File size: 5,238 Bytes
da6d421 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import os
import gradio as gr
import librosa
import numpy as np
from pathlib import Path
import inference.infer_tool as infer_tool
import utils
from inference.infer_tool import Svc
import logging
import webbrowser
import argparse
import soundfile
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, slice_db, noise_scale, pad_seconds):
if input_audio is None:
return "You need to select an audio", None
raw_audio_path = f"raw/{input_audio}"
if "." not in raw_audio_path:
raw_audio_path += ".wav"
infer_tool.format_wav(raw_audio_path)
wav_path = Path(raw_audio_path).with_suffix('.wav')
_audio = model.slice_inference(
wav_path, sid, vc_transform, slice_db,
cluster_infer_ratio=0,
auto_predict_f0=auto_f0,
noice_scale=noise_scale,
pad_seconds=pad_seconds)
model.clear_empty()
return "Success", (44100, _audio)
return vc_fn
def refresh_raw_wav():
return gr.Dropdown.update(choices=os.listdir("raw"))
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")
parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
hubert_model = utils.get_hubert_model().to(args.device)
models = []
raw = os.listdir("raw")
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():
with gr.Row():
vc_input = gr.Dropdown(label="Input audio", choices=raw)
vc_refresh = gr.Button("🔁", variant="primary")
vc_transform = gr.Number(label="vc_transform", value=0)
slice_db = gr.Number(label="slice_db", value=-40)
noise_scale = gr.Number(label="noise_scale", value=0.4)
pad_seconds = gr.Number(label="pad_seconds", value=0.5)
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, slice_db, noise_scale, pad_seconds], [vc_output1, vc_output2])
vc_refresh.click(refresh_raw_wav, [], [vc_input])
if args.colab:
webbrowser.open("http://127.0.0.1:7860")
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) |