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demucs/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

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demucs/__main__.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import sys
import time
from dataclasses import dataclass, field
from fractions import Fraction
import torch as th
from torch import distributed, nn
from torch.nn.parallel.distributed import DistributedDataParallel
from .augment import FlipChannels, FlipSign, Remix, Shift
from .compressed import StemsSet, build_musdb_metadata, get_musdb_tracks
from .model import Demucs
from .parser import get_name, get_parser
from .raw import Rawset
from .tasnet import ConvTasNet
from .test import evaluate
from .train import train_model, validate_model
from .utils import human_seconds, load_model, save_model, sizeof_fmt
@dataclass
class SavedState:
metrics: list = field(default_factory=list)
last_state: dict = None
best_state: dict = None
optimizer: dict = None
def main():
parser = get_parser()
args = parser.parse_args()
name = get_name(parser, args)
print(f"Experiment {name}")
if args.musdb is None and args.rank == 0:
print(
"You must provide the path to the MusDB dataset with the --musdb flag. "
"To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.",
file=sys.stderr)
sys.exit(1)
eval_folder = args.evals / name
eval_folder.mkdir(exist_ok=True, parents=True)
args.logs.mkdir(exist_ok=True)
metrics_path = args.logs / f"{name}.json"
eval_folder.mkdir(exist_ok=True, parents=True)
args.checkpoints.mkdir(exist_ok=True, parents=True)
args.models.mkdir(exist_ok=True, parents=True)
if args.device is None:
device = "cpu"
if th.cuda.is_available():
device = "cuda"
else:
device = args.device
th.manual_seed(args.seed)
# Prevents too many threads to be started when running `museval` as it can be quite
# inefficient on NUMA architectures.
os.environ["OMP_NUM_THREADS"] = "1"
if args.world_size > 1:
if device != "cuda" and args.rank == 0:
print("Error: distributed training is only available with cuda device", file=sys.stderr)
sys.exit(1)
th.cuda.set_device(args.rank % th.cuda.device_count())
distributed.init_process_group(backend="nccl",
init_method="tcp://" + args.master,
rank=args.rank,
world_size=args.world_size)
checkpoint = args.checkpoints / f"{name}.th"
checkpoint_tmp = args.checkpoints / f"{name}.th.tmp"
if args.restart and checkpoint.exists():
checkpoint.unlink()
if args.test:
args.epochs = 1
args.repeat = 0
model = load_model(args.models / args.test)
elif args.tasnet:
model = ConvTasNet(audio_channels=args.audio_channels, samplerate=args.samplerate, X=args.X)
else:
model = Demucs(
audio_channels=args.audio_channels,
channels=args.channels,
context=args.context,
depth=args.depth,
glu=args.glu,
growth=args.growth,
kernel_size=args.kernel_size,
lstm_layers=args.lstm_layers,
rescale=args.rescale,
rewrite=args.rewrite,
sources=4,
stride=args.conv_stride,
upsample=args.upsample,
samplerate=args.samplerate
)
model.to(device)
if args.show:
print(model)
size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters()))
print(f"Model size {size}")
return
optimizer = th.optim.Adam(model.parameters(), lr=args.lr)
try:
saved = th.load(checkpoint, map_location='cpu')
except IOError:
saved = SavedState()
else:
model.load_state_dict(saved.last_state)
optimizer.load_state_dict(saved.optimizer)
if args.save_model:
if args.rank == 0:
model.to("cpu")
model.load_state_dict(saved.best_state)
save_model(model, args.models / f"{name}.th")
return
if args.rank == 0:
done = args.logs / f"{name}.done"
if done.exists():
done.unlink()
if args.augment:
augment = nn.Sequential(FlipSign(), FlipChannels(), Shift(args.data_stride),
Remix(group_size=args.remix_group_size)).to(device)
else:
augment = Shift(args.data_stride)
if args.mse:
criterion = nn.MSELoss()
else:
criterion = nn.L1Loss()
# Setting number of samples so that all convolution windows are full.
# Prevents hard to debug mistake with the prediction being shifted compared
# to the input mixture.
samples = model.valid_length(args.samples)
print(f"Number of training samples adjusted to {samples}")
if args.raw:
train_set = Rawset(args.raw / "train",
samples=samples + args.data_stride,
channels=args.audio_channels,
streams=[0, 1, 2, 3, 4],
stride=args.data_stride)
valid_set = Rawset(args.raw / "valid", channels=args.audio_channels)
else:
if not args.metadata.is_file() and args.rank == 0:
build_musdb_metadata(args.metadata, args.musdb, args.workers)
if args.world_size > 1:
distributed.barrier()
metadata = json.load(open(args.metadata))
duration = Fraction(samples + args.data_stride, args.samplerate)
stride = Fraction(args.data_stride, args.samplerate)
train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"),
metadata,
duration=duration,
stride=stride,
samplerate=args.samplerate,
channels=args.audio_channels)
valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"),
metadata,
samplerate=args.samplerate,
channels=args.audio_channels)
best_loss = float("inf")
for epoch, metrics in enumerate(saved.metrics):
print(f"Epoch {epoch:03d}: "
f"train={metrics['train']:.8f} "
f"valid={metrics['valid']:.8f} "
f"best={metrics['best']:.4f} "
f"duration={human_seconds(metrics['duration'])}")
best_loss = metrics['best']
if args.world_size > 1:
dmodel = DistributedDataParallel(model,
device_ids=[th.cuda.current_device()],
output_device=th.cuda.current_device())
else:
dmodel = model
for epoch in range(len(saved.metrics), args.epochs):
begin = time.time()
model.train()
train_loss = train_model(epoch,
train_set,
dmodel,
criterion,
optimizer,
augment,
batch_size=args.batch_size,
device=device,
repeat=args.repeat,
seed=args.seed,
workers=args.workers,
world_size=args.world_size)
model.eval()
valid_loss = validate_model(epoch,
valid_set,
model,
criterion,
device=device,
rank=args.rank,
split=args.split_valid,
world_size=args.world_size)
duration = time.time() - begin
if valid_loss < best_loss:
best_loss = valid_loss
saved.best_state = {
key: value.to("cpu").clone()
for key, value in model.state_dict().items()
}
saved.metrics.append({
"train": train_loss,
"valid": valid_loss,
"best": best_loss,
"duration": duration
})
if args.rank == 0:
json.dump(saved.metrics, open(metrics_path, "w"))
saved.last_state = model.state_dict()
saved.optimizer = optimizer.state_dict()
if args.rank == 0 and not args.test:
th.save(saved, checkpoint_tmp)
checkpoint_tmp.rename(checkpoint)
print(f"Epoch {epoch:03d}: "
f"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} "
f"duration={human_seconds(duration)}")
del dmodel
model.load_state_dict(saved.best_state)
if args.eval_cpu:
device = "cpu"
model.to(device)
model.eval()
evaluate(model,
args.musdb,
eval_folder,
rank=args.rank,
world_size=args.world_size,
device=device,
save=args.save,
split=args.split_valid,
shifts=args.shifts,
workers=args.eval_workers)
model.to("cpu")
save_model(model, args.models / f"{name}.th")
if args.rank == 0:
print("done")
done.write_text("done")
if __name__ == "__main__":
main()

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demucs/apply.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Code to apply a model to a mix. It will handle chunking with overlaps and
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
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],
weights: tp.Optional[tp.List[tp.List[float]]] = None,
segment: tp.Optional[float] = None):
"""
Represents a bag of models with specific weights.
You should call `apply_model` rather than calling directly the forward here for
optimal performance.
Args:
models (list[nn.Module]): list of Demucs/HDemucs models.
weights (list[list[float]]): list of weights. If None, assumed to
be all ones, otherwise it should be a list of N list (N number of models),
each containing S floats (S number of sources).
segment (None or float): overrides the `segment` attribute of each model
(this is performed inplace, be careful if you reuse the models passed).
"""
super().__init__()
assert len(models) > 0
first = models[0]
for other in models:
assert other.sources == first.sources
assert other.samplerate == first.samplerate
assert other.audio_channels == first.audio_channels
if segment is not None:
other.segment = segment
self.audio_channels = first.audio_channels
self.samplerate = first.samplerate
self.sources = first.sources
self.models = nn.ModuleList(models)
if weights is None:
weights = [[1. for _ in first.sources] for _ in models]
else:
assert len(weights) == len(models)
for weight in weights:
assert len(weight) == len(first.sources)
self.weights = weights
def forward(self, x):
raise NotImplementedError("Call `apply_model` on this.")
class TensorChunk:
def __init__(self, tensor, offset=0, length=None):
total_length = tensor.shape[-1]
assert offset >= 0
assert offset < total_length
if length is None:
length = total_length - offset
else:
length = min(total_length - offset, length)
self.tensor = tensor
self.offset = offset
self.length = length
self.device = tensor.device
@property
def shape(self):
shape = list(self.tensor.shape)
shape[-1] = self.length
return shape
def padded(self, target_length):
delta = target_length - self.length
total_length = self.tensor.shape[-1]
assert delta >= 0
start = self.offset - delta // 2
end = start + target_length
correct_start = max(0, start)
correct_end = min(total_length, end)
pad_left = correct_start - start
pad_right = end - correct_end
out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
assert out.shape[-1] == target_length
return out
def tensor_chunk(tensor_or_chunk):
if isinstance(tensor_or_chunk, TensorChunk):
return tensor_or_chunk
else:
assert isinstance(tensor_or_chunk, th.Tensor)
return TensorChunk(tensor_or_chunk)
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.
Args:
shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
and apply the oppositve shift to the output. This is repeated `shifts` time and
all predictions are averaged. This effectively makes the model time equivariant
and improves SDR by up to 0.2 points.
split (bool): if True, the input will be broken down in 8 seconds extracts
and predictions will be performed individually on each and concatenated.
Useful for model with large memory footprint like Tasnet.
progress (bool): if True, show a progress bar (requires split=True)
device (torch.device, str, or None): if provided, device on which to
execute the computation, otherwise `mix.device` is assumed.
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:
device = th.device(device)
if pool is None:
if num_workers > 0 and device.type == 'cpu':
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,
'transition_power': transition_power,
'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):
out[:, k, :, :] *= inst_weight
totals[k] += inst_weight
estimates += out
del out
for k in range(estimates.shape[1]):
estimates[:, k, :, :] /= totals[k]
return estimates
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)
sum_weight = th.zeros(length, device=mix.device)
segment = int(model.samplerate * model.segment)
stride = int((1 - overlap) * segment)
offsets = range(0, length, stride)
scale = stride / model.samplerate
# We start from a triangle shaped weight, with maximal weight in the middle
# of the segment. Then we normalize and take to the power `transition_power`.
# Large values of transition power will lead to sharper transitions.
weight = th.cat([th.arange(1, segment // 2 + 1, device=device),
th.arange(segment - segment // 2, 0, -1, device=device)])
assert len(weight) == segment
# If the overlap < 50%, this will translate to linear transition when
# transition_power is 1.
weight = (weight / weight.max())**transition_power
futures = []
for offset in offsets:
chunk = TensorChunk(mix, offset, segment)
future = pool.submit(apply_model, model, chunk, **kwargs)
futures.append((future, offset))
offset += segment
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)
sum_weight[offset:offset + segment] += weight[:chunk_length].to(mix.device)
assert sum_weight.min() > 0
out /= sum_weight
return out
elif shifts:
kwargs['shifts'] = 0
max_shift = int(0.5 * model.samplerate)
mix = tensor_chunk(mix)
padded_mix = mix.padded(length + 2 * max_shift)
out = 0
for _ in range(shifts):
offset = random.randint(0, max_shift)
shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
shifted_out = apply_model(model, shifted, **kwargs)
out += shifted_out[..., max_shift - offset:]
out /= shifts
return out
else:
if hasattr(model, 'valid_length'):
valid_length = model.valid_length(length)
else:
valid_length = length
mix = tensor_chunk(mix)
padded_mix = mix.padded(valid_length).to(device)
with th.no_grad():
out = model(padded_mix)
return center_trim(out, length)

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demucs/demucs.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import typing as tp
import julius
import torch
from torch import nn
from torch.nn import functional as F
from .states import capture_init
from .utils import center_trim, unfold
class BLSTM(nn.Module):
"""
BiLSTM with same hidden units as input dim.
If `max_steps` is not None, input will be splitting in overlapping
chunks and the LSTM applied separately on each chunk.
