diff --git a/melo/text/__init__.py b/melo/text/__init__.py
index 6e482be18..8851a8722 100644
--- a/melo/text/__init__.py
+++ b/melo/text/__init__.py
@@ -28,8 +28,10 @@ def get_bert(norm_text, word2ph, language, device):
     from .spanish_bert import get_bert_feature as sp_bert
     from .french_bert import get_bert_feature as fr_bert
     from .korean import get_bert_feature as kr_bert
+    from .turkish import get_bert_feature as tr_bert
 
     lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert, 'ZH_MIX_EN': zh_mix_en_bert, 
-                          'FR': fr_bert, 'SP': sp_bert, 'ES': sp_bert, "KR": kr_bert}
+                          'FR': fr_bert, 'SP': sp_bert, 'ES': sp_bert, "KR": kr_bert, "TR": tr_bert}
     bert = lang_bert_func_map[language](norm_text, word2ph, device)
     return bert
+
diff --git a/melo/text/cleaner.py b/melo/text/cleaner.py
index fdf2609a0..b81008997 100644
--- a/melo/text/cleaner.py
+++ b/melo/text/cleaner.py
@@ -1,9 +1,9 @@
-from . import chinese, japanese, english, chinese_mix, korean, french, spanish
+from . import chinese, japanese, english, chinese_mix, korean, french, spanish, turkish
 from . import cleaned_text_to_sequence
 import copy
 
 language_module_map = {"ZH": chinese, "JP": japanese, "EN": english, 'ZH_MIX_EN': chinese_mix, 'KR': korean,
-                    'FR': french, 'SP': spanish, 'ES': spanish}
+                    'FR': french, 'SP': spanish, 'ES': spanish, "TR": turkish}
 
 
 def clean_text(text, language):
@@ -33,4 +33,4 @@ def text_to_sequence(text, language):
 
 
 if __name__ == "__main__":
-    pass
\ No newline at end of file
+    pass
diff --git a/melo/text/symbols.py b/melo/text/symbols.py
index 46eea5c97..b1547d43f 100644
--- a/melo/text/symbols.py
+++ b/melo/text/symbols.py
@@ -261,16 +261,28 @@
 ]
 num_ru_tones = 1
 
+# Turkish symbols (IPA)
+tr_symbols = [
+    "a", "aː", "b", "d", "dʒ", "e", "f", "ɡ", "h", "ɯ", 
+    "i", "j", "k", "l", "m", "n", "o", "ø", "p", "r", 
+    "s", "ʃ", "t", "tʃ", "u", "y", "v", "z", "ː"
+]
+num_tr_tones = 1
+
 # combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols + kr_symbols + es_symbols + fr_symbols + de_symbols + ru_symbols))
+normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols + kr_symbols + 
+                          es_symbols + fr_symbols + de_symbols + ru_symbols + tr_symbols))
 symbols = [pad] + normal_symbols + pu_symbols
 sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
 
 # combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones + num_es_tones + num_fr_tones + num_de_tones + num_ru_tones
-
+num_tones = (num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones + 
+             num_es_tones + num_fr_tones + num_de_tones + num_ru_tones + num_tr_tones)
 # language maps
-language_id_map = {"ZH": 0, "JP": 1, "EN": 2, "ZH_MIX_EN": 3, 'KR': 4, 'ES': 5, 'SP': 5 ,'FR': 6}
+language_id_map = {
+    "ZH": 0, "JP": 1, "EN": 2, "ZH_MIX_EN": 3, 'KR': 4, 
+    'ES': 5, 'SP': 5, 'FR': 6, 'TR': 7
+}
 num_languages = len(language_id_map.keys())
 
 language_tone_start_map = {
@@ -282,9 +294,11 @@
     "ES": num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones,
     "SP": num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones,
     "FR": num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones + num_es_tones,
+    "TR": num_zh_tones + num_ja_tones + num_en_tones + num_kr_tones + num_es_tones + num_fr_tones
 }
 
