Upload app.py

app.py

@@ -1,8 +1,5 @@
 # flake8: noqa: E402
-
-import sys, os
 import logging
-
 logging.getLogger("numba").setLevel(logging.WARNING)
 logging.getLogger("markdown_it").setLevel(logging.WARNING)
 logging.getLogger("urllib3").setLevel(logging.WARNING)
@@ -13,8 +10,17 @@ logging.basicConfig(
 )
 
 logger = logging.getLogger(__name__)
-
+import datetime
+import numpy as np
 import torch
+from ebooklib import epub
+import PyPDF2
+from PyPDF2 import PdfReader
+import zipfile
+import shutil
+import sys, os
+import json
+from bs4 import BeautifulSoup
 import argparse
 import commons
 import utils
@@ -24,8 +30,21 @@ from text import cleaned_text_to_sequence, get_bert
 from text.cleaner import clean_text
 import gradio as gr
 import webbrowser
-
+import re
+from scipy.io.wavfile import write
+from datetime import datetime
 net_g = None
+BandList = {
+    
+        "PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
+        "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"],
+        "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"],
+        "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"],
+        "Roselia":["友希那","紗夜","リサ","燐子","あこ"],
+        "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"],
+        "Morfonica":["ましろ","瑠唯","つくし","七深","透子"],
+        "MyGo&AveMujica(Part)":["燈","愛音","そよ","立希","楽奈","祥子","睦","海鈴"],
+}
 
 if sys.platform == "darwin" and torch.backends.mps.is_available():
     device = "mps"
@@ -33,6 +52,35 @@ if sys.platform == "darwin" and torch.backends.mps.is_available():
 else:
     device = "cuda"
 
+def is_japanese(string):
+        for ch in string:
+            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
+                return True
+        return False
+
+def extrac(text):
+    text = re.sub("<[^>]*>","",text)
+    result_list = re.split(r'\n', text)
+    final_list = []
+    for i in result_list:
+        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.append(i)
+        '''
+    final_list = [x for x in final_list if x != '']
+    return final_list
 
 def get_text(text, language_str, hps):
     norm_text, phone, tone, word2ph = clean_text(text, language_str)
@@ -99,36 +147,211 @@ def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, langua
             .float()
             .numpy()
         )
+        current_time = datetime.now()
+        print(str(current_time)+':'+str(sid))
         del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
         return audio
 