"""
def __init__(self, dim, layers=1, max_steps=None, skip=False):
super().__init__()
assert max_steps is None or max_steps % 4 == 0
self.max_steps = max_steps
self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
self.linear = nn.Linear(2 * dim, dim)
self.skip = skip
def forward(self, x):
B, C, T = x.shape
y = x
framed = False
if self.max_steps is not None and T > self.max_steps:
width = self.max_steps
stride = width // 2
frames = unfold(x, width, stride)
nframes = frames.shape[2]
framed = True
x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)
x = x.permute(2, 0, 1)
x = self.lstm(x)[0]
x = self.linear(x)
x = x.permute(1, 2, 0)
if framed:
out = []
frames = x.reshape(B, -1, C, width)
limit = stride // 2
for k in range(nframes):
if k == 0:
out.append(frames[:, k, :, :-limit])
elif k == nframes - 1:
out.append(frames[:, k, :, limit:])
else:
out.append(frames[:, k, :, limit:-limit])
out = torch.cat(out, -1)
out = out[..., :T]
x = out
if self.skip:
x = x + y
return x
def rescale_conv(conv, reference):
"""Rescale initial weight scale. It is unclear why it helps but it certainly does.
"""
std = conv.weight.std().detach()
scale = (std / reference)**0.5
conv.weight.data /= scale
if conv.bias is not None:
conv.bias.data /= scale
def rescale_module(module, reference):
for sub in module.modules():
if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):
rescale_conv(sub, reference)
class LayerScale(nn.Module):
"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
This rescales diagonaly residual outputs close to 0 initially, then learnt.
"""
def __init__(self, channels: int, init: float = 0):
super().__init__()
self.scale = nn.Parameter(torch.zeros(channels, requires_grad=True))
self.scale.data[:] = init
def forward(self, x):
return self.scale[:, None] * x
class DConv(nn.Module):
"""
New residual branches in each encoder layer.
This alternates dilated convolutions, potentially with LSTMs and attention.
Also before entering each residual branch, dimension is projected on a smaller subspace,
e.g. of dim `channels // compress`.
"""
def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4,
norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True,
kernel=3, dilate=True):
"""
Args:
channels: input/output channels for residual branch.
compress: amount of channel compression inside the branch.
depth: number of layers in the residual branch. Each layer has its own
projection, and potentially LSTM and attention.
init: initial scale for LayerNorm.
norm: use GroupNorm.
attn: use LocalAttention.
heads: number of heads for the LocalAttention.
ndecay: number of decay controls in the LocalAttention.
lstm: use LSTM.
gelu: Use GELU activation.
kernel: kernel size for the (dilated) convolutions.
dilate: if true, use dilation, increasing with the depth.
"""
super().__init__()
assert kernel % 2 == 1
self.channels = channels
self.compress = compress
self.depth = abs(depth)
dilate = depth > 0
norm_fn: tp.Callable[[int], nn.Module]
norm_fn = lambda d: nn.Identity() # noqa
if norm:
norm_fn = lambda d: nn.GroupNorm(1, d) # noqa
hidden = int(channels / compress)
act: tp.Type[nn.Module]
if gelu:
act = nn.GELU
else:
act = nn.ReLU
self.layers = nn.ModuleList([])
for d in range(self.depth):
dilation = 2 ** d if dilate else 1
padding = dilation * (kernel // 2)
mods = [
nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),
norm_fn(hidden), act(),
nn.Conv1d(hidden, 2 * channels, 1),
norm_fn(2 * channels), nn.GLU(1),
LayerScale(channels, init),
]
if attn:
mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
if lstm:
mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))
layer = nn.Sequential(*mods)
self.layers.append(layer)
def forward(self, x):
for layer in self.layers:
x = x + layer(x)
return x
class LocalState(nn.Module):
"""Local state allows to have attention based only on data (no positional embedding),
but while setting a constraint on the time window (e.g. decaying penalty term).
Also a failed experiments with trying to provide some frequency based attention.
"""
def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):
super().__init__()
assert channels % heads == 0, (channels, heads)
self.heads = heads
self.nfreqs = nfreqs
self.ndecay = ndecay
self.content = nn.Conv1d(channels, channels, 1)
self.query = nn.Conv1d(channels, channels, 1)
self.key = nn.Conv1d(channels, channels, 1)
if nfreqs:
self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
if ndecay:
self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
# Initialize decay close to zero (there is a sigmoid), for maximum initial window.
self.query_decay.weight.data *= 0.01
assert self.query_decay.bias is not None # stupid type checker
self.query_decay.bias.data[:] = -2
self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)
def forward(self, x):
B, C, T = x.shape
heads = self.heads
indexes = torch.arange(T, device=x.device, dtype=x.dtype)
# left index are keys, right index are queries
delta = indexes[:, None] - indexes[None, :]
queries = self.query(x).view(B, heads, -1, T)
keys = self.key(x).view(B, heads, -1, T)
# t are keys, s are queries
dots = torch.einsum("bhct,bhcs->bhts", keys, queries)
dots /= keys.shape[2]**0.5
if self.nfreqs:
periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)
freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))
freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs ** 0.5
dots += torch.einsum("fts,bhfs->bhts", freq_kernel, freq_q)
if self.ndecay:
decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)
decay_q = self.query_decay(x).view(B, heads, -1, T)
decay_q = torch.sigmoid(decay_q) / 2
decay_kernel = - decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5
dots += torch.einsum("fts,bhfs->bhts", decay_kernel, decay_q)
# Kill self reference.
dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
weights = torch.softmax(dots, dim=2)
content = self.content(x).view(B, heads, -1, T)
result = torch.einsum("bhts,bhct->bhcs", weights, content)
if self.nfreqs:
time_sig = torch.einsum("bhts,fts->bhfs", weights, freq_kernel)
result = torch.cat([result, time_sig], 2)
result = result.reshape(B, -1, T)
return x + self.proj(result)
class Demucs(nn.Module):
@capture_init
def __init__(self,
sources,
# Channels
audio_channels=2,
channels=64,
growth=2.,
# Main structure
depth=6,
rewrite=True,
lstm_layers=0,
# Convolutions
kernel_size=8,
stride=4,
context=1,
# Activations
gelu=True,
glu=True,
# Normalization
norm_starts=4,
norm_groups=4,
# DConv residual branch
dconv_mode=1,
dconv_depth=2,
dconv_comp=4,
dconv_attn=4,
dconv_lstm=4,
dconv_init=1e-4,
# Pre/post processing
normalize=True,
resample=True,
# Weight init
rescale=0.1,
# Metadata
samplerate=44100,
segment=4 * 10):
"""
Args:
sources (list[str]): list of source names
audio_channels (int): stereo or mono
channels (int): first convolution channels
depth (int): number of encoder/decoder layers
growth (float): multiply (resp divide) number of channels by that
for each layer of the encoder (resp decoder)
depth (int): number of layers in the encoder and in the decoder.
rewrite (bool): add 1x1 convolution to each layer.
lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated
by default, as this is now replaced by the smaller and faster small LSTMs
in the DConv branches.
kernel_size (int): kernel size for convolutions
stride (int): stride for convolutions
context (int): kernel size of the convolution in the
decoder before the transposed convolution. If > 1,
will provide some context from neighboring time steps.
gelu: use GELU activation function.
glu (bool): use glu instead of ReLU for the 1x1 rewrite conv.
norm_starts: layer at which group norm starts being used.
decoder layers are numbered in reverse order.
norm_groups: number of groups for group norm.
dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.
dconv_depth: depth of residual DConv branch.
dconv_comp: compression of DConv branch.
dconv_attn: adds attention layers in DConv branch starting at this layer.
dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.
dconv_init: initial scale for the DConv branch LayerScale.
normalize (bool): normalizes the input audio on the fly, and scales back
the output by the same amount.
resample (bool): upsample x2 the input and downsample /2 the output.
rescale (int): rescale initial weights of convolutions
to get their standard deviation closer to `rescale`.
samplerate (int): stored as meta information for easing
future evaluations of the model.
segment (float): duration of the chunks of audio to ideally evaluate the model on.
This is used by `demucs.apply.apply_model`.
"""
super().__init__()
self.audio_channels = audio_channels
self.sources = sources
self.kernel_size = kernel_size
self.context = context
self.stride = stride
self.depth = depth
self.resample = resample
self.channels = channels
self.normalize = normalize
self.samplerate = samplerate
self.segment = segment
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
self.skip_scales = nn.ModuleList()
if glu:
activation = nn.GLU(dim=1)
ch_scale = 2
else:
activation = nn.ReLU()
ch_scale = 1
if gelu:
act2 = nn.GELU
else:
act2 = nn.ReLU
in_channels = audio_channels
padding = 0
for index in range(depth):
norm_fn = lambda d: nn.Identity() # noqa
if index >= norm_starts:
norm_fn = lambda d: nn.GroupNorm(norm_groups, d) # noqa
encode = []
encode += [
nn.Conv1d(in_channels, channels, kernel_size, stride),
norm_fn(channels),
act2(),
]
attn = index >= dconv_attn
lstm = index >= dconv_lstm
if dconv_mode & 1:
encode += [DConv(channels, depth=dconv_depth, init=dconv_init,
compress=dconv_comp, attn=attn, lstm=lstm)]
if rewrite:
encode += [
nn.Conv1d(channels, ch_scale * channels, 1),
norm_fn(ch_scale * channels), activation]
self.encoder.append(nn.Sequential(*encode))
decode = []
if index > 0:
out_channels = in_channels
else:
out_channels = len(self.sources) * audio_channels
if rewrite:
decode += [
nn.Conv1d(channels, ch_scale * channels, 2 * context + 1, padding=context),
norm_fn(ch_scale * channels), activation]
if dconv_mode & 2:
decode += [DConv(channels, depth=dconv_depth, init=dconv_init,
compress=dconv_comp, attn=attn, lstm=lstm)]
decode += [nn.ConvTranspose1d(channels, out_channels,
kernel_size, stride, padding=padding)]
if index > 0:
decode += [norm_fn(out_channels), act2()]
self.decoder.insert(0, nn.Sequential(*decode))
in_channels = channels
channels = int(growth * channels)
channels = in_channels
if lstm_layers:
self.lstm = BLSTM(channels, lstm_layers)
else:
self.lstm = None
if rescale:
rescale_module(self, reference=rescale)
def valid_length(self, length):
"""
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolution, e.g. for all
layers, size of the input - kernel_size % stride = 0.
Note that input are automatically padded if necessary to ensure that the output
has the same length as the input.
"""
if self.resample:
length *= 2
for _ in range(self.depth):
length = math.ceil((length - self.kernel_size) / self.stride) + 1
length = max(1, length)
for idx in range(self.depth):
length = (length - 1) * self.stride + self.kernel_size
if self.resample:
length = math.ceil(length / 2)
return int(length)
def forward(self, mix):
x = mix
length = x.shape[-1]
if self.normalize:
mono = mix.mean(dim=1, keepdim=True)
mean = mono.mean(dim=-1, keepdim=True)
std = mono.std(dim=-1, keepdim=True)
x = (x - mean) / (1e-5 + std)
else:
mean = 0
std = 1
delta = self.valid_length(length) - length
x = F.pad(x, (delta // 2, delta - delta // 2))
if self.resample:
x = julius.resample_frac(x, 1, 2)
saved = []
for encode in self.encoder:
x = encode(x)
saved.append(x)
if self.lstm:
x = self.lstm(x)
for decode in self.decoder:
skip = saved.pop(-1)
skip = center_trim(skip, x)
x = decode(x + skip)
if self.resample:
x = julius.resample_frac(x, 2, 1)
x = x * std + mean
x = center_trim(x, length)
x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
return x
def load_state_dict(self, state, strict=True):
# fix a mismatch with previous generation Demucs models.
for idx in range(self.depth):
for a in ['encoder', 'decoder']:
for b in ['bias', 'weight']:
new = f'{a}.{idx}.3.{b}'
old = f'{a}.{idx}.2.{b}'
if old in state and new not in state:
state[new] = state.pop(old)
super().load_state_dict(state, strict=strict)

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demucs/hdemucs.py
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@ -1,761 +0,0 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
This code contains the spectrogram and Hybrid version of Demucs.
"""
from copy import deepcopy
import math
from openunmix.filtering import wiener
import torch
from torch import nn
from torch.nn import functional as F
from .demucs import DConv, rescale_module
from .states import capture_init
from .spec import spectro, ispectro
class ScaledEmbedding(nn.Module):
"""
Boost learning rate for embeddings (with `scale`).
Also, can make embeddings continuous with `smooth`.
"""
def __init__(self, num_embeddings: int, embedding_dim: int,
scale: float = 10., smooth=False):
super().__init__()
self.embedding = nn.Embedding(num_embeddings, embedding_dim)
if smooth:
weight = torch.cumsum(self.embedding.weight.data, dim=0)
# when summing gaussian, overscale raises as sqrt(n), so we nornalize by that.
weight = weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]
self.embedding.weight.data[:] = weight
self.embedding.weight.data /= scale
self.scale = scale
@property
def weight(self):
return self.embedding.weight * self.scale
def forward(self, x):
out = self.embedding(x) * self.scale
return out
class HEncLayer(nn.Module):
def __init__(self, chin, chout, kernel_size=8, stride=4, norm_groups=1, empty=False,
freq=True, dconv=True, norm=True, context=0, dconv_kw={}, pad=True,
rewrite=True):
"""Encoder layer. This used both by the time and the frequency branch.
