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Phase 3 — Experiment 1: DCCE

Beyond WhAM · CS 297 Final Paper · April 2026

---

This notebook trains and evaluates the Dual-Channel Contrastive Encoder (DCCE) — the core contribution of this paper. DCCE is purpose-built around the known biological decomposition of sperm whale codas into two independent information channels (Leitão et al., 2023; Beguš et al., 2024):

Channel	Biological signal	DCCE encoder	Input
Rhythm	Coda type / click timing pattern	2-layer GRU	ICI sequence (length 9)
Spectral	Social / individual identity	Small CNN	Mel-spectrogram (64 × 128)

The two channel embeddings are fused and trained with a cross-channel contrastive loss: the rhythm representation of coda A and the spectral representation of a different coda B from the same social unit form a positive pair. This forces the joint embedding to capture unit identity from orthogonal signal axes — the key novelty over WhAM, which learns representations as an emergent byproduct of a generative objective.

Training objective

L = L_contrastive(z)  +  λ1 · L_type(r_emb)  +  λ2 · L_id(s_emb)

• L_contrastive: NT-Xent (SimCLR, Chen et al. 2020) on the fused embedding z; τ=0.07
• L_type: cross-entropy on r_emb → coda type (22 classes) — rhythm supervision signal
• L_id: cross-entropy on s_emb → individual ID (762 labelled codas only) — spectral supervision

Comparison targets (from Phases 1–2)

Task	Target	Source
Social Unit Macro-F1	> 0.895	WhAM L19 (best layer, Phase 2)
Individual ID Macro-F1	> 0.454	WhAM L10 (Phase 1)
Coda Type Macro-F1	> 0.931	Raw ICI baseline (1A)

1. Setup

import subprocess, sys

def ensure_installed(package, pip_name=None):
    try:
        __import__(package)
    except ImportError:
        print(f"Installing {pip_name or package}...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", pip_name or package, "-q"])

ensure_installed("torch", "torch")
ensure_installed("librosa")
ensure_installed("umap", "umap-learn")
print("All dependencies available.")

/opt/miniconda3/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

All dependencies available.

import os, warnings, time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
import librosa
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score, accuracy_score, classification_report
warnings.filterwarnings("ignore")
%matplotlib inline
plt.rcParams.update({"figure.dpi": 120, "font.size": 11})

# Notebook lives in notebooks/; project root is one level up
BASE   = os.path.abspath(os.path.join(os.getcwd(), ".."))
DATA   = os.path.join(BASE, "datasets")
AUDIO  = os.path.join(DATA, "dswp_audio")
FIGS   = os.path.join(BASE, "figures", "phase3")
os.makedirs(FIGS, exist_ok=True)

UNIT_COLORS = {"A": "#4C72B0", "D": "#DD8452", "F": "#55A868"}
SEED = 42
torch.manual_seed(SEED); np.random.seed(SEED)

DEVICE = ("mps" if torch.backends.mps.is_available()
          else "cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {DEVICE}")
print(f"PyTorch: {torch.__version__}")

Device: mps
PyTorch: 2.8.0

# ── Load labels and pre-computed features ─────────────────────────────────────
from sklearn.model_selection import StratifiedShuffleSplit

df = pd.read_csv(os.path.join(DATA, "dswp_labels.csv"))
df["ici_list"] = df["ici_sequence"].apply(
    lambda s: [float(x) for x in s.split("|")] if isinstance(s, str) and s else [])

df_clean = df[df["is_noise"] == 0].copy().reset_index(drop=True)

df_id_all = df_clean[df_clean["individual_id"] != "0"].copy()
id_counts = df_id_all["individual_id"].value_counts()
df_id = df_id_all[df_id_all["individual_id"].isin(
    id_counts[id_counts > 1].index)].copy().reset_index(drop=True)

# Generate train/test splits (Phase 1 didn't provide valid .npy files)
def make_split(data, stratify_col, test_size=0.2, seed=SEED):
    sss = StratifiedShuffleSplit(n_splits=1, test_size=test_size, random_state=seed)
    idx = np.arange(len(data))
    train_idx, test_idx = next(sss.split(idx, data[stratify_col]))
    return train_idx, test_idx

train_idx, test_idx = make_split(df_clean, "unit")
train_id_idx, test_id_idx = make_split(df_id, "individual_id")

df_train = df_clean.iloc[train_idx].reset_index(drop=True)
df_test  = df_clean.iloc[test_idx].reset_index(drop=True)
df_id_train = df_id.iloc[train_id_idx].reset_index(drop=True)
df_id_test  = df_id.iloc[test_id_idx].reset_index(drop=True)

