Merge pull request #23 from shahules786/dev

rename package

.flake8

@@ -1,5 +1,5 @@
 [flake8]
-per-file-ignores = __init__.py:F401
+per-file-ignores = "mayavoz/model/__init__.py:F401"
 ignore = E203, E266, E501, W503
 # line length is intentionally set to 80 here because black uses Bugbear
 # See https://github.com/psf/black/blob/master/README.md#line-length for more details

.github/workflows/ci.yaml

@@ -1,13 +1,13 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Enhancer
+name: mayavoz
 
 on:
   push:
     branches: [ dev ]
   pull_request:
-    branches: [ dev ]
+    branches: [ main ]
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -40,12 +40,12 @@ jobs:
         sudo apt-get install libsndfile1
         pip install -r requirements.txt
         pip install black pytest-cov
-    - name: Install enhancer
+    - name: Install mayavoz
       run: |
           pip install -e .[dev,testing]
     - name: Run black
       run:
-        black --check .  --exclude enhancer/version.py
+        black --check .  --exclude mayavoz/version.py
     - name: Test with pytest
       run:
-        pytest tests --cov=enhancer/
+        pytest tests --cov=mayavoz/

.pre-commit-config.yaml

@@ -23,6 +23,7 @@ repos:
       hooks:
       - id: flake8
         args: ['--ignore=E203,E501,F811,E712,W503']
+        exclude: __init__.py
 
     # Formatting, Whitespace, etc
     - repo: https://github.com/pre-commit/pre-commit-hooks

enhancer/cli/train_config/dataset/Vctk_local.yaml

@@ -1,13 +0,0 @@
-_target_: enhancer.data.dataset.EnhancerDataset
-name : vctk
-root_dir : /Users/shahules/Myprojects/enhancer/datasets/vctk
-duration : 1.0
-sampling_rate: 16000
-batch_size: 64
-num_workers : 0
-
-files:
-  train_clean : clean_testset_wav
-  test_clean : clean_testset_wav
-  train_noisy : noisy_testset_wav
-  test_noisy : noisy_testset_wav

enhancer/data/__init__.py

@@ -1 +0,0 @@
-from enhancer.data.dataset import EnhancerDataset

enhancer/models/__init__.py

@@ -1,3 +0,0 @@
-from enhancer.models.demucs import Demucs
-from enhancer.models.model import Model
-from enhancer.models.waveunet import WaveUnet

enhancer/models/complexnn/__init__.py

@@ -1,5 +0,0 @@
-from enhancer.models.complexnn.conv import ComplexConv2d  # noqa
-from enhancer.models.complexnn.conv import ComplexConvTranspose2d  # noqa
-from enhancer.models.complexnn.rnn import ComplexLSTM  # noqa
-from enhancer.models.complexnn.utils import ComplexBatchNorm2D  # noqa
-from enhancer.models.complexnn.utils import ComplexRelu  # noqa

enhancer/utils/__init__.py

@@ -1,3 +0,0 @@
-from enhancer.utils.config import Files
-from enhancer.utils.io import Audio
-from enhancer.utils.utils import check_files

environment.yml

@@ -1,4 +1,4 @@
-name: enhancer
+name: mayavoz
 
 dependencies:
   - pip=21.0.1

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

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 root_dir : /Users/shahules/Myprojects/MS-SNSD
 name : dns-2020
 duration : 2.0

mayavoz/cli/train_config/dataset/Vctk.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 name : vctk
 root_dir : /scratch/c.sistc3/DS_10283_2791
 duration : 4.5

mayavoz/cli/train_config/mlflow/experiment.yaml

@@ -1,2 +1,2 @@
-experiment_name : shahules/enhancer
+experiment_name : shahules/mayavoz
 run_name : Demucs + Vtck with stride + augmentations

mayavoz/cli/train_config/model/DCCRN.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.dccrn.DCCRN
+_target_: mayavoz.models.dccrn.DCCRN
 num_channels: 1
 sampling_rate : 16000
 complex_lstm : True

mayavoz/cli/train_config/model/Demucs.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.demucs.Demucs
+_target_: mayavoz.models.demucs.Demucs
 num_channels: 1
 resample: 4
 sampling_rate : 16000

mayavoz/cli/train_config/model/WaveUnet.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.waveunet.WaveUnet
+_target_: mayavoz.models.waveunet.WaveUnet
 num_channels : 1
 depth : 9
 initial_output_channels: 24

mayavoz/data/__init__.py

@@ -0,0 +1 @@
+from mayavoz.data.dataset import MayaDataset

mayavoz/data/dataset.py

@@ -11,11 +11,11 @@ import torch.nn.functional as F
 from torch.utils.data import DataLoader, Dataset, RandomSampler
 from torch_audiomentations import Compose
 
