Make style

TTS/tts/configs/xtts_config.py

@@ -84,7 +84,24 @@ class XttsConfig(BaseTTSConfig):
     audio: XttsAudioConfig = field(default_factory=XttsAudioConfig)
     model_dir: str = None
     languages: List[str] = field(
-        default_factory=lambda: ["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn", "hu", "ko", "ja"]
+        default_factory=lambda: [
+            "en",
+            "es",
+            "fr",
+            "de",
+            "it",
+            "pt",
+            "pl",
+            "tr",
+            "ru",
+            "nl",
+            "cs",
+            "ar",
+            "zh-cn",
+            "hu",
+            "ko",
+            "ja",
+        ]
     )
 
     # inference params

TTS/tts/layers/xtts/gpt.py

@@ -13,6 +13,7 @@ from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
 from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
 from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
 
+
 def null_position_embeddings(range, dim):
     return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
 
@@ -186,7 +187,9 @@ class GPT(nn.Module):
     def get_grad_norm_parameter_groups(self):
         return {
             "conditioning_encoder": list(self.conditioning_encoder.parameters()),
-            "conditioning_perceiver": list(self.conditioning_perceiver.parameters()) if self.use_perceiver_resampler else None,
+            "conditioning_perceiver": list(self.conditioning_perceiver.parameters())
+            if self.use_perceiver_resampler
+            else None,
             "gpt": list(self.gpt.parameters()),
             "heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
         }
@@ -355,9 +358,9 @@ class GPT(nn.Module):
         if not return_latent:
             if cond_input.ndim == 4:
                 cond_input = cond_input.squeeze(1)
-            conds = self.conditioning_encoder(cond_input) # (b, d, s)
+            conds = self.conditioning_encoder(cond_input)  # (b, d, s)
             if self.use_perceiver_resampler:
-                conds = self.conditioning_perceiver(conds.permute(0, 2, 1)).transpose(1, 2) # (b, d, 32)
+                conds = self.conditioning_perceiver(conds.permute(0, 2, 1)).transpose(1, 2)  # (b, d, 32)
         else:
             # already computed
             conds = cond_input.unsqueeze(1)

TTS/tts/layers/xtts/perceiver_encoder.py

@@ -16,8 +16,10 @@ from einops.layers.torch import Rearrange
 def exists(val):
     return val is not None
 
+
 def once(fn):
     called = False
+
     @wraps(fn)
     def inner(x):
         nonlocal called
@@ -25,19 +27,17 @@ def once(fn):
             return
         called = True
         return fn(x)
+
     return inner
 
+
 print_once = once(print)
 
 # main class
 
+
 class Attend(nn.Module):
-    def __init__(
-        self,
-        dropout = 0.,
-        causal = False,
-        use_flash = False
-    ):
+    def __init__(self, dropout=0.0, causal=False, use_flash=False):
         super().__init__()
         self.dropout = dropout
         self.attn_dropout = nn.Dropout(dropout)
@@ -46,23 +46,25 @@ class Attend(nn.Module):
         self.register_buffer("mask", None, persistent=False)
 
         self.use_flash = use_flash
-        assert not (use_flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
+        assert not (
+            use_flash and version.parse(torch.__version__) < version.parse("2.0.0")
+        ), "in order to use flash attention, you must be using pytorch 2.0 or above"
 
         # determine efficient attention configs for cuda and cpu
-        self.config = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
+        self.config = namedtuple("EfficientAttentionConfig", ["enable_flash", "enable_math", "enable_mem_efficient"])
         self.cpu_config = self.config(True, True, True)
         self.cuda_config = None
 
         if not torch.cuda.is_available() or not use_flash:
             return
 
-        device_properties = torch.cuda.get_device_properties(torch.device('cuda'))
+        device_properties = torch.cuda.get_device_properties(torch.device("cuda"))
 
         if device_properties.major == 8 and device_properties.minor == 0:
-            print_once('A100 GPU detected, using flash attention if input tensor is on cuda')
+            print_once("A100 GPU detected, using flash attention if input tensor is on cuda")
             self.cuda_config = self.config(True, False, False)
         else:
-            print_once('Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda')
+            print_once("Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda")
             self.cuda_config = self.config(False, True, True)
 
     def get_mask(self, n, device):
@@ -73,23 +75,23 @@ class Attend(nn.Module):
         self.register_buffer("mask", mask, persistent=False)
         return mask
 
-    def flash_attn(self, q, k, v, mask = None):
+    def flash_attn(self, q, k, v, mask=None):
         _, heads, q_len, _, k_len, is_cuda = *q.shape, k.shape[-2], q.is_cuda
 
         # Recommended for multi-query single-key-value attention by Tri Dao
         # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
 
         if k.ndim == 3:
-            k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
+            k = rearrange(k, "b ... -> b 1 ...").expand_as(q)
 
         if v.ndim == 3:
-            v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
+            v = rearrange(v, "b ... -> b 1 ...").expand_as(q)
 