 
 def tts_fn(
-    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
+    text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,LongSentence
 ):
-    with torch.no_grad():
-        audio = infer(
-            text,
-            sdp_ratio=sdp_ratio,
-            noise_scale=noise_scale,
-            noise_scale_w=noise_scale_w,
-            length_scale=length_scale,
-            sid=speaker,
-            language=language,
-        )
-        torch.cuda.empty_cache()
-    return "Success", (hps.data.sampling_rate, audio)
+    if not LongSentence:
+        with torch.no_grad():
+            audio = infer(
+                text,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+                sid=speaker,
+                language= "JP" if is_japanese(text) else "ZH",
+            )
+            torch.cuda.empty_cache()
+        return (hps.data.sampling_rate, audio)
+    else:
+        audiopath = 'voice.wav'
+        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 = []
+        for sentence in final_list:
+            with torch.no_grad():
+                audio = infer(
+                    sentence,
+                    sdp_ratio=sdp_ratio,
+                    noise_scale=noise_scale,
+                    noise_scale_w=noise_scale_w,
+                    length_scale=length_scale,
+                    sid=speaker,
+                    language= "JP" if is_japanese(text) else "ZH",
+                )
+            audio_fin.append(audio)
+        return (hps.data.sampling_rate, np.concatenate(audio_fin))
+
+def split_into_sentences(text):
+    """将文本分割为句子，基于中文的标点符号"""
+    sentences = re.split(r'(?<=[。！？…\n])', text)
+    return [sentence.strip() for sentence in sentences if sentence]
+
+
+def seconds_to_ass_time(seconds):
+    """将秒数转换为ASS时间格式"""
+    hours = int(seconds / 3600)
+    minutes = int((seconds % 3600) / 60)
+    seconds = int(seconds) % 60
+    milliseconds = int((seconds - int(seconds)) * 1000)
+    return "{:01d}:{:02d}:{:02d}.{:02d}".format(hours, minutes, seconds, int(milliseconds / 10))
+
+def generate_audio_and_srt_for_group(group, outputPath, group_index, sampling_rate, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
+    audio_fin = []
+    ass_entries = []
+    start_time = 0
+
+    ass_header = """[Script Info]
+; Script generated by OpenAI Assistant
+Title: Audiobook
+ScriptType: v4.00+
+WrapStyle: 0
+PlayResX: 640
+PlayResY: 360
+ScaledBorderAndShadow: yes
+[V4+ Styles]
+Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
+Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,1,2,10,10,10,1
+[Events]
+Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
+"""
+
+    for sentence in group:
+        try:
+            print(sentence)
+            FakeSpeaker = sentence.split("|")[0]
+            print(FakeSpeaker)
+            SpeakersList = re.split('\n', spealerList)
+            if FakeSpeaker in list(hps.data.spk2id.keys()):
+                speaker = FakeSpeaker
+            for i in SpeakersList:
+                if FakeSpeaker == i.split("|")[1]:
+                    speaker = i.split("|")[0]
+            speaker_ids = hps.data.spk2id
+            
+            _, audio = tts_fn(sentence.split("|")[-1], speaker=speaker, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, LongSentence=True)
+            silence_frames = int(silenceTime * 44010)
+            silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
+            audio_fin.append(audio)
+            audio_fin.append(silence_data)
+
+            duration = len(audio) / sampling_rate
+            end_time = start_time + duration + silenceTime
+            ass_entries.append("Dialogue: 0,{},{},".format(seconds_to_ass_time(start_time), seconds_to_ass_time(end_time)) + "Default,,0,0,0,,{}".format(sentence.replace("|","：")))
+            start_time = end_time
+        except:
+            pass
+    wav_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.wav')
+    ass_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.ass')
+
+    write(wav_filename, sampling_rate, np.concatenate(audio_fin))
+
+    with open(ass_filename, 'w', encoding='utf-8') as f:
+        f.write(ass_header + '\n'.join(ass_entries))
+    return (hps.data.sampling_rate, np.concatenate(audio_fin))
+def extract_text_from_epub(file_path):
+    book = epub.read_epub(file_path)
+    content = []
+    for item in book.items:
+        if isinstance(item, epub.EpubHtml):
+            soup = BeautifulSoup(item.content, 'html.parser')
+            content.append(soup.get_text())
+    return '\n'.join(content)
+
+def extract_text_from_pdf(file_path):
+    with open(file_path, 'rb') as file:
+        reader = PdfReader(file)
+        content = [page.extract_text() for page in reader.pages]
+    return '\n'.join(content)
+
+def extract_text_from_game2(data):
+    current_content = []
+
+    def _extract(data, current_data=None):
+        nonlocal current_content
+        
+        if current_data is None:
+            current_data = {}
+
+        if isinstance(data, dict):
+            if 'name' in data and 'body' in data:
+                current_name = data['name']
+                current_body = data['body'].replace('\n', '')
+                current_content.append(f"{current_name}|{current_body}")
+
+            for key, value in data.items():
+                _extract(value, dict(current_data))
+
+        elif isinstance(data, list):
+            for item in data:
+                _extract(item, dict(current_data))
+
+    _extract(data)
+    return '\n'.join(current_content)
+
+def extract_text_from_file(inputFile):
+    file_extension = os.path.splitext(inputFile)[1].lower()
+    