Args:
chin: number of input channels.
chout: number of output channels.
norm_groups: number of groups for group norm.
empty: used to make a layer with just the first conv. this is used
before merging the time and freq. branches.
freq: this is acting on frequencies.
dconv: insert DConv residual branches.
norm: use GroupNorm.
context: context size for the 1x1 conv.
dconv_kw: list of kwargs for the DConv class.
pad: pad the input. Padding is done so that the output size is
always the input size / stride.
rewrite: add 1x1 conv at the end of the layer.
"""
super().__init__()
norm_fn = lambda d: nn.Identity() # noqa
if norm:
norm_fn = lambda d: nn.GroupNorm(norm_groups, d) # noqa
if pad:
pad = kernel_size // 4
else:
pad = 0
klass = nn.Conv1d
self.freq = freq
self.kernel_size = kernel_size
self.stride = stride
self.empty = empty
self.norm = norm
self.pad = pad
if freq:
kernel_size = [kernel_size, 1]
stride = [stride, 1]
pad = [pad, 0]
klass = nn.Conv2d
self.conv = klass(chin, chout, kernel_size, stride, pad)
if self.empty:
return
self.norm1 = norm_fn(chout)
self.rewrite = None
if rewrite:
self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)
self.norm2 = norm_fn(2 * chout)
self.dconv = None
if dconv:
self.dconv = DConv(chout, **dconv_kw)
def forward(self, x, inject=None):
"""
`inject` is used to inject the result from the time branch into the frequency branch,
when both have the same stride.
"""
if not self.freq and x.dim() == 4:
B, C, Fr, T = x.shape
x = x.view(B, -1, T)
if not self.freq:
le = x.shape[-1]
if not le % self.stride == 0:
x = F.pad(x, (0, self.stride - (le % self.stride)))
y = self.conv(x)
if self.empty:
return y
if inject is not None:
assert inject.shape[-1] == y.shape[-1], (inject.shape, y.shape)
if inject.dim() == 3 and y.dim() == 4:
inject = inject[:, :, None]
y = y + inject
y = F.gelu(self.norm1(y))
if self.dconv:
if self.freq:
B, C, Fr, T = y.shape
y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
y = self.dconv(y)
if self.freq:
y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
if self.rewrite:
z = self.norm2(self.rewrite(y))
z = F.glu(z, dim=1)
else:
z = y
return z
class MultiWrap(nn.Module):
"""
Takes one layer and replicate it N times. each replica will act
on a frequency band. All is done so that if the N replica have the same weights,
then this is exactly equivalent to applying the original module on all frequencies.
This is a bit over-engineered to avoid edge artifacts when splitting
the frequency bands, but it is possible the naive implementation would work as well...
"""
def __init__(self, layer, split_ratios):
"""
Args:
layer: module to clone, must be either HEncLayer or HDecLayer.
split_ratios: list of float indicating which ratio to keep for each band.
"""
super().__init__()
self.split_ratios = split_ratios
self.layers = nn.ModuleList()
self.conv = isinstance(layer, HEncLayer)
assert not layer.norm
assert layer.freq
assert layer.pad
if not self.conv:
assert not layer.context_freq
for k in range(len(split_ratios) + 1):
lay = deepcopy(layer)
if self.conv:
lay.conv.padding = (0, 0)
else:
lay.pad = False
for m in lay.modules():
if hasattr(m, 'reset_parameters'):
m.reset_parameters()
self.layers.append(lay)
def forward(self, x, skip=None, length=None):
B, C, Fr, T = x.shape
ratios = list(self.split_ratios) + [1]
start = 0
outs = []
for ratio, layer in zip(ratios, self.layers):
if self.conv:
pad = layer.kernel_size // 4
if ratio == 1:
limit = Fr
frames = -1
else:
limit = int(round(Fr * ratio))
le = limit - start
if start == 0:
le += pad
frames = round((le - layer.kernel_size) / layer.stride + 1)
limit = start + (frames - 1) * layer.stride + layer.kernel_size
if start == 0:
limit -= pad
assert limit - start > 0, (limit, start)
assert limit <= Fr, (limit, Fr)
y = x[:, :, start:limit, :]
if start == 0:
y = F.pad(y, (0, 0, pad, 0))
if ratio == 1:
y = F.pad(y, (0, 0, 0, pad))
outs.append(layer(y))
start = limit - layer.kernel_size + layer.stride
else:
if ratio == 1:
limit = Fr
else:
limit = int(round(Fr * ratio))
last = layer.last
layer.last = True
y = x[:, :, start:limit]
s = skip[:, :, start:limit]
out, _ = layer(y, s, None)
if outs:
outs[-1][:, :, -layer.stride:] += (
out[:, :, :layer.stride] - layer.conv_tr.bias.view(1, -1, 1, 1))
out = out[:, :, layer.stride:]
if ratio == 1:
out = out[:, :, :-layer.stride // 2, :]
if start == 0:
out = out[:, :, layer.stride // 2:, :]
outs.append(out)
layer.last = last
start = limit
out = torch.cat(outs, dim=2)
if not self.conv and not last:
out = F.gelu(out)
if self.conv:
return out
else:
return out, None
class HDecLayer(nn.Module):
def __init__(self, chin, chout, last=False, kernel_size=8, stride=4, norm_groups=1, empty=False,
freq=True, dconv=True, norm=True, context=1, dconv_kw={}, pad=True,
context_freq=True, rewrite=True):
"""
Same as HEncLayer but for decoder. See `HEncLayer` for documentation.
"""
super().__init__()
norm_fn = lambda d: nn.Identity() # noqa
if norm:
norm_fn = lambda d: nn.GroupNorm(norm_groups, d) # noqa
if pad:
pad = kernel_size // 4
else:
pad = 0
self.pad = pad
self.last = last
self.freq = freq
self.chin = chin
self.empty = empty
self.stride = stride
self.kernel_size = kernel_size
self.norm = norm
self.context_freq = context_freq
klass = nn.Conv1d
klass_tr = nn.ConvTranspose1d
if freq:
kernel_size = [kernel_size, 1]
stride = [stride, 1]
klass = nn.Conv2d
klass_tr = nn.ConvTranspose2d
self.conv_tr = klass_tr(chin, chout, kernel_size, stride)
self.norm2 = norm_fn(chout)
if self.empty:
return
self.rewrite = None
if rewrite:
if context_freq:
self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)
else:
self.rewrite = klass(chin, 2 * chin, [1, 1 + 2 * context], 1,
[0, context])
self.norm1 = norm_fn(2 * chin)
self.dconv = None
if dconv:
self.dconv = DConv(chin, **dconv_kw)
def forward(self, x, skip, length):
if self.freq and x.dim() == 3:
B, C, T = x.shape
x = x.view(B, self.chin, -1, T)
if not self.empty:
x = x + skip
if self.rewrite:
y = F.glu(self.norm1(self.rewrite(x)), dim=1)
else:
y = x
if self.dconv:
if self.freq:
B, C, Fr, T = y.shape
y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
y = self.dconv(y)
if self.freq:
y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
else:
y = x
assert skip is None
z = self.norm2(self.conv_tr(y))
if self.freq:
if self.pad:
z = z[..., self.pad:-self.pad, :]
else:
z = z[..., self.pad:self.pad + length]
assert z.shape[-1] == length, (z.shape[-1], length)
if not self.last:
z = F.gelu(z)
return z, y
class HDemucs(nn.Module):
"""
Spectrogram and hybrid Demucs model.
The spectrogram model has the same structure as Demucs, except the first few layers are over the
frequency axis, until there is only 1 frequency, and then it moves to time convolutions.
Frequency layers can still access information across time steps thanks to the DConv residual.
Hybrid model have a parallel time branch. At some layer, the time branch has the same stride
as the frequency branch and then the two are combined. The opposite happens in the decoder.
Models can either use naive iSTFT from masking, Wiener filtering ([Ulhih et al. 2017]),
or complex as channels (CaC) [Choi et al. 2020]. Wiener filtering is based on
Open Unmix implementation [Stoter et al. 2019].
The loss is always on the temporal domain, by backpropagating through the above
output methods and iSTFT. This allows to define hybrid models nicely. However, this breaks
a bit Wiener filtering, as doing more iteration at test time will change the spectrogram
contribution, without changing the one from the waveform, which will lead to worse performance.
I tried using the residual option in OpenUnmix Wiener implementation, but it didn't improve.
CaC on the other hand provides similar performance for hybrid, and works naturally with
hybrid models.
This model also uses frequency embeddings are used to improve efficiency on convolutions
over the freq. axis, following [Isik et al. 2020] (https://arxiv.org/pdf/2008.04470.pdf).
Unlike classic Demucs, there is no resampling here, and normalization is always applied.
"""
@capture_init
def __init__(self,
sources,
# Channels
audio_channels=2,
channels=48,
channels_time=None,
growth=2,
# STFT
nfft=4096,
wiener_iters=0,
end_iters=0,
wiener_residual=False,
cac=True,
# Main structure
depth=6,
rewrite=True,
hybrid=True,
hybrid_old=False,
# Frequency branch
multi_freqs=None,
multi_freqs_depth=2,
freq_emb=0.2,
emb_scale=10,
emb_smooth=True,
# Convolutions
kernel_size=8,
time_stride=2,
stride=4,
context=1,
context_enc=0,
# Normalization
norm_starts=4,
norm_groups=4,
# DConv residual branch
dconv_mode=1,
dconv_depth=2,
dconv_comp=4,
dconv_attn=4,
dconv_lstm=4,
dconv_init=1e-4,
# Weight init
rescale=0.1,
# Metadata
samplerate=44100,
segment=4 * 10):
"""
Args:
sources (list[str]): list of source names.
audio_channels (int): input/output audio channels.
channels (int): initial number of hidden channels.
channels_time: if not None, use a different `channels` value for the time branch.
growth: increase the number of hidden channels by this factor at each layer.
nfft: number of fft bins. Note that changing this require careful computation of
various shape parameters and will not work out of the box for hybrid models.
wiener_iters: when using Wiener filtering, number of iterations at test time.
end_iters: same but at train time. For a hybrid model, must be equal to `wiener_iters`.
wiener_residual: add residual source before wiener filtering.
cac: uses complex as channels, i.e. complex numbers are 2 channels each
in input and output. no further processing is done before ISTFT.
depth (int): number of layers in the encoder and in the decoder.
rewrite (bool): add 1x1 convolution to each layer.
hybrid (bool): make a hybrid time/frequency domain, otherwise frequency only.
hybrid_old: some models trained for MDX had a padding bug. This replicates
this bug to avoid retraining them.
multi_freqs: list of frequency ratios for splitting frequency bands with `MultiWrap`.
multi_freqs_depth: how many layers to wrap with `MultiWrap`. Only the outermost
layers will be wrapped.
freq_emb: add frequency embedding after the first frequency layer if > 0,
the actual value controls the weight of the embedding.
emb_scale: equivalent to scaling the embedding learning rate
emb_smooth: initialize the embedding with a smooth one (with respect to frequencies).
kernel_size: kernel_size for encoder and decoder layers.
stride: stride for encoder and decoder layers.
time_stride: stride for the final time layer, after the merge.
context: context for 1x1 conv in the decoder.
context_enc: context for 1x1 conv in the encoder.
norm_starts: layer at which group norm starts being used.
decoder layers are numbered in reverse order.
norm_groups: number of groups for group norm.
dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.
dconv_depth: depth of residual DConv branch.
dconv_comp: compression of DConv branch.
dconv_attn: adds attention layers in DConv branch starting at this layer.
dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.
dconv_init: initial scale for the DConv branch LayerScale.