         # Check if mask exists and expand to compatible shape
         # The mask is B L, so it would have to be expanded to B H N L
 
         if exists(mask):
-            mask = rearrange(mask, 'b j -> b 1 1 j')
+            mask = rearrange(mask, "b j -> b 1 1 j")
             mask = mask.expand(-1, heads, q_len, -1)
 
         # Check if there is a compatible device for flash attention
@@ -100,15 +102,12 @@ class Attend(nn.Module):
 
         with torch.backends.cuda.sdp_kernel(**config._asdict()):
             out = F.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask = mask,
-                dropout_p = self.dropout if self.training else 0.,
-                is_causal = self.causal
+                q, k, v, attn_mask=mask, dropout_p=self.dropout if self.training else 0.0, is_causal=self.causal
             )
 
         return out
 
-    def forward(self, q, k, v, mask = None):
+    def forward(self, q, k, v, mask=None):
         """
         einstein notation
         b - batch
@@ -122,9 +121,9 @@ class Attend(nn.Module):
         scale = q.shape[-1] ** -0.5
 
         if self.use_flash:
-            return self.flash_attn(q, k, v, mask = mask)
+            return self.flash_attn(q, k, v, mask=mask)
 
-        kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
+        kv_einsum_eq = "b j d" if k.ndim == 3 else "b h j d"
 
         # similarity
 
@@ -133,7 +132,7 @@ class Attend(nn.Module):
         # key padding mask
 
         if exists(mask):
-            mask = rearrange(mask, 'b j -> b 1 1 j')
+            mask = rearrange(mask, "b j -> b 1 1 j")
             sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max)
 
         # causal mask
@@ -153,6 +152,7 @@ class Attend(nn.Module):
 
         return out
 
+
 def Sequential(*mods):
     return nn.Sequential(*filter(exists, mods))
 

TTS/tts/layers/xtts/tokenizer.py

@@ -161,9 +161,9 @@ _abbreviations = {
     "hu": [
         (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
         for x in [
-            ("dr", "doktor"),     # doctor
-            ("b", "bácsi"),   # Mr.
-            ("nőv", "nővér"),     # nurse
+            ("dr", "doktor"),  # doctor
+            ("b", "bácsi"),  # Mr.
+            ("nőv", "nővér"),  # nurse
             # Add other Hungarian abbreviations here if needed.
         ]
     ],
@@ -171,9 +171,8 @@ _abbreviations = {
         (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
         for x in [
             # Korean doesn't typically use abbreviations in the same way as Latin-based scripts.
-
         ]
-    ]
+    ],
 }
 
 
@@ -354,7 +353,7 @@ _symbols_multilingual = {
             ("#", " kettőskereszt "),
             ("$", " dollár "),
             ("£", " font "),
-            ("°", " fok ")
+            ("°", " fok "),
         ]
     ],
     "ko": [
@@ -367,9 +366,9 @@ _symbols_multilingual = {
             ("#", " 번호 "),
             ("$", " 달러 "),
             ("£", " 파운드 "),
-            ("°", " 도 ")
+            ("°", " 도 "),
         ]
-    ]
+    ],
 }
 
 
@@ -463,14 +462,9 @@ def _expand_ordinal(m, lang="en"):
 def _expand_number(m, lang="en"):
     return num2words(int(m.group(0)), lang=lang if lang != "cs" else "cz")
 
-<<<<<<< HEAD
 
 def expand_numbers_multilingual(text, lang="en"):
-    if lang == "zh-cn":
-=======
-def expand_numbers_multilingual(text, lang='en'):
     if lang == "zh" or lang == "zh-cn":
->>>>>>> Update model entry
         text = zh_num2words()(text)
     else:
         if lang in ["en", "ru"]:
@@ -521,24 +515,15 @@ def basic_cleaners(text):
 
 def chinese_transliterate(text):
     return "".join(
-        p[0] for p in pypinyin.pinyin(text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True)
+        [p[0] for p in pypinyin.pinyin(text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True)]
     )
 
 
-
 def japanese_cleaners(text, katsu):
     text = katsu.romaji(text)
     text = lowercase(text)
     return text
 
-<<<<<<< HEAD
-
-class VoiceBpeTokenizer:
-    def __init__(self, vocab_file=None, preprocess=None):
-        self.tokenizer = None
-        self.katsu = None
-=======
->>>>>>> Update model entry
 
 def korean_cleaners(text):
     r = Transliter(academic)
@@ -559,32 +544,14 @@ def preprocess_text(txt, lang):
     return txt
 
