diff --git a/src/utils.py b/src/utils.py
index 3da36af..a8c175a 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -37,6 +37,8 @@ from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_
 from tortoise.utils.text import split_and_recombine_text
 from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram, do_gc
 
+from whisper.normalizers.english import EnglishTextNormalizer
+
 MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
 
 WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
@@ -1193,7 +1195,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
 	messages.append(f"Sliced segments: {files} => {segments}.")
 	return "\n".join(messages)
 
-def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=True ):
+def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=False ):
 	indir = f'./training/{voice}/'
 	infile = f'{indir}/whisper.json'
 	messages = []
@@ -1208,6 +1210,9 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 		'validation': [],
 	}
 
+	normalizer = EnglishTextNormalizer() if normalize else None
+
+	errored = 0
 	for filename in results:
 		result = results[filename]
 		use_segment = use_segments
@@ -1215,7 +1220,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 		# check if unsegmented text exceeds 200 characters
 		if not use_segment:
 			if len(result['text']) > 200:
-				message = f"Text length too long (200 < {len(text)}), using segments: {filename}"
+				message = f"Text length too long (200 < {len(result['text'])}), using segments: {filename}"
 				print(message)
 				messages.append(message)
 				use_segment = True
@@ -1225,6 +1230,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 			path = f'{indir}/audio/{filename}'
 			if not os.path.exists(path):
 				messages.append(f"Missing source audio: {filename}")
+				errored += 1
 				continue
 
 			metadata = torchaudio.info(path)
@@ -1253,15 +1259,17 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 				message = f"Missing source audio: {file}"
 				print(message)
 				messages.append(message)
+				errored += 1
 				continue
 			
 			text = segment['text'].strip()
-			normalized_text = text
+			normalized_text = normalizer(text) if normalize and result['language'] == "en" else text
 
 			if len(text) > 200:
 				message = f"Text length too long (200 < {len(text)}), skipping... {file}"
 				print(message)
 				messages.append(message)
+				errored += 1
 				continue
 
 			waveform, sample_rate = torchaudio.load(path)
@@ -1271,6 +1279,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 				message = f"{error}, skipping... {file}"
 				print(message)
 				messages.append(message)
+				errored += 1
 				continue
 
 			culled = len(text) < text_length
@@ -1279,8 +1288,13 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 				duration = num_channels * num_frames / sample_rate
 				culled = duration < audio_length
 
-			# lines['training' if not culled else 'validation'].append(f'audio/{file}|{text}|{normalized_text}')
-			lines['training' if not culled else 'validation'].append(f'audio/{file}|{text}')
+			# for when i add in a little treat ;), as it requires normalized text
+			if normalize and length(normalized_text) < 200:
+				line = f'audio/{file}|{text}|{normalized_text}'
+			else:
+				line = f'audio/{file}|{text}'
+
+			lines['training' if not culled else 'validation'].append(line) 
 
 	training_joined = "\n".join(lines['training'])
 	validation_joined = "\n".join(lines['validation'])
@@ -1291,7 +1305,7 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 	with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
 		f.write(validation_joined)
 
-	messages.append(f"Prepared {len(lines['training'])} lines (validation: {len(lines['validation'])}).\n{training_joined}\n\n{validation_joined}")
+	messages.append(f"Prepared {len(lines['training'])} lines (validation: {len(lines['validation'])}, culled: {errored}).\n{training_joined}\n\n{validation_joined}")
 	return "\n".join(messages)
 
 def calc_iterations( epochs, lines, batch_size ):