rmv cli

recipes/Valentini-dataset/28spk/cli/train.py

@@ -1,120 +0,0 @@
-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/Valentini-dataset/28spk/cli/train_config/config.yaml

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

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

@@ -1,12 +0,0 @@
-_target_: mayavoz.data.dataset.MayaDataset
-root_dir : /Users/shahules/Myprojects/MS-SNSD
-name : MS-SNSD
-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/Valentini-dataset/28spk/cli/train_config/dataset/Vctk.yaml

@@ -1,13 +0,0 @@
-_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/Valentini-dataset/28spk/cli/train_config/hyperparameters/default.yaml

@@ -1,7 +0,0 @@
-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/Valentini-dataset/28spk/cli/train_config/mlflow/experiment.yaml

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

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

@@ -1,25 +0,0 @@
-_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/Valentini-dataset/28spk/cli/train_config/model/Demucs.yaml

@@ -1,16 +0,0 @@
-_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/Valentini-dataset/28spk/cli/train_config/model/WaveUnet.yaml

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

recipes/Valentini-dataset/28spk/cli/train_config/optimizer/Adam.yaml

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

recipes/Valentini-dataset/28spk/cli/train_config/trainer/default.yaml

@@ -1,46 +0,0 @@
-_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/Valentini-dataset/28spk/cli/train_config/trainer/fastrun_dev.yaml

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