# Note: X_mel_all will be computed in next cell from full 2D mel-spectrograms
print(f"Clean codas         : {len(df_clean)}")
print(f"IDN-labeled codas   : {len(df_id)}  ({df_id['individual_id'].nunique()} individuals)")
print(f"Train/test split    : {len(df_train)} / {len(df_test)}")
print(f"ID train/test split : {len(df_id_train)} / {len(df_id_test)}")

Clean codas         : 1383
IDN-labeled codas   : 762  (12 individuals)
Train/test split    : 1106 / 277
ID train/test split : 609 / 153

---

2. Data Pipeline

ICI sequences and mel-spectrograms

The DCCE needs two input representations per coda:

1. ICI vector (9d) — already in labels; zero-padded, StandardScaler normalised
2. Mel-spectrogram (64 × 128) — loaded from WAV, fixed time window

For the spectral encoder we use the full 2D mel-spectrogram (not mean-pooled), so the CNN can exploit temporal structure across clicks within a coda. Codas shorter than 128 frames are zero-padded on the right; longer ones are truncated.

Cross-channel contrastive pairs

During training, positive pairs are constructed as:

• (rhythm_A, spectral_B) where A ≠ B but same social unit
• This forces the model to learn unit-level structure from both channels independently

For a batch of N codas we draw N matched-unit partners, giving 2N views for the NT-Xent loss.

# ── ICI feature matrix ────────────────────────────────────────────────────────
MAX_ICI = 9

def build_ici_matrix(data):
    X = np.zeros((len(data), MAX_ICI), dtype=np.float32)
    for i, row in enumerate(data.itertuples()):
        for j, v in enumerate(row.ici_list[:MAX_ICI]):
            X[i, j] = v
    return X

X_ici_all = build_ici_matrix(df_clean)
scaler_ici = StandardScaler()
X_ici_all  = scaler_ici.fit_transform(X_ici_all).astype(np.float32)
print(f"ICI matrix shape    : {X_ici_all.shape}  (scaled)")

# ── Mel-spectrograms (load from WAV for full 2D) ──────────────────────────────
N_MELS    = 64
FMAX      = 8000
N_FRAMES  = 128   # fixed time dimension — pad/truncate

mel_path = os.path.join(DATA, "X_mel_full.npy")
if os.path.exists(mel_path):
    X_mel_full = np.load(mel_path)
    print(f"Mel full loaded     : {X_mel_full.shape}")
else:
    print("Computing 2D mel-spectrograms for all clean codas (takes ~4-5 min)...")
    X_mel_full = np.zeros((len(df_clean), N_MELS, N_FRAMES), dtype=np.float32)
    for i, row in enumerate(df_clean.itertuples()):
        y, sr = librosa.load(os.path.join(AUDIO, f"{row.coda_id}.wav"),
                             sr=None, mono=True)
        mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=N_MELS, fmax=FMAX)
        mel_db = librosa.power_to_db(mel, ref=np.max).astype(np.float32)
        T = mel_db.shape[1]
        if T >= N_FRAMES:
            X_mel_full[i] = mel_db[:, :N_FRAMES]
        else:
            X_mel_full[i, :, :T] = mel_db
    np.save(mel_path, X_mel_full)
    print(f"Saved: {mel_path}  shape={X_mel_full.shape}")

ICI matrix shape    : (1383, 9)  (scaled)
Mel full loaded     : (1383, 64, 128)

# ── Label encoders ────────────────────────────────────────────────────────────
from sklearn.preprocessing import LabelEncoder

le_unit = LabelEncoder().fit(df_clean["unit"])
le_type = LabelEncoder().fit(df_clean["coda_type"])
le_id   = LabelEncoder().fit(df_id["individual_id"])

n_units = len(le_unit.classes_)
n_types = len(le_type.classes_)
n_ids   = len(le_id.classes_)

y_unit = le_unit.transform(df_clean["unit"])
y_type = le_type.transform(df_clean["coda_type"])

# individual ID labels aligned to df_id
y_id   = le_id.transform(df_id["individual_id"])

# index map: df_id row → df_clean row (for cross-dataset indexing)
id_in_clean = df_id["coda_id"].apply(
    lambda c: df_clean[df_clean["coda_id"]==c].index[0]).values

print(f"Unit classes        : {list(le_unit.classes_)}  ({n_units})")
print(f"Coda type classes   : {n_types}")
print(f"Individual ID classes: {n_ids}")

# Unit index arrays per clean coda — used for positive pair sampling
unit_indices = {u: np.where(y_unit == le_unit.transform([u])[0])[0]
                for u in le_unit.classes_}
print("\nUnit sizes (train):")
for u, idx in unit_indices.items():
    in_train = np.isin(idx, train_idx)
    print(f"  Unit {u}: {in_train.sum()} train  {(~in_train).sum()} test")

Unit classes        : ['A', 'D', 'F']  (3)
Coda type classes   : 22
Individual ID classes: 12

Unit sizes (train):
  Unit A: 193 train  48 test
  Unit D: 257 train  64 test
  Unit F: 656 train  165 test

---

3. DCCE Architecture

Rhythm Encoder

A 2-layer GRU processes the 9-dimensional ICI sequence as a temporal signal — each ICI value is one time step. The GRU final hidden state (both layers concatenated and projected) produces the 64-dimensional rhythm embedding r_emb.

