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",
"teio": "https://huggingface.co/spaces/sayashi/sovits-teio",
"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"
"\n\n"
"[](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n"
"[](https://huggingface.co/spaces/sayashi/sovits-models?duplicate=true)\n\n"
"[](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)