From 8ccb4d8b7d39f39069c32b0e34c959998e7962ac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9E=97=E5=8C=971941783147?= <11103332+linbei-1941783147@user.noreply.gitee.com> Date: Wed, 24 Jan 2024 22:55:18 +0800 Subject: [PATCH] clean branch --- .gitignore | 5 - config.py | 7 - tools/uvr5/uvr5_weights/.gitignore | 2 - voice_loader.py | 371 ----------------------------- 4 files changed, 385 deletions(-) delete mode 100644 tools/uvr5/uvr5_weights/.gitignore delete mode 100644 voice_loader.py diff --git a/.gitignore b/.gitignore index f5533033..f8305fc7 100644 --- a/.gitignore +++ b/.gitignore @@ -4,8 +4,3 @@ __pycache__ env runtime .idea -TEMP -ffmpeg.exe -ffprobe.exe -GPT_weights/ -SoVITS_weights/ diff --git a/config.py b/config.py index 1dd51c6f..8b5f378f 100644 --- a/config.py +++ b/config.py @@ -1,12 +1,5 @@ import sys,os -#model type name -MODEL_TYPE_GPT = "GPT" -MODEL_TYPE_SOVITS = "SOVITS" - -#model folder path -MODEL_FOLDER_PATH_GPT = "GPT_weights" -MODEL_FOLDER_PATH_SOVITS = "SoVITS_weights" # 推理用的指定模型 sovits_path = "" diff --git a/tools/uvr5/uvr5_weights/.gitignore b/tools/uvr5/uvr5_weights/.gitignore deleted file mode 100644 index d6b7ef32..00000000 --- a/tools/uvr5/uvr5_weights/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -* -!.gitignore diff --git a/voice_loader.py b/voice_loader.py deleted file mode 100644 index c9c44561..00000000 --- a/voice_loader.py +++ /dev/null @@ -1,371 +0,0 @@ -import config -import sys,os -import gradio as gr -import torch -import numpy as np -import librosa,torch -from transformers import AutoModelForMaskedLM, AutoTokenizer -from feature_extractor import cnhubert -from module.models import SynthesizerTrn -from AR.models.t2s_lightning_module import Text2SemanticLightningModule -from text import cleaned_text_to_sequence -from text.cleaner import clean_text -from time import time as ttime -from module.mel_processing import spectrogram_torch -from my_utils import load_audio -from tools.i18n.i18n import I18nAuto - -hps = None -ssl_model = None -vq_model = None -t2s_model = None -is_half = config.is_half -hz = 50 -max_sec = None -top_k = None - -#后期可能将这里个path分离成变量 -bert_path = config.bert_path -cnhubert_base_path = config.cnhubert_path -cnhubert.cnhubert_base_path = cnhubert_base_path -device = "cuda" #不确定能否支持cpu,先预留 - -tokenizer = None -bert_model = None - -i18n = I18nAuto() -cwd = os.getcwd() -sys.path.append(cwd) - -SUPPORT_LANGUAGE = [i18n("中文"),i18n("英文"),i18n("日文")] - -dict_language={ - i18n("中文"):"zh", - i18n("英文"):"en", - i18n("日文"):"ja" -} - -def read_model_path(model_type): - model_list = [] - if model_type == config.MODEL_TYPE_GPT: - folder_path = os.path.join(cwd,config.MODEL_FOLDER_PATH_GPT) - file_type = ".ckpt" - elif model_type == config.MODEL_TYPE_SOVITS: - folder_path = os.path.join(cwd,config.MODEL_FOLDER_PATH_SOVITS) - file_type = ".pth" - for root, dirs, files in os.walk(folder_path): - for file_name in files: - if file_name.endswith(file_type): - file_path = os.path.join(root, file_name) - model_list.append((file_name,file_path)) - return model_list - -def refresh_model_list(): - gpt_choices = read_model_path(config.MODEL_TYPE_GPT) - sovits_choices = read_model_path(config.MODEL_TYPE_SOVITS) - return gr.Dropdown(choices=sorted(gpt_choices),value=gpt_choices[0]if len(gpt_choices)>0 else "",interactive=True),gr.Dropdown(choices=sorted(sovits_choices),value=sovits_choices[0]if len(sovits_choices)>0 else None,interactive=True) - -def get_bert_feature(text, word2ph): - global tokenizer,bert_model - with torch.no_grad(): - inputs = tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model - res = bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] - assert len(word2ph) == len(text) - phone_level_feature = [] - for i in range(len(word2ph)): - repeat_feature = res[i].repeat(word2ph[i], 1) - phone_level_feature.append(repeat_feature) - phone_level_feature = torch.cat(phone_level_feature, dim=0) - # if(is_half==True):phone_level_feature=phone_level_feature.half() - return phone_level_feature.T - -class DictToAttrRecursive(dict): - def __init__(self, input_dict): - super().