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import time |
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import matplotlib.pyplot as plt |
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import IPython.display as ipd |
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import re |
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import os |
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import json |
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import math |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from torch.utils.data import DataLoader |
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import gradio as gr |
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import commons |
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import utils |
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from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import text_to_sequence |
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import unicodedata |
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from scipy.io.wavfile import write |
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def get_text(text, hps): |
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text_norm = text_to_sequence(text, hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = torch.LongTensor(text_norm) |
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return text_norm |
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def get_label(text, label): |
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if f'[{label}]' in text: |
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return True, text.replace(f'[{label}]', '') |
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else: |
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return False, text |
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def selection(speaker): |
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if speaker == "高咲侑": |
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spk = 0 |
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return spk |
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elif speaker == "歩夢": |
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spk = 1 |
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return spk |
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elif speaker == "かすみ": |
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spk = 2 |
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return spk |
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elif speaker == "しずく": |
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spk = 3 |
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return spk |
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elif speaker == "果林": |
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spk = 4 |
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return spk |
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elif speaker == "愛": |
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spk = 5 |
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return spk |
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elif speaker == "彼方": |
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spk = 6 |
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return spk |
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elif speaker == "せつ菜": |
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spk = 7 |
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return spk |
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elif speaker == "エマ": |
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spk = 8 |
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return spk |
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elif speaker == "璃奈": |
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spk = 9 |
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return spk |
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elif speaker == "栞子": |
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spk = 10 |
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return spk |
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elif speaker == "ランジュ": |
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spk = 11 |
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return spk |
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elif speaker == "ミア": |
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spk = 12 |
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return spk |
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elif speaker == "派蒙": |
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spk = 16 |
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return spk |
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def sle(language,tts_input0): |
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if language == "中文": |
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tts_input1 = "[ZH]" + tts_input0.replace('\n','。').replace(' ',',') + "[ZH]" |
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return tts_input1 |
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if language == "英文": |
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tts_input1 = "[EN]" + tts_input0.replace('\n','.').replace(' ',',') + "[EN]" |
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return tts_input1 |
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elif language == "日文": |
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tts_input1 = "[JA]" + tts_input0.replace('\n','。').replace(' ',',') + "[JA]" |
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return tts_input1 |
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def infer(language,text,speaker_id, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ): |
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speaker_id = int(selection(speaker_id)) |
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answer = sle(language,text) |
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stn_tst = get_text(answer, hps_ms) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(dev) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) |
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sid = torch.LongTensor([speaker_id]).to(dev) |
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t1 = time.time() |
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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() |
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t2 = time.time() |
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spending_time = "推理时间:"+str(t2-t1)+"s" |
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image = '1.png' |
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print(spending_time) |
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return (hps_ms.data.sampling_rate, audio),image |
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lan = ["中文","日文","英文"] |
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idols = ["高咲侑","歩夢","かすみ","しずく","果林","愛","彼方","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","派蒙"] |
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dev = torch.device("cpu") |
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hps_ms = utils.get_hparams_from_file("config.json") |
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net_g_ms = SynthesizerTrn( |
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len(symbols), |
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hps_ms.data.filter_length // 2 + 1, |
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hps_ms.train.segment_size // hps_ms.data.hop_length, |
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n_speakers=hps_ms.data.n_speakers, |
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**hps_ms.model).to(dev) |
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_ = net_g_ms.eval() |
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_ = utils.load_checkpoint("G_874000.pth", net_g_ms, None) |
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app = gr.Blocks() |
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with app: |
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with gr.Tabs(): |
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with gr.TabItem("Basic"): |
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tts_input1 = gr.TextArea(label="VITS模型,绝赞训练中", value="一次審査、二次審査、それぞれの欄に記入をお願いします。") |
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language = gr.Dropdown(label="选择语言",choices=lan, value="日文", interactive=True) |
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para_input1 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声比例", value=0.667) |
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para_input2 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声偏差", value=0.8) |
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para_input3 = gr.Slider(minimum= 0.1,maximum=10,label="更改时间比例", value=1) |
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tts_submit = gr.Button("Generate", variant="primary") |
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speaker1 = gr.Dropdown(label="选择说话人",choices=idols, value="かすみ", interactive=True) |
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tts_output2 = gr.Audio(label="Output") |
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tts_output3 = gr.Image(label = "Model") |
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tts_submit.click(infer, [language,tts_input1,speaker1,para_input1,para_input2,para_input3], [tts_output2,tts_output3]) |
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app.launch() |