Delete app.py

app.py

@@ -1,97 +0,0 @@
-import os
-
-os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
-
-import logging
-
-numba_logger = logging.getLogger('numba')
-numba_logger.setLevel(logging.WARNING)
-
-import librosa
-
-import matplotlib.pyplot as plt
-import IPython.display as ipd
-
-import os
-import json
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-
-import commons
-import utils
-from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text.cleaners import japanese_phrase_cleaners
-from text import cleaned_text_to_sequence
-from pypinyin import lazy_pinyin, Style
-
-from scipy.io.wavfile import write
-
-def get_text(text, hps):
-    text_norm = cleaned_text_to_sequence(text)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-# hps_ms = utils.get_hparams_from_file("./configs/vctk_base.json")
-
-
-hps = utils.get_hparams_from_file("./configs/tokaiteio.json")
-# net_g_ms = SynthesizerTrn(
-#     len(symbols),
-#     hps_ms.data.filter_length // 2 + 1,
-#     hps_ms.train.segment_size // hps.data.hop_length,
-#     n_speakers=hps_ms.data.n_speakers,
-#     **hps_ms.model)
-
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    **hps.model)
-_ = net_g.eval()
-
-
-def tts(text):
-    if len(text) > 150:
-        return "Error: Text is too long", None
-    stn_tst = get_text(text, hps)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().numpy()
-    ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))
-
-
-def tts_fn(text, speaker_id):
-    if len(text) > 150:
-        return "Error: Text is too long", None
-    stn_tst = get_text(text, hps)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
-            0, 0].data.cpu().float().numpy()
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == '__main__':
-    _ = utils.load_checkpoint("G_50000.pth", net_g, None)
-
-    app = gr.Blocks()
-
-    with app:
-        with gr.Tabs():
-            with gr.Column():
-                tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="こんにちは。")
-                tts_submit = gr.Button("Generate", variant="primary")
-                tts_output1 = gr.Textbox(label="Output Message")
-                tts_output2 = gr.Audio(label="Output Audio")
-
-        tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output1, tts_output2])
-
-    app.launch()