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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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9
GPT_SoVITS/BigVGAN/tests/test_activation.py
View File

@@ -3,6 +3,7 @@
import os
import sys
# to import modules from parent_dir
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(parent_dir)
@@ -24,14 +25,10 @@ def test_anti_alias_activation():
data = torch.rand((10, 10, 200), device="cuda")
# Check activations.Snake cuda vs. torch
fused_anti_alias_activation = activation1d.Activation1d(
activation=Snake(10), fused=True
).cuda()
fused_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=True).cuda()
fused_activation_output = fused_anti_alias_activation(data)
torch_anti_alias_activation = activation1d.Activation1d(
activation=Snake(10), fused=False
).cuda()
torch_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=False).cuda()
torch_activation_output = torch_anti_alias_activation(data)
test_result = (fused_activation_output - torch_activation_output).abs()

10
GPT_SoVITS/BigVGAN/tests/test_activation_snake_beta.py
View File

@@ -3,6 +3,7 @@
import os
import sys
# to import modules from parent_dir
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(parent_dir)
@@ -24,14 +25,10 @@ def test_anti_alias_activation():
data = torch.rand((10, 10, 200), device="cuda")
# Check activations, Snake CUDA vs. Torch
fused_anti_alias_activation = activation1d.Activation1d(
activation=SnakeBeta(10), fused=True
).cuda()
fused_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=True).cuda()
fused_activation_output = fused_anti_alias_activation(data)
torch_anti_alias_activation = activation1d.Activation1d(
activation=SnakeBeta(10), fused=False
).cuda()
torch_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=False).cuda()
torch_activation_output = torch_anti_alias_activation(data)
test_result = (fused_activation_output - torch_activation_output).abs()
@@ -57,7 +54,6 @@ def test_anti_alias_activation():
)
if __name__ == "__main__":
from alias_free_activation.cuda import load

38
GPT_SoVITS/BigVGAN/tests/test_cuda_vs_torch_model.py
View File

@@ -42,9 +42,7 @@ def generate_soundwave(duration=5.0, sr=24000):
def get_mel(x, h):
return mel_spectrogram(
x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax
)
return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
def load_checkpoint(filepath, device):
@@ -56,9 +54,7 @@ def load_checkpoint(filepath, device):
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Test script to check CUDA kernel correctness."
)
parser = argparse.ArgumentParser(description="Test script to check CUDA kernel correctness.")
parser.add_argument(
"--checkpoint_file",
type=str,
@@ -91,27 +87,25 @@ if __name__ == "__main__":
# define number of samples and length of mel frame to benchmark
num_sample = 10
num_mel_frame = 16384
# CUDA kernel correctness check
diff = 0.0
for i in tqdm(range(num_sample)):
# Random mel
data = torch.rand((1, h.num_mels, num_mel_frame), device="cuda")
with torch.inference_mode():
audio_original = generator_original(data)
with torch.inference_mode():
audio_cuda_kernel = generator_cuda_kernel(data)
# Both outputs should be (almost) the same
test_result = (audio_original - audio_cuda_kernel).abs()
diff += test_result.mean(dim=-1).item()
diff /= num_sample
if (
diff <= 2e-3
): # We can expect a small difference (~1e-3) which does not affect perceptual quality
if diff <= 2e-3: # We can expect a small difference (~1e-3) which does not affect perceptual quality
print(
f"\n[Success] test CUDA fused vs. plain torch BigVGAN inference"
f"\n > mean_difference={diff}"
@@ -125,9 +119,9 @@ if __name__ == "__main__":
f"\n > fused_values={audio_cuda_kernel[-1][-1][-30:].tolist()}, "
f"\n > torch_values={audio_original[-1][-1][-30:].tolist()}"
)
del data, audio_original, audio_cuda_kernel
# Variables for tracking total time and VRAM usage
toc_total_original = 0
toc_total_cuda_kernel = 0
@@ -145,10 +139,10 @@ if __name__ == "__main__":
audio_original = generator_original(data)
torch.cuda.synchronize()
toc = time() - tic
toc_total_original += toc
toc_total_original += toc
vram_used_original_total += torch.cuda.max_memory_allocated(device="cuda")
del data, audio_original
torch.cuda.empty_cache()
@@ -163,11 +157,11 @@ if __name__ == "__main__":
torch.cuda.synchronize()
toc = time() - tic
toc_total_cuda_kernel += toc
audio_length_total += audio_cuda_kernel.shape[-1]
vram_used_cuda_kernel_total += torch.cuda.max_memory_allocated(device="cuda")
del data, audio_cuda_kernel
torch.cuda.empty_cache()
@@ -175,8 +169,8 @@ if __name__ == "__main__":
audio_second = audio_length_total / h.sampling_rate
khz_original = audio_length_total / toc_total_original / 1000
khz_cuda_kernel = audio_length_total / toc_total_cuda_kernel / 1000
vram_used_original_gb = vram_used_original_total / num_sample / (1024 ** 3)
vram_used_cuda_kernel_gb = vram_used_cuda_kernel_total / num_sample / (1024 ** 3)
vram_used_original_gb = vram_used_original_total / num_sample / (1024**3)
vram_used_cuda_kernel_gb = vram_used_cuda_kernel_total / num_sample / (1024**3)
# Print results
print(