I cannot believe it's not actually called Wand DB (added wandb logging support since I think it would have been a much better way to look at my metrics)

.gitignore

@@ -6,3 +6,4 @@ __pycache__
 /vall_e/version.py
 /.cache
 /voices
+/wandb

vall_e/config.py

@@ -695,6 +695,8 @@ class Trainer:
 	check_for_oom: bool = True # checks for OOMs thrown during forward/backwards
 	gc_mode: str | None = None # deprecated, but marks when to do GC
 
+	wandb: bool = True # use wandb, if available
+
 	weight_dtype: str = "float16" # dtype to have the model under
 
 	amp: bool = False # automatic mixed precision

vall_e/engines/__init__.py

@@ -28,6 +28,12 @@ try:
 except Exception as e:
 	pass
 
+try:
+	import wandb
+except Exception as e:
+	_logger.warning(f'Failed to import wandb: {str(e)}')
+	wandb = None
+
 from functools import cache
 
 @cache
@@ -278,4 +284,11 @@ def load_engines(training=True, **model_kwargs):
 		if cfg.optimizations.model_offloading:
 			engine.module = ml.offload_model( engine.module, policy=cfg.optimizations.model_offloading )
 
+		# setup wandb
+		if engine._training and cfg.trainer.wandb and wandb is not None:
+			engine.wandb = wandb.init(project=name)
+			engine.wandb.watch(engine.module)
+		else:
+			engine.wandb = None
+
 	return engines

vall_e/engines/base.py

@@ -554,23 +554,22 @@ class Engines(dict[str, Engine]):
 			if grad_norm is not None:
 				grad_norm /= loss_scale
 
-			stats.update(
-				flatten_dict(
-					{
-						name.split("-")[0]: dict(
-							**engine_stats,
-							lr=engine.get_lr()[0],
-							grad_norm=grad_norm.item() if isinstance( grad_norm, torch.Tensor ) else grad_norm,
-							loss_scale=loss_scale if loss_scale != 1 else None,
-							elapsed_time=elapsed_time,
-							engine_step=engine.global_step,
-							samples_processed=engine.global_samples,
-							tokens_processed=engine.tokens_processed,
-						)
-					}
-				),
+			model_stats = dict(
+				**engine_stats,
+				lr=engine.get_lr()[0],
+				grad_norm=grad_norm.item() if isinstance( grad_norm, torch.Tensor ) else grad_norm,
+				loss_scale=loss_scale if loss_scale != 1 else None,
+				elapsed_time=elapsed_time,
+				engine_step=engine.global_step,
+				samples_processed=engine.global_samples,
+				tokens_processed=engine.tokens_processed,
 			)
 		
+			if engine.wandb is not None:
+				engine.wandb.log(model_stats)
+
+			stats.update(flatten_dict({name.split("-")[0]: model_stats}))
+
 		self._update()
 
 		if len(self.keys()) > 1: