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| import gradio as gr | |
| import edge_tts | |
| import asyncio | |
| import librosa | |
| import soundfile | |
| import io | |
| import argparse | |
| import numpy as np | |
| from inference.infer_tool import Svc | |
| def get_or_create_eventloop(): | |
| try: | |
| return asyncio.get_event_loop() | |
| except RuntimeError as ex: | |
| if "There is no current event loop in thread" in str(ex): | |
| loop = asyncio.new_event_loop() | |
| asyncio.set_event_loop(loop) | |
| return asyncio.get_event_loop() | |
| def tts_get_voices_list(): | |
| voices = [] | |
| tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) | |
| for item in tts_voice_list: | |
| voices.append(item['ShortName']) | |
| return voices | |
| def infer(txt, tts_voice, input_audio, predict_f0, audio_mode): | |
| if audio_mode: | |
| if input_audio is None: | |
| return 'Please upload your audio file' | |
| sampling_rate, audio = input_audio | |
| duration = audio.shape[0] / sampling_rate | |
| if duration > 30: | |
| return 'The audio file is too long, Please upload audio file that less than 30 seconds' | |
| 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 != 16000: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) | |
| raw_path = io.BytesIO() | |
| soundfile.write(raw_path, audio, 16000, format="wav") | |
| raw_path.seek(0) | |
| model = Svc(fr"Herta-Svc/G_10000.pth", f"Herta-Svc/config.json", device = 'cpu') | |
| out_audio, out_sr = model.infer('speaker0', 0, raw_path, auto_predict_f0 = predict_f0,) | |
| return (44100, out_audio.cpu().numpy()) | |
| tts = asyncio.run(edge_tts.Communicate(txt, tts_voice).save('audio.mp3')) | |
| audio, sr = librosa.load('audio.mp3', sr=16000, mono=True) | |
| raw_path = io.BytesIO() | |
| soundfile.write(raw_path, audio, 16000, format="wav") | |
| raw_path.seek(0) | |
| model = Svc(fr"Herta-Svc/G_10000.pth", f"Herta-Svc/config.json", device = 'cpu') | |
| out_audio, out_sr = model.infer('speaker0', 0, raw_path, auto_predict_f0 = True,) | |
| return (44100, out_audio.cpu().numpy()) | |
| def change_to_audio_mode(audio_mode): | |
| if audio_mode: | |
| return gr.Audio.update(visible = True), gr.Textbox.update(visible= False), gr.Dropdown.update(visible = False), gr.Checkbox.update(value = True) | |
| else: | |
| return gr.Audio.update(visible = False), gr.Textbox.update(visible= True), gr.Dropdown.update(visible = True), gr.Checkbox.update(value = False) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument('--api', action="store_true", default=False) | |
| parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
| args = parser.parse_args() | |
| loop = asyncio.new_event_loop() | |
| asyncio.set_event_loop(loop) | |
| with gr.Blocks() as app: | |
| with gr.Tabs(): | |
| with gr.TabItem('Herta'): | |
| title = gr.Label('Herta Sovits Model') | |
| cover = gr.Markdown('<div align="center">' | |
| f'<img style="width:auto;height:300px;" src="file/Herta-Svc/herta.png">' | |
| '</div>') | |
| tts_text = gr.Textbox(label="TTS text (100 words limitation)") | |
| audio_input = gr.Audio(label = 'Please upload audio file that less than 30 seconds', visible = False) | |
| tts_voice = gr.Dropdown(choices= tts_get_voices_list()) | |
| predict_f0 = gr.Checkbox(label = 'Auto predict F0', value = False) | |
| audio_mode = gr.Checkbox(label = 'Upload audio instead', value = False) | |
| audio_output = gr.Audio(label="Output Audio") | |
| btn_submit = gr.Button("Generate") | |
| btn_submit.click(infer, [tts_text, tts_voice, audio_input, predict_f0, audio_mode], [audio_output]) | |
| audio_mode.change(change_to_audio_mode, audio_mode, [audio_input, tts_text, tts_voice]) | |
| app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) | |