From 8630a9d9ebff5a0d1b767e2d12e821472766d658 Mon Sep 17 00:00:00 2001 From: Ke Date: Sun, 21 Jan 2024 14:19:14 +0800 Subject: [PATCH] Update inference_webui.py Integrate inference webui to main window. --- GPT_SoVITS/inference_webui.py | 439 +++++++++++++++------------------- 1 file changed, 199 insertions(+), 240 deletions(-) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index e5e604f5..550722fb 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -1,66 +1,17 @@ import os - -gpt_path = os.environ.get( - "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" -) -sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") -cnhubert_base_path = os.environ.get( - "cnhubert_base_path", "pretrained_models/chinese-hubert-base" -) -bert_path = os.environ.get( - "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large" -) -infer_ttswebui = os.environ.get("infer_ttswebui", 9872) -infer_ttswebui = int(infer_ttswebui) -if "_CUDA_VISIBLE_DEVICES" in os.environ: - os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] -is_half = eval(os.environ.get("is_half", "True")) -import gradio as gr -from transformers import AutoModelForMaskedLM, AutoTokenizer import numpy as np import librosa,torch from feature_extractor import cnhubert -cnhubert.cnhubert_base_path=cnhubert_base_path from module.models import SynthesizerTrn from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from transformers import AutoModelForMaskedLM, AutoTokenizer 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 -device = "cuda" -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) - - -# bert_model=bert_model.to(device) -def get_bert_feature(text, word2ph): - 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 - - -n_semantic = 1024 - -dict_s2=torch.load(sovits_path,map_location="cpu") -hps=dict_s2["config"] class DictToAttrRecursive(dict): def __init__(self, input_dict): @@ -90,40 +41,205 @@ class DictToAttrRecursive(dict): raise AttributeError(f"Attribute {item} not found") -hps = DictToAttrRecursive(hps) +class Inference: + def __init__(self, is_half, GPT_weight_root, SoVITS_weight_root): + self.n_semantic = 1024 + self.model_loaded = False + self.is_half = is_half + self.GPT_weight_root = GPT_weight_root + self.SoVITS_weight_root = SoVITS_weight_root -hps.model.semantic_frame_rate = "25hz" -dict_s1 = torch.load(gpt_path, map_location="cpu") -config = dict_s1["config"] -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 == True: - 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)) -hz = 50 -max_sec = config["data"]["max_sec"] -# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo -t2s_model = Text2SemanticLightningModule(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)) + def update_envs(self, gpt_path, sovits_path, cnhubert_base_path, bert_path): + self.gpt_path = os.path.join(self.GPT_weight_root, gpt_path) + self.sovits_path = os.path.join(self.SoVITS_weight_root, sovits_path) + self.cnhubert_base_path = cnhubert_base_path + self.bert_path = bert_path + + cnhubert.cnhubert_base_path=cnhubert_base_path + + yield self.load_model() + + def load_model(self, device='cuda'): + try: + # Load bert model + self.device = device + self.tokenizer = AutoTokenizer.from_pretrained(self.bert_path) + self.bert_model = AutoModelForMaskedLM.from_pretrained(self.bert_path) + if self.is_half == True: + self.bert_model = self.bert_model.half().to(device) + else: + self.bert_model = self.bert_model.to(device) + + # Load ssl model + dict_s1 = torch.load(self.gpt_path, map_location="cpu") + self.config = dict_s1["config"] + self.ssl_model = cnhubert.get_model() + if self.is_half == True: + self.ssl_model = self.ssl_model.half().to(device) + else: + self.ssl_model = self.ssl_model.to(device) + + dict_s2=torch.load(self.sovits_path,map_location="cpu") + self.hps=dict_s2["config"] + self.hps = DictToAttrRecursive(self.hps) + self.hps.model.semantic_frame_rate = "25hz" + + # Load vq model + self.vq_model = SynthesizerTrn( + self.hps.data.filter_length // 2 + 1, + self.hps.train.segment_size // self.hps.data.hop_length, + n_speakers=self.hps.data.n_speakers, + **self.hps.model + ) + if self.is_half == True: + self.vq_model = self.vq_model.half().to(device) + else: + self.vq_model = self.vq_model.to(device) + self.vq_model.eval() + self.vq_model.load_state_dict(dict_s2["weight"], strict=False) + + # Load t2s model + # t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo + self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False) + self.t2s_model.load_state_dict(dict_s1["weight"]) + if self.is_half == True: + self.t2s_model = self.t2s_model.half() + self.t2s_model = self.t2s_model.to(device) + self.t2s_model.eval() + total = sum([param.nelement() for param in self.t2s_model.parameters()]) + print("Number of parameter: %.2fM" % (total / 1e6)) + + self.model_loaded = True + return '模型加载成功' + except Exception as e: + return f'模型加载失败：{e}' + + def unload_model(self): + if self.model_loaded: + try: + del self.bert_model, self.ssl_model, self.hps, self.vq_model, self.t2s_model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + self.model_loaded = False + yield '模型卸载成功' + except Exception as e: + yield f'模型卸载失败：{e}' + else: + yield '模型未加载' + + def get_bert_feature(self, text, word2ph): + with torch.no_grad(): + inputs = self.tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(self.device) #####输入是long不用管精度问题，精度随bert_model + res = self.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 + + + def get_tts_wav(self, ref_wav_path, prompt_text, prompt_language, text, text_language): + if not self.model_loaded: + return + hz = 50 + dict_language = {"中文": "zh", "英文": "en", "日文": "ja"} + t0 = ttime() + prompt_text = prompt_text.strip("\n") + prompt_language, text = prompt_language, text.strip("\n") + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙 + wav16k = torch.from_numpy(wav16k) + if self.is_half == True: + wav16k = wav16k.half().to(self.device) + else: + wav16k = wav16k.to(self.device) + ssl_content = self.ssl_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = self.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 = [] + zero_wav = np.zeros( + int(self.hps.data.sampling_rate * 0.3), + dtype=np.float16 if self.is_half == True else np.float32, + ) + 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 = self.get_bert_feature(norm_text1, word2ph1).to(self.device) + else: + bert1 = torch.zeros( + (1024, len(phones1)), + dtype=torch.float16 if self.is_half == True else torch.float32, + ).to(self.device) + if text_language == "zh": + bert2 = self.get_bert_feature(norm_text2, word2ph2).to(self.device) + else: + bert2 = torch.zeros((1024, len(phones2))).to(bert1) + bert = torch.cat([bert1, bert2], 1) + + all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(self.device).unsqueeze(0) + bert = bert.to(self.device).unsqueeze(0) + all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(self.device) + prompt = prompt_semantic.unsqueeze(0).to(self.device) + t2 = ttime() + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + pred_semantic, idx = self.t2s_model.model.infer_panel( + all_phoneme_ids, + all_phoneme_len, + prompt, + bert, + # prompt_phone_len=ph_offset, + top_k=self.config["inference"]["top_k"], + early_stop_num=hz * self.config["data"]["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(self.hps, ref_wav_path) # .to(device) + if self.is_half == True: + refer = refer.half().to(self.device) + else: + refer = refer.to(self.device) + # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] + audio = ( + self.vq_model.decode( + pred_semantic, torch.LongTensor(phones2).to(self.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 self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( + np.int16 + ) + def get_spepc(hps, filename): @@ -142,119 +258,9 @@ def get_spepc(hps, filename): return spec -dict_language = {"中文": "zh", "英文": "en", "日文": "ja"} - - -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language): - t0 = ttime() - prompt_text = prompt_text.strip("\n") - prompt_language, text = prompt_language, text.strip("\n") - with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙 - wav16k = torch.from_numpy(wav16k) - if is_half == True: - wav16k = wav16k.half().to(device) - else: - wav16k = wav16k.to(device) - 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 = [] - zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), - dtype=np.float16 if is_half == True else np.float32, - ) - 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=config["inference"]["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): + splits = { "，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } # 不考虑省略号 todo_text = todo_text.replace("……", "。").replace("——", "，") if todo_text[-1] not in splits: todo_text += "。" @@ -314,50 +320,3 @@ def cut3(inp): inp = inp.strip("\n") return "\n".join(["%s。" % item for item in inp.strip("。").split("。")]) - -with gr.Blocks(title="GPT-SoVITS WebUI") as app: - gr.Markdown( - value="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.
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." - ) - # with gr.Tabs(): - # with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): - with gr.Group(): - gr.Markdown(value="*请上传并填写参考信息") - with gr.Row(): - inp_ref = gr.Audio(label="请上传参考音频", type="filepath") - prompt_text = gr.Textbox(label="参考音频的文本", value="") - prompt_language = gr.Dropdown( - label="参考音频的语种", choices=["中文", "英文", "日文"], value="中文" - ) - gr.Markdown(value="*请填写需要合成的目标文本") - with gr.Row(): - text = gr.Textbox(label="需要合成的文本", value="") - text_language = gr.Dropdown( - label="需要合成的语种", choices=["中文", "英文", "日文"], value="中文" - ) - inference_button = gr.Button("合成语音", variant="primary") - output = gr.Audio(label="输出的语音") - inference_button.click( - get_tts_wav, - [inp_ref, prompt_text, prompt_language, text, text_language], - [output], - ) - - gr.Markdown(value="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="需要合成的切分前文本", value="") - button1 = gr.Button("凑五句一切", variant="primary") - button2 = gr.Button("凑50字一切", variant="primary") - button3 = gr.Button("按中文句号。切", variant="primary") - text_opt = gr.Textbox(label="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]) - gr.Markdown(value="后续将支持混合语种编码文本输入。") - -app.queue(concurrency_count=511, max_size=1022).launch( - server_name="0.0.0.0", - inbrowser=True, - server_port=infer_ttswebui, - quiet=True, -)