rescale: weight recaling trick
"""
super().__init__()
self.cac = cac
self.wiener_residual = wiener_residual
self.audio_channels = audio_channels
self.sources = sources
self.kernel_size = kernel_size
self.context = context
self.stride = stride
self.depth = depth
self.channels = channels
self.samplerate = samplerate
self.segment = segment
self.nfft = nfft
self.hop_length = nfft // 4
self.wiener_iters = wiener_iters
self.end_iters = end_iters
self.freq_emb = None
self.hybrid = hybrid
self.hybrid_old = hybrid_old
if hybrid_old:
assert hybrid, "hybrid_old must come with hybrid=True"
if hybrid:
assert wiener_iters == end_iters
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
if hybrid:
self.tencoder = nn.ModuleList()
self.tdecoder = nn.ModuleList()
chin = audio_channels
chin_z = chin # number of channels for the freq branch
if self.cac:
chin_z *= 2
chout = channels_time or channels
chout_z = channels
freqs = nfft // 2
for index in range(depth):
lstm = index >= dconv_lstm
attn = index >= dconv_attn
norm = index >= norm_starts
freq = freqs > 1
stri = stride
ker = kernel_size
if not freq:
assert freqs == 1
ker = time_stride * 2
stri = time_stride
pad = True
last_freq = False
if freq and freqs <= kernel_size:
ker = freqs
pad = False
last_freq = True
kw = {
'kernel_size': ker,
'stride': stri,
'freq': freq,
'pad': pad,
'norm': norm,
'rewrite': rewrite,
'norm_groups': norm_groups,
'dconv_kw': {
'lstm': lstm,
'attn': attn,
'depth': dconv_depth,
'compress': dconv_comp,
'init': dconv_init,
'gelu': True,
}
}
kwt = dict(kw)
kwt['freq'] = 0
kwt['kernel_size'] = kernel_size
kwt['stride'] = stride
kwt['pad'] = True
kw_dec = dict(kw)
multi = False
if multi_freqs and index < multi_freqs_depth:
multi = True
kw_dec['context_freq'] = False
if last_freq:
chout_z = max(chout, chout_z)
chout = chout_z
enc = HEncLayer(chin_z, chout_z,
dconv=dconv_mode & 1, context=context_enc, **kw)
if hybrid and freq:
tenc = HEncLayer(chin, chout, dconv=dconv_mode & 1, context=context_enc,
empty=last_freq, **kwt)
self.tencoder.append(tenc)
if multi:
enc = MultiWrap(enc, multi_freqs)
self.encoder.append(enc)
if index == 0:
chin = self.audio_channels * len(self.sources)
chin_z = chin
if self.cac:
chin_z *= 2
dec = HDecLayer(chout_z, chin_z, dconv=dconv_mode & 2,
last=index == 0, context=context, **kw_dec)
if multi:
dec = MultiWrap(dec, multi_freqs)
if hybrid and freq:
tdec = HDecLayer(chout, chin, dconv=dconv_mode & 2, empty=last_freq,
last=index == 0, context=context, **kwt)
self.tdecoder.insert(0, tdec)
self.decoder.insert(0, dec)
chin = chout
chin_z = chout_z
chout = int(growth * chout)
chout_z = int(growth * chout_z)
if freq:
if freqs <= kernel_size:
freqs = 1
else:
freqs //= stride
if index == 0 and freq_emb:
self.freq_emb = ScaledEmbedding(
freqs, chin_z, smooth=emb_smooth, scale=emb_scale)
self.freq_emb_scale = freq_emb
if rescale:
rescale_module(self, reference=rescale)
def _spec(self, x):
hl = self.hop_length
nfft = self.nfft
x0 = x # noqa
if self.hybrid:
# We re-pad the signal in order to keep the property
# that the size of the output is exactly the size of the input
# divided by the stride (here hop_length), when divisible.
# This is achieved by padding by 1/4th of the kernel size (here nfft).
# which is not supported by torch.stft.
# Having all convolution operations follow this convention allow to easily
# align the time and frequency branches later on.
assert hl == nfft // 4
le = int(math.ceil(x.shape[-1] / hl))
pad = hl // 2 * 3
if not self.hybrid_old:
x = F.pad(x, (pad, pad + le * hl - x.shape[-1]), mode='reflect')
else:
x = F.pad(x, (pad, pad + le * hl - x.shape[-1]))
z = spectro(x, nfft, hl)[..., :-1, :]
if self.hybrid:
assert z.shape[-1] == le + 4, (z.shape, x.shape, le)
z = z[..., 2:2+le]
return z
def _ispec(self, z, length=None, scale=0):
hl = self.hop_length // (4 ** scale)
z = F.pad(z, (0, 0, 0, 1))
if self.hybrid:
z = F.pad(z, (2, 2))
pad = hl // 2 * 3
if not self.hybrid_old:
le = hl * int(math.ceil(length / hl)) + 2 * pad
else:
le = hl * int(math.ceil(length / hl))
x = ispectro(z, hl, length=le)
if not self.hybrid_old:
x = x[..., pad:pad + length]
else:
x = x[..., :length]
else:
x = ispectro(z, hl, length)
return x
def _magnitude(self, z):
# return the magnitude of the spectrogram, except when cac is True,
# in which case we just move the complex dimension to the channel one.
if self.cac:
B, C, Fr, T = z.shape
m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)
m = m.reshape(B, C * 2, Fr, T)
else:
m = z.abs()
return m
def _mask(self, z, m):
# Apply masking given the mixture spectrogram `z` and the estimated mask `m`.
# If `cac` is True, `m` is actually a full spectrogram and `z` is ignored.
niters = self.wiener_iters
if self.cac:
B, S, C, Fr, T = m.shape
out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)
out = torch.view_as_complex(out.contiguous())
return out
if self.training:
niters = self.end_iters
if niters < 0:
z = z[:, None]
return z / (1e-8 + z.abs()) * m
else:
return self._wiener(m, z, niters)
def _wiener(self, mag_out, mix_stft, niters):
# apply wiener filtering from OpenUnmix.
init = mix_stft.dtype
wiener_win_len = 300
residual = self.wiener_residual
B, S, C, Fq, T = mag_out.shape
mag_out = mag_out.permute(0, 4, 3, 2, 1)
mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))
outs = []
for sample in range(B):
pos = 0
out = []
for pos in range(0, T, wiener_win_len):
frame = slice(pos, pos + wiener_win_len)
z_out = wiener(
mag_out[sample, frame], mix_stft[sample, frame], niters,
residual=residual)
out.append(z_out.transpose(-1, -2))
outs.append(torch.cat(out, dim=0))
out = torch.view_as_complex(torch.stack(outs, 0))
out = out.permute(0, 4, 3, 2, 1).contiguous()
if residual:
out = out[:, :-1]
assert list(out.shape) == [B, S, C, Fq, T]
return out.to(init)
def forward(self, mix):
x = mix
length = x.shape[-1]
z = self._spec(mix)
mag = self._magnitude(z)
x = mag
B, C, Fq, T = x.shape
# unlike previous Demucs, we always normalize because it is easier.
mean = x.mean(dim=(1, 2, 3), keepdim=True)
std = x.std(dim=(1, 2, 3), keepdim=True)
x = (x - mean) / (1e-5 + std)
# x will be the freq. branch input.
if self.hybrid:
# Prepare the time branch input.
xt = mix
meant = xt.mean(dim=(1, 2), keepdim=True)
stdt = xt.std(dim=(1, 2), keepdim=True)
xt = (xt - meant) / (1e-5 + stdt)
# okay, this is a giant mess I know...
saved = [] # skip connections, freq.
saved_t = [] # skip connections, time.
lengths = [] # saved lengths to properly remove padding, freq branch.
lengths_t = [] # saved lengths for time branch.
for idx, encode in enumerate(self.encoder):
lengths.append(x.shape[-1])
inject = None
if self.hybrid and idx < len(self.tencoder):
# we have not yet merged branches.
lengths_t.append(xt.shape[-1])
tenc = self.tencoder[idx]
xt = tenc(xt)
if not tenc.empty:
# save for skip connection
saved_t.append(xt)
else:
# tenc contains just the first conv., so that now time and freq.
# branches have the same shape and can be merged.
inject = xt
x = encode(x, inject)
if idx == 0 and self.freq_emb is not None:
# add frequency embedding to allow for non equivariant convolutions
# over the frequency axis.
frs = torch.arange(x.shape[-2], device=x.device)
emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
x = x + self.freq_emb_scale * emb
saved.append(x)
x = torch.zeros_like(x)
if self.hybrid:
xt = torch.zeros_like(x)
# initialize everything to zero (signal will go through u-net skips).
for idx, decode in enumerate(self.decoder):
skip = saved.pop(-1)
x, pre = decode(x, skip, lengths.pop(-1))
# `pre` contains the output just before final transposed convolution,
# which is used when the freq. and time branch separate.
if self.hybrid:
offset = self.depth - len(self.tdecoder)
if self.hybrid and idx >= offset:
tdec = self.tdecoder[idx - offset]
length_t = lengths_t.pop(-1)
if tdec.empty:
assert pre.shape[2] == 1, pre.shape
pre = pre[:, :, 0]
xt, _ = tdec(pre, None, length_t)
else:
skip = saved_t.pop(-1)
xt, _ = tdec(xt, skip, length_t)
# Let's make sure we used all stored skip connections.
assert len(saved) == 0
assert len(lengths_t) == 0
assert len(saved_t) == 0
S = len(self.sources)
x = x.view(B, S, -1, Fq, T)
x = x * std[:, None] + mean[:, None]
zout = self._mask(z, x)
x = self._ispec(zout, length)
if self.hybrid:
xt = xt.view(B, S, -1, length)
xt = xt * stdt[:, None] + meant[:, None]
x = xt + x
return x

218
demucs/model.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch as th
from torch import nn
from .utils import capture_init, center_trim
class BLSTM(nn.Module):
def __init__(self, dim, layers=1):
super().__init__()
self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
self.linear = nn.Linear(2 * dim, dim)
def forward(self, x):
x = x.permute(2, 0, 1)
x = self.lstm(x)[0]
x = self.linear(x)
x = x.permute(1, 2, 0)
return x
def rescale_conv(conv, reference):
std = conv.weight.std().detach()
scale = (std / reference)**0.5
conv.weight.data /= scale
if conv.bias is not None:
conv.bias.data /= scale
def rescale_module(module, reference):
for sub in module.modules():
if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
rescale_conv(sub, reference)
def upsample(x, stride):
"""
Linear upsampling, the output will be `stride` times longer.
"""
batch, channels, time = x.size()
weight = th.arange(stride, device=x.device, dtype=th.float) / stride
x = x.view(batch, channels, time, 1)
out = x[..., :-1, :] * (1 - weight) + x[..., 1:, :] * weight
return out.reshape(batch, channels, -1)
def downsample(x, stride):
"""
Downsample x by decimation.
"""
return x[:, :, ::stride]
class Demucs(nn.Module):
@capture_init
def __init__(self,
sources=4,
audio_channels=2,
channels=64,
depth=6,
rewrite=True,
glu=True,
upsample=False,
rescale=0.1,
kernel_size=8,
stride=4,
growth=2.,
lstm_layers=2,
context=3,
samplerate=44100):
"""
Args:
sources (int): number of sources to separate
audio_channels (int): stereo or mono
channels (int): first convolution channels
depth (int): number of encoder/decoder layers
rewrite (bool): add 1x1 convolution to each encoder layer
and a convolution to each decoder layer.
For the decoder layer, `context` gives the kernel size.
glu (bool): use glu instead of ReLU
upsample (bool): use linear upsampling with convolutions
Wave-U-Net style, instead of transposed convolutions
rescale (int): rescale initial weights of convolutions
to get their standard deviation closer to `rescale`
kernel_size (int): kernel size for convolutions
stride (int): stride for convolutions
growth (float): multiply (resp divide) number of channels by that
for each layer of the encoder (resp decoder)
lstm_layers (int): number of lstm layers, 0 = no lstm
context (int): kernel size of the convolution in the
decoder before the transposed convolution. If > 1,
will provide some context from neighboring time
steps.
"""
super().__init__()
self.audio_channels = audio_channels
self.sources = sources
self.kernel_size = kernel_size
self.context = context
self.stride = stride
self.depth = depth
self.upsample = upsample
self.channels = channels
self.samplerate = samplerate
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
self.final = None
if upsample:
self.final = nn.Conv1d(channels + audio_channels, sources * audio_channels, 1)
stride = 1
if glu:
activation = nn.GLU(dim=1)
ch_scale = 2
else:
activation = nn.ReLU()
ch_scale = 1
in_channels = audio_channels
for index in range(depth):
encode = []
encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
if rewrite:
encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
self.encoder.append(nn.Sequential(*encode))
decode = []
if index > 0:
out_channels = in_channels
else:
if upsample:
out_channels = channels
else:
out_channels = sources * audio_channels
if rewrite:
decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
if upsample:
decode += [
nn.Conv1d(channels, out_channels, kernel_size, stride=1),
]
else:
decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
if index > 0:
decode.append(nn.ReLU())
self.decoder.insert(0, nn.Sequential(*decode))
in_channels = channels
channels = int(growth * channels)
channels = in_channels
if lstm_layers:
self.lstm = BLSTM(channels, lstm_layers)
else:
self.lstm = None
if rescale:
rescale_module(self, reference=rescale)
def valid_length(self, length):
"""
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolutions, e.g. for all
layers, size of the input - kernel_size % stride = 0.
