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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)
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