-from enhancer.data.fileprocessor import Fileprocessor
-from enhancer.utils import check_files
-from enhancer.utils.config import Files
-from enhancer.utils.io import Audio
-from enhancer.utils.random import create_unique_rng
+from mayavoz.data.fileprocessor import Fileprocessor
+from mayavoz.utils import check_files
+from mayavoz.utils.config import Files
+from mayavoz.utils.io import Audio
+from mayavoz.utils.random import create_unique_rng
 
 LARGE_NUM = 2147483647
 
@@ -248,7 +248,7 @@ class TaskDataset(pl.LightningDataModule):
         )
 
 
-class EnhancerDataset(TaskDataset):
+class MayaDataset(TaskDataset):
     """
     Dataset object for creating clean-noisy speech enhancement datasets
     paramters:
@@ -258,7 +258,7 @@ class EnhancerDataset(TaskDataset):
         root directory of the dataset containing clean/noisy folders
     files : Files
         dataclass containing train_clean, train_noisy, test_clean, test_noisy
-        folder names (refer enhancer.utils.Files dataclass)
+        folder names (refer mayavoz.utils.Files dataclass)
     min_valid_minutes: float
         minimum validation split size time in minutes
         algorithm randomly select n speakers (>=min_valid_minutes) from train data to form validation data.

mayavoz/inference.py

@@ -8,7 +8,7 @@ from librosa import load as load_audio
 from scipy.io import wavfile
 from scipy.signal import get_window
 
-from enhancer.utils import Audio
+from mayavoz.utils import Audio
 
 
 class Inference:

mayavoz/loss.py

@@ -192,7 +192,7 @@ class Si_snr(nn.Module):
         super().__init__()
 
         self.loss_fun = ScaleInvariantSignalNoiseRatio(**kwargs)
-        self.higher_better = True
+        self.higher_better = False
         self.name = "si_snr"
 
     def forward(self, prediction: torch.Tensor, target: torch.Tensor):
@@ -203,7 +203,7 @@ class Si_snr(nn.Module):
                     got {prediction.size()} and {target.size()} instead"""
             )
 
-        return self.loss_fun(prediction, target)
+        return -1 * self.loss_fun(prediction, target)
 
 
 LOSS_MAP = {

mayavoz/models/__init__.py

@@ -0,0 +1,3 @@
+from mayavoz.models.demucs import Demucs
+from mayavoz.models.model import Mayamodel
+from mayavoz.models.waveunet import WaveUnet

mayavoz/models/complexnn/__init__.py

@@ -0,0 +1,5 @@
+from mayavoz.models.complexnn.conv import ComplexConv2d  # noqa
+from mayavoz.models.complexnn.conv import ComplexConvTranspose2d  # noqa
+from mayavoz.models.complexnn.rnn import ComplexLSTM  # noqa
+from mayavoz.models.complexnn.utils import ComplexBatchNorm2D  # noqa
+from mayavoz.models.complexnn.utils import ComplexRelu  # noqa

mayavoz/models/dccrn.py

@@ -5,18 +5,18 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from enhancer.data import EnhancerDataset
-from enhancer.models import Model
-from enhancer.models.complexnn import (
+from mayavoz.data import MayaDataset
+from mayavoz.models import Mayamodel
+from mayavoz.models.complexnn import (
     ComplexBatchNorm2D,
     ComplexConv2d,
     ComplexConvTranspose2d,
     ComplexLSTM,
     ComplexRelu,
 )
-from enhancer.models.complexnn.utils import complex_cat
-from enhancer.utils.transforms import ConviSTFT, ConvSTFT
-from enhancer.utils.utils import merge_dict
+from mayavoz.models.complexnn.utils import complex_cat
+from mayavoz.utils.transforms import ConviSTFT, ConvSTFT
+from mayavoz.utils.utils import merge_dict
 
 
 class DCCRN_ENCODER(nn.Module):
@@ -98,7 +98,7 @@ class DCCRN_DECODER(nn.Module):
         return self.decoder(waveform)
 