If the mixture has a valid length, the estimated sources
will have exactly the same length when context = 1. If context > 1,
the two signals can be center trimmed to match.
For training, extracts should have a valid length.For evaluation
on full tracks we recommend passing `pad = True` to :method:`forward`.
"""
for _ in range(self.depth):
if self.upsample:
length = math.ceil(length / self.stride) + self.kernel_size - 1
else:
length = math.ceil((length - self.kernel_size) / self.stride) + 1
length = max(1, length)
length += self.context - 1
for _ in range(self.depth):
if self.upsample:
length = length * self.stride + self.kernel_size - 1
else:
length = (length - 1) * self.stride + self.kernel_size
return int(length)
def forward(self, mix):
x = mix
saved = [x]
for encode in self.encoder:
x = encode(x)
saved.append(x)
if self.upsample:
x = downsample(x, self.stride)
if self.lstm:
x = self.lstm(x)
for decode in self.decoder:
if self.upsample:
x = upsample(x, stride=self.stride)
skip = center_trim(saved.pop(-1), x)
x = x + skip
x = decode(x)
if self.final:
skip = center_trim(saved.pop(-1), x)
x = th.cat([x, skip], dim=1)
x = self.final(x)
x = x.view(x.size(0), self.sources, self.audio_channels, x.size(-1))
return x

202
demucs/model_v2.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import julius
from torch import nn
from .utils import capture_init, center_trim
class BLSTM(nn.Module):
def __init__(self, dim, layers=1):
super().__init__()
self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
self.linear = nn.Linear(2 * dim, dim)
def forward(self, x):
x = x.permute(2, 0, 1)
x = self.lstm(x)[0]
x = self.linear(x)
x = x.permute(1, 2, 0)
return x
def rescale_conv(conv, reference):
std = conv.weight.std().detach()
scale = (std / reference)**0.5
conv.weight.data /= scale
if conv.bias is not None:
conv.bias.data /= scale
def rescale_module(module, reference):
for sub in module.modules():
if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)):
rescale_conv(sub, reference)
class Demucs(nn.Module):
@capture_init
def __init__(self,
sources,
audio_channels=2,
channels=64,
depth=6,
rewrite=True,
glu=True,
rescale=0.1,
resample=True,
kernel_size=8,
stride=4,
growth=2.,
lstm_layers=2,
context=3,
normalize=False,
samplerate=44100,
segment_length=4 * 10 * 44100):
"""
Args:
sources (list[str]): list of source names
audio_channels (int): stereo or mono
channels (int): first convolution channels
depth (int): number of encoder/decoder layers
rewrite (bool): add 1x1 convolution to each encoder layer
and a convolution to each decoder layer.
For the decoder layer, `context` gives the kernel size.
glu (bool): use glu instead of ReLU
resample_input (bool): upsample x2 the input and downsample /2 the output.
rescale (int): rescale initial weights of convolutions
to get their standard deviation closer to `rescale`
kernel_size (int): kernel size for convolutions
stride (int): stride for convolutions
growth (float): multiply (resp divide) number of channels by that
for each layer of the encoder (resp decoder)
lstm_layers (int): number of lstm layers, 0 = no lstm
context (int): kernel size of the convolution in the
decoder before the transposed convolution. If > 1,
will provide some context from neighboring time
steps.
samplerate (int): stored as meta information for easing
future evaluations of the model.
segment_length (int): stored as meta information for easing
future evaluations of the model. Length of the segments on which
the model was trained.
"""
super().__init__()
self.audio_channels = audio_channels
self.sources = sources
self.kernel_size = kernel_size
self.context = context
self.stride = stride
self.depth = depth
self.resample = resample
self.channels = channels
self.normalize = normalize
self.samplerate = samplerate
self.segment_length = segment_length
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
if glu:
activation = nn.GLU(dim=1)
ch_scale = 2
else:
activation = nn.ReLU()
ch_scale = 1
in_channels = audio_channels
for index in range(depth):
encode = []
encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()]
if rewrite:
encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation]
self.encoder.append(nn.Sequential(*encode))
decode = []
if index > 0:
out_channels = in_channels
else:
out_channels = len(self.sources) * audio_channels
if rewrite:
decode += [nn.Conv1d(channels, ch_scale * channels, context), activation]
decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)]
if index > 0:
decode.append(nn.ReLU())
self.decoder.insert(0, nn.Sequential(*decode))
in_channels = channels
channels = int(growth * channels)
channels = in_channels
if lstm_layers:
self.lstm = BLSTM(channels, lstm_layers)
else:
self.lstm = None
if rescale:
rescale_module(self, reference=rescale)
def valid_length(self, length):
"""
Return the nearest valid length to use with the model so that
there is no time steps left over in a convolutions, e.g. for all
layers, size of the input - kernel_size % stride = 0.
If the mixture has a valid length, the estimated sources
will have exactly the same length when context = 1. If context > 1,
the two signals can be center trimmed to match.
For training, extracts should have a valid length.For evaluation
on full tracks we recommend passing `pad = True` to :method:`forward`.
"""
if self.resample:
length *= 2
for _ in range(self.depth):
length = math.ceil((length - self.kernel_size) / self.stride) + 1
length = max(1, length)
length += self.context - 1
for _ in range(self.depth):
length = (length - 1) * self.stride + self.kernel_size
if self.resample:
length = math.ceil(length / 2)
return int(length)
def forward(self, mix):
x = mix
if self.normalize:
mono = mix.mean(dim=1, keepdim=True)
mean = mono.mean(dim=-1, keepdim=True)
std = mono.std(dim=-1, keepdim=True)
else:
mean = 0
std = 1
x = (x - mean) / (1e-5 + std)
if self.resample:
x = julius.resample_frac(x, 1, 2)
saved = []
for encode in self.encoder:
x = encode(x)
saved.append(x)
if self.lstm:
x = self.lstm(x)
for decode in self.decoder:
skip = center_trim(saved.pop(-1), x)
x = x + skip
x = decode(x)
if self.resample:
x = julius.resample_frac(x, 2, 1)
x = x * std + mean
x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
return x

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demucs/pretrained.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Loading pretrained models.
"""
import logging
from pathlib import Path
import typing as tp
from dora.log import fatal
import logging
from diffq import DiffQuantizer
import torch.hub
from .model import Demucs
from .tasnet_v2 import ConvTasNet
from .utils import set_state
from .hdemucs import HDemucs
from .repo import RemoteRepo, LocalRepo, ModelOnlyRepo, BagOnlyRepo, AnyModelRepo, ModelLoadingError # noqa
logger = logging.getLogger(__name__)
ROOT_URL = "https://dl.fbaipublicfiles.com/demucs/mdx_final/"
REMOTE_ROOT = Path(__file__).parent / 'remote'
SOURCES = ["drums", "bass", "other", "vocals"]
def demucs_unittest():
model = HDemucs(channels=4, sources=SOURCES)
return model
def add_model_flags(parser):
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument("-s", "--sig", help="Locally trained XP signature.")
group.add_argument("-n", "--name", default="mdx_extra_q",
help="Pretrained model name or signature. Default is mdx_extra_q.")
parser.add_argument("--repo", type=Path,
help="Folder containing all pre-trained models for use with -n.")
def get_model(name: str,
repo: tp.Optional[Path] = None):
"""`name` must be a bag of models name or a pretrained signature
from the remote AWS model repo or the specified local repo if `repo` is not None.
"""
if name == 'demucs_unittest':
return demucs_unittest()
model_repo: ModelOnlyRepo
if repo is None:
remote_files = [line.strip()
for line in (REMOTE_ROOT / 'files.txt').read_text().split('\n')
if line.strip()]
model_repo = RemoteRepo(ROOT_URL, remote_files)
bag_repo = BagOnlyRepo(REMOTE_ROOT, model_repo)
else:
if not repo.is_dir():
fatal(f"{repo} must exist and be a directory.")
model_repo = LocalRepo(repo)
bag_repo = BagOnlyRepo(repo, model_repo)
any_repo = AnyModelRepo(model_repo, bag_repo)
return any_repo.get_model(name)
def get_model_from_args(args):
"""
Load local model package or pre-trained model.
"""
return get_model(name=args.name, repo=args.repo)
logger = logging.getLogger(__name__)
ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/"
PRETRAINED_MODELS = {
'demucs': 'e07c671f',
'demucs48_hq': '28a1282c',
'demucs_extra': '3646af93',
'demucs_quantized': '07afea75',
'tasnet': 'beb46fac',
'tasnet_extra': 'df3777b2',
'demucs_unittest': '09ebc15f',
}
SOURCES = ["drums", "bass", "other", "vocals"]
def get_url(name):
sig = PRETRAINED_MODELS[name]
return ROOT + name + "-" + sig[:8] + ".th"
def is_pretrained(name):
return name in PRETRAINED_MODELS
def load_pretrained(name):
if name == "demucs":
return demucs(pretrained=True)
elif name == "demucs48_hq":
return demucs(pretrained=True, hq=True, channels=48)
elif name == "demucs_extra":
return demucs(pretrained=True, extra=True)
elif name == "demucs_quantized":
return demucs(pretrained=True, quantized=True)
elif name == "demucs_unittest":
return demucs_unittest(pretrained=True)
elif name == "tasnet":
return tasnet(pretrained=True)
elif name == "tasnet_extra":
return tasnet(pretrained=True, extra=True)
else:
raise ValueError(f"Invalid pretrained name {name}")
def _load_state(name, model, quantizer=None):
url = get_url(name)
state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
set_state(model, quantizer, state)
if quantizer:
quantizer.detach()
def demucs_unittest(pretrained=True):
model = Demucs(channels=4, sources=SOURCES)
if pretrained:
_load_state('demucs_unittest', model)
return model
def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):
if not pretrained and (extra or quantized or hq):
raise ValueError("if extra or quantized is True, pretrained must be True.")
model = Demucs(sources=SOURCES, channels=channels)
if pretrained:
name = 'demucs'
if channels != 64:
name += str(channels)
quantizer = None
if sum([extra, quantized, hq]) > 1:
raise ValueError("Only one of extra, quantized, hq, can be True.")
if quantized:
quantizer = DiffQuantizer(model, group_size=8, min_size=1)
name += '_quantized'
if extra:
name += '_extra'
if hq:
name += '_hq'
_load_state(name, model, quantizer)
return model
def tasnet(pretrained=True, extra=False):
if not pretrained and extra:
raise ValueError("if extra is True, pretrained must be True.")
model = ConvTasNet(X=10, sources=SOURCES)
if pretrained:
name = 'tasnet'
if extra:
name = 'tasnet_extra'
_load_state(name, model)
return model

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demucs/repo.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Represents a model repository, including pre-trained models and bags of models.
A repo can either be the main remote repository stored in AWS, or a local repository
with your own models.
"""
from hashlib import sha256
from pathlib import Path
import typing as tp
import torch
import yaml
from .apply import BagOfModels, Model
from .states import load_model
AnyModel = tp.Union[Model, BagOfModels]
class ModelLoadingError(RuntimeError):
pass
def check_checksum(path: Path, checksum: str):
sha = sha256()
with open(path, 'rb') as file:
while True:
buf = file.read(2**20)
if not buf:
break
sha.update(buf)
actual_checksum = sha.hexdigest()[:len(checksum)]
if actual_checksum != checksum:
raise ModelLoadingError(f'Invalid checksum for file {path}, '
f'expected {checksum} but got {actual_checksum}')
class ModelOnlyRepo:
"""Base class for all model only repos.
"""
def has_model(self, sig: str) -> bool:
raise NotImplementedError()
def get_model(self, sig: str) -> Model:
raise NotImplementedError()
class RemoteRepo(ModelOnlyRepo):
def __init__(self, root_url: str, remote_files: tp.List[str]):
if not root_url.endswith('/'):
root_url += '/'
self._models: tp.Dict[str, str] = {}
for file in remote_files:
sig, checksum = file.split('.')[0].split('-')
assert sig not in self._models
self._models[sig] = root_url + file
def has_model(self, sig: str) -> bool:
return sig in self._models
def get_model(self, sig: str) -> Model:
try:
url = self._models[sig]
except KeyError:
raise ModelLoadingError(f'Could not find a pre-trained model with signature {sig}.')
pkg = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
return load_model(pkg)
class LocalRepo(ModelOnlyRepo):
def __init__(self, root: Path):
self.root = root
self.scan()
def scan(self):
self._models = {}
self._checksums = {}
for file in self.root.iterdir():
if file.suffix == '.th':
if '-' in file.stem:
xp_sig, checksum = file.stem.split('-')
self._checksums[xp_sig] = checksum
else:
xp_sig = file.stem
if xp_sig in self._models:
print('Whats xp? ', xp_sig)
raise ModelLoadingError(
f'Duplicate pre-trained model exist for signature {xp_sig}. '
'Please delete all but one.')
self._models[xp_sig] = file
def has_model(self, sig: str) -> bool:
return sig in self._models
def get_model(self, sig: str) -> Model:
try:
file = self._models[sig]
except KeyError:
raise ModelLoadingError(f'Could not find pre-trained model with signature {sig}.')
if sig in self._checksums:
check_checksum(file, self._checksums[sig])
return load_model(file)
class BagOnlyRepo:
"""Handles only YAML files containing bag of models, leaving the actual
model loading to some Repo.
