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chore: import upstream snapshot with attribution
2026-07-13 12:40:30 +08:00

33 lines
781 B
Python

# Copyright (c) Meta Platforms, Inc. and 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 random
import torch
from audiocraft.modules.lstm import StreamableLSTM
class TestStreamableLSTM:
def test_lstm(self):
B, C, T = 4, 2, random.randint(1, 100)
lstm = StreamableLSTM(C, 3, skip=False)
x = torch.randn(B, C, T)
y = lstm(x)
print(y.shape)
assert y.shape == torch.Size([B, C, T])
def test_lstm_skip(self):
B, C, T = 4, 2, random.randint(1, 100)
lstm = StreamableLSTM(C, 3, skip=True)
x = torch.randn(B, C, T)
y = lstm(x)
assert y.shape == torch.Size([B, C, T])