__init__(input_dict) - for key, value in input_dict.items(): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - self[key] = value - setattr(self, key, value) - - def __getattr__(self, item): - try: - return self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - def __setattr__(self, key, value): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - super(DictToAttrRecursive, self).__setitem__(key, value) - super().__setattr__(key, value) - - def __delattr__(self, item): - try: - del self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - -def get_spepc(hps, filename): - audio = load_audio(filename, int(hps.data.sampling_rate)) - audio = torch.FloatTensor(audio) - audio_norm = audio - audio_norm = audio_norm.unsqueeze(0) - spec = spectrogram_torch( - audio_norm, - hps.data.filter_length, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - center=False, - ) - return spec - -def load_models(sovits_path, gpt_path): - global tokenizer,bert_model,hps,ssl_model,vq_model,t2s_model,is_half,hz,max_sec,top_k - print(f"SoVITS model path: {sovits_path}") - print(f"GPT model path: {gpt_path}") - - if sovits_path is None or gpt_path is None: - print("Choose both of two models before loading") - return "请正确选择两个模型",gr.Button(interactive=False) - - torch.cuda.empty_cache() - - tokenizer = AutoTokenizer.from_pretrained(bert_path) - bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) - if is_half == True: - bert_model = bert_model.half().to(device) - else: - bert_model = bert_model.to(device) - - dict_s2=torch.load(sovits_path,map_location="cpu") - hps=dict_s2["config"] - hps = DictToAttrRecursive(hps) - hps.model.semantic_frame_rate = "25hz" - - dict_s1 = torch.load(gpt_path, map_location="cpu") - dict_s1_config = dict_s1["config"] - max_sec = dict_s1_config["data"]["max_sec"] - top_k=dict_s1_config["inference"]["top_k"] - - ssl_model = cnhubert.get_model() - if is_half == True: - ssl_model = ssl_model.half().to(device) - else: - ssl_model = ssl_model.to(device) - - vq_model = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model - ) - if is_half: - vq_model = vq_model.half().to(device) - else: - vq_model = vq_model.to(device) - vq_model.eval() - print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) - - t2s_model = Text2SemanticLightningModule(dict_s1_config, "ojbk", is_train=False) - t2s_model.load_state_dict(dict_s1["weight"]) - if is_half == True: - t2s_model = t2s_model.half() - t2s_model = t2s_model.to(device) - t2s_model.eval() - total = sum([param.nelement() for param in t2s_model.parameters()]) - print("Number of parameter: %.2fM" % (total / 1e6)) - - #加载模型成功 - return "模型加载成功",gr.Button(interactive=True) - - -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language): - global hps,ssl_model,vq_model,t2s_model,is_half,hz,max_sec,top_k - t0 = ttime() - prompt_text = prompt_text.strip("\n") - prompt_language, text = prompt_language, text.strip("\n") - zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), - dtype=np.float16 if is_half else np.float32, - ) - with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) - wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) - if is_half == True: - wav16k = wav16k.half().to(device) - zero_wav_torch = zero_wav_torch.half().to(device) - else: - wav16k = wav16k.to(device) - zero_wav_torch = zero_wav_torch.to(device) - wav16k=torch.cat([wav16k,zero_wav_torch]) - ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ - "last_hidden_state" - ].transpose( - 1, 2 - ) # .float() - codes = vq_model.extract_latent(ssl_content) - prompt_semantic = codes[0, 0] - t1 = ttime() - prompt_language = dict_language[prompt_language] - text_language = dict_language[text_language] - phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language) - phones1 = cleaned_text_to_sequence(phones1) - texts = text.split("\n") - audio_opt = [] - for text in texts: - # 解决输入目标文本的空行导致报错的问题 - if (len(text.strip()) == 0): - continue - phones2, word2ph2, norm_text2 = clean_text(text, text_language) - phones2 = cleaned_text_to_sequence(phones2) - if prompt_language == "zh": - bert1 = get_bert_feature(norm_text1, word2ph1).to(device) - else: - bert1 = torch.zeros( - (1024, len(phones1)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - if text_language == "zh": - bert2 = get_bert_feature(norm_text2, word2ph2).to(device) - else: - bert2 = torch.zeros((1024, len(phones2))).to(bert1) - bert = torch.cat([bert1, bert2], 1) - - all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) - prompt = prompt_semantic.unsqueeze(0).to(device) - t2 = ttime() - with torch.no_grad(): - # pred_semantic = t2s_model.model.infer( - pred_semantic, idx = t2s_model.model.infer_panel( - all_phoneme_ids, - all_phoneme_len, - prompt, - bert, - # prompt_phone_len=ph_offset, - top_k=top_k, - early_stop_num=hz * max_sec, - ) - t3 = ttime() - # print(pred_semantic.shape,idx) - pred_semantic = pred_semantic[:, -idx:].unsqueeze( - 0 - ) # .unsqueeze(0)#mq要多unsqueeze一次 - refer = get_spepc(hps, ref_wav_path) # .to(device) - if is_half == True: - refer = refer.half().to(device) - else: - refer = refer.to(device) - # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] - audio = ( - vq_model.decode( - pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer - ) - .detach() - .cpu() - .numpy()[0, 0] - ) ###试试重建不带上prompt部分 - audio_opt.append(audio) - audio_opt.append(zero_wav) - t4 = ttime() - print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( - np.int16 - ) - -splits = { - "，", - "。", - "？", - "！", - ",", - ".", - "?", - "!", - "~", - ":", - "：", - "—", - "…", -} # 不考虑省略号 - - -def split(todo_text): - todo_text = todo_text.replace("……", "。").replace("——", "，") - if todo_text[-1] not in splits: - todo_text += "。" - i_split_head = i_split_tail = 0 - len_text = len(todo_text) - todo_texts = [] - while 1: - if i_split_head >= len_text: - break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 - if todo_text[i_split_head] in splits: - i_split_head += 1 - todo_texts.append(todo_text[i_split_tail:i_split_head]) - i_split_tail = i_split_head - else: - i_split_head += 1 - return todo_texts - - -def start_webui(): - - ngpu = torch.cuda.device_count() - gpu_list = [] - for i in range(ngpu): - gpu_list.append((torch.cuda.get_device_name(i),i)) - print(gpu_list) - - gpt_choices = read_model_path(config.MODEL_TYPE_GPT) - sovits_choices = read_model_path(config.MODEL_TYPE_SOVITS) - - with gr.Blocks() as demo: - with gr.Row(): - message_text = gr.Textbox("信息",interactive=False) - with gr.Accordion(label="设备"): - with gr.Row(): - cuda_device_index = gr.Dropdown(choices=gpu_list,value=0 if len(gpu_list)>0 else None,label="CUDA设备",interactive=True) - with gr.Accordion(label="模型"): - with gr.Row(): - gpt_dropdown = gr.Dropdown(choices=sorted(gpt_choices),value=gpt_choices[0][1]if len(gpt_choices)>0 else None,label="选择GPT模型",interactive=True) - sovits_dropdown = gr.Dropdown(choices=sorted(sovits_choices),value=sovits_choices[0][1]if len(sovits_choices)>0 else None,label="选择SoVITS模型",interactive=True) - with gr.Row(): - model_load_button = gr.Button("加载模型",variant="primary") - model_refresh_button = gr.Button("刷新模型", variant="secondary") - with gr.Accordion(label="参考"): - with gr.Group(): - with gr.Row(): - with gr.Row(): - ref_wav_path = gr.Audio(label="参考音频", type="filepath", scale=3) - ref_language = gr.Dropdown(choices=SUPPORT_LANGUAGE,value=i18n("中文"),label="参考语种",interactive=True,min_width=50, scale=1) - ref_text = gr.TextArea(label="参考文本",scale=1) - with gr.Row(): - output_language = gr.Dropdown(choices=SUPPORT_LANGUAGE,value=i18n("中文"),label="合成语种",interactive=True, scale=2) - preprocess_output_text_button = gr.Button("合成文本预处理",variant="primary",scale=3) - inference_button = gr.Button(i18n("合成语音"), interactive=False,variant="primary") - output_text = gr.TextArea(label="合成文本",interactive=True) - output_audio = gr.Audio(label="输出结果") - model_load_button.click(load_models,[gpt_dropdown,sovits_dropdown],[message_text,inference_button]) - model_refresh_button.click(refresh_model_list,[],[gpt_dropdown,sovits_dropdown]) - inference_button.click( - get_tts_wav, - [ref_wav_path, ref_text, ref_language, output_text, output_language], - [output_audio], - ) - demo.queue(max_size=1022).launch(server_port=2777) - -if __name__ == "__main__": - start_webui() - - - - -