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import gradio as gr |
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import os |
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os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..') |
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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 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, cleaned_text_to_sequence |
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from text.cleaners import japanese_cleaners |
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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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hps = utils.get_hparams_from_file("configs/japanese_base.json") |
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net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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**hps.model) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint("MyDrive/japanese_base/G_42000.pth", net_g, None) |
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def tts(text): |
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if len(text) > 150: |
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return "Error: Text is too long", None |
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stn_tst = get_text(text, hps) |
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with torch.no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) |
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audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1.25)[0][ |
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0, 0].data.float().numpy() |
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return "Success", (hps.data.sampling_rate, audio) |
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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("AI koni"): |
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tts_input1 = gr.TextArea(label="Text in Japanese (150 words limitation)", value="γγγ«γ‘γ―γ") |
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tts_submit = gr.Button("Generate", variant="primary") |
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tts_output1 = gr.Textbox(label="Message") |
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tts_output2 = gr.Audio(label="Output") |
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tts_submit.click(tts, [tts_input1], [tts_output1, tts_output2]) |
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app.launch() |
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