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85f7f46 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | 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
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