heimoshuiyu/ultimatevocalremovergui
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This commit is contained in:
Anjok07
2022-08-23 15:04:39 -05:00
committed by GitHub
parent f7e37b492b
commit 7778d4c3fb
19 changed files with 1735 additions and 581 deletions
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70
demucs/utils.py
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@@ -22,6 +22,7 @@ import socket
import tempfile
import warnings
import zlib
import tkinter as tk
from diffq import UniformQuantizer, DiffQuantizer
import torch as th
@@ -228,7 +229,7 @@ def tensor_chunk(tensor_or_chunk):
return TensorChunk(tensor_or_chunk)
def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
def apply_model_v1(model, mix, gui_progress_bar: tk.Variable, widget_text: tk.Text, update_prog, total_files, file_num, inference_type, shifts=None, split=False, progress=False, segmen=True):
"""
Apply model to a given mixture.
@@ -242,6 +243,10 @@ def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
Useful for model with large memory footprint like Tasnet.
progress (bool): if True, show a progress bar (requires split=True)
"""
base_text = 'File {file_num}/{total_files} '.format(file_num=file_num,
total_files=total_files)
channels, length = mix.size()
device = mix.device
if split:
@@ -249,11 +254,31 @@ def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
shift = model.samplerate * 10
offsets = range(0, length, shift)
scale = 10
progress_bar = 0
prog_bar = 0
if progress:
offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
for offset in offsets:
if segmen:
fut_length = len(offsets)
send_back = fut_length * 2
progress_bar += 100
prog_bar += 1
if inference_type == 'demucs_only':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.1 + (1.7/send_back * prog_bar)))
elif inference_type == 'inference_mdx':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.35 + (1.05/send_back * prog_bar)))
elif inference_type == 'inference_vr':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.6 + (0.7/send_back * prog_bar)))
step = (progress_bar / fut_length)
percent_prog = f"{base_text}Demucs v1 Inference Progress: {prog_bar}/{fut_length} | {round(step)}%"
widget_text.percentage(percent_prog)
#gui_progress_bar.set(step)
chunk = mix[..., offset:offset + shift]
chunk_out = apply_model_v1(model, chunk, shifts=shifts)
chunk_out = apply_model_v1(model, chunk, gui_progress_bar, widget_text, update_prog, total_files, file_num, inference_type, shifts=shifts)
out[..., offset:offset + shift] = chunk_out
offset += shift
return out
@@ -265,7 +290,7 @@ def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
out = 0
for offset in offsets[:shifts]:
shifted = mix[..., offset:offset + length + max_shift]
shifted_out = apply_model_v1(model, shifted)
shifted_out = apply_model_v1(model, shifted, gui_progress_bar, widget_text, update_prog, total_files, file_num, inference_type)
out += shifted_out[..., max_shift - offset:max_shift - offset + length]
out /= shifts
return out
@@ -277,8 +302,8 @@ def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
out = model(padded.unsqueeze(0))[0]
return center_trim(out, mix)
def apply_model_v2(model, mix, shifts=None, split=False,
overlap=0.25, transition_power=1., progress=False):
def apply_model_v2(model, mix, gui_progress_bar: tk.Variable, widget_text: tk.Text, update_prog, total_files, file_num, inference_type, shifts=None, split=False,
overlap=0.25, transition_power=1., progress=False, segmen=True):
"""
Apply model to a given mixture.
@@ -292,6 +317,16 @@ def apply_model_v2(model, mix, shifts=None, split=False,
Useful for model with large memory footprint like Tasnet.
progress (bool): if True, show a progress bar (requires split=True)
"""
global prog_space
global percent_prog
percent_prog = 0
base_text = 'File {file_num}/{total_files} '.format(file_num=file_num,
total_files=total_files)
#widget_text.remove(percent_prog)
assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
device = mix.device
channels, length = mix.shape
@@ -313,9 +348,30 @@ def apply_model_v2(model, mix, shifts=None, split=False,
# If the overlap < 50%, this will translate to linear transition when
# transition_power is 1.
weight = (weight / weight.max())**transition_power
progress_bar = 0
prog_bar = 0
for offset in offsets:
if segmen:
fut_length = len(offsets)
send_back = fut_length * 2
progress_bar += 100
prog_bar += 1
if inference_type == 'demucs_only':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.1 + (1.7/send_back * prog_bar)))
elif inference_type == 'inference_mdx':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.35 + (1.05/send_back * prog_bar)))
elif inference_type == 'inference_vr':
update_prog(gui_progress_bar, total_files, file_num,
step=(0.6 + (0.7/send_back * prog_bar)))
step = (progress_bar / fut_length)
percent_prog = f"{base_text}Demucs v2 Inference Progress: {prog_bar}/{fut_length} | {round(step)}%"
prog_space = len(percent_prog)
prog_space = prog_bar*prog_space
widget_text.percentage(percent_prog)
chunk = TensorChunk(mix, offset, segment)
chunk_out = apply_model_v2(model, chunk, shifts=shifts)
chunk_out = apply_model_v2(model, chunk, gui_progress_bar, widget_text, update_prog, total_files, file_num, inference_type, shifts=shifts)
chunk_length = chunk_out.shape[-1]
out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out
sum_weight[offset:offset + segment] += weight[:chunk_length]
@@ -331,7 +387,7 @@ def apply_model_v2(model, mix, shifts=None, split=False,
for _ in range(shifts):
offset = random.randint(0, max_shift)
shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
shifted_out = apply_model_v2(model, shifted)
shifted_out = apply_model_v2(model, shifted, gui_progress_bar, widget_text, update_prog, total_files, file_num, inference_type)
out += shifted_out[..., max_shift - offset:]
out /= shifts
return out