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configuration_dualturn.py

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+from transformers import PretrainedConfig
+
+
+class DualTurnConfig(PretrainedConfig):
+    """Configuration for DualTurnModel.
+
+    The backbone is a two-stream transformer (TurnTakingModel) that encodes
+    dual-channel audio via Mimi and predicts per-frame turn-taking signals.
+
+    The endpoint classifier is a lightweight sklearn model (logistic regression
+    or gradient boosting) that converts the 10 backbone signals at a VAD-offset
+    anchor into P(ST) — probability the user has finished their turn.
+    """
+
+    model_type = "dualturn"
+
+    def __init__(
+        self,
+        # ── Backbone ──────────────────────────────────────────────────────
+        backbone_input_mode: str = "continuous",    # "continuous" | "discrete"
+        mimi_sample_rate: int = 24_000,             # Mimi encoder input SR
+        mimi_frame_rate: float = 12.5,              # frames per second
+        mimi_frame_ms: float = 80.0,                # ms per frame
+        # ── Signals ───────────────────────────────────────────────────────
+        signal_keys: list = None,
+        # ── Endpoint classifier ───────────────────────────────────────────
+        st_threshold: float = 0.30,
+        vad_edge_threshold: float = 0.50,           # vad_user edge for anchor
+        agent_voice_min: float = 0.15,              # min agent VAD to consider voicing
+        fvad_alpha_short: float = 0.3,              # Silero smoothing (fast)
+        fvad_alpha_long: float = 0.7,               # Silero smoothing (slow)
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.backbone_input_mode = backbone_input_mode
+        self.mimi_sample_rate    = mimi_sample_rate
+        self.mimi_frame_rate     = mimi_frame_rate
+        self.mimi_frame_ms       = mimi_frame_ms
+        self.signal_keys = signal_keys or [
+            "vad_user", "vad_agent",
+            "eot_user", "eot_agent",
+            "bot_user", "bot_agent",
+            "fvad_user_short", "fvad_user_long",
+            "fvad_agent_short", "fvad_agent_long",
+        ]
+        self.st_threshold        = st_threshold
+        self.vad_edge_threshold  = vad_edge_threshold
+        self.agent_voice_min     = agent_voice_min
+        self.fvad_alpha_short    = fvad_alpha_short
+        self.fvad_alpha_long     = fvad_alpha_long