diff --git a/TTS/config/shared_configs.py b/TTS/config/shared_configs.py
index 3ea49796..94964497 100644
--- a/TTS/config/shared_configs.py
+++ b/TTS/config/shared_configs.py
@@ -222,6 +222,7 @@ class BaseDatasetConfig(Coqpit):
     meta_file_train: str = ""
     ignored_speakers: List[str] = None
     language: str = ""
+    speech_style: str = ""
     meta_file_val: str = ""
     meta_file_attn_mask: str = ""
 
diff --git a/TTS/tts/configs/shared_configs.py b/TTS/tts/configs/shared_configs.py
index dcc862e8..70b922af 100644
--- a/TTS/tts/configs/shared_configs.py
+++ b/TTS/tts/configs/shared_configs.py
@@ -279,3 +279,5 @@ class BaseTTSConfig(BaseTrainingConfig):
     speaker_weighted_sampler_alpha: float = 1.0
     use_language_weighted_sampler: bool = False
     language_weighted_sampler_alpha: float = 1.0
+    use_style_weighted_sampler: bool = False
+    style_weighted_sampler_alpha: float = 1.0
diff --git a/TTS/tts/datasets/__init__.py b/TTS/tts/datasets/__init__.py
index 6c7c9edd..64017a21 100644
--- a/TTS/tts/datasets/__init__.py
+++ b/TTS/tts/datasets/__init__.py
@@ -103,13 +103,14 @@ def load_tts_samples(
         meta_file_val = dataset["meta_file_val"]
         ignored_speakers = dataset["ignored_speakers"]
         language = dataset["language"]
+        speech_style = dataset["speech_style"]
 
         # setup the right data processor
         if formatter is None:
             formatter = _get_formatter_by_name(name)
         # load train set
         meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
-        meta_data_train = [{**item, **{"language": language}} for item in meta_data_train]
+        meta_data_train = [{**item, **{"language": language, "speech_style": speech_style}} for item in meta_data_train]
 
         print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
         # load evaluation split if set
diff --git a/TTS/tts/models/base_tts.py b/TTS/tts/models/base_tts.py
index 71da495c..2ccc6143 100644
--- a/TTS/tts/models/base_tts.py
+++ b/TTS/tts/models/base_tts.py
@@ -14,6 +14,7 @@ from TTS.model import BaseTrainerModel
 from TTS.tts.datasets.dataset import TTSDataset
 from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
 from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights, get_speaker_manager
+from TTS.tts.utils.emotions import get_speech_style_balancer_weights
 from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
 
@@ -250,6 +251,14 @@ class BaseTTS(BaseTrainerModel):
             else:
                 weights = get_speaker_balancer_weights(data_items) * alpha
 
+        if getattr(config, "use_style_weighted_sampler", False):
+            alpha = getattr(config, "style_weighted_sampler_alpha", 1.0)
+            print(" > Using Speech Style weighted sampler with alpha:", alpha)
+            if weights is not None:
+                weights += get_speech_style_balancer_weights(data_items) * alpha
+            else:
+                weights = get_speech_style_balancer_weights(data_items) * alpha
+
         if weights is not None:
             sampler = WeightedRandomSampler(weights, len(weights))
         else:
diff --git a/TTS/tts/utils/emotions.py b/TTS/tts/utils/emotions.py
index 909772ad..1fea49ae 100644
--- a/TTS/tts/utils/emotions.py
+++ b/TTS/tts/utils/emotions.py
@@ -1,5 +1,7 @@
 import json
 import os
+import torch
+import numpy as np
 from typing import Any, List
 
 import fsspec
@@ -203,3 +205,16 @@ def get_emotion_manager(c: Coqpit, restore_path: str = None, out_path: str = Non
         else:
             emotion_manager.save_ids_to_file(out_file_path)
     return emotion_manager
+
+
+def get_speech_style_balancer_weights(items: list):
+    style_names = np.array([item["speech_style"] for item in items])
+    unique_style_names = np.unique(style_names).tolist()
+    style_ids = [unique_style_names.index(s) for s in style_names]
+    style_count = np.array([len(np.where(style_names == s)[0]) for s in unique_style_names])
+    weight_style = 1.0 / style_count
+    # get weight for each sample
+    dataset_samples_weight = np.array([weight_style[s] for s in style_ids])
+    # normalize
+    dataset_samples_weight = dataset_samples_weight / np.linalg.norm(dataset_samples_weight)
+    return torch.from_numpy(dataset_samples_weight).float()
diff --git a/tests/tts_tests/test_vits_d_vector_with_external_emotion_train.py b/tests/tts_tests/test_vits_d_vector_with_external_emotion_train.py
index 55f0492d..19a781dd 100644
--- a/tests/tts_tests/test_vits_d_vector_with_external_emotion_train.py
+++ b/tests/tts_tests/test_vits_d_vector_with_external_emotion_train.py
@@ -45,6 +45,7 @@ config.model_args.d_vector_dim = 256
 config.model_args.use_external_emotions_embeddings = True
 config.model_args.use_emotion_embedding = False
 config.model_args.emotion_embedding_dim = 256
+config.model_args.emotion_just_encoder = True
 config.model_args.external_emotions_embs_file = "tests/data/ljspeech/speakers.json"
 
 # consistency loss