 
-class DCCRN(Model):
+class DCCRN(Mayamodel):
 
     STFT_DEFAULTS = {
         "window_len": 400,
@@ -134,13 +134,13 @@ class DCCRN(Model):
         num_channels: int = 1,
         sampling_rate=16000,
         lr: float = 1e-3,
-        dataset: Optional[EnhancerDataset] = None,
+        dataset: Optional[MayaDataset] = None,
         duration: Optional[float] = None,
         loss: Union[str, List, Any] = "mse",
         metric: Union[str, List] = "mse",
     ):
         duration = (
-            dataset.duration if isinstance(dataset, EnhancerDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else None
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:

mayavoz/models/demucs.py

@@ -5,10 +5,10 @@ from typing import List, Optional, Union
 import torch.nn.functional as F
 from torch import nn
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.models.model import Model
-from enhancer.utils.io import Audio as audio
-from enhancer.utils.utils import merge_dict
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.models.model import Mayamodel
+from mayavoz.utils.io import Audio as audio
+from mayavoz.utils.utils import merge_dict
 
 
 class DemucsLSTM(nn.Module):
@@ -88,7 +88,7 @@ class DemucsDecoder(nn.Module):
         return out
 
 
-class Demucs(Model):
+class Demucs(Mayamodel):
     """
     Demucs model from https://arxiv.org/pdf/1911.13254.pdf
     parameters:
@@ -102,8 +102,8 @@ class Demucs(Model):
             sampling rate of input audio
         lr : float, defaults to 1e-3
             learning rate used for training
-        dataset: EnhancerDataset, optional
-            EnhancerDataset object containing train/validation data for training
+        dataset: MayaDataset, optional
+            MayaDataset object containing train/validation data for training
         duration : float, optional
             chunk duration in seconds
         loss : string or List of strings
@@ -135,13 +135,13 @@ class Demucs(Model):
         sampling_rate=16000,
         normalize=True,
         lr: float = 1e-3,
-        dataset: Optional[EnhancerDataset] = None,
+        dataset: Optional[MayaDataset] = None,
         loss: Union[str, List] = "mse",
         metric: Union[str, List] = "mse",
         floor=1e-3,
     ):
         duration = (
-            dataset.duration if isinstance(dataset, EnhancerDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else None
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:

mayavoz/models/model.py

@@ -13,20 +13,21 @@ from pytorch_lightning.utilities.cloud_io import load as pl_load
 from torch import nn
 from torch.optim import Adam
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.inference import Inference
-from enhancer.loss import LOSS_MAP, LossWrapper
-from enhancer.version import __version__
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.inference import Inference
+from mayavoz.loss import LOSS_MAP, LossWrapper
+from mayavoz.version import __version__
 
 CACHE_DIR = os.getenv(
     "ENHANCER_CACHE",
-    os.path.expanduser("~/.cache/torch/enhancer"),
+    os.path.expanduser("~/.cache/torch/mayavoz"),
 )
 HF_TORCH_WEIGHTS = "pytorch_model.ckpt"
 DEFAULT_DEVICE = "cpu"
+SAVE_NAME = "enhancer"
 
 
-class Model(pl.LightningModule):
+class Mayamodel(pl.LightningModule):
     """
     Base class for all models
     parameters:
@@ -36,8 +37,8 @@ class Model(pl.LightningModule):
             audio sampling rate
         lr: float, optional
             learning rate for model training
-        dataset: EnhancerDataset, optional
-            Enhancer dataset used for training/validation
+        dataset: MayaDataset, optional
+            mayavoz dataset used for training/validation
         duration: float, optional
             duration used for training/inference
         loss : string or List of strings or custom loss (nn.Module), default to "mse"
@@ -50,15 +51,13 @@ class Model(pl.LightningModule):
         num_channels: int = 1,
         sampling_rate: int = 16000,
         lr: float = 1e-3,
-        dataset: Optional[EnhancerDataset] = None,
+        dataset: Optional[MayaDataset] = None,
         duration: Optional[float] = None,
         loss: Union[str, List] = "mse",
         metric: Union[str, List, Any] = "mse",
     ):
         super().__init__()
-        assert (
-            num_channels == 1
-        ), "Enhancer only support for mono channel models"
+        assert num_channels == 1, "mayavoz only support for mono channel models"
         self.dataset = dataset
         self.save_hyperparameters(
             "num_channels", "sampling_rate", "lr", "loss", "metric", "duration"
@@ -235,8 +234,8 @@ class Model(pl.LightningModule):
 
     def on_save_checkpoint(self, checkpoint):
 
