diff --git a/src/f5_tts/model/trainer.py b/src/f5_tts/model/trainer.py
index 58b582d..08903d6 100644
--- a/src/f5_tts/model/trainer.py
+++ b/src/f5_tts/model/trainer.py
@@ -191,7 +191,7 @@ class Trainer:
             from f5_tts.infer.utils_infer import load_vocoder, nfe_step, cfg_strength, sway_sampling_coef
 
             vocoder = load_vocoder()
-            target_sample_rate = self.model.mel_spec.mel_stft.sample_rate
+            target_sample_rate = self.accelerator.unwrap_model(self.model).mel_spec.mel_stft.sample_rate
             log_samples_path = f"{self.checkpoint_path}/samples"
             os.makedirs(log_samples_path, exist_ok=True)
 
@@ -314,12 +314,12 @@ class Trainer:
                 if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
                     self.save_checkpoint(global_step)
 
-                    if self.log_samples:
-                        ref_audio, ref_audio_len = vocoder.decode([batch["mel"][0]].cpu()), mel_lengths[0]
+                    if self.log_samples and self.accelerator.is_local_main_process:
+                        ref_audio, ref_audio_len = vocoder.decode(batch["mel"][0].unsqueeze(0).cpu()), mel_lengths[0]
                         torchaudio.save(f"{log_samples_path}/step_{global_step}_ref.wav", ref_audio, target_sample_rate)
                         with torch.inference_mode():
-                            generated, _ = self.model.sample(
-                                cond=[mel_spec[0][:ref_audio_len]],
+                            generated, _ = self.accelerator.unwrap_model(self.model).sample(
+                                cond=mel_spec[0][:ref_audio_len].unsqueeze(0),
                                 text=[text_inputs[0] + [" "] + text_inputs[0]],
                                 duration=ref_audio_len * 2,
                                 steps=nfe_step,
diff --git a/src/f5_tts/train/train.py b/src/f5_tts/train/train.py
index a40efba..94fe9b5 100644
--- a/src/f5_tts/train/train.py
+++ b/src/f5_tts/train/train.py
@@ -83,6 +83,7 @@ def main():
         wandb_run_name=exp_name,
         wandb_resume_id=wandb_resume_id,
         last_per_steps=last_per_steps,
+        log_samples=True,
     )
 
     train_dataset = load_dataset(dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)