Merge pull request #208 from kan-bayashi/minor_update_5

parallel_wavegan/datasets/__init__.py

@@ -1,2 +1,2 @@
-from parallel_wavegan.datasets.audio_mel_dataset import *  # NOQA
-from parallel_wavegan.datasets.scp_dataset import *  # NOQA
+from .audio_mel_dataset import *  # NOQA
+from .scp_dataset import *  # NOQA

parallel_wavegan/layers/__init__.py

@@ -1,5 +1,5 @@
-from parallel_wavegan.layers.causal_conv import *  # NOQA
-from parallel_wavegan.layers.pqmf import *  # NOQA
-from parallel_wavegan.layers.residual_block import *  # NOQA
-from parallel_wavegan.layers.residual_stack import *  # NOQA
-from parallel_wavegan.layers.upsample import *  # NOQA
+from .causal_conv import *  # NOQA
+from .pqmf import *  # NOQA
+from .residual_block import *  # NOQA
+from .residual_stack import *  # NOQA
+from .upsample import *  # NOQA

parallel_wavegan/losses/__init__.py

@@ -1 +1 @@
-from parallel_wavegan.losses.stft_loss import *  # NOQA
+from .stft_loss import *  # NOQA

parallel_wavegan/models/__init__.py

@@ -1,2 +1,2 @@
-from parallel_wavegan.models.melgan import *  # NOQA
-from parallel_wavegan.models.parallel_wavegan import *  # NOQA
+from .melgan import *  # NOQA
+from .parallel_wavegan import *  # NOQA

parallel_wavegan/optimizers/__init__.py

@@ -1,2 +1,3 @@
 from torch.optim import *  # NOQA
-from parallel_wavegan.optimizers.radam import *  # NOQA
+
+from .radam import *  # NOQA

parallel_wavegan/utils/__init__.py

@@ -1 +1 @@
-from parallel_wavegan.utils.utils import *  # NOQA
+from .utils import *  # NOQA

parallel_wavegan/utils/utils.py

@@ -18,11 +18,6 @@
 import torch
 import yaml
 
-import parallel_wavegan.models
-
-from parallel_wavegan.layers import PQMF
-
-
 PRETRAINED_MODEL_LIST = {
     "ljspeech_parallel_wavegan.v1": "1PdZv37JhAQH6AwNh31QlqruqrvjTBq7U",
     "ljspeech_parallel_wavegan.v1.long": "1A9TsrD9fHxFviJVFjCk5W6lkzWXwhftv",
@@ -291,6 +286,9 @@ def load_model(checkpoint, config=None):
         with open(config) as f:
             config = yaml.load(f, Loader=yaml.Loader)
 
+    # lazy load for circular error
+    import parallel_wavegan.models
+
     # get model and load parameters
     model_class = getattr(
         parallel_wavegan.models,
@@ -303,6 +301,9 @@ def load_model(checkpoint, config=None):
 
     # add pqmf if needed
     if config["generator_params"]["out_channels"] > 1:
+        # lazy load for circular error
+        from parallel_wavegan.layers import PQMF
+
         pqmf_params = {}
         if LooseVersion(config.get("version", "0.1.0")) <= LooseVersion("0.4.2"):
             # For compatibility, here we set default values in version <= 0.4.2
@@ -333,10 +334,15 @@ def download_pretrained_model(tag, download_dir=None):
     output_path = f"{download_dir}/{tag}.tar.gz"
     os.makedirs(f"{download_dir}", exist_ok=True)
     if not os.path.exists(output_path):
+        # lazy load for compatibility
         import gdown
+
         gdown.download(f"https://drive.google.com/uc?id={id_}", output_path, quiet=False)
         with tarfile.open(output_path, 'r:*') as tar:
-            tar.extractall(f"{download_dir}/{tag}")
+            for member in tar.getmembers():
+                if member.isreg():
+                    member.name = os.path.basename(member.name)
+                    tar.extract(member, f"{download_dir}/{tag}")
     checkpoint_path = find_files(f"{download_dir}/{tag}", "checkpoint*.pkl")
 
     return checkpoint_path[0]