"""
def __init__(self, root: Path, model_repo: ModelOnlyRepo):
self.root = root
self.model_repo = model_repo
self.scan()
def scan(self):
self._bags = {}
for file in self.root.iterdir():
if file.suffix == '.yaml':
self._bags[file.stem] = file
def has_model(self, name: str) -> bool:
return name in self._bags
def get_model(self, name: str) -> BagOfModels:
try:
yaml_file = self._bags[name]
except KeyError:
raise ModelLoadingError(f'{name} is neither a single pre-trained model or '
'a bag of models.')
bag = yaml.safe_load(open(yaml_file))
signatures = bag['models']
models = [self.model_repo.get_model(sig) for sig in signatures]
weights = bag.get('weights')
segment = bag.get('segment')
return BagOfModels(models, weights, segment)
class AnyModelRepo:
def __init__(self, model_repo: ModelOnlyRepo, bag_repo: BagOnlyRepo):
self.model_repo = model_repo
self.bag_repo = bag_repo
def has_model(self, name_or_sig: str) -> bool:
return self.model_repo.has_model(name_or_sig) or self.bag_repo.has_model(name_or_sig)
def get_model(self, name_or_sig: str) -> AnyModel:
if self.model_repo.has_model(name_or_sig):
return self.model_repo.get_model(name_or_sig)
else:
return self.bag_repo.get_model(name_or_sig)

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demucs/spec.py
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@ -1,41 +0,0 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Conveniance wrapper to perform STFT and iSTFT"""
import torch as th
def spectro(x, n_fft=512, hop_length=None, pad=0):
*other, length = x.shape
x = x.reshape(-1, length)
z = th.stft(x,
n_fft * (1 + pad),
hop_length or n_fft // 4,
window=th.hann_window(n_fft).to(x),
win_length=n_fft,
normalized=True,
center=True,
return_complex=True,
pad_mode='reflect')
_, freqs, frame = z.shape
return z.view(*other, freqs, frame)
def ispectro(z, hop_length=None, length=None, pad=0):
*other, freqs, frames = z.shape
n_fft = 2 * freqs - 2
z = z.view(-1, freqs, frames)
win_length = n_fft // (1 + pad)
x = th.istft(z,
n_fft,
hop_length,
window=th.hann_window(win_length).to(z.real),
win_length=win_length,
normalized=True,
length=length,
center=True)
_, length = x.shape
return x.view(*other, length)

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demucs/states.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Utilities to save and load models.
"""
from contextlib import contextmanager
import functools
import hashlib
import inspect
import io
from pathlib import Path
import warnings
from omegaconf import OmegaConf
from diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state
import torch
def get_quantizer(model, args, optimizer=None):
"""Return the quantizer given the XP quantization args."""
quantizer = None
if args.diffq:
quantizer = DiffQuantizer(
model, min_size=args.min_size, group_size=args.group_size)
if optimizer is not None:
quantizer.setup_optimizer(optimizer)
elif args.qat:
quantizer = UniformQuantizer(
model, bits=args.qat, min_size=args.min_size)
return quantizer
def load_model(path_or_package, strict=False):
"""Load a model from the given serialized model, either given as a dict (already loaded)
or a path to a file on disk."""
if isinstance(path_or_package, dict):
package = path_or_package
elif isinstance(path_or_package, (str, Path)):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
path = path_or_package
package = torch.load(path, 'cpu')
else:
raise ValueError(f"Invalid type for {path_or_package}.")
klass = package["klass"]
args = package["args"]
kwargs = package["kwargs"]
if strict:
model = klass(*args, **kwargs)
else:
sig = inspect.signature(klass)
for key in list(kwargs):
if key not in sig.parameters:
warnings.warn("Dropping inexistant parameter " + key)
del kwargs[key]
model = klass(*args, **kwargs)
state = package["state"]
set_state(model, state)
return model
def get_state(model, quantizer, half=False):
"""Get the state from a model, potentially with quantization applied.
If `half` is True, model are stored as half precision, which shouldn't impact performance
but half the state size."""
if quantizer is None:
dtype = torch.half if half else None
state = {k: p.data.to(device='cpu', dtype=dtype) for k, p in model.state_dict().items()}
else:
state = quantizer.get_quantized_state()
state['__quantized'] = True
return state
def set_state(model, state, quantizer=None):
"""Set the state on a given model."""
if state.get('__quantized'):
if quantizer is not None:
quantizer.restore_quantized_state(model, state['quantized'])
else:
restore_quantized_state(model, state)
else:
model.load_state_dict(state)
return state
def save_with_checksum(content, path):
"""Save the given value on disk, along with a sha256 hash.
Should be used with the output of either `serialize_model` or `get_state`."""
buf = io.BytesIO()
torch.save(content, buf)
sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
path = path.parent / (path.stem + "-" + sig + path.suffix)
path.write_bytes(buf.getvalue())
def serialize_model(model, training_args, quantizer=None, half=True):
args, kwargs = model._init_args_kwargs
klass = model.__class__
state = get_state(model, quantizer, half)
return {
'klass': klass,
'args': args,
'kwargs': kwargs,
'state': state,
'training_args': OmegaConf.to_container(training_args, resolve=True),
}
def copy_state(state):
return {k: v.cpu().clone() for k, v in state.items()}
@contextmanager
def swap_state(model, state):
"""
Context manager that swaps the state of a model, e.g:
# model is in old state
with swap_state(model, new_state):
# model in new state
# model back to old state
"""
old_state = copy_state(model.state_dict())
model.load_state_dict(state, strict=False)
try:
yield
finally:
model.load_state_dict(old_state)
def capture_init(init):
@functools.wraps(init)
def __init__(self, *args, **kwargs):
self._init_args_kwargs = (args, kwargs)
init(self, *args, **kwargs)
return __init__

447
demucs/tasnet.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Created on 2018/12
# Author: Kaituo XU
# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels
# Here is the original license:
# The MIT License (MIT)
#
# Copyright (c) 2018 Kaituo XU
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import capture_init
EPS = 1e-8
def overlap_and_add(signal, frame_step):
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor
subframe_step = frame_step // subframe_length
subframes_per_frame = frame_length // subframe_length
output_size = frame_step * (frames - 1) + frame_length
output_subframes = output_size // subframe_length
subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)
frame = torch.arange(0, output_subframes,
device=signal.device).unfold(0, subframes_per_frame, subframe_step)
frame = frame.long() # signal may in GPU or CPU
frame = frame.contiguous().view(-1)
result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
result.index_add_(-2, frame, subframe_signal)
result = result.view(*outer_dimensions, -1)
return result
class ConvTasNet(nn.Module):
@capture_init
def __init__(self,
N=256,
L=20,
B=256,
H=512,
P=3,
X=8,
R=4,
C=4,
audio_channels=1,
samplerate=44100,
norm_type="gLN",
causal=False,
mask_nonlinear='relu'):
"""
Args:
N: Number of filters in autoencoder
L: Length of the filters (in samples)
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
C: Number of speakers
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(ConvTasNet, self).__init__()
# Hyper-parameter
self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C
self.norm_type = norm_type
self.causal = causal
self.mask_nonlinear = mask_nonlinear
self.audio_channels = audio_channels
self.samplerate = samplerate
# Components
self.encoder = Encoder(L, N, audio_channels)
self.separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type, causal, mask_nonlinear)
self.decoder = Decoder(N, L, audio_channels)
# init
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_normal_(p)
def valid_length(self, length):
return length
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
est_source: [M, C, T]
"""
mixture_w = self.encoder(mixture)
est_mask = self.separator(mixture_w)
est_source = self.decoder(mixture_w, est_mask)
# T changed after conv1d in encoder, fix it here
T_origin = mixture.size(-1)
T_conv = est_source.size(-1)
est_source = F.pad(est_source, (0, T_origin - T_conv))
return est_source
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N, audio_channels):
super(Encoder, self).__init__()
# Hyper-parameter
self.L, self.N = L, N
# Components
# 50% overlap
self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
"""
mixture_w = F.relu(self.conv1d_U(mixture)) # [M, N, K]
return mixture_w
class Decoder(nn.Module):
def __init__(self, N, L, audio_channels):
super(Decoder, self).__init__()
# Hyper-parameter
self.N, self.L = N, L
self.audio_channels = audio_channels
# Components
self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [M, N, K]
est_mask: [M, C, N, K]
Returns:
est_source: [M, C, T]
"""
# D = W * M
source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [M, C, N, K]
source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N]
# S = DV
est_source = self.basis_signals(source_w) # [M, C, K, ac * L]
m, c, k, _ = est_source.size()
est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()
est_source = overlap_and_add(est_source, self.L // 2) # M x C x ac x T
return est_source
class TemporalConvNet(nn.Module):
def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False, mask_nonlinear='relu'):
"""
Args:
N: Number of filters in autoencoder
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
C: Number of speakers
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(TemporalConvNet, self).__init__()
# Hyper-parameter
self.C = C
self.mask_nonlinear = mask_nonlinear
# Components
# [M, N, K] -> [M, N, K]
layer_norm = ChannelwiseLayerNorm(N)
# [M, N, K] -> [M, B, K]
bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
# [M, B, K] -> [M, B, K]
repeats = []
for r in range(R):
blocks = []
for x in range(X):
dilation = 2**x
padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
blocks += [
TemporalBlock(B,
H,
P,
stride=1,
padding=padding,
dilation=dilation,
norm_type=norm_type,
causal=causal)
]
repeats += [nn.Sequential(*blocks)]
temporal_conv_net = nn.Sequential(*repeats)
# [M, B, K] -> [M, C*N, K]
mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
# Put together
self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net,
mask_conv1x1)
def forward(self, mixture_w):
"""
Keep this API same with TasNet
Args:
mixture_w: [M, N, K], M is batch size
returns:
est_mask: [M, C, N, K]
"""
M, N, K = mixture_w.size()
score = self.network(mixture_w) # [M, N, K] -> [M, C*N, K]
score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K]
if self.mask_nonlinear == 'softmax':
est_mask = F.softmax(score, dim=1)
elif self.mask_nonlinear == 'relu':
est_mask = F.relu(score)
else:
raise ValueError("Unsupported mask non-linear function")
return est_mask
class TemporalBlock(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(TemporalBlock, self).__init__()
# [M, B, K] -> [M, H, K]
conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
prelu = nn.PReLU()
norm = chose_norm(norm_type, out_channels)
# [M, H, K] -> [M, B, K]
dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding,
dilation, norm_type, causal)
# Put together
self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)
def forward(self, x):
"""
Args:
x: [M, B, K]
Returns:
[M, B, K]
"""
residual = x
out = self.net(x)
# TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?
return out + residual # look like w/o F.relu is better than w/ F.relu
# return F.relu(out + residual)
class DepthwiseSeparableConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(DepthwiseSeparableConv, self).__init__()
# Use `groups` option to implement depthwise convolution
# [M, H, K] -> [M, H, K]
depthwise_conv = nn.Conv1d(in_channels,
in_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
bias=False)
if causal:
chomp = Chomp1d(padding)
prelu = nn.PReLU()
norm = chose_norm(norm_type, in_channels)
# [M, H, K] -> [M, B, K]
pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)
# Put together
if causal:
self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)
else:
self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)
def forward(self, x):
"""
Args:
x: [M, H, K]
Returns:
result: [M, B, K]
"""
return self.net(x)
class Chomp1d(nn.Module):
"""To ensure the output length is the same as the input.
"""
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
"""
Args:
x: [M, H, Kpad]
Returns:
[M, H, K]
"""
return x[:, :, :-self.chomp_size].contiguous()
def chose_norm(norm_type, channel_size):
"""The input of normlization will be (M, C, K), where M is batch size,
C is channel size and K is sequence length.
"""
if norm_type == "gLN":
return GlobalLayerNorm(channel_size)
elif norm_type == "cLN":
return ChannelwiseLayerNorm(channel_size)
elif norm_type == "id":
return nn.Identity()
else: # norm_type == "BN":
# Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
# along M and K, so this BN usage is right.
return nn.BatchNorm1d(channel_size)
# TODO: Use nn.LayerNorm to impl cLN to speed up
class ChannelwiseLayerNorm(nn.Module):
"""Channel-wise Layer Normalization (cLN)"""
def __init__(self, channel_size):
super(ChannelwiseLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
cLN_y: [M, N, K]
"""
mean = torch.mean(y, dim=1, keepdim=True) # [M, 1, K]
var = torch.var(y, dim=1, keepdim=True, unbiased=False) # [M, 1, K]
cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return cLN_y
class GlobalLayerNorm(nn.Module):
"""Global Layer Normalization (gLN)"""
def __init__(self, channel_size):
super(GlobalLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
gLN_y: [M, N, K]
"""
# TODO: in torch 1.0, torch.mean() support dim list
mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) # [M, 1, 1]
var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return gLN_y
if __name__ == "__main__":
torch.manual_seed(123)
M, N, L, T = 2, 3, 4, 12
K = 2 * T // L - 1
B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False
mixture = torch.randint(3, (M, T))
# test Encoder
encoder = Encoder(L, N)
encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())
mixture_w = encoder(mixture)
print('mixture', mixture)
print('U', encoder.conv1d_U.weight)
print('mixture_w', mixture_w)
print('mixture_w size', mixture_w.size())
# test TemporalConvNet
separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)
est_mask = separator(mixture_w)
print('est_mask', est_mask)
# test Decoder
decoder = Decoder(N, L)
est_mask = torch.randint(2, (B, K, C, N))
est_source = decoder(mixture_w, est_mask)
print('est_source', est_source)
# test Conv-TasNet
conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)
est_source = conv_tasnet(mixture)
print('est_source', est_source)
print('est_source size', est_source.size())

452
demucs/tasnet_v2.py
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@ -1,452 +0,0 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Created on 2018/12
# Author: Kaituo XU
# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels
# Here is the original license:
# The MIT License (MIT)
#
# Copyright (c) 2018 Kaituo XU
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import capture_init
EPS = 1e-8
def overlap_and_add(signal, frame_step):
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor
subframe_step = frame_step // subframe_length
subframes_per_frame = frame_length // subframe_length
output_size = frame_step * (frames - 1) + frame_length
output_subframes = output_size // subframe_length
subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)
frame = torch.arange(0, output_subframes,
device=signal.device).unfold(0, subframes_per_frame, subframe_step)
frame = frame.long() # signal may in GPU or CPU
frame = frame.contiguous().view(-1)
result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
result.index_add_(-2, frame, subframe_signal)
result = result.view(*outer_dimensions, -1)
return result
class ConvTasNet(nn.Module):
@capture_init
def __init__(self,
sources,
N=256,
L=20,
B=256,
H=512,
P=3,
X=8,
R=4,
audio_channels=2,
norm_type="gLN",
causal=False,
mask_nonlinear='relu',
samplerate=44100,
segment_length=44100 * 2 * 4):
"""
Args:
sources: list of sources
N: Number of filters in autoencoder
L: Length of the filters (in samples)
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(ConvTasNet, self).__init__()
# Hyper-parameter
self.sources = sources
self.C = len(sources)
self.N, self.L, self.B, self.H, self.P, self.X, self.R = N, L, B, H, P, X, R
self.norm_type = norm_type
self.causal = causal
self.mask_nonlinear = mask_nonlinear
self.audio_channels = audio_channels
self.samplerate = samplerate
self.segment_length = segment_length
# Components
self.encoder = Encoder(L, N, audio_channels)
self.separator = TemporalConvNet(
N, B, H, P, X, R, self.C, norm_type, causal, mask_nonlinear)
self.decoder = Decoder(N, L, audio_channels)
# init
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_normal_(p)
def valid_length(self, length):
return length
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
est_source: [M, C, T]
"""
mixture_w = self.encoder(mixture)
est_mask = self.separator(mixture_w)
est_source = self.decoder(mixture_w, est_mask)
# T changed after conv1d in encoder, fix it here
T_origin = mixture.size(-1)
T_conv = est_source.size(-1)
est_source = F.pad(est_source, (0, T_origin - T_conv))
return est_source
class Encoder(nn.Module):
"""Estimation of the nonnegative mixture weight by a 1-D conv layer.
"""
def __init__(self, L, N, audio_channels):
super(Encoder, self).__init__()
# Hyper-parameter
self.L, self.N = L, N
# Components
# 50% overlap
self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False)
def forward(self, mixture):
"""
Args:
mixture: [M, T], M is batch size, T is #samples
Returns:
mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
"""
mixture_w = F.relu(self.conv1d_U(mixture)) # [M, N, K]
return mixture_w
class Decoder(nn.Module):
def __init__(self, N, L, audio_channels):
super(Decoder, self).__init__()
# Hyper-parameter
self.N, self.L = N, L
self.audio_channels = audio_channels
# Components
self.basis_signals = nn.Linear(N, audio_channels * L, bias=False)
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [M, N, K]
est_mask: [M, C, N, K]
Returns:
est_source: [M, C, T]
"""
# D = W * M
source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [M, C, N, K]
source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N]
# S = DV
est_source = self.basis_signals(source_w) # [M, C, K, ac * L]
m, c, k, _ = est_source.size()
est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous()
est_source = overlap_and_add(est_source, self.L // 2) # M x C x ac x T
return est_source
class TemporalConvNet(nn.Module):
def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False, mask_nonlinear='relu'):
"""
Args:
N: Number of filters in autoencoder
B: Number of channels in bottleneck 1 × 1-conv block
H: Number of channels in convolutional blocks
P: Kernel size in convolutional blocks
X: Number of convolutional blocks in each repeat
R: Number of repeats
C: Number of speakers
norm_type: BN, gLN, cLN
causal: causal or non-causal
mask_nonlinear: use which non-linear function to generate mask
"""
super(TemporalConvNet, self).__init__()
# Hyper-parameter
self.C = C
self.mask_nonlinear = mask_nonlinear
# Components
# [M, N, K] -> [M, N, K]
layer_norm = ChannelwiseLayerNorm(N)
# [M, N, K] -> [M, B, K]
bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
# [M, B, K] -> [M, B, K]
repeats = []
for r in range(R):
blocks = []
for x in range(X):
dilation = 2**x
padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
blocks += [
TemporalBlock(B,
H,
P,
stride=1,
padding=padding,
dilation=dilation,
norm_type=norm_type,
causal=causal)
]
repeats += [nn.Sequential(*blocks)]
temporal_conv_net = nn.Sequential(*repeats)
# [M, B, K] -> [M, C*N, K]
mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False)
# Put together
self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net,
mask_conv1x1)
def forward(self, mixture_w):
"""
Keep this API same with TasNet
Args:
mixture_w: [M, N, K], M is batch size
returns:
est_mask: [M, C, N, K]
"""
M, N, K = mixture_w.size()
score = self.network(mixture_w) # [M, N, K] -> [M, C*N, K]
score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K]
if self.mask_nonlinear == 'softmax':
est_mask = F.softmax(score, dim=1)
elif self.mask_nonlinear == 'relu':
est_mask = F.relu(score)
else:
raise ValueError("Unsupported mask non-linear function")
return est_mask
class TemporalBlock(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(TemporalBlock, self).__init__()
# [M, B, K] -> [M, H, K]
conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
prelu = nn.PReLU()
norm = chose_norm(norm_type, out_channels)
# [M, H, K] -> [M, B, K]
dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding,
dilation, norm_type, causal)
# Put together
self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)
def forward(self, x):
"""
Args:
x: [M, B, K]
Returns:
[M, B, K]
"""
residual = x
out = self.net(x)
# TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?
return out + residual # look like w/o F.relu is better than w/ F.relu
# return F.relu(out + residual)
class DepthwiseSeparableConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
norm_type="gLN",
causal=False):
super(DepthwiseSeparableConv, self).__init__()
# Use `groups` option to implement depthwise convolution
# [M, H, K] -> [M, H, K]
depthwise_conv = nn.Conv1d(in_channels,
in_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
bias=False)
if causal:
chomp = Chomp1d(padding)
prelu = nn.PReLU()
norm = chose_norm(norm_type, in_channels)
# [M, H, K] -> [M, B, K]
pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)
# Put together
if causal:
self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv)
else:
self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv)
def forward(self, x):
"""
Args:
x: [M, H, K]
Returns:
result: [M, B, K]
"""
return self.net(x)
class Chomp1d(nn.Module):
"""To ensure the output length is the same as the input.
"""
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
"""
Args:
x: [M, H, Kpad]
Returns:
[M, H, K]
"""
return x[:, :, :-self.chomp_size].contiguous()
def chose_norm(norm_type, channel_size):
"""The input of normlization will be (M, C, K), where M is batch size,
C is channel size and K is sequence length.
"""
if norm_type == "gLN":
return GlobalLayerNorm(channel_size)
elif norm_type == "cLN":
return ChannelwiseLayerNorm(channel_size)
elif norm_type == "id":
return nn.Identity()
else: # norm_type == "BN":
# Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
# along M and K, so this BN usage is right.
return nn.BatchNorm1d(channel_size)
# TODO: Use nn.LayerNorm to impl cLN to speed up
class ChannelwiseLayerNorm(nn.Module):
"""Channel-wise Layer Normalization (cLN)"""
def __init__(self, channel_size):
super(ChannelwiseLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
cLN_y: [M, N, K]
"""
mean = torch.mean(y, dim=1, keepdim=True) # [M, 1, K]
var = torch.var(y, dim=1, keepdim=True, unbiased=False) # [M, 1, K]
cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return cLN_y
class GlobalLayerNorm(nn.Module):
"""Global Layer Normalization (gLN)"""
def __init__(self, channel_size):
super(GlobalLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
"""
Args:
y: [M, N, K], M is batch size, N is channel size, K is length
Returns:
gLN_y: [M, N, K]
"""
# TODO: in torch 1.0, torch.mean() support dim list
mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) # [M, 1, 1]
var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
return gLN_y
if __name__ == "__main__":
torch.manual_seed(123)
M, N, L, T = 2, 3, 4, 12
K = 2 * T // L - 1
B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False
mixture = torch.randint(3, (M, T))
# test Encoder
encoder = Encoder(L, N)
encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())
mixture_w = encoder(mixture)
print('mixture', mixture)
print('U', encoder.conv1d_U.weight)
print('mixture_w', mixture_w)
print('mixture_w size', mixture_w.size())
# test TemporalConvNet
separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)
est_mask = separator(mixture_w)
print('est_mask', est_mask)
# test Decoder
decoder = Decoder(N, L)
est_mask = torch.randint(2, (B, K, C, N))
est_source = decoder(mixture_w, est_mask)
print('est_source', est_source)
# test Conv-TasNet
conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type)
est_source = conv_tasnet(mixture)
print('est_source', est_source)
print('est_source size', est_source.size())

187
demucs/utils-.py
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@ -1,187 +0,0 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import errno
import functools
import gzip
import os
import random
import socket
import tempfile
import warnings
from contextlib import contextmanager
import torch as th
import tqdm
from torch import distributed
from torch.nn import functional as F
def center_trim(tensor, reference):
"""
Center trim `tensor` with respect to `reference`, along the last dimension.
`reference` can also be a number, representing the length to trim to.
If the size difference != 0 mod 2, the extra sample is removed on the right side.
"""
if hasattr(reference, "size"):
reference = reference.size(-1)
delta = tensor.size(-1) - reference
if delta < 0:
raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
if delta:
tensor = tensor[..., delta // 2:-(delta - delta // 2)]
return tensor
def average_metric(metric, count=1.):
"""
Average `metric` which should be a float across all hosts. `count` should be
the weight for this particular host (i.e. number of examples).
"""
metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
return metric[1].item() / metric[0].item()
def free_port(host='', low=20000, high=40000):
"""
Return a port number that is most likely free.
This could suffer from a race condition although
it should be quite rare.
"""
sock = socket.socket()
while True:
port = random.randint(low, high)
try:
sock.bind((host, port))
except OSError as error:
if error.errno == errno.EADDRINUSE:
continue
raise
return port
def sizeof_fmt(num, suffix='B'):
"""
Given `num` bytes, return human readable size.
Taken from https://stackoverflow.com/a/1094933
"""
for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
num /= 1024.0
return "%.1f%s%s" % (num, 'Yi', suffix)
def human_seconds(seconds, display='.2f'):
"""
Given `seconds` seconds, return human readable duration.
"""
value = seconds * 1e6
ratios = [1e3, 1e3, 60, 60, 24]
names = ['us', 'ms', 's', 'min', 'hrs', 'days']
last = names.pop(0)
for name, ratio in zip(names, ratios):
if value / ratio < 0.3:
break
value /= ratio
last = name
return f"{format(value, display)} {last}"
def apply_model_v1(model, mix, shifts=None, split=False, progress=False):
"""
Apply model to a given mixture.
