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ea2601f | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | """Abstract base class and shared types for cry classifiers."""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
import numpy as np
# ── Label sets ────────────────────────────────────────────────────────────────
LABELS_5CLASS = ["belly_pain", "burping", "discomfort", "hungry", "tired"]
LABELS_8CLASS = [
"hungry", "burping", "scared", "belly_pain",
"discomfort", "cold_hot", "lonely", "tired",
]
LABEL_EMOJI: dict[str, str] = {
"belly_pain": "😣",
"burping": "🫧",
"discomfort": "😖",
"hungry": "🍼",
"tired": "😴",
"scared": "😨",
"cold_hot": "🌡️",
"lonely": "🥺",
"cry": "✅",
"not_cry": "❌",
}
# What each cry label means — shown in the terminal legend
LABEL_MEANING: dict[str, str] = {
"belly_pain": "Baby has stomach cramps or gas — try gentle tummy massage or bicycle legs",
"burping": "Baby needs to burp — hold upright and pat back gently",
"discomfort": "General discomfort — check diaper, clothing, temperature, or position",
"hungry": "Baby is hungry — time to feed",
"tired": "Baby is sleepy or overtired — needs soothing and rest",
"scared": "Baby is startled or frightened — comfort and hold close",
"cold_hot": "Baby is too cold or too warm — adjust clothing or room temperature",
"lonely": "Baby wants attention or closeness — pick up and cuddle",
}
def display_label(raw: str) -> str:
"""Return an emoji-prefixed human-friendly label."""
emoji = LABEL_EMOJI.get(raw, "❓")
name = raw.replace("_", " ").title()
return f"{emoji} {name}"
# ── Prediction dataclass ─────────────────────────────────────────────────────
@dataclass
class CryPrediction:
model_name: str
label: str # raw label
display_label: str # emoji + human name
confidence: float # 0.0 – 1.0
latency_ms: float # inference time in ms
error: str | None = None
# ── Abstract classifier ──────────────────────────────────────────────────────
class CryClassifier(ABC):
name: str = "unnamed"
description: str = ""
def __init__(self) -> None:
self._loaded = False
@abstractmethod
def load(self) -> None:
"""Download weights (if needed) and initialize the model."""
@abstractmethod
def predict(self, audio_np: np.ndarray, sr: int) -> CryPrediction:
"""Run inference on a single audio window and return a prediction."""
def is_loaded(self) -> bool:
return self._loaded
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