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Running
on
CPU Upgrade
| import json | |
| 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, hps): | |
| 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 create_tts_fn(model, hps, speaker_ids): | |
| def tts_fn(text, speaker, speed): | |
| if len(text) > 150: | |
| return "Error: Text is too long", None | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps) | |
| 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 / speed)[0][0, 0].data.cpu().float().numpy() | |
| return "Success", (hps.data.sampling_rate, audio) | |
| return tts_fn | |
| def create_vc_fn(model, hps, speaker_ids): | |
| def vc_fn(original_speaker, target_speaker, input_audio): | |
| if input_audio is None: | |
| return "You need to upload an audio", None | |
| sampling_rate, audio = input_audio | |
| duration = audio.shape[0] / sampling_rate | |
| if duration > 30: | |
| return "Error: Audio is too long", None | |
| original_speaker_id = speaker_ids[original_speaker] | |
| target_speaker_id = speaker_ids[target_speaker] | |
| 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 "Success", (hps.data.sampling_rate, audio) | |
| return vc_fn | |
| if __name__ == '__main__': | |
| models = [] | |
| with open("saved_model/names.json", "r", encoding="utf-8") as f: | |
| models_names = json.load(f) | |
| for i, models_name in models_names.items(): | |
| config_path = f"saved_model/{i}/config.json" | |
| model_path = f"saved_model/{i}/model.pth" | |
| cover_path = f"saved_model/{i}/cover.jpg" | |
| 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() | |
| speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"] | |
| speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"] | |
| models.append((models_name, cover_path, speakers, | |
| create_tts_fn(model, hps, speaker_ids), create_vc_fn(model, hps, speaker_ids))) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# Moe Japanese TTS And Voice Conversion Using VITS Model\n\n" | |
| "\n\n" | |
| "unofficial demo for \n\n" | |
| "- [https://github.com/CjangCjengh/MoeGoe](https://github.com/CjangCjengh/MoeGoe)\n" | |
| "- [https://github.com/Francis-Komizu/VITS](https://github.com/Francis-Komizu/VITS)" | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("TTS"): | |
| with gr.Tabs(): | |
| for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models): | |
| with gr.TabItem(f"model{i}"): | |
| with gr.Column(): | |
| gr.Markdown(f"## {models_name}\n\n" | |
| f"") | |
| tts_input1 = gr.TextArea(label="Text (150 words limitation)", value="γγγ«γ‘γ―γ") | |
| tts_input2 = gr.Dropdown(label="Speaker", choices=speakers, | |
| type="index", value=speakers[0]) | |
| tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1) | |
| 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_input3], | |
| [tts_output1, tts_output2]) | |
| with gr.TabItem("Voice Conversion"): | |
| with gr.Tabs(): | |
| for i, (models_name, cover_path, speakers, tts_fn, vc_fn) in enumerate(models): | |
| with gr.TabItem(f"model{i}"): | |
| gr.Markdown(f"## {models_name}\n\n" | |
| f"") | |
| vc_input1 = gr.Dropdown(label="Original Speaker", choices=speakers, type="index", | |
| value=speakers[0]) | |
| vc_input2 = gr.Dropdown(label="Target Speaker", choices=speakers, type="index", | |
| value=speakers[1]) | |
| vc_input3 = gr.Audio(label="Input Audio (30s limitation)") | |
| vc_submit = gr.Button("Convert", variant="primary") | |
| vc_output1 = gr.Textbox(label="Output Message") | |
| vc_output2 = gr.Audio(label="Output Audio") | |
| vc_submit.click(vc_fn, [vc_input1, vc_input2, vc_input3], [vc_output1, vc_output2]) | |
| app.launch() | |