diff --git a/src/utils.py b/src/utils.py
index 466460c..508a7e6 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -49,6 +49,8 @@ GENERATE_SETTINGS_ARGS = None
 LEARNING_RATE_SCHEMES = {"Multistep": "MultiStepLR", "Cos. Annealing": "CosineAnnealingLR_Restart"}
 LEARNING_RATE_SCHEDULE = [ 9, 18, 25, 33 ]
 
+RESAMPLERS = {}
+
 args = None
 tts = None
 tts_loading = False
@@ -59,6 +61,23 @@ training_state = None
 
 current_voice = None
 
+def resample( waveform, input_rate, output_rate=44100 ):
+	if input_rate == output_rate:
+		return waveform, output_rate
+
+	key = f'{input_rate}:{output_rate}'
+	if not key in RESAMPLERS:
+		RESAMPLERS[key] = torchaudio.transforms.Resample(
+			input_rate,
+			output_rate,
+			lowpass_filter_width=16,
+			rolloff=0.85,
+			resampling_method="kaiser_window",
+			beta=8.555504641634386,
+		)
+
+	return RESAMPLERS[key]( waveform ), output_rate
+
 def generate(**kwargs):
 	parameters = {}
 	parameters.update(kwargs)
@@ -199,17 +218,6 @@ def generate(**kwargs):
 	os.makedirs(outdir, exist_ok=True)
 
 	audio_cache = {}
-	resample = None
-
-	if tts.output_sample_rate != args.output_sample_rate:
-		resampler = torchaudio.transforms.Resample(
-			tts.output_sample_rate,
-			args.output_sample_rate,
-			lowpass_filter_width=16,
-			rolloff=0.85,
-			resampling_method="kaiser_window",
-			beta=8.555504641634386,
-		)
 
 	volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
 
@@ -343,8 +351,7 @@ def generate(**kwargs):
 	for k in audio_cache:
 		audio = audio_cache[k]['audio']
 
-		if resampler is not None:
-			audio = resampler(audio)
+		audio, _ = resample(audio, tts.output_sample_rate, args.output_sample_rate)
 		if volume_adjust is not None:
 			audio = volume_adjust(audio)
 
@@ -1098,12 +1105,14 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 
 		if basename in results and skip_existings:
 			print(f"Skipping already parsed file: {basename}")
-			continue
+		else:
+			results[basename] = whisper_transcribe(file, language=language)
 
-		results[basename] = whisper_transcribe(file, language=language)
-
-		# lazy copy
 		waveform, sample_rate = torchaudio.load(file)
+		# resample to the input rate, since it'll get resampled for training anyways
+		# this should also "help" increase throughput a bit when filling the dataloaders
+		waveform, sample_rate = resample(waveform, sample_rate, tts.input_sample_rate if tts is not None else 22050)
+
 		torchaudio.save(f"{indir}/audio/{basename}", waveform, sample_rate)
 
 		with open(infile, 'w', encoding="utf-8") as f:
@@ -1111,8 +1120,6 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
 
 		do_gc()
 
-	unload_whisper()
-
 	return f"Processed dataset to: {indir}"
 
 def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0 ):
@@ -1154,9 +1161,11 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0 ):
 
 			if trim_silence:
 				sliced = torchaudio.functional.vad( sliced, sample_rate )
-
-			segments +=1
+			
+			sliced, sample_rate = resample( sample_rate, 22050 )
 			torchaudio.save(f"{indir}/audio/{file}", sliced, sample_rate)
+			
+			segments +=1
 
 	messages.append(f"Sliced segments: {files} => {segments}.")
 	return "\n".join(messages)
@@ -1500,20 +1509,7 @@ def import_voices(files, saveAs=None, progress=None):
 				if not voicefixer:
 					load_voicefixer()
 
-				# resample to best bandwidth since voicefixer will do it anyways through librosa
-				if sample_rate != 44100:
-					print(f"Resampling imported voice sample: {path}")
-					resampler = torchaudio.transforms.Resample(
-						sample_rate,
-						44100,
-						lowpass_filter_width=16,
-						rolloff=0.85,
-						resampling_method="kaiser_window",
-						beta=8.555504641634386,
-					)
-					waveform = resampler(waveform)
-					sample_rate = 44100
-
+				waveform, sample_rate = resample(waveform, sample_rate, 44100)
 				torchaudio.save(path, waveform, sample_rate)
 
 				print(f"Running 'voicefixer' on voice sample: {path}")