Args:
shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
and apply the oppositve shift to the output. This is repeated `shifts` time and
all predictions are averaged. This effectively makes the model time equivariant
and improves SDR by up to 0.2 points.
split (bool): if True, the input will be broken down in 8 seconds extracts
and predictions will be performed individually on each and concatenated.
Useful for model with large memory footprint like Tasnet.
progress (bool): if True, show a progress bar (requires split=True)
"""
channels, length = mix.size()
device = mix.device
if split:
out = th.zeros(4, channels, length, device=device)
shift = model.samplerate * 10
offsets = range(0, length, shift)
scale = 10
if progress:
offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
for offset in offsets:
chunk = mix[..., offset:offset + shift]
chunk_out = apply_model_v1(model, chunk, shifts=shifts)
out[..., offset:offset + shift] = chunk_out
offset += shift
return out
elif shifts:
max_shift = int(model.samplerate / 2)
mix = F.pad(mix, (max_shift, max_shift))
offsets = list(range(max_shift))
random.shuffle(offsets)
out = 0
for offset in offsets[:shifts]:
shifted = mix[..., offset:offset + length + max_shift]
shifted_out = apply_model_v1(model, shifted)
out += shifted_out[..., max_shift - offset:max_shift - offset + length]
out /= shifts
return out
else:
valid_length = model.valid_length(length)
print('valid_length: ', valid_length)
delta = valid_length - length
padded = F.pad(mix, (delta // 2, delta - delta // 2))
with th.no_grad():
out = model(padded.unsqueeze(0))[0]
return center_trim(out, mix)
@contextmanager
def temp_filenames(count, delete=True, **kwargs):
names = []
try:
for _ in range(count):
names.append(tempfile.NamedTemporaryFile(delete=False).name)
yield names
finally:
if delete:
for name in names:
os.unlink(name)
def load_model(path):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
load_from = path
if str(path).endswith(".gz"):
load_from = gzip.open(path, "rb")
klass, args, kwargs, state = th.load(load_from, 'cpu')
model = klass(*args, **kwargs)
model.load_state_dict(state)
return model
def save_model(model, path):
args, kwargs = model._init_args_kwargs
klass = model.__class__
state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
save_to = path
if str(path).endswith(".gz"):
save_to = gzip.open(path, "wb", compresslevel=5)
th.save((klass, args, kwargs, state), save_to)
def capture_init(init):
@functools.wraps(init)
def __init__(self, *args, **kwargs):
self._init_args_kwargs = (args, kwargs)
init(self, *args, **kwargs)
return __init__

533
demucs/utils.py
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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
from contextlib import contextmanager
import math
import os
import tempfile
import typing as tp
import errno
import functools
import hashlib
import inspect
import io
import os
import random
import socket
import tempfile
import warnings
import zlib
import tkinter as tk
from diffq import UniformQuantizer, DiffQuantizer
import torch as th
import tqdm
from torch import distributed
from torch.nn import functional as F
import torch
def unfold(a, kernel_size, stride):
"""Given input of size [*OT, T], output Tensor of size [*OT, F, K]
with K the kernel size, by extracting frames with the given stride.
This will pad the input so that `F = ceil(T / K)`.
see https://github.com/pytorch/pytorch/issues/60466
"""
*shape, length = a.shape
n_frames = math.ceil(length / stride)
tgt_length = (n_frames - 1) * stride + kernel_size
a = F.pad(a, (0, tgt_length - length))
strides = list(a.stride())
assert strides[-1] == 1, 'data should be contiguous'
strides = strides[:-1] + [stride, 1]
return a.as_strided([*shape, n_frames, kernel_size], strides)
def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):
"""
Center trim `tensor` with respect to `reference`, along the last dimension.
`reference` can also be a number, representing the length to trim to.
If the size difference != 0 mod 2, the extra sample is removed on the right side.
"""
ref_size: int
if isinstance(reference, torch.Tensor):
ref_size = reference.size(-1)
else:
ref_size = reference
delta = tensor.size(-1) - ref_size
if delta < 0:
raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
if delta:
tensor = tensor[..., delta // 2:-(delta - delta // 2)]
return tensor
def pull_metric(history: tp.List[dict], name: str):
out = []
for metrics in history:
metric = metrics
for part in name.split("."):
metric = metric[part]
out.append(metric)
return out
def EMA(beta: float = 1):
"""
Exponential Moving Average callback.
Returns a single function that can be called to repeatidly update the EMA
with a dict of metrics. The callback will return
the new averaged dict of metrics.
Note that for `beta=1`, this is just plain averaging.
"""
fix: tp.Dict[str, float] = defaultdict(float)
total: tp.Dict[str, float] = defaultdict(float)
def _update(metrics: dict, weight: float = 1) -> dict:
nonlocal total, fix
for key, value in metrics.items():
total[key] = total[key] * beta + weight * float(value)
fix[key] = fix[key] * beta + weight
return {key: tot / fix[key] for key, tot in total.items()}
return _update
def sizeof_fmt(num: float, suffix: str = 'B'):
"""
Given `num` bytes, return human readable size.
Taken from https://stackoverflow.com/a/1094933
"""
for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
num /= 1024.0
return "%.1f%s%s" % (num, 'Yi', suffix)
@contextmanager
def temp_filenames(count: int, delete=True):
names = []
try:
for _ in range(count):
names.append(tempfile.NamedTemporaryFile(delete=False).name)
yield names
finally:
if delete:
for name in names:
os.unlink(name)
def average_metric(metric, count=1.):
"""
Average `metric` which should be a float across all hosts. `count` should be
the weight for this particular host (i.e. number of examples).
"""
metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
return metric[1].item() / metric[0].item()
def free_port(host='', low=20000, high=40000):
"""
Return a port number that is most likely free.
This could suffer from a race condition although
it should be quite rare.
"""
sock = socket.socket()
while True:
port = random.randint(low, high)
try:
sock.bind((host, port))
except OSError as error:
if error.errno == errno.EADDRINUSE:
continue
raise
return port
def sizeof_fmt(num, suffix='B'):
"""
Given `num` bytes, return human readable size.
Taken from https://stackoverflow.com/a/1094933
"""
for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
num /= 1024.0
return "%.1f%s%s" % (num, 'Yi', suffix)
def human_seconds(seconds, display='.2f'):
"""
Given `seconds` seconds, return human readable duration.
"""
value = seconds * 1e6
ratios = [1e3, 1e3, 60, 60, 24]
names = ['us', 'ms', 's', 'min', 'hrs', 'days']
last = names.pop(0)
for name, ratio in zip(names, ratios):
if value / ratio < 0.3:
break
value /= ratio
last = name
return f"{format(value, display)} {last}"
class TensorChunk:
def __init__(self, tensor, offset=0, length=None):
total_length = tensor.shape[-1]
assert offset >= 0
assert offset < total_length
if length is None:
length = total_length - offset
else:
length = min(total_length - offset, length)
self.tensor = tensor
self.offset = offset
self.length = length
self.device = tensor.device
@property
def shape(self):
shape = list(self.tensor.shape)
shape[-1] = self.length
return shape
def padded(self, target_length):
delta = target_length - self.length
total_length = self.tensor.shape[-1]
assert delta >= 0
start = self.offset - delta // 2
end = start + target_length
correct_start = max(0, start)
correct_end = min(total_length, end)
pad_left = correct_start - start
pad_right = end - correct_end
out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
assert out.shape[-1] == target_length
return out
def tensor_chunk(tensor_or_chunk):
if isinstance(tensor_or_chunk, TensorChunk):
return tensor_or_chunk
else:
assert isinstance(tensor_or_chunk, th.Tensor)
return TensorChunk(tensor_or_chunk)
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.
Args:
shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
and apply the oppositve shift to the output. This is repeated `shifts` time and
all predictions are averaged. This effectively makes the model time equivariant
and improves SDR by up to 0.2 points.
split (bool): if True, the input will be broken down in 8 seconds extracts
and predictions will be performed individually on each and concatenated.
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:
out = th.zeros(4, channels, length, device=device)
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, 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
elif shifts:
max_shift = int(model.samplerate / 2)
mix = F.pad(mix, (max_shift, max_shift))
offsets = list(range(max_shift))
random.shuffle(offsets)
out = 0
for offset in offsets[:shifts]:
shifted = mix[..., offset:offset + length + max_shift]
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
else:
valid_length = model.valid_length(length)
delta = valid_length - length
padded = F.pad(mix, (delta // 2, delta - delta // 2))
with th.no_grad():
out = model(padded.unsqueeze(0))[0]
return center_trim(out, mix)
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.
Args:
shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
and apply the oppositve shift to the output. This is repeated `shifts` time and
all predictions are averaged. This effectively makes the model time equivariant
and improves SDR by up to 0.2 points.
split (bool): if True, the input will be broken down in 8 seconds extracts
and predictions will be performed individually on each and concatenated.
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
if split:
out = th.zeros(len(model.sources), channels, length, device=device)
sum_weight = th.zeros(length, device=device)
segment = model.segment_length
stride = int((1 - overlap) * segment)
offsets = range(0, length, stride)
scale = stride / model.samplerate
if progress:
offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
# We start from a triangle shaped weight, with maximal weight in the middle
# of the segment. Then we normalize and take to the power `transition_power`.
# Large values of transition power will lead to sharper transitions.
weight = th.cat([th.arange(1, segment // 2 + 1),
th.arange(segment - segment // 2, 0, -1)]).to(device)
assert len(weight) == segment
# 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, 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]
offset += segment
assert sum_weight.min() > 0
out /= sum_weight
return out
elif shifts:
max_shift = int(0.5 * model.samplerate)
mix = tensor_chunk(mix)
padded_mix = mix.padded(length + 2 * max_shift)
out = 0
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, gui_progress_bar, widget_text, update_prog, total_files, file_num, inference_type)
out += shifted_out[..., max_shift - offset:]
out /= shifts
return out
else:
valid_length = model.valid_length(length)
mix = tensor_chunk(mix)
padded_mix = mix.padded(valid_length)
with th.no_grad():
out = model(padded_mix.unsqueeze(0))[0]
return center_trim(out, length)
@contextmanager
def temp_filenames(count, delete=True):
names = []
try:
for _ in range(count):
names.append(tempfile.NamedTemporaryFile(delete=False).name)
yield names
finally:
if delete:
for name in names:
os.unlink(name)
def get_quantizer(model, args, optimizer=None):
quantizer = None
if args.diffq:
quantizer = DiffQuantizer(
model, min_size=args.q_min_size, group_size=8)
if optimizer is not None:
quantizer.setup_optimizer(optimizer)
elif args.qat:
quantizer = UniformQuantizer(
model, bits=args.qat, min_size=args.q_min_size)
return quantizer
def load_model(path, strict=False):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
load_from = path
package = th.load(load_from, 'cpu')
klass = package["klass"]
args = package["args"]
kwargs = package["kwargs"]
if strict:
model = klass(*args, **kwargs)
else:
sig = inspect.signature(klass)
for key in list(kwargs):
if key not in sig.parameters:
warnings.warn("Dropping inexistant parameter " + key)
del kwargs[key]
model = klass(*args, **kwargs)
state = package["state"]
training_args = package["training_args"]
quantizer = get_quantizer(model, training_args)
set_state(model, quantizer, state)
return model
def get_state(model, quantizer):
if quantizer is None:
state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
else:
state = quantizer.get_quantized_state()
buf = io.BytesIO()
th.save(state, buf)
state = {'compressed': zlib.compress(buf.getvalue())}
return state
def set_state(model, quantizer, state):
if quantizer is None:
model.load_state_dict(state)
else:
buf = io.BytesIO(zlib.decompress(state["compressed"]))
state = th.load(buf, "cpu")
quantizer.restore_quantized_state(state)
return state
def save_state(state, path):
buf = io.BytesIO()
th.save(state, buf)
sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
path = path.parent / (path.stem + "-" + sig + path.suffix)
path.write_bytes(buf.getvalue())
def save_model(model, quantizer, training_args, path):
args, kwargs = model._init_args_kwargs
klass = model.__class__
state = get_state(model, quantizer)
save_to = path
package = {
'klass': klass,
'args': args,
'kwargs': kwargs,
'state': state,
'training_args': training_args,
}
th.save(package, save_to)
def capture_init(init):
@functools.wraps(init)
def __init__(self, *args, **kwargs):
self._init_args_kwargs = (args, kwargs)
init(self, *args, **kwargs)
return __init__
class DummyPoolExecutor:
class DummyResult:
def __init__(self, func, *args, **kwargs):
self.func = func
self.args = args
self.kwargs = kwargs
def result(self):
return self.func(*self.args, **self.kwargs)
def __init__(self, workers=0):
pass
def submit(self, func, *args, **kwargs):
return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_tb):
return