 
-<<<<<<< HEAD
-    def preprocess_text(self, txt, lang):
-        if lang in ["en", "es", "fr", "de", "pt", "it", "pl", "ar", "cs", "ru", "nl", "tr", "zh-cn"]:
-            txt = multilingual_cleaners(txt, lang)
-            if lang == "zh-cn":
-                txt = chinese_transliterate(txt)
-        elif lang == "ja":
-            if self.katsu is None:
-                import cutlet
+DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json")
 
-                self.katsu = cutlet.Cutlet()
-            txt = japanese_cleaners(txt, self.katsu)
-        else:
-            raise NotImplementedError()
-        return txt
-=======
-DEFAULT_VOCAB_FILE = os.path.join(
-    os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json"
-)
 
 class VoiceBpeTokenizer:
     def __init__(self, vocab_file=None):
         self.tokenizer = None
         if vocab_file is not None:
             self.tokenizer = Tokenizer.from_file(vocab_file)
->>>>>>> Update model entry
 
     def encode(self, txt, lang):
         txt = preprocess_text(txt, lang)
@@ -611,139 +578,145 @@ class VoiceBpeTokenizer:
 def test_expand_numbers_multilingual():
     test_cases = [
         # English
-        ("In 12.5 seconds.", 'In twelve point five seconds.', 'en'),
-        ("There were 50 soldiers.", 'There were fifty soldiers.', 'en'),
-        ("This is a 1st test", 'This is a first test', 'en'),
-        ("That will be $20 sir.", 'That will be twenty dollars sir.', 'en'),
-        ("That will be 20€ sir.", 'That will be twenty euro sir.', 'en'),
-        ("That will be 20.15€ sir.", 'That will be twenty euro, fifteen cents sir.', 'en'),
-        ("That's 100,000.5.", 'That\'s one hundred thousand point five.', 'en'),
+        ("In 12.5 seconds.", "In twelve point five seconds.", "en"),
+        ("There were 50 soldiers.", "There were fifty soldiers.", "en"),
+        ("This is a 1st test", "This is a first test", "en"),
+        ("That will be $20 sir.", "That will be twenty dollars sir.", "en"),
+        ("That will be 20€ sir.", "That will be twenty euro sir.", "en"),
+        ("That will be 20.15€ sir.", "That will be twenty euro, fifteen cents sir.", "en"),
+        ("That's 100,000.5.", "That's one hundred thousand point five.", "en"),
         # French
-        ("En 12,5 secondes.", 'En douze virgule cinq secondes.', 'fr'),
-        ("Il y avait 50 soldats.", 'Il y avait cinquante soldats.', 'fr'),
-        ("Ceci est un 1er test", 'Ceci est un premier test', 'fr'),
-        ("Cela vous fera $20 monsieur.", 'Cela vous fera vingt dollars monsieur.', 'fr'),
-        ("Cela vous fera 20€ monsieur.", 'Cela vous fera vingt euros monsieur.', 'fr'),
-        ("Cela vous fera 20,15€ monsieur.", 'Cela vous fera vingt euros et quinze centimes monsieur.', 'fr'),
-        ("Ce sera 100.000,5.", 'Ce sera cent mille virgule cinq.', 'fr'),
+        ("En 12,5 secondes.", "En douze virgule cinq secondes.", "fr"),
+        ("Il y avait 50 soldats.", "Il y avait cinquante soldats.", "fr"),
+        ("Ceci est un 1er test", "Ceci est un premier test", "fr"),
+        ("Cela vous fera $20 monsieur.", "Cela vous fera vingt dollars monsieur.", "fr"),
+        ("Cela vous fera 20€ monsieur.", "Cela vous fera vingt euros monsieur.", "fr"),
+        ("Cela vous fera 20,15€ monsieur.", "Cela vous fera vingt euros et quinze centimes monsieur.", "fr"),
+        ("Ce sera 100.000,5.", "Ce sera cent mille virgule cinq.", "fr"),
         # German
-        ("In 12,5 Sekunden.", 'In zwölf Komma fünf Sekunden.', 'de'),
-        ("Es gab 50 Soldaten.", 'Es gab fünfzig Soldaten.', 'de'),
-        ("Dies ist ein 1. Test", 'Dies ist ein erste Test', 'de'), # Issue with gender
-        ("Das macht $20 Herr.", 'Das macht zwanzig Dollar Herr.', 'de'),
-        ("Das macht 20€ Herr.", 'Das macht zwanzig Euro Herr.', 'de'),
-        ("Das macht 20,15€ Herr.", 'Das macht zwanzig Euro und fünfzehn Cent Herr.', 'de'),
+        ("In 12,5 Sekunden.", "In zwölf Komma fünf Sekunden.", "de"),
+        ("Es gab 50 Soldaten.", "Es gab fünfzig Soldaten.", "de"),
