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59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
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# LICENSE is in incl_licenses directory.
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import glob
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import os
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import torch
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from torch.nn.utils import weight_norm
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def apply_weight_norm(m):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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weight_norm(m)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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def load_checkpoint(filepath, device):
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assert os.path.isfile(filepath)
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print(f"Loading '{filepath}'")
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checkpoint_dict = torch.load(filepath, map_location=device)
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print("Complete.")
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return checkpoint_dict
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def save_checkpoint(filepath, obj):
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print(f"Saving checkpoint to {filepath}")
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torch.save(obj, filepath)
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print("Complete.")
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def scan_checkpoint(cp_dir, prefix, renamed_file=None):
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# Fallback to original scanning logic first
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pattern = os.path.join(cp_dir, prefix + "????????")
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cp_list = glob.glob(pattern)
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if len(cp_list) > 0:
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last_checkpoint_path = sorted(cp_list)[-1]
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print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'")
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return last_checkpoint_path
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# If no pattern-based checkpoints are found, check for renamed file
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if renamed_file:
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renamed_path = os.path.join(cp_dir, renamed_file)
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if os.path.isfile(renamed_path):
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print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'")
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return renamed_path
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return None
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