Merge pull request #24 from shahules786/dev

Minor improvements/bug fixes

.gitignore

@@ -1,4 +1,8 @@
 #local
+cleaned_my_voice.wav
+lightning_logs/
+my_voice.wav
+pretrained/
 *.ckpt
 *_local.yaml
 cli/train_config/dataset/Vctk_local.yaml

mayavoz/__init__.py

@@ -1 +1,2 @@
 __import__("pkg_resources").declare_namespace(__name__)
+from mayavoz.models import Mayamodel

mayavoz/cli/train.py

@@ -19,9 +19,9 @@ JOB_ID = os.environ.get("SLURM_JOBID", "0")
 
 
 @hydra.main(config_path="train_config", config_name="config")
-def main(config: DictConfig):
+def train(config: DictConfig):
 
-    OmegaConf.save(config, "config_log.yaml")
+    OmegaConf.save(config, "config.yaml")
 
     callbacks = []
     logger = MLFlowLogger(
@@ -96,7 +96,7 @@ def main(config: DictConfig):
     trainer.test(model)
 
     logger.experiment.log_artifact(
-        logger.run_id, f"{trainer.default_root_dir}/config_log.yaml"
+        logger.run_id, f"{trainer.default_root_dir}/config.yaml"
     )
 
     saved_location = os.path.join(
@@ -117,4 +117,4 @@ def main(config: DictConfig):
 
 
 if __name__ == "__main__":
-    main()
+    train()

mayavoz/cli/train_config/dataset/DNS-2020.yaml

@@ -1,10 +1,10 @@
 _target_: mayavoz.data.dataset.MayaDataset
-root_dir : /Users/shahules/Myprojects/MS-SNSD
 name : dns-2020
+root_dir : /Users/shahules/Myprojects/MS-SNSD
 duration : 2.0
 sampling_rate: 16000
 batch_size: 32
-valid_size: 0.05
+min_valid_minutes: 15
 files:
   train_clean : CleanSpeech_training
   test_clean : CleanSpeech_training

mayavoz/data/dataset.py

@@ -1,6 +1,8 @@
 import math
 import multiprocessing
 import os
+import sys
+import warnings
 from pathlib import Path
 from typing import Optional
 
@@ -80,6 +82,21 @@ class TaskDataset(pl.LightningDataModule):
         self._validation = []
         if num_workers is None:
             num_workers = multiprocessing.cpu_count() // 2
+        if num_workers is None:
+            num_workers = multiprocessing.cpu_count() // 2
+
+        if (
+            num_workers > 0
+            and sys.platform == "darwin"
+            and sys.version_info[0] >= 3
+            and sys.version_info[1] >= 8
+        ):
+            warnings.warn(
+                "num_workers > 0 is not supported with macOS and Python 3.8+: "
+                "setting num_workers = 0."
+            )
+            num_workers = 0
+
         self.num_workers = num_workers
         if min_valid_minutes > 0.0:
             self.min_valid_minutes = min_valid_minutes

mayavoz/data/fileprocessor.py

@@ -93,7 +93,7 @@ class Fileprocessor:
     def from_name(cls, name: str, clean_dir, noisy_dir, matching_function=None):
 
         if matching_function is None:
-            if name.lower() == "vctk":
+            if name.lower() in ("vctk", "valentini"):
                 return cls(clean_dir, noisy_dir, ProcessorFunctions.one_to_one)
             elif name.lower() == "dns-2020":
                 return cls(clean_dir, noisy_dir, ProcessorFunctions.one_to_many)

mayavoz/loss.py

@@ -1,4 +1,4 @@
-import logging
+import warnings
 
 import numpy as np
 import torch
@@ -134,7 +134,7 @@ class Pesq:
             try:
                 pesq_values.append(self.pesq(pred.squeeze(), target_.squeeze()))
             except Exception as e:
-                logging.warning(f"{e} error occured while calculating PESQ")
+                warnings.warn(f"{e} error occured while calculating PESQ")
         return torch.tensor(np.mean(pesq_values))
 
 

mayavoz/models/dccrn.py

@@ -1,4 +1,4 @@
-import logging
+import warnings
 from typing import Any, List, Optional, Tuple, Union
 
 import torch
@@ -140,11 +140,11 @@ class DCCRN(Mayamodel):
         metric: Union[str, List] = "mse",
     ):
         duration = (
-            dataset.duration if isinstance(dataset, MayaDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else duration
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:
-                logging.warning(
+                warnings.warn(
                     f"model sampling rate {sampling_rate} should match dataset sampling rate {dataset.sampling_rate}"
                 )
                 sampling_rate = dataset.sampling_rate

mayavoz/models/demucs.py

@@ -1,5 +1,5 @@
-import logging
 import math
+import warnings
 from typing import List, Optional, Union
 
 import torch.nn.functional as F
@@ -136,16 +136,17 @@ class Demucs(Mayamodel):
         normalize=True,
         lr: float = 1e-3,
         dataset: Optional[MayaDataset] = None,
+        duration: Optional[float] = None,
         loss: Union[str, List] = "mse",
         metric: Union[str, List] = "mse",
         floor=1e-3,
     ):
         duration = (
-            dataset.duration if isinstance(dataset, MayaDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else duration
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:
-                logging.warning(
+                warnings.warn(
                     f"model sampling rate {sampling_rate} should match dataset sampling rate {dataset.sampling_rate}"
                 )
                 sampling_rate = dataset.sampling_rate

mayavoz/models/model.py

@@ -24,7 +24,7 @@ CACHE_DIR = os.getenv(
 )
 HF_TORCH_WEIGHTS = "pytorch_model.ckpt"
 DEFAULT_DEVICE = "cpu"
-SAVE_NAME = "enhancer"
+SAVE_NAME = "mayavoz"
 
 
 class Mayamodel(pl.LightningModule):

mayavoz/models/waveunet.py

@@ -1,4 +1,4 @@
-import logging
+import warnings
 from typing import List, Optional, Union
 
 import torch
@@ -103,11 +103,11 @@ class WaveUnet(Mayamodel):
         metric: Union[str, List] = "mse",
     ):
         duration = (
-            dataset.duration if isinstance(dataset, MayaDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else duration
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:
-                logging.warning(
+                warnings.warn(
                     f"model sampling rate {sampling_rate} should match dataset sampling rate {dataset.sampling_rate}"
                 )
                 sampling_rate = dataset.sampling_rate

tests/models/demucs_test.py

@@ -15,7 +15,9 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(
+        name="vctk", root_dir=root_dir, files=files, sampling_rate=16000
+    )
     return dataset
 
 

tests/models/test_dccrn.py

@@ -15,7 +15,9 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(
+        name="vctk", root_dir=root_dir, files=files, sampling_rate=16000
+    )
     return dataset
 
 

tests/models/test_waveunet.py

@@ -15,7 +15,9 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(
+        name="vctk", root_dir=root_dir, files=files, sampling_rate=16000
+    )
     return dataset