+        ("Dies ist ein 1. Test", "Dies ist ein erste Test", "de"),  # Issue with gender
+        ("Das macht $20 Herr.", "Das macht zwanzig Dollar Herr.", "de"),
+        ("Das macht 20€ Herr.", "Das macht zwanzig Euro Herr.", "de"),
+        ("Das macht 20,15€ Herr.", "Das macht zwanzig Euro und fünfzehn Cent Herr.", "de"),
         # Spanish
-        ("En 12,5 segundos.", 'En doce punto cinco segundos.', 'es'),
-        ("Había 50 soldados.", 'Había cincuenta soldados.', 'es'),
-        ("Este es un 1er test", 'Este es un primero test', 'es'),
-        ("Eso le costará $20 señor.", 'Eso le costará veinte dólares señor.', 'es'),
-        ("Eso le costará 20€ señor.", 'Eso le costará veinte euros señor.', 'es'),
-        ("Eso le costará 20,15€ señor.", 'Eso le costará veinte euros con quince céntimos señor.', 'es'),
+        ("En 12,5 segundos.", "En doce punto cinco segundos.", "es"),
+        ("Había 50 soldados.", "Había cincuenta soldados.", "es"),
+        ("Este es un 1er test", "Este es un primero test", "es"),
+        ("Eso le costará $20 señor.", "Eso le costará veinte dólares señor.", "es"),
+        ("Eso le costará 20€ señor.", "Eso le costará veinte euros señor.", "es"),
+        ("Eso le costará 20,15€ señor.", "Eso le costará veinte euros con quince céntimos señor.", "es"),
         # Italian
-        ("In 12,5 secondi.", 'In dodici virgola cinque secondi.', 'it'),
-        ("C'erano 50 soldati.", "C'erano cinquanta soldati.", 'it'),
-        ("Questo è un 1° test", 'Questo è un primo test', 'it'),
-        ("Ti costerà $20 signore.", 'Ti costerà venti dollari signore.', 'it'),
-        ("Ti costerà 20€ signore.", 'Ti costerà venti euro signore.', 'it'),
-        ("Ti costerà 20,15€ signore.", 'Ti costerà venti euro e quindici centesimi signore.', 'it'),
+        ("In 12,5 secondi.", "In dodici virgola cinque secondi.", "it"),
+        ("C'erano 50 soldati.", "C'erano cinquanta soldati.", "it"),
+        ("Questo è un 1° test", "Questo è un primo test", "it"),
+        ("Ti costerà $20 signore.", "Ti costerà venti dollari signore.", "it"),
+        ("Ti costerà 20€ signore.", "Ti costerà venti euro signore.", "it"),
+        ("Ti costerà 20,15€ signore.", "Ti costerà venti euro e quindici centesimi signore.", "it"),
         # Portuguese
-        ("Em 12,5 segundos.", 'Em doze vírgula cinco segundos.', 'pt'),
-        ("Havia 50 soldados.", 'Havia cinquenta soldados.', 'pt'),
-        ("Este é um 1º teste", 'Este é um primeiro teste', 'pt'),
-        ("Isso custará $20 senhor.", 'Isso custará vinte dólares senhor.', 'pt'),
-        ("Isso custará 20€ senhor.", 'Isso custará vinte euros senhor.', 'pt'),
-        ("Isso custará 20,15€ senhor.", 'Isso custará vinte euros e quinze cêntimos senhor.', 'pt'), # "cêntimos" should be "centavos" num2words issue
+        ("Em 12,5 segundos.", "Em doze vírgula cinco segundos.", "pt"),
+        ("Havia 50 soldados.", "Havia cinquenta soldados.", "pt"),
+        ("Este é um 1º teste", "Este é um primeiro teste", "pt"),
+        ("Isso custará $20 senhor.", "Isso custará vinte dólares senhor.", "pt"),
+        ("Isso custará 20€ senhor.", "Isso custará vinte euros senhor.", "pt"),
+        (
+            "Isso custará 20,15€ senhor.",
+            "Isso custará vinte euros e quinze cêntimos senhor.",
+            "pt",
+        ),  # "cêntimos" should be "centavos" num2words issue
         # Polish
-        ("W 12,5 sekundy.", 'W dwanaście przecinek pięć sekundy.', 'pl'),
-        ("Było 50 żołnierzy.", 'Było pięćdziesiąt żołnierzy.', 'pl'),
-        ("To będzie kosztować 20€ panie.", 'To będzie kosztować dwadzieścia euro panie.', 'pl'),
-        ("To będzie kosztować 20,15€ panie.", 'To będzie kosztować dwadzieścia euro, piętnaście centów panie.', 'pl'),
+        ("W 12,5 sekundy.", "W dwanaście przecinek pięć sekundy.", "pl"),
+        ("Było 50 żołnierzy.", "Było pięćdziesiąt żołnierzy.", "pl"),
+        ("To będzie kosztować 20€ panie.", "To będzie kosztować dwadzieścia euro panie.", "pl"),
+        ("To będzie kosztować 20,15€ panie.", "To będzie kosztować dwadzieścia euro, piętnaście centów panie.", "pl"),
