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| import os | |
| import numpy as np | |
| import torch | |
| from torch import no_grad, LongTensor | |
| import argparse | |
| import commons | |
| from mel_processing import spectrogram_torch | |
| import utils | |
| from models import SynthesizerTrn | |
| import gradio as gr | |
| import librosa | |
| import webbrowser | |
| from text import text_to_sequence, _clean_text | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| language_marks = { | |
| "Japanese": "", | |
| "日本語": "[JA]", | |
| "简体中文": "[ZH]", | |
| "English": "[EN]", | |
| "Mix": "", | |
| } | |
| lang = ['日本語', '简体中文', 'English', 'Mix'] | |
| def get_text(text, hps, is_symbol): | |
| text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else 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, language, speed): | |
| if language is not None: | |
| text = language_marks[language] + text + language_marks[language] | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps, False) | |
| with no_grad(): | |
| x_tst = stn_tst.unsqueeze(0).to(device) | |
| x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) | |
| sid = LongTensor([speaker_id]).to(device) | |
| 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() | |
| del stn_tst, x_tst, x_tst_lengths, sid | |
| 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, record_audio, upload_audio): | |
| input_audio = record_audio if record_audio is not None else upload_audio | |
| if input_audio is None: | |
| return "You need to record or upload an audio", None | |
| sampling_rate, audio = input_audio | |
| 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) | |
| with no_grad(): | |
| y = torch.FloatTensor(audio) | |
| y = y / max(-y.min(), y.max()) / 0.99 | |
| y = y.to(device) | |
| 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).to(device) | |
| spec_lengths = LongTensor([spec.size(-1)]).to(device) | |
| sid_src = LongTensor([original_speaker_id]).to(device) | |
| sid_tgt = LongTensor([target_speaker_id]).to(device) | |
| audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ | |
| 0, 0].data.cpu().float().numpy() | |
| del y, spec, spec_lengths, sid_src, sid_tgt | |
| return "Success", (hps.data.sampling_rate, audio) | |
| return vc_fn | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model") | |
| parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file") | |
| parser.add_argument("--share", default=False, help="make link public (used in colab)") | |
| args = parser.parse_args() | |
| hps = utils.get_hparams_from_file(args.config_dir) | |
| net_g = 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).to(device) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(args.model_dir, net_g, None) | |
| speaker_ids = hps.speakers | |
| speakers = list(hps.speakers.keys()) | |
| tts_fn = create_tts_fn(net_g, hps, speaker_ids) | |
| vc_fn = create_vc_fn(net_g, hps, speaker_ids) | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tab("Text-to-Speech"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| textbox = gr.TextArea(label="Text", | |
| placeholder="Type your sentence here", | |
| value="こんにちわ。", elem_id=f"tts-input") | |
| # select character | |
| char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character') | |
| language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language') | |
| duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, | |
| label='速度 Speed') | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Message") | |
| audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") | |
| btn = gr.Button("Generate!") | |
| btn.click(tts_fn, | |
| inputs=[textbox, char_dropdown, language_dropdown, duration_slider,], | |
| outputs=[text_output, audio_output], | |
| api_name= "vits") | |
| with gr.Tab("Voice Conversion"): | |
| gr.Markdown(""" | |
| 录制或上传声音，并选择要转换的音色。 | |
| """) | |
| with gr.Column(): | |
| record_audio = gr.Audio(label="record your voice", source="microphone") | |
| upload_audio = gr.Audio(label="or upload audio here", source="upload") | |
| source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker") | |
| target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker") | |
| with gr.Column(): | |
| message_box = gr.Textbox(label="Message") | |
| converted_audio = gr.Audio(label='converted audio') | |
| btn = gr.Button("Convert!") | |
| btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], | |
| outputs=[message_box, converted_audio]) | |
| webbrowser.open("http://127.0.0.1:7860") | |
| app.launch(share=args.share) | |