v0.18.2

TTS/.models.json

@@ -18,12 +18,12 @@
                 "xtts_v1.1": {
                     "description": "XTTS-v1.1 by Coqui with 14 languages, cross-language voice cloning and reference leak fixed.",
                     "hf_url": [
-                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1/model.pth",
-                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1/config.json",
-                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1/vocab.json",
-                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1/hash.md5"
+                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/model.pth",
+                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/config.json",
+                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/vocab.json",
+                        "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/hash.md5"
                     ],
-                    "model_hash": "10163afc541dc86801b33d1f3217b456",
+                    "model_hash": "ae9e4b39e095fd5728fe7f7931ec66ad",
                     "default_vocoder": null,
                     "commit": "82910a63",
                     "license": "CPML",

TTS/VERSION

@@ -1 +1 @@
-0.18.0
+0.18.2

TTS/tts/configs/xtts_config.py

@@ -78,13 +78,13 @@ class XttsConfig(BaseTTSConfig):
     )
 
     # inference params
-    temperature: float = 0.2
+    temperature: float = 0.85
     length_penalty: float = 1.0
     repetition_penalty: float = 2.0
     top_k: int = 50
-    top_p: float = 0.8
+    top_p: float = 0.85
     cond_free_k: float = 2.0
     diffusion_temperature: float = 1.0
-    num_gpt_outputs: int = 16
+    num_gpt_outputs: int = 1
     decoder_iterations: int = 30
     decoder_sampler: str = "ddim"

TTS/tts/layers/xtts/tokenizer.py

@@ -6,7 +6,6 @@
 from tokenizers import Tokenizer
 
 import pypinyin
-import cutlet
 from num2words import num2words
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
 
@@ -484,10 +483,13 @@ def preprocess_text(self, txt, lang):
             if lang == "zh-cn":
                 txt = chinese_transliterate(txt)
         elif lang == "ja":
+            assert txt[:4] == "[ja]", "Japanese speech should start with the [ja] token."
+            txt = txt[4:]
             if self.katsu is None:
                 import cutlet
                 self.katsu = cutlet.Cutlet()
             txt = japanese_cleaners(txt, self.katsu)
+            txt = "[ja]" + txt
         else:
             raise NotImplementedError()
         return txt

TTS/tts/models/xtts.py

@@ -821,8 +821,6 @@ def load_checkpoint(
             self.tokenizer = VoiceBpeTokenizer(vocab_file=vocab_path)
 
         self.init_models()
-        if eval:
-            self.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache)
 
         checkpoint = load_fsspec(model_path, map_location=torch.device("cpu"))["model"]
         ignore_keys = ["diffusion_decoder", "vocoder"] if self.args.use_hifigan or self.args.use_ne_hifigan else []
@@ -831,7 +829,14 @@ def load_checkpoint(
         for key in list(checkpoint.keys()):
             if key.split(".")[0] in ignore_keys:
                 del checkpoint[key]
-        self.load_state_dict(checkpoint, strict=strict)
+
+        # deal with v1 and v1.1. V1 has the init_gpt_for_inference keys, v1.1 do not
+        try:
+            self.load_state_dict(checkpoint, strict=strict)
+        except:
+            if eval:
+                self.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache)
+            self.load_state_dict(checkpoint, strict=strict)
 
         if eval:
             if hasattr(self, "hifigan_decoder"): self.hifigan_decoder.eval()