diff --git a/README.md b/README.md
index ca7942d..57174ac 100755
--- a/README.md
+++ b/README.md
@@ -64,6 +64,7 @@ If you're interested in creating an HDF5 copy of your dataset, simply invoke: `p
 
 5. Train the model using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
 * If distributing your training (for example, multi-GPU), use `deepspeed --module vall_e.train yaml="./data/config.yaml"`
+  + if you're not using the `deepspeed` backend, set `trainer.ddp = True` in the config YAML, then launch with `torchrun --nnodes=1 --nproc-per-node=4 -m vall_e.train yaml="./data/config.yaml"`
 
 You may quit your training any time by just entering `quit` in your CLI. The latest checkpoint will be automatically saved.
 
diff --git a/vall_e/utils/trainer.py b/vall_e/utils/trainer.py
index 31b0b12..21e061b 100755
--- a/vall_e/utils/trainer.py
+++ b/vall_e/utils/trainer.py
@@ -173,7 +173,10 @@ def train(
 
 
 		elapsed_time = stats.get("elapsed_time", 0)
-		metrics = json.dumps(stats)
+		try:
+			metrics = json.dumps(stats)
+		except Exception as e:
+			metrics = str(stats)
 
 		if cfg.trainer.no_logger:
 			tqdm.write(f"Training Metrics: {truncate_json(metrics)}.")