-        checkpoint["enhancer"] = {
-            "version": {"enhancer": __version__, "pytorch": torch.__version__},
+        checkpoint[SAVE_NAME] = {
+            "version": {SAVE_NAME: __version__, "pytorch": torch.__version__},
             "architecture": {
                 "module": self.__class__.__module__,
                 "class": self.__class__.__name__,
@@ -289,8 +288,8 @@ class Model(pl.LightningModule):
 
         Returns
         -------
-        model : Model
-            Model
+        model : Mayamodel
+            Mayamodel
 
         See also
         --------
@@ -319,7 +318,7 @@ class Model(pl.LightningModule):
             )
             model_path_pl = cached_download(
                 url=url,
-                library_name="enhancer",
+                library_name="mayavoz",
                 library_version=__version__,
                 cache_dir=cached_dir,
                 use_auth_token=use_auth_token,
@@ -329,8 +328,8 @@ class Model(pl.LightningModule):
             map_location = torch.device(DEFAULT_DEVICE)
 
         loaded_checkpoint = pl_load(model_path_pl, map_location)
-        module_name = loaded_checkpoint["enhancer"]["architecture"]["module"]
-        class_name = loaded_checkpoint["enhancer"]["architecture"]["class"]
+        module_name = loaded_checkpoint[SAVE_NAME]["architecture"]["module"]
+        class_name = loaded_checkpoint[SAVE_NAME]["architecture"]["class"]
         module = import_module(module_name)
         Klass = getattr(module, class_name)
 

mayavoz/models/waveunet.py

@@ -5,8 +5,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.models.model import Model
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.models.model import Mayamodel
 
 
 class WavenetDecoder(nn.Module):
@@ -66,7 +66,7 @@ class WavenetEncoder(nn.Module):
         return self.encoder(waveform)
 
 
-class WaveUnet(Model):
+class WaveUnet(Mayamodel):
     """
     Wave-U-Net model from  https://arxiv.org/pdf/1811.11307.pdf
     parameters:
@@ -80,8 +80,8 @@ class WaveUnet(Model):
             sampling rate of input audio
         lr : float, defaults to 1e-3
             learning rate used for training
-        dataset: EnhancerDataset, optional
-            EnhancerDataset object containing train/validation data for training
+        dataset: MayaDataset, optional
+            MayaDataset object containing train/validation data for training
         duration : float, optional
             chunk duration in seconds
         loss : string or List of strings
@@ -97,13 +97,13 @@ class WaveUnet(Model):
         initial_output_channels: int = 24,
         sampling_rate: int = 16000,
         lr: float = 1e-3,
-        dataset: Optional[EnhancerDataset] = None,
+        dataset: Optional[MayaDataset] = None,
         duration: Optional[float] = None,
         loss: Union[str, List] = "mse",
         metric: Union[str, List] = "mse",
     ):
         duration = (
-            dataset.duration if isinstance(dataset, EnhancerDataset) else None
+            dataset.duration if isinstance(dataset, MayaDataset) else None
         )
         if dataset is not None:
             if sampling_rate != dataset.sampling_rate:

mayavoz/utils/__init__.py

@@ -0,0 +1,3 @@
+from mayavoz.utils.config import Files
+from mayavoz.utils.io import Audio
+from mayavoz.utils.utils import check_files

mayavoz/utils/utils.py

@@ -1,7 +1,7 @@
 import os
 from typing import Optional
 
-from enhancer.utils.config import Files
+from mayavoz.utils.config import Files
 
 
 def check_files(root_dir: str, files: Files):

notebooks/Getting_started.ipynb

@@ -374,7 +374,7 @@
     "```\n",
     "\n",
     "```yaml\n",
-    "_target_: enhancer.models.demucs.Demucs\n",
+    "_target_: mayavoz.models.demucs.Demucs\n",
     "num_channels: 1\n",
     "resample: 4\n",
     "sampling_rate : 16000\n",
@@ -405,9 +405,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "enhancer",
+   "display_name": "mayavoz",
    "language": "python",
-   "name": "enhancer"
+   "name": "mayavoz"
   },
   "language_info": {
    "codemirror_mode": {

recipes/DNS/DNS-2020/cli/train.py

@@ -0,0 +1,120 @@
+import os
+from types import MethodType
+
+import hydra
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning.callbacks import (
+    EarlyStopping,
+    LearningRateMonitor,
+    ModelCheckpoint,
+)
+from pytorch_lightning.loggers import MLFlowLogger
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+
+# from torch_audiomentations import Compose, Shift
+
+os.environ["HYDRA_FULL_ERROR"] = "1"
+JOB_ID = os.environ.get("SLURM_JOBID", "0")
+
+
+@hydra.main(config_path="train_config", config_name="config")
+def main(config: DictConfig):
+
+    OmegaConf.save(config, "config_log.yaml")
+
+    callbacks = []
+    logger = MLFlowLogger(
+        experiment_name=config.mlflow.experiment_name,
+        run_name=config.mlflow.run_name,
+        tags={"JOB_ID": JOB_ID},
+    )
+
+    parameters = config.hyperparameters
+    # apply_augmentations = Compose(
+    #     [
+    #         Shift(min_shift=0.5, max_shift=1.0, shift_unit="seconds", p=0.5),
+    #     ]
+    # )
+
+    dataset = instantiate(config.dataset, augmentations=None)
+    model = instantiate(
+        config.model,
+        dataset=dataset,
+        lr=parameters.get("lr"),
+        loss=parameters.get("loss"),
+        metric=parameters.get("metric"),
+    )
+
+    direction = model.valid_monitor
+    checkpoint = ModelCheckpoint(
+        dirpath="./model",
+        filename=f"model_{JOB_ID}",
+        monitor="valid_loss",
+        verbose=False,
+        mode=direction,
+        every_n_epochs=1,
+    )
+    callbacks.append(checkpoint)
+    callbacks.append(LearningRateMonitor(logging_interval="epoch"))
+
+    if parameters.get("Early_stop", False):
+        early_stopping = EarlyStopping(
+            monitor="val_loss",
+            mode=direction,
+            min_delta=0.0,
+            patience=parameters.get("EarlyStopping_patience", 10),
+            strict=True,
+            verbose=False,
+        )
+        callbacks.append(early_stopping)
+
+    def configure_optimizers(self):
+        optimizer = instantiate(
+            config.optimizer,
+            lr=parameters.get("lr"),
+            params=self.parameters(),
+        )
+        scheduler = ReduceLROnPlateau(
+            optimizer=optimizer,
+            mode=direction,
+            factor=parameters.get("ReduceLr_factor", 0.1),
+            verbose=True,
+            min_lr=parameters.get("min_lr", 1e-6),
+            patience=parameters.get("ReduceLr_patience", 3),
+        )
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": scheduler,
+            "monitor": f'valid_{parameters.get("ReduceLr_monitor", "loss")}',
+        }
+
+    model.configure_optimizers = MethodType(configure_optimizers, model)
+
+    trainer = instantiate(config.trainer, logger=logger, callbacks=callbacks)
+    trainer.fit(model)
+    trainer.test(model)
+
+    logger.experiment.log_artifact(
+        logger.run_id, f"{trainer.default_root_dir}/config_log.yaml"
+    )
+
+    saved_location = os.path.join(
+        trainer.default_root_dir, "model", f"model_{JOB_ID}.ckpt"
+    )
+    if os.path.isfile(saved_location):
+        logger.experiment.log_artifact(logger.run_id, saved_location)
+        logger.experiment.log_param(
+            logger.run_id,
+            "num_train_steps_per_epoch",
+            dataset.train__len__() / dataset.batch_size,
+        )
+        logger.experiment.log_param(
+            logger.run_id,
+            "num_valid_steps_per_epoch",
+            dataset.val__len__() / dataset.batch_size,
+        )
+
+
+if __name__ == "__main__":
+    main()

recipes/DNS/DNS-2020/cli/train_config/config.yaml

@@ -0,0 +1,7 @@
+defaults:
+  - model : Demucs
+  - dataset : Vctk
+  - optimizer : Adam
+  - hyperparameters : default
+  - trainer : default
+  - mlflow : experiment

