feat(models): expand chatterbox-turbo params (seed, top_k, exaggeration, cfg_weight, etc.)

server/models/chatterbox_turbo.py

@@ -31,10 +31,42 @@ class Adapter:
     ]
     supports_voice_clone: ClassVar[bool] = True
     params: ClassVar[list[ParamSpec]] = [
-        ParamSpec(name="cfg_weight", label="CFG weight", type="float",
-                  default=0.5, min=0.0, max=1.0, step=0.05),
-        ParamSpec(name="temperature", label="Temperature", type="float",
-                  default=0.8, min=0.1, max=1.5, step=0.05),
+        ParamSpec(
+            name="temperature", label="Temperature", type="float",
+            default=0.8, min=0.1, max=1.5, step=0.05,
+            group="basic",
+        ),
+        ParamSpec(
+            name="top_p", label="Top p", type="float",
+            default=0.95, min=0.0, max=1.0, step=0.01,
+            group="basic",
+        ),
+        ParamSpec(
+            name="repetition_penalty", label="Repetition penalty", type="float",
+            default=1.2, min=1.0, max=3.0, step=0.05,
+            group="basic",
+        ),
+        ParamSpec(
+            name="seed", label="Seed", type="int",
+            default=-1, min=-1, step=1,
+            help="-1 draws a random seed each time.",
+            group="advanced",
+        ),
+        ParamSpec(
+            name="top_k", label="Top k", type="int",
+            default=1000, min=1, max=4000, step=1,
+            group="advanced",
+        ),
+        ParamSpec(
+            name="exaggeration", label="Exaggeration", type="float",
+            default=0.0, min=0.0, max=2.0, step=0.05,
+            group="advanced",
+        ),
+        ParamSpec(
+            name="cfg_weight", label="CFG weight", type="float",
+            default=0.0, min=0.0, max=1.0, step=0.05,
+            group="advanced",
+        ),
     ]
 
     def __init__(self, device: str) -> None:
@@ -62,8 +94,12 @@ class Adapter:
         wav = self._model.generate(
             text,
             audio_prompt_path=reference_wav_path,
-            cfg_weight=float(params.get("cfg_weight", 0.5)),
+            exaggeration=float(params.get("exaggeration", 0.0)),
+            cfg_weight=float(params.get("cfg_weight", 0.0)),
             temperature=float(params.get("temperature", 0.8)),
+            top_p=float(params.get("top_p", 0.95)),
+            top_k=int(params.get("top_k", 1000)),
+            repetition_penalty=float(params.get("repetition_penalty", 1.2)),
         )
         import numpy as np
         import torch