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【Hackathon 8th No.9】在 PaddleSpeech 中复现 DAC 训练需要用到的 loss #3954

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【Hackathon 8th No.9】在 PaddleSpeech 中复现 DAC 训练需要用到的 loss #3954

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2c04e0a
[Hackathon 7th No.56] 在 PaddleSpeech 中复现 DAC 训练需要用到的 loss
suzakuwcx Dec 17, 2024
b741545
Using pre-commit to format code
suzakuwcx Dec 17, 2024
37f60d6
t2s/modules/losses.py: Add a 'clamp_eps' parameter to dynamically adj…
suzakuwcx Dec 28, 2024
c1a8f99
tests/unit/tts/test_losses.py: Add gradient tests and update precisio…
suzakuwcx Dec 29, 2024
fd5365c
tests/unit/tts/test_losses.py: Add error message on assert failed
suzakuwcx Jan 1, 2025
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tests/unit/tts/test_losses.py: Add error message on assert failed
@suzakuwcx
suzakuwcx committed Jan 1, 2025
commit fd5365c5b6e42f2feab0c15fb54c89c2519e18cf
37 changes: 23 additions & 14 deletions tests/unit/tts/test_losses.py
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Original file line number Diff line number Diff line change
@@ -31,10 +31,13 @@ def test_dac_losses():
loss_1.backward()
loss_1_grad = signal.audio_data.grad.sum()

assert abs((loss_1.item() - loss_origin['stft/loss'].item()) /
loss_1.item()) < 1e-5
assert abs((loss_1_grad.item() - loss_origin['stft/grad'].sum().item())
/ loss_1_grad.item()) < 1e-5
assert abs(
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suggest use np.testing.assert_allclose, these losses can pass 1e-6?

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Currently not. After debugging, I find out that the loss is generated by 'paddle.signal.stft' (without cuda), so I have to compare the implement with '_VF' and paddle. I'm sure that the loss can decrease to 0 if fixing this

(loss_1.item() - loss_origin['stft/loss'].item()) /
loss_1.item()) < 1e-5, r"value incorrect for 'MultiScaleSTFTLoss'"
assert abs(
(loss_1_grad.item() - loss_origin['stft/grad'].sum().item()
) / loss_1_grad.
item()) < 1e-5, r"gradient incorrect for 'MultiScaleSTFTLoss'"

signal.audio_data.clear_grad()
recons.audio_data.clear_grad()
@@ -43,11 +46,13 @@ def test_dac_losses():
loss_2.backward()
loss_2_grad = signal.audio_data.grad.sum()

assert abs((loss_2.item() - loss_origin['mel/loss'].item()) /
loss_2.item()) < 1e-5
assert abs(
(loss_2.item() - loss_origin['mel/loss'].item()) / loss_2.
item()) < 1e-5, r"value incorrect for 'MultiMelSpectrogramLoss'"
assert abs(
(signal.audio_data.grad.sum().item() -
loss_origin['mel/grad'].sum().item()) / loss_2_grad.item()) < 1e-5
loss_origin['mel/grad'].sum().item()) / loss_2_grad.
item()) < 1e-5, r"gradient incorrect for 'MultiMelSpectrogramLoss'"

signal.audio_data.clear_grad()
recons.audio_data.clear_grad()
@@ -60,10 +65,11 @@ def test_dac_losses():
loss_1.backward()
loss_1_grad = signal.audio_data.grad.sum()

assert abs(loss_1.item() - loss_origin['stft/loss']
.item()) / loss_1.item() < 1e-5
assert abs(loss_1.item() - loss_origin['stft/loss'].item(
)) / loss_1.item() < 1e-5, r"value incorrect for 'MultiScaleSTFTLoss'"
assert abs(loss_1_grad.item() - loss_origin['stft/grad'].sum()
.item()) / loss_1_grad.item() < 1e-5
.item()) / loss_1_grad.item(
) < 1e-5, r"gradient incorrect for 'MultiScaleSTFTLoss'"

signal.audio_data.clear_grad()
recons.audio_data.clear_grad()
@@ -72,7 +78,10 @@ def test_dac_losses():
loss_2.backward()
loss_2_grad = signal.audio_data.grad.sum()

assert abs((loss_2.item() - loss_origin['mel/loss'].item()) /
loss_2.item()) < 1e-5
assert abs((loss_2_grad.item() - loss_origin['mel/grad'].sum().item()) /
loss_2_grad.item()) < 1e-5
assert abs(
(loss_2.item() - loss_origin['mel/loss'].item()) / loss_2.
item()) < 1e-5, r"value incorrect for 'MultiMelSpectrogramLoss'"
assert abs(
(loss_2_grad.item() - loss_origin['mel/grad'].sum().item()
) / loss_2_grad.
item()) < 1e-5, r"gradient incorrect for 'MultiMelSpectrogramLoss'"