Update app.py

app.py

@@ -14,170 +14,246 @@ import gradio as gr
 import time
 import datetime
 import os
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-def selection(speaker):
-    if speaker == "高咲侑":
-        spk = 0
-        return spk
+class VitsGradio:
+    def __init__(self):
+        self.dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.lan = ["中文","日文","自动","手动"]
+        self.idols = ["歩夢","かすみ","しずく","果林","愛","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","派蒙"]
+        self.modelPaths = []
+        for root,dirs,files in os.walk("checkpoints"):
+            for dir in dirs:
+                self.modelPaths.append(dir)
+        with gr.Blocks() as self.Vits:
+            gr.Markdown(
+            "## <center> Lovelive虹团中日双语VITS\n"
+            "### <center> 请不要生成会对个人以及企划造成侵害的内容\n"
+            "<div align='center'>目前有标贝普通话版，去标贝版，少歌模型目前还是大饼状态</div>"
+            '<div align="center"><a>参数说明:由于爱抖露们过于有感情，合成日语时建议将噪声比例调节至0.2-0.3区间，噪声偏差对应着每个字之间的间隔，对普通话影响较大，duration代表整体语速</div>'
+            '<div align="center"><a>合成前请先选择模型，否则第一次合成不一定成功。长段落/小说合成建议colab或本地运行</div>')
+            with gr.Tab("TTS合成"):
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            with gr.Column():
+                                input1 = gr.TextArea(label="Text", value="为什么你会那么熟练啊？你和雪菜亲过多少次了")
+                                input2 = gr.Dropdown(label="Language", choices=self.lan, value="自动", interactive=True)
+                                input3 = gr.Dropdown(label="Speaker", choices=self.idols, value="歩夢", interactive=True)
+                                btnVC = gr.Button("Submit")
+                            with gr.Column():
+                                input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.267)
+                                input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.7)
+                                input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1)
+                                output1 = gr.Audio(label="采样率22050")
+                btnVC.click(self.infer, inputs=[input1, input2, input3, input4, input5, input6], outputs=[output1])
+            with gr.Tab("选择模型"):
+                with gr.Column():
+                    modelstrs = gr.Dropdown(label = "模型", choices = self.modelPaths, value = self.modelPaths[0], type = "value")
+                    btnMod = gr.Button("载入模型")
+                    statusa = gr.TextArea()
+                    btnMod.click(self.loadCk, inputs=[modelstrs], outputs = [statusa])
+            with gr.Tab("小说合成(带字幕)"):
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            with gr.Column():
+                                input1 = gr.TextArea(label="建议colab或本地克隆后运行本仓库", value="为什么你会那么熟练啊？你和雪菜亲过多少次了")
+                                input2 = gr.Dropdown(label="Language", choices=self.lan, value="自动", interactive=True)
+                                input3 = gr.Dropdown(label="Speaker", choices=self.idols, value="歩夢", interactive=True)
+                                btnVC = gr.Button("Submit")
+                            with gr.Column():
+                                input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.267)
+                                input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.7)
+                                input6 = gr.Slider(minimum=0.1, maximum=10, label="Duration", value=1)
+                                output1 = gr.Audio(label="采样率22050")
+                                subtitle = gr.outputs.File(label="字幕文件：subtitles.srt")
+                btnVC.click(self.infer2, inputs=[input1, input2, input3, input4, input5, input6], outputs=[output1,subtitle])
+    
+    def loadCk(self,path):
+        self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
+        self.net_g = SynthesizerTrn(
+            len(symbols),
+            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).to(self.dev)
+        _ = self.net_g.eval()
+        _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.net_g)
+        return "success"
+
+    def get_text(self,text):
+        text_norm = text_to_sequence(text, self.hps.data.text_cleaners)
+        if self.hps.data.add_blank:
+            text_norm = commons.intersperse(text_norm, 0)
+        text_norm = torch.LongTensor(text_norm)
+        return text_norm
+    
+    def is_japanese(self,string):
+        for ch in string:
+            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
+                return True
+        return False
+    
+    def is_english(self,string):
+        import re
+        pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$')
+        if pattern.fullmatch(string):
+            return True
+        else:
+            return False
+    
+    def selection(self,speaker):
+        if speaker == "高咲侑":
+            spk = 0
+            return spk
 
-    elif speaker == "歩夢":
-        spk = 1
-        return spk
+        elif speaker == "歩夢":
+            spk = 1
+            return spk
 
-    elif speaker == "かすみ":
-        spk = 2
-        return spk
+        elif speaker == "かすみ":
+            spk = 2
+            return spk
 
-    elif speaker == "しずく":
-        spk = 3
-        return spk
+        elif speaker == "しずく":
+            spk = 3
+            return spk
 
-    elif speaker == "果林":
-        spk = 4
-        return spk
+        elif speaker == "果林":
+            spk = 4
+            return spk
     
-    elif speaker == "愛":
-        spk = 5
-        return spk
+        elif speaker == "愛":
+            spk = 5
+            return spk
 