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

@@ -0,0 +1,12 @@
+_target_: mayavoz.data.dataset.MayaDataset
+root_dir : /Users/shahules/Myprojects/MS-SNSD
+name : dns-2020
+duration : 2.0
+sampling_rate: 16000
+batch_size: 32
+valid_size: 0.05
+files:
+  train_clean : CleanSpeech_training
+  test_clean : CleanSpeech_training
+  train_noisy : NoisySpeech_training
+  test_noisy : NoisySpeech_training

recipes/DNS/DNS-2020/cli/train_config/dataset/Vctk.yaml

@@ -0,0 +1,13 @@
+_target_: mayavoz.data.dataset.MayaDataset
+name : vctk
+root_dir : /scratch/c.sistc3/DS_10283_2791
+duration : 4.5
+stride : 2
+sampling_rate: 16000
+batch_size: 32
+valid_minutes : 15
+files:
+  train_clean : clean_trainset_28spk_wav
+  test_clean : clean_testset_wav
+  train_noisy : noisy_trainset_28spk_wav
+  test_noisy : noisy_testset_wav

recipes/DNS/DNS-2020/cli/train_config/hyperparameters/default.yaml

@@ -0,0 +1,7 @@
+loss : mae
+metric : [stoi,pesq,si-sdr]
+lr : 0.0003
+ReduceLr_patience : 5
+ReduceLr_factor : 0.2
+min_lr : 0.000001
+EarlyStopping_factor : 10

recipes/DNS/DNS-2020/cli/train_config/mlflow/experiment.yaml

@@ -0,0 +1,2 @@
+experiment_name : shahules/mayavoz
+run_name : Demucs + Vtck with stride + augmentations

recipes/DNS/DNS-2020/cli/train_config/model/DCCRN.yaml

@@ -0,0 +1,25 @@
+_target_: mayavoz.models.dccrn.DCCRN
+num_channels: 1
+sampling_rate : 16000
+complex_lstm : True
+complex_norm : True
+complex_relu : True
+masking_mode : True
+
+encoder_decoder:
+  initial_output_channels : 32
+  depth : 6
+  kernel_size : 5
+  growth_factor : 2
+  stride : 2
+  padding : 2
+  output_padding : 1
+
+lstm:
+  num_layers : 2
+  hidden_size : 256
+
+stft:
+  window_len : 400
+  hop_size : 100
+  nfft : 512

recipes/DNS/DNS-2020/cli/train_config/model/Demucs.yaml

@@ -0,0 +1,16 @@
+_target_: mayavoz.models.demucs.Demucs
+num_channels: 1
+resample: 4
+sampling_rate : 16000
+
+encoder_decoder:
+  depth: 4
+  initial_output_channels: 64
+  kernel_size: 8
+  stride: 4
+  growth_factor: 2
+  glu: True
+
+lstm:
+  bidirectional: False
+  num_layers: 2

recipes/DNS/DNS-2020/cli/train_config/model/WaveUnet.yaml

@@ -0,0 +1,5 @@
+_target_: mayavoz.models.waveunet.WaveUnet
+num_channels : 1
+depth : 9
+initial_output_channels: 24
+sampling_rate : 16000

recipes/DNS/DNS-2020/cli/train_config/optimizer/Adam.yaml

@@ -0,0 +1,6 @@
+_target_: torch.optim.Adam
+lr: 1e-3
+betas: [0.9, 0.999]
+eps: 1e-08
+weight_decay: 0
+amsgrad: False

