference_button.click(
|
||||
get_tts_wav,
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language,how_to_cut],
|
||||
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut],
|
||||
[output],
|
||||
)
|
||||
|
||||
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="")
|
||||
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
|
||||
button1 = gr.Button(i18n("凑四句一切"), variant="primary")
|
||||
button2 = gr.Button(i18n("凑50字一切"), variant="primary")
|
||||
button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
|
||||
button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
|
||||
button5 = gr.Button(i18n("按标点符号切"), variant="primary")
|
||||
text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
|
||||
button1.click(cut1, [text_inp], [text_opt])
|
||||
button2.click(cut2, [text_inp], [text_opt])
|
||||
button3.click(cut3, [text_inp], [text_opt])
|
||||
button4.click(cut4, [text_inp], [text_opt])
|
||||
button5.click(cut5, [text_inp], [text_opt])
|
||||
gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
|
||||
|
||||
app.queue(concurrency_count=511, max_size=1022).launch(
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user