-    elif speaker == "彼方":
-        spk = 6
-        return spk
+        elif speaker == "彼方":
+            spk = 6
+            return spk
 
-    elif speaker == "せつ菜":
-        spk = 7
-        return spk
-    elif speaker == "エマ":
-        spk = 8
-        return spk
-    elif speaker == "璃奈":
-        spk = 9
-        return spk
-    elif speaker == "栞子":
-        spk = 10
-        return spk
-    elif speaker == "ランジュ":
-        spk = 11
-        return spk
-    elif speaker == "ミア":
-        spk = 12
-        return spk
-    elif speaker == "三色绘恋1":
-        spk = 13
-        return spk
-    elif speaker == "三色绘恋2":
-        spk = 15
-    elif speaker == "派蒙":
-        spk = 16
-        return spk
-def is_japanese(string):
-        for ch in string:
-            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
-                return True
-        return False
-def is_english(string):
-    import re
-    pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$')
-    if pattern.fullmatch(string):
-        return True
-    else:
-        return False
-def sle(language,tts_input0):
-    if language == "中文":
-        tts_input1 = "[ZH]" + tts_input0.replace('\n','。').replace(' ',',') + "[ZH]"
-        return tts_input1
-    if language == "自动":
-        tts_input1 = f"[JA]{tts_input0}[JA]" if is_japanese(tts_input0) else f"[ZH]{tts_input0}[ZH]"
-        return tts_input1
-    elif language == "日文":
-        tts_input1 = "[JA]" + tts_input0.replace('\n','。').replace(' ',',') + "[JA]"
-        return tts_input1
-def extrac(text):
-    text = re.sub("<[^>]*>","",text)
-    result_list = re.split(r'\n', text)
-    final_list = []
-    for i in result_list:
-        if is_english(i):
-            i = romajitable.to_kana(i).katakana
-        i = i.replace('\n','').replace(' ','')
-        #Current length of single sentence: 20 
-        if len(i)>1:
-            if len(i) > 20:
-                try:
-                    cur_list = re.split(r'。|！', i)
-                    for i in cur_list:
-                        if len(i)>1:
-                            final_list.append(i+'。')
-                except:
-                    pass
-            else:
-                final_list.append(i)
-    final_list = [x for x in final_list if x != '']
-    print(final_list)
-    return final_list
-def infer(text ,language, speaker_id,n_scale= 0.667,n_scale_w = 0.8, l_scale = 1):
-    speaker_id = int(selection(speaker_id))
-    a = ['【','[','(','（']
-    b = ['】',']',')','）']
-    for i in a:
-        text = text.replace(i,'<')
-    for i in b:
-        text = text.replace(i,'>')
-    final_list = extrac(text.replace('“','').replace('”',''))
-    audio_fin = []
-    c = 0
-    t = datetime.timedelta(seconds=0)
-    f1 = open("subtitles.srt",'w',encoding='utf-8')
-    for sentence in final_list:
-        c +=1
-        stn_tst = get_text(sle(language,sentence), hps_ms)
-        with torch.no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(dev)
-            x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
-            sid = torch.LongTensor([speaker_id]).to(dev)
+        elif speaker == "せつ菜":
+            spk = 7
+            return spk
+        elif speaker == "エマ":
+            spk = 8
+            return spk
+        elif speaker == "璃奈":
+            spk = 9
+            return spk
+        elif speaker == "栞子":
+            spk = 10
+            return spk
+        elif speaker == "ランジュ":
+            spk = 11
+            return spk
+        elif speaker == "ミア":
+            spk = 12
+            return spk
+        elif speaker == "派蒙":
+            spk = 16
+            return spk
+    
+    def sle(self,language,text):
+        text = text.replace('\n','。').replace(' ',',')
+        if language == "中文":
+            tts_input1 = "[ZH]" + text + "[ZH]"
+            return tts_input1
+        elif language == "自动":
+            tts_input1 = f"[JA]{text}[JA]" if self.is_japanese(text) else f"[ZH]{text}[ZH]"
+            return tts_input1
+        elif language == "日文":
+            tts_input1 = "[JA]" + text + "[JA]"
+            return tts_input1
+        elif language == "英文":
+            tts_input1 = "[EN]" + text + "[EN]"
+            return tts_input1
+        elif language == "手动":
+            return text
+    
+    def extrac(self,text):
+        text = re.sub("<[^>]*>","",text)
+        result_list = re.split(r'\n', text)
+        final_list = []
+        for i in result_list:
+            if self.is_english(i):
+                i = romajitable.to_kana(i).katakana
+            i = i.replace('\n','').replace(' ','')
+            #Current length of single sentence: 20 
+            if len(i)>1:
+                if len(i) > 20:
+                    try:
+                        cur_list = re.split(r'。|！', i)
+                        for i in cur_list:
+                            if len(i)>1:
+                                final_list.append(i+'。')
+                    except:
+                        pass
+                else:
+                    final_list.append(i)
+        final_list = [x for x in final_list if x != '']
+        print(final_list)
+        return final_list
+
+    def infer(self, text ,language, speaker_id,n_scale= 0.667,n_scale_w = 0.8, l_scale = 1):
+        try:
+            speaker_id = int(self.selection(speaker_id))
             t1 = time.time()
-            audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy()
-            t2 = time.time()
-            spending_time = "第"+str(c)+"句的推理时间为："+str(t2-t1)+"s"
-            print(spending_time)
-            time_start = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
-            last_time = datetime.timedelta(seconds=len(audio)/float(22050))
-            t+=last_time
-            time_end = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
-            print(time_end)
-            f1.write(str(c-1)+'\n'+time_start+' --> '+time_end+'\n'+sentence+'\n\n')
-            audio_fin.append(audio)
-    file_path = "subtitles.srt"
-    return (hps_ms.data.sampling_rate, np.concatenate(audio_fin)),file_path
-lan = ["中文","日文","自动"]
-idols = ["高咲侑","歩夢","かすみ","しずく","果林","愛","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","派蒙"]
-hps_ms = utils.get_hparams_from_file("lovelive/config.json")
-net_g_ms = SynthesizerTrn(
-    len(symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(dev)
-_ = net_g_ms.eval()
-_ = utils.load_checkpoint("lovelive/G_936000.pth", net_g_ms)
-inputs = [gr.TextArea(label="如需实现快速合成，建议在colab上克隆后运行本仓库", value="为什么你会那么熟练啊？你和雪菜亲过多少次了？我想做只属于你一个人的学院偶像，所以，请只注视我一个人，好吗？【中文】\nなんでそんなに慣れてんだよっ？せつ菜と…何回キスしたんだよ？どこまであたしを置いてきぼりにすれば気が済むんだよ？[日文]\nI can't choose just one（English）"),
-           gr.Dropdown(label="选择语言，目前勉强可以做到自动识别",choices=lan, value="自动", interactive=True),
-           gr.Dropdown(label="选择说话人",choices=idols, value="歩夢", interactive=True),
-           gr.Slider(minimum= 0,maximum=1.0,label="更改噪声比例，以控制情感", value=0.267),
-           gr.Slider(minimum= 0,maximum=1.0,label="更改噪声偏差，以控制音素长短", value=0.7),
-           gr.Slider(minimum= 0.1,maximum=10,label="更改时间比例", value=1)]
-outputs=[gr.Audio(label="采样率22050"), gr.outputs.File(label="字幕文件：subtitles.srt")]
-iface = gr.Interface(
-    fn=infer,
-    inputs=inputs,
-    outputs=outputs,
-    title="Vits",
-    description="虹团11人模型",
-)
-iface.launch()
+            stn_tst = self.get_text(self.sle(language,text))
+            with torch.no_grad():
+                x_tst = stn_tst.unsqueeze(0).to(self.dev)
+                x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(self.dev)
+                sid = torch.LongTensor([speaker_id]).to(self.dev)
+                audio = self.net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy()
+                t2 = time.time()
+                spending_time = "推理时间为："+str(t2-t1)+"s"
+                print(spending_time)
+            return (self.hps.data.sampling_rate, audio)
+        except:
+            self.hps = utils.get_hparams_from_file(f"checkpoints/biaobei/config.json")
+            self.net_g = SynthesizerTrn(
+                len(symbols),
+                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).to(self.dev)
+            _ = self.net_g.eval()
+            _ = utils.load_checkpoint(f"checkpoints/biaobei/model.pth", self.net_g)
+
+    def infer2(self, text ,language, speaker_id,n_scale= 0.667,n_scale_w = 0.8, l_scale = 1):
+        speaker_id = int(self.selection(speaker_id))
+        a = ['【','[','(','（']
+        b = ['】',']',')','）']
+        for i in a:
+            text = text.replace(i,'<')
+        for i in b:
+            text = text.replace(i,'>')
+        final_list = self.extrac(text.replace('“','').replace('”',''))
+        audio_fin = []
+        c = 0
+        t = datetime.timedelta(seconds=0)
+        f1 = open("subtitles.srt",'w',encoding='utf-8')
+        for sentence in final_list:
+            c +=1
+            stn_tst = self.get_text(self.sle(language,text))
+            with torch.no_grad():
+                x_tst = stn_tst.unsqueeze(0).to(self.dev)
+                x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(self.dev)
+                sid = torch.LongTensor([speaker_id]).to(self.dev)
+                t1 = time.time()
+                audio = self.net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy()
+                t2 = time.time()
+                spending_time = "第"+str(c)+"句的推理时间为："+str(t2-t1)+"s"
+                print(spending_time)
+                time_start = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
+                last_time = datetime.timedelta(seconds=len(audio)/float(22050))
+                t+=last_time
+                time_end = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
+                print(time_end)
+                f1.write(str(c-1)+'\n'+time_start+' --> '+time_end+'\n'+sentence+'\n\n')
+                audio_fin.append(audio)
+        file_path = "subtitles.srt"
+        return (self.hps.data.sampling_rate, np.concatenate(audio_fin)),file_path
+
+grVits = VitsGradio()
+
+grVits.Vits.launch()
+