 if __name__ == "__main__":
     a = set(zh_symbols)
     b = set(en_symbols)
     print(sorted(a & b))
+
diff --git a/melo/text/turkish.py b/melo/text/turkish.py
new file mode 100644
index 000000000..86a735c11
--- /dev/null
+++ b/melo/text/turkish.py
@@ -0,0 +1,122 @@
+import re
+from transformers import AutoTokenizer
+from . import symbols
+
+def distribute_phone(n_phone, n_word):
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+def text_normalize(text):
+    # Basic Turkish text normalization
+    # Convert to lowercase while preserving Turkish characters
+    text = text.replace("I", "ı").lower()
+    text = text.lower()
+    # Replace multiple spaces with single space
+    text = re.sub(r'\s+', ' ', text)
+    # Remove unnecessary punctuation
+    text = re.sub(r'[^\w\s.,!?;:əğıöüşçİĞÖÜŞÇ]', '', text)
+    return text.strip()
+
+def post_replace_ph(ph):
+    rep_map = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "...": "…"
+    }
+    if ph in rep_map.keys():
+        ph = rep_map[ph]
+    if ph in symbols:
+        return ph
+    if ph not in symbols:
+        ph = "UNK"
+    return ph
+
+def refine_ph(phn):
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    return phn.lower(), tone
+
+def tr_to_ipa(text):
+    """
+    Convert Turkish text to IPA
+    This is a basic implementation - you might want to expand this based on Turkish phonology rules
+    """
+    tr_to_ipa_dict = {
+        'a': 'a', 'e': 'e', 'ı': 'ɯ', 'i': 'i', 
+        'o': 'o', 'ö': 'ø', 'u': 'u', 'ü': 'y',
+        'b': 'b', 'c': 'dʒ', 'ç': 'tʃ', 'd': 'd',
+        'f': 'f', 'g': 'ɡ', 'ğ': 'ː', 'h': 'h',
+        'j': 'ʒ', 'k': 'k', 'l': 'l', 'm': 'm',
+        'n': 'n', 'p': 'p', 'r': 'r', 's': 's',
+        'ş': 'ʃ', 't': 't', 'v': 'v', 'y': 'j',
+        'z': 'z'
+    }
+    return ''.join(tr_to_ipa_dict.get(char, char) for char in text.lower())
+
+# Initialize the Turkish BERT tokenizer
+model_id = 'ytu-ce-cosmos/turkish-base-bert-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def g2p(text, pad_start_end=True, tokenized=None):
+    if tokenized is None:
+        tokenized = tokenizer.tokenize(text)
+    
+    phs = []
+    ph_groups = []
+    for t in tokenized:
+        if not t.startswith("##"):  # Note: Turkish BERT uses ## for subwords
+            ph_groups.append([t])
+        else:
+            ph_groups[-1].append(t.replace("##", ""))
+    
+    phones = []
+    tones = []
+    word2ph = []
+    
+    for group in ph_groups:
+        w = "".join(group)
+        phone_len = 0
+        word_len = len(group)
+        if w == '[UNK]':
+            phone_list = ['UNK']
+        else:
+            phone_list = list(filter(lambda p: p != " ", tr_to_ipa(w)))
+        
+        for ph in phone_list:
+            phones.append(ph)
+            tones.append(0)  # Turkish is not a tonal language
+            phone_len += 1
+        aaa = distribute_phone(phone_len, word_len)
+        word2ph += aaa
+
+    if pad_start_end:
+        phones = ["_"] + phones + ["_"]
+        tones = [0] + tones + [0]
+        word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+def get_bert_feature(text, word2ph, device=None):
+    from text import turkish_bert
+    return turkish_bert.get_bert_feature(text, word2ph, device=device)
+
+if __name__ == "__main__":
+    text = "Merhaba, nasılsın? Ben iyiyim."
+    text = text_normalize(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = get_bert_feature(text, word2ph)
+    print(phones)
+    print(len(phones), tones, sum(word2ph), bert.shape)
diff --git a/melo/text/turkish_bert.py b/melo/text/turkish_bert.py
new file mode 100644
index 000000000..cdfcd22d2
--- /dev/null
+++ b/melo/text/turkish_bert.py
@@ -0,0 +1,39 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import sys
+
+model_id = 'ytu-ce-cosmos/turkish-base-bert-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = None
+
+def get_bert_feature(text, word2ph, device=None):
+    global model
+    if (
+        sys.platform == "darwin"
+        and torch.backends.mps.is_available()
+        and device == "cpu"
+    ):
+        device = "mps"
+    if not device:
+        device = "cuda"
+    if model is None:
+        model = AutoModelForMaskedLM.from_pretrained(model_id).to(
+            device
+        )
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        
+    assert inputs["input_ids"].shape[-1] == len(word2ph)
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
diff --git a/melo/utils.py b/melo/utils.py
index bafca5a6d..1e3fd6308 100644
--- a/melo/utils.py
+++ b/melo/utils.py
@@ -42,7 +42,7 @@ def get_text_for_tts_infer(text, language_str, hps, device, symbol_to_id=None):
         if language_str == "ZH":
             bert = bert
             ja_bert = torch.zeros(768, len(phone))
-        elif language_str in ["JP", "EN", "ZH_MIX_EN", 'KR', 'SP', 'ES', 'FR', 'DE', 'RU']:
+        elif language_str in ["JP", "EN", "ZH_MIX_EN", 'KR', 'SP', 'ES', 'FR', 'DE', 'RU', 'TR']:
             ja_bert = bert
             bert = torch.zeros(1024, len(phone))
         else: