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import os |
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import numpy as np |
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import torch |
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from torch import no_grad, LongTensor |
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import argparse |
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import commons |
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from mel_processing import spectrogram_torch |
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import utils |
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from models import SynthesizerTrn |
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import gradio as gr |
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import librosa |
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import webbrowser |
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from text import text_to_sequence, _clean_text |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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language_marks = { |
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"Japanese": "", |
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"日本語": "[JA]", |
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"简体中文": "[ZH]", |
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"English": "[EN]", |
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"Mix": "", |
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} |
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lang = ['日本語', '简体中文', 'English', 'Mix'] |
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def get_text(text, hps, is_symbol): |
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text_norm = text_to_sequence( |
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text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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def create_tts_fn(model, hps, speaker_ids): |
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def tts_fn(text, speaker, language, speed): |
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if language is not None: |
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text = language_marks[language] + text + language_marks[language] |
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speaker_id = speaker_ids[speaker] |
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stn_tst = get_text(text, hps, False) |
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with no_grad(): |
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x_tst = stn_tst.unsqueeze(0).to(device) |
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x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) |
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sid = LongTensor([speaker_id]).to(device) |
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, |
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() |
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del stn_tst, x_tst, x_tst_lengths, sid |
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return "Success", (hps.data.sampling_rate, audio) |
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return tts_fn |
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def create_vc_fn(model, hps, speaker_ids): |
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def vc_fn(original_speaker, target_speaker, record_audio, upload_audio): |
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input_audio = record_audio if record_audio is not None else upload_audio |
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if input_audio is None: |
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return "You need to record or upload an audio", None |
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sampling_rate, audio = input_audio |
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original_speaker_id = speaker_ids[original_speaker] |
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target_speaker_id = speaker_ids[target_speaker] |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != hps.data.sampling_rate: |
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audio = librosa.resample( |
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audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) |
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with no_grad(): |
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y = torch.FloatTensor(audio) |
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y = y / max(-y.min(), y.max()) / 0.99 |
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y = y.to(device) |
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y = y.unsqueeze(0) |
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spec = spectrogram_torch(y, hps.data.filter_length, |
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hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, |
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center=False).to(device) |
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spec_lengths = LongTensor([spec.size(-1)]).to(device) |
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sid_src = LongTensor([original_speaker_id]).to(device) |
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sid_tgt = LongTensor([target_speaker_id]).to(device) |
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audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ |
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0, 0].data.cpu().float().numpy() |
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del y, spec, spec_lengths, sid_src, sid_tgt |
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return "Success", (hps.data.sampling_rate, audio) |
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return vc_fn |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_dir", default="./models/G_9700.pth", |
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help="directory to your fine-tuned model") |
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parser.add_argument("--config_dir", default="./configs/modified_finetune_speaker.json", |
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help="directory to your model config file") |
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parser.add_argument("--share", action="store_true", default=False, |
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help="make link public (used in colab)") |
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args = parser.parse_args() |
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hps = utils.get_hparams_from_file(args.config_dir) |
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net_g = SynthesizerTrn( |
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len(hps.symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(args.model_dir, net_g, None) |
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speaker_ids = hps.speakers |
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speakers = list(hps.speakers.keys()) |
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tts_fn = create_tts_fn(net_g, hps, speaker_ids) |
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vc_fn = create_vc_fn(net_g, hps, speaker_ids) |
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app = gr.Blocks() |
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with app: |
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with gr.Tab("Text-to-Speech"): |
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with gr.Row(): |
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with gr.Column(): |
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textbox = gr.TextArea(label="Text", |
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placeholder="Type your sentence here", |
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value="新たなキャラを解放できるようになったようですね。", elem_id=f"tts-input") |
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char_dropdown = gr.Dropdown( |
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choices=speakers, value=speakers[0], label='character') |
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language_dropdown = gr.Dropdown( |
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choices=lang, value=lang[0], label='language') |
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, |
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label='速度 Speed') |
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with gr.Column(): |
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text_output = gr.Textbox(label="Message") |
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audio_output = gr.Audio( |
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label="Output Audio", elem_id="tts-audio") |
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btn = gr.Button("Generate!") |
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btn.click(tts_fn, |
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inputs=[textbox, char_dropdown, |
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language_dropdown, duration_slider, ], |
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outputs=[text_output, audio_output]) |
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with gr.Tab("Voice Conversion"): |
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gr.Markdown(""" |
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录制或上传声音，并选择要转换的音色。 |
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""") |
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with gr.Column(): |
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record_audio = gr.Audio( |
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label="record your voice", source="microphone") |
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upload_audio = gr.Audio( |
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label="or upload audio here", source="upload") |
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source_speaker = gr.Dropdown( |
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choices=speakers, value=speakers[0], label="source speaker") |
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target_speaker = gr.Dropdown( |
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choices=speakers, value=speakers[0], label="target speaker") |
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with gr.Column(): |
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message_box = gr.Textbox(label="Message") |
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converted_audio = gr.Audio(label='converted audio') |
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btn = gr.Button("Convert!") |
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btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], |
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outputs=[message_box, converted_audio]) |
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webbrowser.open("http://127.0.0.1:7899") |
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app.queue(concurrency_count=1, api_open=True).launch(share=args.share) |
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