feat(models): chatterbox-turbo and chatterbox-mtl adapters

server/models/chatterbox_mtl.py

@@ -0,0 +1,96 @@
+"""Chatterbox Multilingual adapter (23 languages)."""
+from __future__ import annotations
+
+import io
+from typing import Any, ClassVar
+
+import soundfile as sf
+
+from server.schemas import Lang, ParamSpec
+
+
+_MTL_LANGS: list[Lang] = [
+    Lang(code="ar", label="Arabic"),
+    Lang(code="da", label="Danish"),
+    Lang(code="de", label="German"),
+    Lang(code="el", label="Greek"),
+    Lang(code="en", label="English"),
+    Lang(code="es", label="Spanish"),
+    Lang(code="fi", label="Finnish"),
+    Lang(code="fr", label="French"),
+    Lang(code="he", label="Hebrew"),
+    Lang(code="hi", label="Hindi"),
+    Lang(code="it", label="Italian"),
+    Lang(code="ja", label="Japanese"),
+    Lang(code="ko", label="Korean"),
+    Lang(code="ms", label="Malay"),
+    Lang(code="nl", label="Dutch"),
+    Lang(code="no", label="Norwegian"),
+    Lang(code="pl", label="Polish"),
+    Lang(code="pt", label="Portuguese"),
+    Lang(code="ru", label="Russian"),
+    Lang(code="sv", label="Swedish"),
+    Lang(code="sw", label="Swahili"),
+    Lang(code="tr", label="Turkish"),
+    Lang(code="zh", label="Chinese"),
+]
+
+
+class Adapter:
+    id: ClassVar[str] = "chatterbox-mtl"
+    label: ClassVar[str] = "Chatterbox Multilingual"
+    description: ClassVar[str] = (
+        "23-language voice cloning. Pick a language at generate time."
+    )
+    languages: ClassVar[list[Lang]] = _MTL_LANGS
+    paralinguistic_tags: ClassVar[list[str]] = []  # TBD on first manual run
+    supports_voice_clone: ClassVar[bool] = True
+    params: ClassVar[list[ParamSpec]] = [
+        ParamSpec(name="exaggeration", label="Exaggeration", type="float",
+                  default=0.5, min=0.0, max=2.0, step=0.05),
+        ParamSpec(name="cfg_weight", label="CFG weight", type="float",
+                  default=0.5, min=0.0, max=1.0, step=0.05),
+    ]
+
+    def __init__(self, device: str) -> None:
+        self.device = device
+        self._model = None
+
+    def load(self) -> None:
+        from chatterbox.mtl_tts import ChatterboxMultilingualTTS
+
+        self._model = ChatterboxMultilingualTTS.from_pretrained(device=self.device)
+
+    def unload(self) -> None:
+        self._model = None
+
+    def generate(
+        self,
+        text: str,
+        reference_wav_path: str | None,
+        language: str | None,
+        params: dict[str, Any],
+    ) -> tuple[bytes, int]:
+        if self._model is None:
+            raise RuntimeError("model not loaded")
+        if not language:
+            raise ValueError("language is required for chatterbox-mtl")
+        wav = self._model.generate(
+            text,
+            language_id=language,
+            audio_prompt_path=reference_wav_path,
+            exaggeration=float(params.get("exaggeration", 0.5)),
+            cfg_weight=float(params.get("cfg_weight", 0.5)),
+        )
+        import numpy as np
+        import torch
+
+        if hasattr(wav, "detach"):
+            wav = wav.detach().cpu().numpy()
+        if isinstance(wav, torch.Tensor):  # pragma: no cover
+            wav = wav.numpy()
+        arr = np.asarray(wav).squeeze()
+        sr = getattr(self._model, "sr", 24000)
+        buf = io.BytesIO()
+        sf.write(buf, arr, sr, format="WAV", subtype="PCM_16")
+        return buf.getvalue(), sr

server/models/chatterbox_turbo.py

@@ -0,0 +1,66 @@
+"""Chatterbox Turbo adapter — fast English with paralinguistic tags."""
+from __future__ import annotations
+
+import io
+from typing import Any, ClassVar
+
+import soundfile as sf
+
+from server.schemas import Lang, ParamSpec
+
+
+class Adapter:
+    id: ClassVar[str] = "chatterbox-turbo"
+    label: ClassVar[str] = "Chatterbox Turbo"
+    description: ClassVar[str] = (
+        "Faster, lower-VRAM English variant. Supports [laugh], [cough], [chuckle] tags."
+    )
+    languages: ClassVar[list[Lang]] = [Lang(code="en", label="English")]
+    paralinguistic_tags: ClassVar[list[str]] = ["[laugh]", "[cough]", "[chuckle]"]
+    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),
+    ]
+
+    def __init__(self, device: str) -> None:
+        self.device = device
+        self._model = None
+
+    def load(self) -> None:
+        from chatterbox.tts_turbo import ChatterboxTurboTTS
+
+        self._model = ChatterboxTurboTTS.from_pretrained(device=self.device)
+
+    def unload(self) -> None:
+        self._model = None
+
+    def generate(
+        self,
+        text: str,
+        reference_wav_path: str | None,
+        language: str | None,
+        params: dict[str, Any],
+    ) -> tuple[bytes, int]:
+        if self._model is None:
+            raise RuntimeError("model not loaded")
+        wav = self._model.generate(
+            text,
+            audio_prompt_path=reference_wav_path,
+            cfg_weight=float(params.get("cfg_weight", 0.5)),
+            temperature=float(params.get("temperature", 0.8)),
+        )
+        import numpy as np
+        import torch
+
+        if hasattr(wav, "detach"):
+            wav = wav.detach().cpu().numpy()
+        if isinstance(wav, torch.Tensor):  # pragma: no cover
+            wav = wav.numpy()
+        arr = np.asarray(wav).squeeze()
+        sr = getattr(self._model, "sr", 24000)
+        buf = io.BytesIO()
+        sf.write(buf, arr, sr, format="WAV", subtype="PCM_16")
+        return buf.getvalue(), sr

tests/test_adapter_contract.py

@@ -8,6 +8,8 @@ from server.schemas import ParamSpec
 
 ADAPTER_MODULES = [
     "server.models.chatterbox_en",
+    "server.models.chatterbox_turbo",
+    "server.models.chatterbox_mtl",
 ]