recipes/DNS/DNS-2020/cli/train_config/trainer/default.yaml

@@ -0,0 +1,46 @@
+_target_: pytorch_lightning.Trainer
+accelerator: gpu
+accumulate_grad_batches: 1
+amp_backend: native
+auto_lr_find: True
+auto_scale_batch_size: False
+auto_select_gpus: True
+benchmark: False
+check_val_every_n_epoch: 1
+detect_anomaly: False
+deterministic: False
+devices: 2
+enable_checkpointing: True
+enable_model_summary: True
+enable_progress_bar: True
+fast_dev_run: False
+gpus: null
+gradient_clip_val: 0
+gradient_clip_algorithm: norm
+ipus: null
+limit_predict_batches: 1.0
+limit_test_batches: 1.0
+limit_train_batches: 1.0
+limit_val_batches: 1.0
+log_every_n_steps: 50
+max_epochs: 200
+max_steps: -1
+max_time: null
+min_epochs: 1
+min_steps: null
+move_metrics_to_cpu: False
+multiple_trainloader_mode: max_size_cycle
+num_nodes: 1
+num_processes: 1
+num_sanity_val_steps: 2
+overfit_batches: 0.0
+precision: 32
+profiler: null
+reload_dataloaders_every_n_epochs: 0
+replace_sampler_ddp: True
+strategy: ddp
+sync_batchnorm: False
+tpu_cores: null
+track_grad_norm: -1
+val_check_interval: 1.0
+weights_save_path: null

recipes/DNS/DNS-2020/cli/train_config/trainer/fastrun_dev.yaml

@@ -0,0 +1,2 @@
+_target_: pytorch_lightning.Trainer
+fast_dev_run: True

recipes/Valentini-dataset/28spk/Demucs/train_config/dataset/Vctk.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 name : vctk
 root_dir : /scratch/c.sistc3/DS_10283_2791
 duration : 4.5

recipes/Valentini-dataset/28spk/Demucs/train_config/mlflow/experiment.yaml

@@ -1,2 +1,2 @@
-experiment_name : shahules/enhancer
+experiment_name : shahules/mayavoz
 run_name : baseline

recipes/Valentini-dataset/28spk/Demucs/train_config/model/Demucs.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.demucs.Demucs
+_target_: mayavoz.models.demucs.Demucs
 num_channels: 1
 resample: 4
 sampling_rate : 16000

recipes/Valentini-dataset/28spk/WaveUnet/train_config/dataset/Vctk.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 name : vctk
 root_dir : /scratch/c.sistc3/DS_10283_2791
 duration : 2

recipes/Valentini-dataset/28spk/WaveUnet/train_config/mlflow/experiment.yaml

@@ -1,2 +1,2 @@
-experiment_name : shahules/enhancer
+experiment_name : shahules/mayavoz
 run_name : baseline

recipes/Valentini-dataset/28spk/WaveUnet/train_config/model/WaveUnet.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.waveunet.WaveUnet
+_target_: mayavoz.models.waveunet.WaveUnet
 num_channels : 1
 depth : 9
 initial_output_channels: 24

recipes/Valentini-dataset/28spk/cli/train_config/dataset/DNS-2020.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 root_dir : /Users/shahules/Myprojects/MS-SNSD
 name : dns-2020
 duration : 2.0

recipes/Valentini-dataset/28spk/cli/train_config/dataset/Vctk.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.data.dataset.EnhancerDataset
+_target_: mayavoz.data.dataset.MayaDataset
 name : vctk
 root_dir : /scratch/c.sistc3/DS_10283_2791
 duration : 4.5

recipes/Valentini-dataset/28spk/cli/train_config/mlflow/experiment.yaml

@@ -1,2 +1,2 @@
-experiment_name : shahules/enhancer
+experiment_name : shahules/mayavoz
 run_name : Demucs + Vtck with stride + augmentations

recipes/Valentini-dataset/28spk/cli/train_config/model/DCCRN.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.dccrn.DCCRN
+_target_: mayavoz.models.dccrn.DCCRN
 num_channels: 1
 sampling_rate : 16000
 complex_lstm : True

recipes/Valentini-dataset/28spk/cli/train_config/model/Demucs.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.demucs.Demucs
+_target_: mayavoz.models.demucs.Demucs
 num_channels: 1
 resample: 4
 sampling_rate : 16000

recipes/Valentini-dataset/28spk/cli/train_config/model/WaveUnet.yaml

@@ -1,4 +1,4 @@
-_target_: enhancer.models.waveunet.WaveUnet
+_target_: mayavoz.models.waveunet.WaveUnet
 num_channels : 1
 depth : 9
 initial_output_channels: 24

setup.cfg

@@ -3,7 +3,7 @@
 # http://setuptools.readthedocs.io/en/latest/setuptools.html#configuring-setup-using-setup-cfg-files
 
 [metadata]
-name = enhancer
+name = mayavoz
 description = Deep learning for speech enhacement
 author = Shahul Ess
 author-email = shahules786@gmail.com
@@ -53,7 +53,7 @@ cli =
 [options.entry_points]
 
 console_scripts =
-    enhancer-train=enhancer.cli.train:train
+    mayavoz-train=mayavoz.cli.train:train
 
