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Running
on
CPU Upgrade
| 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 | |
| y = torch.FloatTensor(audio.astype(np.float32)) / hps.data.max_wav_value | |
| 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" | |
| "\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") | |
| 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") | |
| vc_input2 = gr.Dropdown(label="Target Speaker", choices=hps.speakers, type="index") | |
| 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() | |