sovits-teio

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sovits-teio / app.py

SayaSS

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e302df6 about 1 year ago

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	import os
	import io
	import gradio as gr
	import librosa
	import numpy as np
	import utils
	from inference.infer_tool import Svc
	import logging
	import soundfile
	import asyncio
	import argparse
	import edge_tts
	import gradio.processing_utils as gr_processing_utils
	logging.getLogger('numba').setLevel(logging.WARNING)
	logging.getLogger('markdown_it').setLevel(logging.WARNING)
	logging.getLogger('urllib3').setLevel(logging.WARNING)
	logging.getLogger('matplotlib').setLevel(logging.WARNING)

	limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces

	audio_postprocess_ori = gr.Audio.postprocess

	def audio_postprocess(self, y):
	data = audio_postprocess_ori(self, y)
	if data is None:
	return None
	return gr_processing_utils.encode_url_or_file_to_base64(data["name"])


	gr.Audio.postprocess = audio_postprocess
	def create_vc_fn(model, sid):
	def vc_fn(input_audio, vc_transform, auto_f0, tts_text, tts_voice, tts_mode):
	if tts_mode:
	if len(tts_text) > 100 and limitation:
	return "Text is too long", None
	if tts_text is None or tts_voice is None:
	return "You need to enter text and select a voice", None
	asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
	audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
	raw_path = io.BytesIO()
	soundfile.write(raw_path, audio, 16000, format="wav")
	raw_path.seek(0)
	out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
	auto_predict_f0=auto_f0,
	)
	return "Success", (44100, out_audio.cpu().numpy())
	if input_audio is None:
	return "You need to upload an audio", None
	sampling_rate, audio = input_audio
	duration = audio.shape[0] / sampling_rate
	if duration > 20 and limitation:
	return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
	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)
	out_audio, out_sr = model.infer(sid, vc_transform, raw_path,
	auto_predict_f0=auto_f0,
	)
	return "Success", (44100, out_audio.cpu().numpy())
	return vc_fn

	def change_to_tts_mode(tts_mode):
	if tts_mode:
	return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), gr.Checkbox.update(value=True)
	else:
	return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), 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()
	hubert_model = utils.get_hubert_model().to(args.device)
	models = []
	others = {
	"rudolf": "https://huggingface.co/spaces/sayashi/sovits-rudolf",
	"alice": "https://huggingface.co/spaces/sayashi/sovits-models",
	"goldship": "https://huggingface.co/spaces/sayashi/sovits-goldship",
	"tannhauser": "https://huggingface.co/spaces/sayashi/sovits-tannhauser"
	}
	voices = []
	tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
	for r in tts_voice_list:
	voices.append(f"{r['ShortName']}-{r['Gender']}")
	for f in os.listdir("models"):
	name = f
	model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device)
	cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None
	models.append((name, cover, create_vc_fn(model, name)))
	with gr.Blocks() as app:
	gr.Markdown(
	"# <center> Sovits Models\n"
	"## <center> The input audio should be clean and pure voice without background music.\n"
	"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n"
	"[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n"
	"[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/sovits-teio?duplicate=true)\n\n"
	"[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/svc-develop-team/so-vits-svc)"

	)
	with gr.Tabs():
	for (name, cover, vc_fn) in models:
	with gr.TabItem(name):
	with gr.Row():
	gr.Markdown(
	'<div align="center">'
	f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
	'</div>'
	)
	with gr.Row():
	with gr.Column():
	vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
	vc_transform = gr.Number(label="vc_transform", value=0)
	auto_f0 = gr.Checkbox(label="auto_f0", value=False)
	tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
	tts_text = gr.Textbox(visible=False, label="TTS text (100 words limitation)" if limitation else "TTS text")
	tts_voice = gr.Dropdown(choices=voices, visible=False)
	vc_submit = gr.Button("Generate", variant="primary")
	with gr.Column():
	vc_output1 = gr.Textbox(label="Output Message")
	vc_output2 = gr.Audio(label="Output Audio")
	vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2])
	tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice, auto_f0])
	for category, link in others.items():
	with gr.TabItem(category):
	gr.Markdown(
	f'''
	<center>
	<h2>Click to Go</h2>
	<a href="{link}">
	<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg"
	</a>
	</center>
	'''
	)
	app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)