 [test]
 # py.test options when running `python setup.py test`
@@ -66,7 +66,7 @@ extras = True
 # e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml
 # in order to write a coverage file that can be read by Jenkins.
 addopts =
-    --cov enhancer --cov-report term-missing
+    --cov mayavoz --cov-report term-missing
     --verbose
 norecursedirs =
     dist

setup.py

@@ -33,15 +33,15 @@ elif sha != "Unknown":
     version += "+" + sha[:7]
 print("-- Building version " + version)
 
-version_path = ROOT_DIR / "enhancer" / "version.py"
+version_path = ROOT_DIR / "mayavoz" / "version.py"
 
 with open(version_path, "w") as f:
     f.write("__version__ = '{}'\n".format(version))
 
 if __name__ == "__main__":
     setup(
-        name="enhancer",
-        namespace_packages=["enhancer"],
+        name="mayavoz",
+        namespace_packages=["mayavoz"],
         version=version,
         packages=find_packages(),
         install_requires=requirements,

tests/loss_function_test.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from enhancer.loss import mean_absolute_error, mean_squared_error
+from mayavoz.loss import mean_absolute_error, mean_squared_error
 
 loss_functions = [mean_absolute_error(), mean_squared_error()]
 

tests/models/complexnn_test.py

@@ -1,8 +1,8 @@
 import torch
 
-from enhancer.models.complexnn.conv import ComplexConv2d, ComplexConvTranspose2d
-from enhancer.models.complexnn.rnn import ComplexLSTM
-from enhancer.models.complexnn.utils import ComplexBatchNorm2D
+from mayavoz.models.complexnn.conv import ComplexConv2d, ComplexConvTranspose2d
+from mayavoz.models.complexnn.rnn import ComplexLSTM
+from mayavoz.models.complexnn.utils import ComplexBatchNorm2D
 
 
 def test_complexconv2d():

tests/models/demucs_test.py

@@ -1,9 +1,9 @@
 import pytest
 import torch
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.models import Demucs
-from enhancer.utils.config import Files
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.models import Demucs
+from mayavoz.utils.config import Files
 
 
 @pytest.fixture
@@ -15,7 +15,7 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = EnhancerDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
     return dataset
 
 

tests/models/test_dccrn.py

@@ -1,9 +1,9 @@
 import pytest
 import torch
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.models.dccrn import DCCRN
-from enhancer.utils.config import Files
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.models.dccrn import DCCRN
+from mayavoz.utils.config import Files
 
 
 @pytest.fixture
@@ -15,7 +15,7 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = EnhancerDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
     return dataset
 
 

tests/models/test_waveunet.py

@@ -1,9 +1,9 @@
 import pytest
 import torch
 
-from enhancer.data.dataset import EnhancerDataset
-from enhancer.models import WaveUnet
-from enhancer.utils.config import Files
+from mayavoz.data.dataset import MayaDataset
+from mayavoz.models import WaveUnet
+from mayavoz.utils.config import Files
 
 
 @pytest.fixture
@@ -15,7 +15,7 @@ def vctk_dataset():
         test_clean="clean_testset_wav",
         test_noisy="noisy_testset_wav",
     )
-    dataset = EnhancerDataset(name="vctk", root_dir=root_dir, files=files)
+    dataset = MayaDataset(name="vctk", root_dir=root_dir, files=files)
     return dataset
 
 

tests/test_inference.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from enhancer.inference import Inference
+from mayavoz.inference import Inference
 
 
 @pytest.mark.parametrize(

tests/transforms_test.py

@@ -1,6 +1,6 @@
 import torch
 
-from enhancer.utils.transforms import ConviSTFT, ConvSTFT
+from mayavoz.utils.transforms import ConviSTFT, ConvSTFT
 
 
 def test_stft_istft():

tests/utils_test.py

@@ -2,8 +2,8 @@ import numpy as np
 import pytest
 import torch
 
-from enhancer.data.fileprocessor import Fileprocessor
-from enhancer.utils.io import Audio
+from mayavoz.data.fileprocessor import Fileprocessor
+from mayavoz.utils.io import Audio
 
 
 def test_io_channel():