diff --git a/tortoise_tts/emb/mel.py b/tortoise_tts/emb/mel.py
index 6af96a1..4084829 100755
--- a/tortoise_tts/emb/mel.py
+++ b/tortoise_tts/emb/mel.py
@@ -80,6 +80,8 @@ def format_diffusion_conditioning( sample, device, do_normalization=False ):
 # encode a wav to conditioning latents + mel codes
 @torch.inference_mode()
 def encode(wav: Tensor, sr: int = cfg.sample_rate, device="cuda"):
+	wav = torchaudio.functional.resample(wav, sr, 22050)
+
 	dvae = load_model("dvae", device=device)
 	unified_voice = load_model("unified_voice", device=device)
 	diffusion = load_model("diffusion", device=device)
diff --git a/tortoise_tts/train.py b/tortoise_tts/train.py
index eb45ac1..444cfd8 100755
--- a/tortoise_tts/train.py
+++ b/tortoise_tts/train.py
@@ -4,7 +4,7 @@ from .config import cfg
 from .data import create_train_val_dataloader
 from .emb import mel
 
-from .utils import setup_logging, to_device, trainer, flatten_dict, do_gc
+from .utils import setup_logging, to_device, trainer, flatten_dict, do_gc, wrapper as ml
 from .utils.distributed import is_global_leader
 
 import auraloss
@@ -165,22 +165,24 @@ def run_eval(engines, eval_name, dl):
 					break
 
 			# diffusion pass
-			output_seq_len = latents.shape[1] * 4 * 24000 // 22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
-			output_shape = (latents.shape[0], 100, output_seq_len)
-			precomputed_embeddings = diffusion.timestep_independent(latents, diffusion_latents, output_seq_len, False)
+			with ml.auto_unload(diffusion, enabled=True):
+				output_seq_len = latents.shape[1] * 4 * 24000 // 22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
+				output_shape = (latents.shape[0], 100, output_seq_len)
+				precomputed_embeddings = diffusion.timestep_independent(latents, diffusion_latents, output_seq_len, False)
 
-			noise = torch.randn(output_shape, device=latents.device) * temperature
-			mel = diffuser.p_sample_loop(
-				diffusion,
-				output_shape,
-				noise=noise,
-				model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
-				progress=True
-			)
-			mels = denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
+				noise = torch.randn(output_shape, device=latents.device) * temperature
+				mel = diffuser.p_sample_loop(
+					diffusion,
+					output_shape,
+					noise=noise,
+					model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
+					progress=True
+				)
+				mels = denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
 
 			# vocoder pass
-			wavs = vocoder.inference(mels)
+			with ml.auto_unload(vocoder, enabled=True):
+				wavs = vocoder.inference(mels)
 
 			return wavs