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import tqdm
import torch
import random
import typing as tp
from torch import nn
from torch.nn import functional as F
from concurrent.futures import ThreadPoolExecutor
from .demucs import Demucs
from .hdemucs import HDemucs
from .htdemucs import HTDemucs
from .utils import center_trim
Model = tp.Union[Demucs, HDemucs, HTDemucs]
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
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):
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 = [[1.0 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("`apply_model`")
class TensorChunk:
def __init__(self, tensor, offset=0, length=None):
total_length = tensor.shape[-1]
assert offset >= 0
assert offset < total_length
length = total_length - offset if length is None else min(total_length - offset, length)
if isinstance(tensor, TensorChunk):
self.tensor = tensor.tensor
self.offset = offset + tensor.offset
else:
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, torch.Tensor)
return TensorChunk(tensor_or_chunk)
def apply_model(model, mix, shifts=1, split=True, overlap=0.25, transition_power=1.0, static_shifts=1, set_progress_bar=None, device=None, progress=False, num_workers=0, pool=None):
global fut_length
global bag_num
global prog_bar
device = mix.device if device is None else torch.device(device)
if pool is None: pool = ThreadPoolExecutor(num_workers) if num_workers > 0 and device.type == "cpu" else DummyPoolExecutor()
kwargs = {
"shifts": shifts,
"split": split,
"overlap": overlap,
"transition_power": transition_power,
"progress": progress,
"device": device,
"pool": pool,
"set_progress_bar": set_progress_bar,
"static_shifts": static_shifts,
}
if isinstance(model, BagOfModels):
estimates = 0
totals = [0] * len(model.sources)
bag_num = len(model.models)
fut_length = 0
prog_bar = 0
current_model = 0
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)
model.eval()
assert transition_power >= 1
batch, channels, length = mix.shape
if 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
elif split:
kwargs["split"] = False
out = torch.zeros(batch, len(model.sources), channels, length, device=mix.device)
sum_weight = torch.zeros(length, device=mix.device)
segment = int(model.samplerate * model.segment)
stride = int((1 - overlap) * segment)
offsets = range(0, length, stride)
weight = torch.cat([torch.arange(1, segment // 2 + 1, device=device), torch.arange(segment - segment // 2, 0, -1, device=device)])
assert len(weight) == segment
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
if progress: futures = tqdm.tqdm(futures)
for future, offset in futures:
if set_progress_bar:
fut_length = len(futures) * bag_num * static_shifts
prog_bar += 1
set_progress_bar(0.1, (0.8 / fut_length * prog_bar))
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
else:
valid_length = model.valid_length(length) if hasattr(model, "valid_length") else length
mix = tensor_chunk(mix)
padded_mix = mix.padded(valid_length).to(device)
with torch.no_grad():
out = model(padded_mix)
return center_trim(out, length)
def demucs_segments(demucs_segment, demucs_model):
if demucs_segment == "Default":
segment = None
if isinstance(demucs_model, BagOfModels):
if segment is not None:
for sub in demucs_model.models:
sub.segment = segment
else:
if segment is not None: sub.segment = segment
else:
try:
segment = int(demucs_segment)
if isinstance(demucs_model, BagOfModels):
if segment is not None:
for sub in demucs_model.models:
sub.segment = segment
else:
if segment is not None: sub.segment = segment
except:
segment = None
if isinstance(demucs_model, BagOfModels):
if segment is not None:
for sub in demucs_model.models:
sub.segment = segment
else:
if segment is not None: sub.segment = segment
return demucs_model |