+    if file_extension == ".epub":
+        return extract_text_from_epub(inputFile)
+    elif file_extension == ".pdf":
+        return extract_text_from_pdf(inputFile)
+    elif file_extension == ".txt":
+        with open(inputFile, 'r', encoding='utf-8') as f:
+            return f.read()
+    elif file_extension == ".asset":
+        with open(inputFile, 'r', encoding='utf-8') as f:
+            content =  json.load(f)
+        return extract_text_from_game2(content) if extract_text_from_game2(content) != '' else extract_text_from_game2(content)
+    else:
+        raise ValueError(f"Unsupported file format: {file_extension}")
+
+def audiobook(inputFile, groupsize, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
+    directory_path = "books"
+    output_path = "books/audiobook_part_1.wav"
+ 
+    if os.path.exists(directory_path):
+        shutil.rmtree(directory_path)
+
+    os.makedirs(directory_path)
+    text = extract_text_from_file(inputFile.name)
+    sentences = split_into_sentences(text)
+    GROUP_SIZE = groupsize
+    for i in range(0, len(sentences), GROUP_SIZE):
+        group = sentences[i:i+GROUP_SIZE]
+        if spealerList == "":
+            spealerList = "无"
+        result = generate_audio_and_srt_for_group(group,directory_path, i//GROUP_SIZE + 1, 44100, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime)
+        if not torch.cuda.is_available():
+            return result
+    return result
+
+def loadmodel(model):
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
+    return "success"
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "-m", "--model", default="./logs/Mygo/G_63000.pth", help="path of your model"
+        "-m", "--model", default="./logs/BangDream/G_45000.pth", help="path of your model"
     )
     parser.add_argument(
         "-c",
         "--config",
-        default="./logs/Mygo/config.json",
+        default="configs/config.json",
         help="path of your config file",
     )
     parser.add_argument(
@@ -142,8 +365,6 @@ if __name__ == "__main__":
     if args.debug:
         logger.info("Enable DEBUG-LEVEL log")
         logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config)
-
     device = (
         "cuda:0"
         if torch.cuda.is_available()
@@ -153,6 +374,7 @@ if __name__ == "__main__":
             else "cpu"
         )
     )
+    hps = utils.get_hparams_from_file(args.config)
     net_g = SynthesizerTrn(
         len(symbols),
         hps.data.filter_length // 2 + 1,
@@ -160,65 +382,126 @@ if __name__ == "__main__":
         n_speakers=hps.data.n_speakers,
         **hps.model,
     ).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
-
+    loadmodel(args.model)
     speaker_ids = hps.data.spk2id
     speakers = list(speaker_ids.keys())
     languages = ["ZH", "JP"]
+    examples = [
+        ["filelist/Scenarioband6-018.asset", 500, "つくし", "ましろ|真白\n七深|七深\n透子|透子\nつくし|筑紫\n瑠唯|瑠唯\nそよ|素世\n祥子|祥子", "扩展功能"],
+    ]
+    modelPaths = []
+    for dirpath, dirnames, filenames in os.walk("./logs/Bangdream/"):
+        for filename in filenames:
+            modelPaths.append(os.path.join(dirpath, filename))
     with gr.Blocks() as app:
-        for name in speakers:
-            with gr.TabItem(name):
+        gr.Markdown(
+             f"少歌邦邦全员TTS,使用本模型请严格遵守法律法规!\n 发布二创作品请注明项目和本模型作者<a href='https://space.bilibili.com/19874615/'>B站@Mahiroshi</a>及项目链接\n从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看使用说明</a>"
+        )
+        for band in BandList:
+            with gr.TabItem(band):
+                for name in BandList[band]:
+                    with gr.TabItem(name):
+                        with gr.Row():
+                            with gr.Column():
+                                with gr.Row():
+                                    gr.Markdown(
+                                        '<div align="center">'
+                                        f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' 
+                                        '</div>'
+                                    )
+                                length_scale = gr.Slider(
+                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
+                                    )
+                                with gr.Accordion(label="切换模型(合成中文建议切换为早期模型)", open=False):
+                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
+                                    btnMod = gr.Button("载入模型")
+                                    statusa = gr.TextArea()
+                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
+                            with gr.Column():
+                                text = gr.TextArea(
+                                    label="输入纯日语或者中文",
+                                    placeholder="输入纯日语或者中文",
+                                    value="有个人躺在地上，哀嚎......\n有个人睡着了，睡在盒子里。\n我要把它打开，看看他的梦是什么。",
+                                )                                
+                                btn = gr.Button("点击生成", variant="primary")
+                                audio_output = gr.Audio(label="Output Audio")
+                                with gr.Accordion(label="其它参数设定", open=False):
+                                    sdp_ratio = gr.Slider(
+                                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
+                                    )
+                                    noise_scale = gr.Slider(
+                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
+                                    )
+                                    noise_scale_w = gr.Slider(
+                                        minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
+                                    )
+                                    LongSentence = gr.Checkbox(value=True, label="Generate LongSentence")
+                                    speaker = gr.Dropdown(
+                                        choices=speakers, value=name, label="说话人"
+                                    )
+                    btn.click(
+                        tts_fn,
+                        inputs=[
+                            text,
+                            speaker,
+                            sdp_ratio,
+                            noise_scale,
+                            noise_scale_w,
+                            length_scale,
+                            LongSentence,
+                        ],
+                        outputs=[audio_output],
+                    )
+        for i in examples:
+            with gr.Tab(i[-1]):
                 with gr.Row():
                     with gr.Column():
-                        with gr.Row():
-                                                gr.Markdown(
-                                                    '<div align="center">'
-                                                    f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' 
-                                                    '</div>'
-                                                )
-                        text_output = gr.Textbox(label="Message")
-                        speaker = gr.Dropdown(
-                            choices=speakers, value=name, label="Speaker"
+                        gr.Markdown(
+                                        f"从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看自制galgame使用说明\n</a>"
+                                    )
+                        inputFile = gr.inputs.File(label="上传txt(可设置角色对应表)、epub或mobi文件")
+                        groupSize = gr.Slider(
+                        minimum=10, maximum=1000,value = i[1], step=1, label="当个音频文件包含的最大字数"
                         )
-                        text_output = gr.Textbox(label="Message")
-                        language = gr.Dropdown(
-                            choices=languages, value=languages[1], label="Language"
+                        silenceTime = gr.Slider(
+                        minimum=0, maximum=1, value=0.5, step=0.1, label="句子的间隔"
                         )
-                    with gr.Column():
-                        text = gr.TextArea(
-                            label="Text",
-                            placeholder="Input Text Here",
-                            value="私たちは、一緒にはいられない。",
+                        spealerList = gr.TextArea(
+                                            label="角色对应表",
+                                            placeholder="左边是你想要在每一句话合成中用到的speaker(见角色清单)右边是你上传文本时分隔符左边设置的说话人:{ChoseSpeakerFromConfigList1}|{SeakerInUploadText1}\n{ChoseSpeakerFromConfigList2}|{SeakerInUploadText2}\n{ChoseSpeakerFromConfigList3}|{SeakerInUploadText3}\n",
+                                            value = i[3],
+                        )                  
+                        speaker = gr.Dropdown(
+                            choices=speakers, value = i[2], label="选择默认说话人"
                         )
-                        audio_output = gr.Audio(label="Output Audio")
-                        btn = gr.Button("Generate!", variant="primary")
+                    with gr.Column():
                         sdp_ratio = gr.Slider(
-                            minimum=0, maximum=1, value=0.2, step=0.01, label="SDP Ratio"
+                        minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
                         )
                         noise_scale = gr.Slider(
-                            minimum=0.1, maximum=2, value=0.6, step=0.01, label="Noise Scale"
+                            minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
                         )
                         noise_scale_w = gr.Slider(
-                            minimum=0.1, maximum=2, value=0.8, step=0.01, label="Noise Scale W"
+                            minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
                         )
                         length_scale = gr.Slider(
-                            minimum=0.1, maximum=2, value=1, step=0.01, label="Length Scale"
+                            minimum=0.1, maximum=2, value=1, step=0.01, label="生成长度"
                         )
-
-            btn.click(
-                tts_fn,
-                inputs=[
-                    text,
-                    speaker,
-                    sdp_ratio,
-                    noise_scale,
-                    noise_scale_w,
-                    length_scale,
-                    language,
-                ],
-                outputs=[text_output, audio_output],
-            )
-
-    app.launch()
+                        LastAudioOutput = gr.Audio(label="当用cuda在本地运行时才能在book文件夹下浏览全部合成内容")
+                        btn2 = gr.Button("点击生成", variant="primary")
+                    btn2.click(
+                        audiobook,
+                        inputs=[
+                            inputFile,
+                            groupSize,
+                            speaker,
+                            sdp_ratio,
+                            noise_scale,
+                            noise_scale_w,
+                            length_scale,
+                            spealerList,
+                            silenceTime
+                        ],
+                        outputs=[LastAudioOutput],
+                    )
+app.launch()