         # Arabic
-        ("في الـ 12,5 ثانية.", 'في الـ اثنا عشر  , خمسون ثانية.', 'ar'),
-        ("كان هناك 50 جنديًا.", 'كان هناك خمسون جنديًا.', 'ar'),
+        ("في الـ 12,5 ثانية.", "في الـ اثنا عشر  , خمسون ثانية.", "ar"),
+        ("كان هناك 50 جنديًا.", "كان هناك خمسون جنديًا.", "ar"),
         # ("ستكون النتيجة $20 يا سيد.", 'ستكون النتيجة عشرون دولار يا سيد.', 'ar'), # $ and € are mising from num2words
         # ("ستكون النتيجة 20€ يا سيد.", 'ستكون النتيجة عشرون يورو يا سيد.', 'ar'),
         # Czech
-        ("Za 12,5 vteřiny.", 'Za dvanáct celá pět vteřiny.', 'cs'),
-        ("Bylo tam 50 vojáků.", 'Bylo tam padesát vojáků.', 'cs'),
-        ("To bude stát 20€ pane.", 'To bude stát dvacet euro pane.', 'cs'),
-        ("To bude 20.15€ pane.", 'To bude dvacet euro, patnáct centů pane.', 'cs'),
+        ("Za 12,5 vteřiny.", "Za dvanáct celá pět vteřiny.", "cs"),
+        ("Bylo tam 50 vojáků.", "Bylo tam padesát vojáků.", "cs"),
+        ("To bude stát 20€ pane.", "To bude stát dvacet euro pane.", "cs"),
+        ("To bude 20.15€ pane.", "To bude dvacet euro, patnáct centů pane.", "cs"),
         # Russian
-        ("Через 12.5 секунды.", 'Через двенадцать запятая пять секунды.', 'ru'),
-        ("Там было 50 солдат.", 'Там было пятьдесят солдат.', 'ru'),
-        ("Это будет 20.15€ сэр.", 'Это будет двадцать евро, пятнадцать центов сэр.', 'ru'),
-        ("Это будет стоить 20€ господин.", 'Это будет стоить двадцать евро господин.', 'ru'),
+        ("Через 12.5 секунды.", "Через двенадцать запятая пять секунды.", "ru"),
+        ("Там было 50 солдат.", "Там было пятьдесят солдат.", "ru"),
+        ("Это будет 20.15€ сэр.", "Это будет двадцать евро, пятнадцать центов сэр.", "ru"),
+        ("Это будет стоить 20€ господин.", "Это будет стоить двадцать евро господин.", "ru"),
         # Dutch
-        ("In 12,5 seconden.", 'In twaalf komma vijf seconden.', 'nl'),
-        ("Er waren 50 soldaten.", 'Er waren vijftig soldaten.', 'nl'),
-        ("Dat wordt dan $20 meneer.", 'Dat wordt dan twintig dollar meneer.', 'nl'),
-        ("Dat wordt dan 20€ meneer.", 'Dat wordt dan twintig euro meneer.', 'nl'),
+        ("In 12,5 seconden.", "In twaalf komma vijf seconden.", "nl"),
+        ("Er waren 50 soldaten.", "Er waren vijftig soldaten.", "nl"),
+        ("Dat wordt dan $20 meneer.", "Dat wordt dan twintig dollar meneer.", "nl"),
+        ("Dat wordt dan 20€ meneer.", "Dat wordt dan twintig euro meneer.", "nl"),
         # Chinese (Simplified)
-        ("在12.5秒内", '在十二点五秒内', 'zh'),
-        ("有50名士兵", '有五十名士兵', 'zh'),
+        ("在12.5秒内", "在十二点五秒内", "zh"),
+        ("有50名士兵", "有五十名士兵", "zh"),
         # ("那将是$20先生", '那将是二十美元先生', 'zh'), currency doesn't work
         # ("那将是20€先生", '那将是二十欧元先生', 'zh'),
         # Turkish
         # ("12,5 saniye içinde.", 'On iki virgül beş saniye içinde.', 'tr'), # decimal doesn't work for TR
-        ("50 asker vardı.", 'elli asker vardı.', 'tr'),
-        ("Bu 1. test", 'Bu birinci test', 'tr'),
+        ("50 asker vardı.", "elli asker vardı.", "tr"),
+        ("Bu 1. test", "Bu birinci test", "tr"),
         # ("Bu 100.000,5.", 'Bu yüz bin virgül beş.', 'tr'),
         # Hungarian
-        ("12,5 másodperc alatt.", 'tizenkettő egész öt tized másodperc alatt.', 'hu'),
-        ("50 katona volt.", 'ötven katona volt.', 'hu'),
-        ("Ez az 1. teszt", 'Ez az első teszt', 'hu'),
+        ("12,5 másodperc alatt.", "tizenkettő egész öt tized másodperc alatt.", "hu"),
+        ("50 katona volt.", "ötven katona volt.", "hu"),
+        ("Ez az 1. teszt", "Ez az első teszt", "hu"),
         # Korean
-        ("12.5 초 안에.", '십이 점 다섯 초 안에.', 'ko'),
-        ("50 명의 병사가 있었다.", '오십 명의 병사가 있었다.', 'ko'),
-        ("이것은 1 번째 테스트입니다", '이것은 첫 번째 테스트입니다', 'ko'),
+        ("12.5 초 안에.", "십이 점 다섯 초 안에.", "ko"),
+        ("50 명의 병사가 있었다.", "오십 명의 병사가 있었다.", "ko"),
+        ("이것은 1 번째 테스트입니다", "이것은 첫 번째 테스트입니다", "ko"),
     ]
     for a, b, lang in test_cases:
         out = expand_numbers_multilingual(a, lang=lang)
         assert out == b, f"'{out}' vs '{b}'"
 
