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import os
os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
import librosa
import numpy as np
import torch
from torch import no_grad, LongTensor
import commons
import utils
import gradio as gr
from models import SynthesizerTrn
from text import text_to_sequence
from mel_processing import spectrogram_torch
def get_text(text):
text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
if hps.data.add_blank:
text_norm = commons.intersperse(text_norm, 0)
text_norm = LongTensor(text_norm)
return text_norm
def tts_fn(text, speaker_id):
stn_tst = get_text(text)
with no_grad():
x_tst = stn_tst.unsqueeze(0)
x_tst_lengths = LongTensor([stn_tst.size(0)])
sid = LongTensor([speaker_id])
audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][
0, 0].data.cpu().float().numpy()
return hps.data.sampling_rate, audio
def vc_fn(original_speaker_id, target_speaker_id, input_audio):
sampling_rate, audio = input_audio
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != hps.data.sampling_rate:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
y = torch.FloatTensor(audio)
y = y.unsqueeze(0)
spec = spectrogram_torch(y, hps.data.filter_length,
hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
center=False)
spec_lengths = LongTensor([spec.size(-1)])
sid_src = LongTensor([original_speaker_id])
sid_tgt = LongTensor([target_speaker_id])
with no_grad():
audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
0, 0].data.cpu().float().numpy()
return hps.data.sampling_rate, audio
if __name__ == '__main__':
config_path = "saved_model/config.json"
model_path = "saved_model/model.pth"
hps = utils.get_hparams_from_file(config_path)
model = SynthesizerTrn(
len(hps.symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
utils.load_checkpoint(model_path, model, None)
model.eval()
app = gr.Blocks()
with app:
gr.Markdown("# Moe Japanese TTS And Voice Conversion Using VITS Model\n\n"
"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.moegoe)\n\n"
"unofficial demo for [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)"
)
with gr.Tabs():
with gr.TabItem("TTS"):
with gr.Column():
tts_input1 = gr.TextArea(label="Text", value="γγγ«γ‘γ―γ")
tts_input2 = gr.Dropdown(label="Speaker", choices=hps.speakers, type="index", value=hps.speakers[0])
tts_submit = gr.Button("Generate", variant="primary")
tts_output = gr.Audio(label="Output Audio")
with gr.TabItem("Voice Conversion"):
with gr.Column():
vc_input1 = gr.Dropdown(label="Original Speaker", choices=hps.speakers, type="index",
value=hps.speakers[0])
vc_input2 = gr.Dropdown(label="Target Speaker", choices=hps.speakers, type="index",
value=hps.speakers[1])
vc_input3 = gr.Audio(label="Input Audio")
vc_submit = gr.Button("Convert", variant="primary")
vc_output = gr.Audio(label="Output Audio")
tts_submit.click(tts_fn, [tts_input1, tts_input2], [tts_output])
vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output])
app.launch()
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