add MS-SNSD recipes

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train.py

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+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 train(config: DictConfig):
+
+    OmegaConf.save(config, "config.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.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__":
+    train()

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/config.yaml

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+defaults:
+  - model : Demucs
+  - dataset : MS-SNSD
+  - optimizer : Adam
+  - hyperparameters : default
+  - trainer : default
+  - mlflow : experiment

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/dataset/MS-SNSD.yaml

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+_target_: mayavoz.data.dataset.MayaDataset
+name : MS-SDSD
+root_dir : /Users/shahules/Myprojects/MS-SNSD
+duration : 1.5
+stride : 1
+sampling_rate: 16000
+batch_size: 32
+min_valid_minutes: 25
+files:
+  train_clean : CleanSpeech_training
+  test_clean : CleanSpeech_training
+  train_noisy : NoisySpeech_training
+  test_noisy : NoisySpeech_training

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/hyperparameters/default.yaml

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+loss : si-snr
+metric : [stoi,pesq]
+lr : 0.001
+ReduceLr_patience : 10
+ReduceLr_factor : 0.5
+min_lr : 0.000001
+EarlyStopping_factor : 10

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/mlflow/experiment.yaml

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+experiment_name : shahules/mayavoz
+run_name : Demucs + Vtck with stride + augmentations

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/model/DCCRN.yaml

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+_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/MS-SNSD/MS-SNSD-20/DCCRN/train_config/optimizer/Adam.yaml

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

recipes/MS-SNSD/MS-SNSD-20/DCCRN/train_config/trainer/default.yaml

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+_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/MS-SNSD/MS-SNSD-20/DCCRN/train_config/trainer/fastrun_dev.yaml

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+_target_: pytorch_lightning.Trainer
+fast_dev_run: True

recipes/MS-SNSD/MS-SNSD-20/Demucs/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 train(config: DictConfig):
+
+    OmegaConf.save(config, "config.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.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__":
+    train()

recipes/MS-SNSD/MS-SNSD-20/Demucs/train_config/config.yaml

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

recipes/MS-SNSD/MS-SNSD-20/Demucs/train_config/dataset/MS-SNSD.yaml

@@ -0,0 +1,13 @@
+_target_: mayavoz.data.dataset.MayaDataset
+name : MS-SDSD
+root_dir : /Users/shahules/Myprojects/MS-SNSD
+duration : 5
+stride : 1
+sampling_rate: 16000
+batch_size: 32
+min_valid_minutes: 25
+files:
+  train_clean : CleanSpeech_training
+  test_clean : CleanSpeech_training
+  train_noisy : NoisySpeech_training
+  test_noisy : NoisySpeech_training

recipes/MS-SNSD/MS-SNSD-20/Demucs/train_config/hyperparameters/default.yaml

@@ -0,0 +1,7 @@
+loss : mae
+metric : [stoi,pesq]
+lr : 0.0003
+ReduceLr_patience : 10
+ReduceLr_factor : 0.5
+min_lr : 0.000001
+EarlyStopping_factor : 10

recipes/MS-SNSD/MS-SNSD-20/Demucs/train_config/mlflow/experiment.yaml

@@ -0,0 +1,2 @@
+experiment_name : shahules/mayavoz
+run_name : demucs-ms-snsd

recipes/MS-SNSD/MS-SNSD-20/Demucs/train_config/model/Demucs.yaml

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+_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/MS-SNSD/MS-SNSD-20/Demucs/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/MS-SNSD/MS-SNSD-20/Demucs/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/MS-SNSD/MS-SNSD-20/Demucs/train_config/trainer/fastrun_dev.yaml

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