+
 def test_abbreviations_multilingual():
     test_cases = [
         # English
-        ("Hello Mr. Smith.", 'Hello mister Smith.', 'en'),
-        ("Dr. Jones is here.", 'doctor Jones is here.', 'en'),
+        ("Hello Mr. Smith.", "Hello mister Smith.", "en"),
+        ("Dr. Jones is here.", "doctor Jones is here.", "en"),
         # Spanish
-        ("Hola Sr. Garcia.", 'Hola señor Garcia.', 'es'),
-        ("La Dra. Martinez es muy buena.", 'La doctora Martinez es muy buena.', 'es'),
+        ("Hola Sr. Garcia.", "Hola señor Garcia.", "es"),
+        ("La Dra. Martinez es muy buena.", "La doctora Martinez es muy buena.", "es"),
         # French
-        ("Bonjour Mr. Dupond.", 'Bonjour monsieur Dupond.', 'fr'),
-        ("Mme. Moreau est absente aujourd'hui.", 'madame Moreau est absente aujourd\'hui.', 'fr'),
+        ("Bonjour Mr. Dupond.", "Bonjour monsieur Dupond.", "fr"),
+        ("Mme. Moreau est absente aujourd'hui.", "madame Moreau est absente aujourd'hui.", "fr"),
         # German
-        ("Frau Dr. Müller ist sehr klug.", 'Frau doktor Müller ist sehr klug.', 'de'),
+        ("Frau Dr. Müller ist sehr klug.", "Frau doktor Müller ist sehr klug.", "de"),
         # Portuguese
-        ("Olá Sr. Silva.", 'Olá senhor Silva.', 'pt'),
-        ("Dra. Costa, você está disponível?", 'doutora Costa, você está disponível?', 'pt'),
+        ("Olá Sr. Silva.", "Olá senhor Silva.", "pt"),
+        ("Dra. Costa, você está disponível?", "doutora Costa, você está disponível?", "pt"),
         # Italian
-        ("Buongiorno, Sig. Rossi.", 'Buongiorno, signore Rossi.', 'it'),
-        #("Sig.ra Bianchi, posso aiutarti?", 'signora Bianchi, posso aiutarti?', 'it'), # Issue with matching that pattern
+        ("Buongiorno, Sig. Rossi.", "Buongiorno, signore Rossi.", "it"),
+        # ("Sig.ra Bianchi, posso aiutarti?", 'signora Bianchi, posso aiutarti?', 'it'), # Issue with matching that pattern
         # Polish
-        ("Dzień dobry, P. Kowalski.", 'Dzień dobry, pani Kowalski.', 'pl'),
-        ("M. Nowak, czy mogę zadać pytanie?", 'pan Nowak, czy mogę zadać pytanie?', 'pl'),
+        ("Dzień dobry, P. Kowalski.", "Dzień dobry, pani Kowalski.", "pl"),
+        ("M. Nowak, czy mogę zadać pytanie?", "pan Nowak, czy mogę zadać pytanie?", "pl"),
         # Czech
-        ("P. Novák", "pan Novák", 'cs'),
-        ("Dr. Vojtěch", "doktor Vojtěch", 'cs'),
+        ("P. Novák", "pan Novák", "cs"),
+        ("Dr. Vojtěch", "doktor Vojtěch", "cs"),
         # Dutch
-        ("Dhr. Jansen", "de heer Jansen", 'nl'),
-        ("Mevr. de Vries", "mevrouw de Vries", 'nl'),
+        ("Dhr. Jansen", "de heer Jansen", "nl"),
+        ("Mevr. de Vries", "mevrouw de Vries", "nl"),
         # Russian
-        ("Здравствуйте Г-н Иванов.", "Здравствуйте господин Иванов.", 'ru'),
-        ("Д-р Смирнов здесь, чтобы увидеть вас.", "доктор Смирнов здесь, чтобы увидеть вас.", 'ru'),
+        ("Здравствуйте Г-н Иванов.", "Здравствуйте господин Иванов.", "ru"),
+        ("Д-р Смирнов здесь, чтобы увидеть вас.", "доктор Смирнов здесь, чтобы увидеть вас.", "ru"),
         # Turkish
-        ("Merhaba B. Yılmaz.", "Merhaba bay Yılmaz.", 'tr'),
-        ("Dr. Ayşe burada.", "doktor Ayşe burada.", 'tr'),
+        ("Merhaba B. Yılmaz.", "Merhaba bay Yılmaz.", "tr"),
+        ("Dr. Ayşe burada.", "doktor Ayşe burada.", "tr"),
         # Hungarian
-        ("Dr. Szabó itt van.", "doktor Szabó itt van.", 'hu'),
+        ("Dr. Szabó itt van.", "doktor Szabó itt van.", "hu"),
     ]
 
