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Fix tuple syntax and tqdm parameters in SCNet.py and apply.py #21

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Fix tuple syntax and tqdm parameters in SCNet.py and apply.py #21

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7 changes: 4 additions & 3 deletions scnet/SCNet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import torch.nn.functional as F
from collections import deque
from .separation import SeparationNet
import typing as tp
from typing import cast
import math

class Swish(nn.Module):
Expand Down Expand Up @@ -147,7 +147,7 @@ def __init__(self, channels_in, channels_out, band_configs):

# Initializing convolutional layers for each band
self.convtrs = nn.ModuleList([
nn.ConvTranspose2d(channels_in, channels_out, [config['kernel'], 1], [config['stride'], 1])
nn.ConvTranspose2d(channels_in, channels_out, (config['kernel'], 1), (config['stride'], 1))
for _, config in band_configs.items()
])

Expand Down Expand Up @@ -351,7 +351,8 @@ def forward(self, x):
x = self.separation_net(x)

#decoder
for fusion_layer, su_layer in self.decoder:
for dec in self.decoder:
fusion_layer, su_layer = cast(nn.Sequential, dec)
x = fusion_layer(x, save_skip.pop())
x = su_layer(x, save_lengths.pop(), save_original_lengths.pop())

Expand Down
4 changes: 2 additions & 2 deletions scnet/apply.py
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Expand Up @@ -108,7 +108,7 @@ def apply_model(model, mix, shifts=1, split=True, segment=20, samplerate=44100,
segment = int(samplerate * segment)
stride = int((1 - overlap) * segment)
offsets = range(0, length, stride)
scale = stride / samplerate
scale = stride // samplerate
weight = th.cat([th.arange(1, segment // 2 + 1, device=device),
th.arange(segment - segment // 2, 0, -1, device=device)])
assert len(weight) == segment
Expand All @@ -122,7 +122,7 @@ def apply_model(model, mix, shifts=1, split=True, segment=20, samplerate=44100,
futures.append((future, offset))
offset += segment
if progress:
futures = tqdm.tqdm(futures, unit_scale=scale, ncols=120, unit='seconds')
futures = tqdm.tqdm(futures, total=scale, ncols=120, unit='chunks')
for future, offset in futures:
chunk_out = future.result()
chunk_length = chunk_out.shape[-1]
Expand Down