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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 argparse
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):
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
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 = []
for f in os.listdir("models"):
name = f
model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device, hubert_model=hubert_model)
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"
"[Open In Colab](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)"
" without queue and length limitation.\n\n"
"[Original Repo](https://github.com/svc-develop-team/so-vits-svc)\n\n"
"Other models:\n"
"[rudolf](https://huggingface.co/spaces/sayashi/sovits-rudolf)\n"
"[teio](https://huggingface.co/spaces/sayashi/sovits-teio)\n"
"[goldship](https://huggingface.co/spaces/sayashi/sovits-goldship)\n"
"[tannhauser](https://huggingface.co/spaces/sayashi/sovits-tannhauser)\n"
)
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)
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], [vc_output1, vc_output2])
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) |