Spaces:
Paused
Paused
File size: 3,942 Bytes
38548f2 385c19b 38548f2 0293e4c 6db8e8a 38548f2 0293e4c 38548f2 385c19b 38548f2 0293e4c 38548f2 0293e4c 38548f2 24caa86 38548f2 4a7b229 38548f2 24caa86 0293e4c 4a7b229 0293e4c 24caa86 38548f2 24caa86 4a7b229 24caa86 38548f2 24caa86 4a7b229 24caa86 38548f2 0293e4c 38548f2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
from glob import glob
import os
from typing import Tuple
from demucs.separate import main as demucs
import gradio as gr
import numpy as np
import soundfile as sf
from configs.config import Config
from infer.modules.vc.modules import VC
from zero import zero
from model import device
@zero(duration=120)
def infer(
exp_dir: str, original_audio: str, f0add: int, index_rate: float, protect: float
) -> Tuple[int, np.ndarray]:
model = os.path.join(exp_dir, "model.pth")
if not os.path.exists(model):
raise gr.Error("Model not found")
index = glob(f"{exp_dir}/added_*.index")
if index:
index = index[0]
else:
index = None
base = os.path.basename(original_audio)
base = os.path.splitext(base)[0]
demucs(
["--two-stems", "vocals", "-d", str(device), "-n", "htdemucs", original_audio]
)
out = os.path.join("separated", "htdemucs", base, "vocals.wav")
cfg = Config()
vc = VC(cfg)
vc.get_vc(model)
_, wav_opt = vc.vc_single(
0,
out,
f0add,
None,
"rmvpe",
index,
None,
index_rate,
3, # this only has effect when f0_method is "harvest"
0,
1,
protect,
)
sr = wav_opt[0]
data = wav_opt[1]
return sr, data
def merge(exp_dir: str, original_audio: str, vocal: Tuple[int, np.ndarray]) -> str:
base = os.path.basename(original_audio)
base = os.path.splitext(base)[0]
music = os.path.join("separated", "htdemucs", base, "no_vocals.wav")
tmp = os.path.join(exp_dir, "tmp.wav")
sf.write(tmp, vocal[1], vocal[0])
os.system(
f"ffmpeg -i {music} -i {tmp} -filter_complex '[1]volume=2[a];[0][a]amix=inputs=2:duration=first:dropout_transition=2' -ac 2 -y {tmp}.merged.mp3"
)
return f"{tmp}.merged.mp3"
class InferenceTab:
def __init__(self):
pass
def ui(self):
gr.Markdown("# Inference")
gr.Markdown(
"After trained model is pruned, you can use it to infer on new music. \n"
"Upload the original audio and adjust the F0 add value to generate the inferred audio."
)
with gr.Row():
self.original_audio = gr.Audio(
label="Upload original audio",
type="filepath",
show_download_button=True,
)
with gr.Column():
self.f0add = gr.Slider(
label="F0 +/-",
minimum=-16,
maximum=16,
step=1,
value=0,
)
self.index_rate = gr.Slider(
label="Index rate",
minimum=-0,
maximum=1,
step=0.01,
value=0.5,
)
self.protect = gr.Slider(
label="Protect",
minimum=0,
maximum=1,
step=0.01,
value=0.33,
)
self.infer_btn = gr.Button(value="Infer", variant="primary")
with gr.Row():
self.infer_output = gr.Audio(
label="Inferred audio", show_download_button=True, format="mp3"
)
with gr.Row():
self.merge_output = gr.Audio(
label="Merged audio", show_download_button=True, format="mp3"
)
def build(self, exp_dir: gr.Textbox):
self.infer_btn.click(
fn=infer,
inputs=[
exp_dir,
self.original_audio,
self.f0add,
self.index_rate,
self.protect,
],
outputs=[self.infer_output],
).success(
fn=merge,
inputs=[exp_dir, self.original_audio, self.infer_output],
outputs=[self.merge_output],
)
|