     for a, b, lang in test_cases:
         out = expand_abbreviations_multilingual(a, lang=lang)
         assert out == b, f"'{out}' vs '{b}'"
 
+
 def test_symbols_multilingual():
     test_cases = [
         ("I have 14% battery", "I have 14 percent battery", "en"),
@@ -763,14 +736,15 @@ def test_symbols_multilingual():
         ("我的电量为 14%", "我的电量为 14 百分之", "zh"),
         ("Pilim %14 dolu.", "Pilim yüzde 14 dolu.", "tr"),
         ("Az akkumulátorom töltöttsége 14%", "Az akkumulátorom töltöttsége 14 százalék", "hu"),
-        ("배터리 잔량이 14%입니다.", "배터리 잔량이 14 퍼센트입니다.", "ko")
+        ("배터리 잔량이 14%입니다.", "배터리 잔량이 14 퍼센트입니다.", "ko"),
     ]
 
     for a, b, lang in test_cases:
         out = expand_symbols_multilingual(a, lang=lang)
         assert out == b, f"'{out}' vs '{b}'"
 
+
 if __name__ == "__main__":
     test_expand_numbers_multilingual()
     test_abbreviations_multilingual()
-    test_symbols_multilingual()
+    test_symbols_multilingual()

TTS/tts/layers/xtts/trainer/dataset.py

@@ -149,7 +149,11 @@ class XTTSDataset(torch.utils.data.Dataset):
             # if use masking do not use cond_len
             cond_len = torch.nan
         else:
-            ref_sample = sample["reference_path"] if "reference_path" in sample and sample["reference_path"] is not None else audiopath
+            ref_sample = (
+                sample["reference_path"]
+                if "reference_path" in sample and sample["reference_path"] is not None
+                else audiopath
+            )
             cond, cond_len, _ = get_prompt_slice(
                 ref_sample, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval
             )
@@ -210,7 +214,9 @@ class XTTSDataset(torch.utils.data.Dataset):
             "wav_lengths": torch.tensor(wav.shape[-1], dtype=torch.long),
             "filenames": audiopath,
             "conditioning": cond.unsqueeze(1),
-            "cond_lens": torch.tensor(cond_len, dtype=torch.long) if cond_len is not torch.nan else torch.tensor([cond_len]),
+            "cond_lens": torch.tensor(cond_len, dtype=torch.long)
+            if cond_len is not torch.nan
+            else torch.tensor([cond_len]),
             "cond_idxs": torch.tensor(cond_idxs) if cond_idxs is not torch.nan else torch.tensor([cond_idxs]),
         }
         return res

TTS/tts/layers/xtts/trainer/gpt_trainer.py

@@ -213,7 +213,13 @@ class GPTTrainer(BaseTTS):
         cond_lens: long tensor, (b,)
         """
         losses = self.xtts.gpt(
-            text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels=cond_mels, cond_idxs=cond_idxs, cond_lens=cond_lens,
+            text_inputs,
+            text_lengths,
+            audio_codes,
+            wav_lengths,
+            cond_mels=cond_mels,
+            cond_idxs=cond_idxs,
+            cond_lens=cond_lens,
         )
         return losses
 
@@ -227,7 +233,12 @@ class GPTTrainer(BaseTTS):
             print(" | > Synthesizing test sentences.")
             for idx, s_info in enumerate(self.config.test_sentences):
                 wav = self.xtts.synthesize(
-                    s_info["text"], self.config, s_info["speaker_wav"], s_info["language"], gpt_cond_len=3, decoder="ne_hifigan"
+                    s_info["text"],
+                    self.config,
+                    s_info["speaker_wav"],
+                    s_info["language"],
+                    gpt_cond_len=3,
+                    decoder="ne_hifigan",
                 )["wav"]
                 test_audios["{}-audio".format(idx)] = wav
 
@@ -295,7 +306,9 @@ class GPTTrainer(BaseTTS):
         cond_idxs = batch["cond_idxs"]
         cond_lens = batch["cond_lens"]
 
