Update inference_webui.py

Integrate inference webui to main window.

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(
+    def update_envs(self, gpt_path, sovits_path, cnhubert_base_path, bert_path):
-    hps.data.filter_length // 2 + 1,
+        self.gpt_path =  os.path.join(self.GPT_weight_root, gpt_path)
-    hps.train.segment_size // hps.data.hop_length,
+        self.sovits_path = os.path.join(self.SoVITS_weight_root, sovits_path)
-    n_speakers=hps.data.n_speakers,
+        self.cnhubert_base_path = cnhubert_base_path
-    **hps.model
+        self.bert_path = bert_path
-)
+
-if is_half == True:
+        cnhubert.cnhubert_base_path=cnhubert_base_path
-    vq_model = vq_model.half().to(device)
+
-else:
+        yield self.load_model()
-    vq_model = vq_model.to(device)
+    
-vq_model.eval()
+    def load_model(self, device='cuda'):
-print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+        try:
-hz = 50
+            # Load bert model
-max_sec = config["data"]["max_sec"]
+            self.device = device
-# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
+            self.tokenizer = AutoTokenizer.from_pretrained(self.bert_path)
-t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
+            self.bert_model = AutoModelForMaskedLM.from_pretrained(self.bert_path)
-t2s_model.load_state_dict(dict_s1["weight"])
+            if self.is_half == True:
-if is_half == True:
+                self.bert_model = self.bert_model.half().to(device)
-    t2s_model = t2s_model.half()
+            else:
-t2s_model = t2s_model.to(device)
+                self.bert_model = self.bert_model.to(device)
-t2s_model.eval()
+
-total = sum([param.nelement() for param in t2s_model.parameters()])
+            # Load ssl model
-print("Number of parameter: %.2fM" % (total / 1e6))
+            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. <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>."
-    )
-    # 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,
-)