| 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") |
| |
| 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='go') |
| 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) |
|
|
|
|
