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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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demucs/__pycache__/__init__.cpython-39.pyc
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demucs/apply.py
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@@ -10,11 +10,13 @@ inteprolation between chunks, as well as the "shift trick".
from concurrent.futures import ThreadPoolExecutor
import random
import typing as tp
from multiprocessing import Process,Queue,Pipe
import torch as th
from torch import nn
from torch.nn import functional as F
import tqdm
import tkinter as tk
from .demucs import Demucs
from .hdemucs import HDemucs
@@ -22,6 +24,7 @@ from .utils import center_trim, DummyPoolExecutor
Model = tp.Union[Demucs, HDemucs]
progress_bar_num = 0
class BagOfModels(nn.Module):
def __init__(self, models: tp.List[Model],
@@ -107,7 +110,6 @@ class TensorChunk:
assert out.shape[-1] == target_length
return out
def tensor_chunk(tensor_or_chunk):
if isinstance(tensor_or_chunk, TensorChunk):
return tensor_or_chunk
@@ -115,10 +117,9 @@ def tensor_chunk(tensor_or_chunk):
assert isinstance(tensor_or_chunk, th.Tensor)
return TensorChunk(tensor_or_chunk)
def apply_model(model, mix, shifts=1, split=True,
overlap=0.25, transition_power=1., progress=False, device=None,
num_workers=0, pool=None):
def apply_model(model, mix, gui_progress_bar: tk.Variable, widget_text: tk.Text, update_prog, total_files, file_num, inference_type, shifts=1, split=True,
overlap=0.25, transition_power=1., progress=True, device=None,
num_workers=0, pool=None, segmen=False):
"""
Apply model to a given mixture.
@@ -136,6 +137,12 @@ def apply_model(model, mix, shifts=1, split=True,
When `device` is different from `mix.device`, only local computations will
be on `device`, while the entire tracks will be stored on `mix.device`.
"""
base_text = 'File {file_num}/{total_files} '.format(file_num=file_num,
total_files=total_files)
global fut_length
if device is None:
device = mix.device
else:
@@ -145,7 +152,12 @@ def apply_model(model, mix, shifts=1, split=True,
pool = ThreadPoolExecutor(num_workers)
else:
pool = DummyPoolExecutor()
kwargs = {
'gui_progress_bar': gui_progress_bar,
'widget_text': widget_text,
'update_prog': update_prog,
'segmen': segmen,
'shifts': shifts,
'split': split,
'overlap': overlap,
@@ -153,17 +165,35 @@ def apply_model(model, mix, shifts=1, split=True,
'progress': progress,
'device': device,
'pool': pool,
'total_files': total_files,
'file_num': file_num,
'inference_type': inference_type
}
if isinstance(model, BagOfModels):
# Special treatment for bag of model.
# We explicitely apply multiple times `apply_model` so that the random shifts
# are different for each model.
global bag_num
global current_model
global progress_bar
global prog_bar
#global percent_prog_del
#percent_prog_del = gui_progress_bar.get()
progress_bar = 0
prog_bar = 0
estimates = 0
totals = [0] * len(model.sources)
bag_num = len(model.models)
fut_length = 0
current_model = 0 #(bag_num + 1)
for sub_model, weight in zip(model.models, model.weights):
original_model_device = next(iter(sub_model.parameters())).device
sub_model.to(device)
fut_length += fut_length
current_model += 1
out = apply_model(sub_model, mix, **kwargs)
sub_model.to(original_model_device)
for k, inst_weight in enumerate(weight):
@@ -179,6 +209,7 @@ def apply_model(model, mix, shifts=1, split=True,
model.to(device)
assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
batch, channels, length = mix.shape
if split:
kwargs['split'] = False
out = th.zeros(batch, len(model.sources), channels, length, device=mix.device)
@@ -202,9 +233,26 @@ def apply_model(model, mix, shifts=1, split=True,
future = pool.submit(apply_model, model, chunk, **kwargs)
futures.append((future, offset))
offset += segment
if progress:
futures = tqdm.tqdm(futures, unit_scale=scale, ncols=120, unit='seconds')
for future, offset in futures:
if segmen:
fut_length = len(futures)
full_fut_length = (fut_length * bag_num)
send_back = full_fut_length * 2
progress_bar += 100
prog_bar += 1
full_step = (progress_bar / full_fut_length)
percent_prog = f"{base_text}Demucs Inference Progress: {prog_bar}/{full_fut_length} | {round(full_step)}%"
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)))
widget_text.percentage(percent_prog)
#gui_progress_bar.set(step)
chunk_out = future.result()
chunk_length = chunk_out.shape[-1]
out[..., offset:offset + segment] += (weight[:chunk_length] * chunk_out).to(mix.device)
demucs/utils.py
+63 -7
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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