-        loss_text, loss_mel, _ = self.forward(text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens)
+        loss_text, loss_mel, _ = self.forward(
+            text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens
+        )
         loss_dict["loss_text_ce"] = loss_text * self.args.gpt_loss_text_ce_weight
         loss_dict["loss_mel_ce"] = loss_mel * self.args.gpt_loss_mel_ce_weight
         loss_dict["loss"] = loss_dict["loss_text_ce"] + loss_dict["loss_mel_ce"]

TTS/tts/models/xtts.py

@@ -381,7 +381,8 @@ class Xtts(BaseTTS):
             audio_22k = torchaudio.functional.resample(audio, sr, 22050)
         audio_22k = audio_22k[:, : 22050 * length]
         if self.args.gpt_use_perceiver_resampler:
-            mel = wav_to_mel_cloning(audio_22k,
+            mel = wav_to_mel_cloning(
+                audio_22k,
                 mel_norms=self.mel_stats.cpu(),
                 n_fft=2048,
                 hop_length=256,
@@ -391,10 +392,11 @@ class Xtts(BaseTTS):
                 sample_rate=22050,
                 f_min=0,
                 f_max=8000,
-                n_mels=80
+                n_mels=80,
             )
         else:
-            mel = wav_to_mel_cloning(audio_22k,
+            mel = wav_to_mel_cloning(
+                audio_22k,
                 mel_norms=self.mel_stats.cpu(),
                 n_fft=4096,
                 hop_length=1024,
@@ -404,7 +406,7 @@ class Xtts(BaseTTS):
                 sample_rate=22050,
                 f_min=0,
                 f_max=8000,
-                n_mels=80
+                n_mels=80,
             )
         cond_latent = self.gpt.get_style_emb(mel.to(self.device))
         return cond_latent.transpose(1, 2)
@@ -598,7 +600,10 @@ class Xtts(BaseTTS):
             Sample rate is 24kHz.
         """
         (gpt_cond_latent, diffusion_conditioning, speaker_embedding) = self.get_conditioning_latents(
-            audio_path=ref_audio_path, gpt_cond_len=gpt_cond_len, max_ref_length=max_ref_len, sound_norm_refs=sound_norm_refs
+            audio_path=ref_audio_path,
+            gpt_cond_len=gpt_cond_len,
+            max_ref_length=max_ref_len,
+            sound_norm_refs=sound_norm_refs,
         )
 
         return self.inference(
@@ -728,7 +733,12 @@ class Xtts(BaseTTS):
                 )
                 wav = self.vocoder.inference(mel)
 
-        return {"wav": wav.cpu().numpy().squeeze(), "gpt_latents": gpt_latents, "speaker_embedding": speaker_embedding, "diffusion_conditioning": diffusion_conditioning}
+        return {
+            "wav": wav.cpu().numpy().squeeze(),
+            "gpt_latents": gpt_latents,
+            "speaker_embedding": speaker_embedding,
+            "diffusion_conditioning": diffusion_conditioning,
+        }
 
     def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
         """Handle chunk formatting in streaming mode"""

recipes/ljspeech/xtts_v2/train_gpt_xtts.py

@@ -61,13 +61,15 @@ TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/voca
 XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/model.pth"
 
 # XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
-TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, TOKENIZER_FILE_LINK.split("/")[-1]) # vocab.json file
-XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, XTTS_CHECKPOINT_LINK.split("/")[-1]) # model.pth file
+TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, TOKENIZER_FILE_LINK.split("/")[-1])  # vocab.json file
+XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, XTTS_CHECKPOINT_LINK.split("/")[-1])  # model.pth file
 
 # download XTTS v2.0 files if needed
 if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
     print(" > Downloading XTTS v2.0 files!")
-    ModelManager._download_model_files([TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
+    ModelManager._download_model_files(
+        [TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
+    )
 
 
 # Training sentences generations
@@ -92,7 +94,7 @@ def main():
         gpt_num_audio_tokens=8194,
         gpt_start_audio_token=8192,
         gpt_stop_audio_token=8193,
-        use_ne_hifigan=True, # if it is true it will keep the non-enhanced keys on the output checkpoint
+        use_ne_hifigan=True,  # if it is true it will keep the non-enhanced keys on the output checkpoint
         gpt_use_masking_gt_prompt_approach=True,
         gpt_use_perceiver_resampler=True,
     )

tests/api_tests/test_synthesize_api.py

@@ -22,4 +22,8 @@ def test_synthesize():
     )
 
     # test pipe_out command
+<<<<<<< HEAD
     run_cli(f'tts --text "test." --pipe_out --out_path "{output_path}" | aplay')
+=======
+    run_cli('tts --text "test." --pipe_out ' f'--out_path "{output_path}" | aplay')
+>>>>>>> Make style