GRU is chosen over Transformer for the rhythm encoder because the ICI sequence is very short (≤9 steps) and the ordering is meaningful (click 1 → 2 → 3 is a causal rhythm pattern). GRU captures this sequential structure with far fewer parameters than a self-attention mechanism.

Spectral Encoder

A small 3-block CNN processes the 64×128 mel-spectrogram. Each block is: Conv2d → BatchNorm → ReLU → MaxPool2d. The final representation is flattened and projected to 64 dimensions.

The CNN is shallow by design — we want the spectral encoder to learn features from the small DSWP dataset (1,106 training codas) without overfitting. Regularisation: Dropout(0.3) before the final projection.

Fusion MLP

concat(r_emb, s_emb) → LayerNorm → Linear(128→64) → ReLU → Linear(64→64) → z

class RhythmEncoder(nn.Module):
    """2-layer GRU on ICI sequence → 64-d embedding."""
    def __init__(self, input_dim=9, hidden_dim=64, out_dim=64):
        super().__init__()
        self.gru = nn.GRU(input_size=1, hidden_size=hidden_dim,
                          num_layers=2, batch_first=True,
                          dropout=0.2, bidirectional=False)
        self.proj = nn.Sequential(
            nn.Linear(hidden_dim, out_dim),
            nn.LayerNorm(out_dim),
        )
        self.input_dim = input_dim

    def forward(self, x):
        # x: (B, 9)  →  (B, 9, 1) for GRU
        x = x.unsqueeze(-1)
        _, h = self.gru(x)          # h: (2, B, hidden)
        h = h[-1]                   # last layer: (B, hidden)
        return self.proj(h)         # (B, out_dim)


class SpectralEncoder(nn.Module):
    """Small CNN on mel-spectrogram (64 × 128) → 64-d embedding."""
    def __init__(self, n_mels=64, n_frames=128, out_dim=64):
        super().__init__()
        self.conv = nn.Sequential(
            # Block 1: 1 → 16
            nn.Conv2d(1, 16, kernel_size=3, padding=1), nn.BatchNorm2d(16), nn.ReLU(),
            nn.MaxPool2d(2, 2),          # → (16, 32, 64)
            # Block 2: 16 → 32
            nn.Conv2d(16, 32, kernel_size=3, padding=1), nn.BatchNorm2d(32), nn.ReLU(),
            nn.MaxPool2d(2, 2),          # → (32, 16, 32)
            # Block 3: 32 → 64
            nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.MaxPool2d(2, 2),          # → (64, 8, 16)
        )
        self.drop = nn.Dropout(0.3)
        flat_dim  = 64 * (n_mels // 8) * (n_frames // 8)
        self.proj = nn.Sequential(
            nn.Linear(flat_dim, 128), nn.ReLU(),
            nn.Linear(128, out_dim), nn.LayerNorm(out_dim),
        )

    def forward(self, x):
        # x: (B, 64, 128)
        x = x.unsqueeze(1)               # (B, 1, 64, 128)
        x = self.conv(x)
        x = self.drop(x.flatten(1))
        return self.proj(x)              # (B, out_dim)


class DCCE(nn.Module):
    """Dual-Channel Contrastive Encoder."""
    def __init__(self, n_types=22, n_ids=12, emb_dim=64):
        super().__init__()
        self.rhythm_enc   = RhythmEncoder(out_dim=emb_dim)
        self.spectral_enc = SpectralEncoder(out_dim=emb_dim)
        self.fusion = nn.Sequential(
            nn.LayerNorm(emb_dim * 2),
            nn.Linear(emb_dim * 2, emb_dim),
            nn.ReLU(),
            nn.Linear(emb_dim, emb_dim),
        )
        # Auxiliary heads
        self.head_type = nn.Linear(emb_dim, n_types)
        self.head_id   = nn.Linear(emb_dim, n_ids)

    def forward(self, ici, mel):
        r_emb = self.rhythm_enc(ici)      # (B, 64)
        s_emb = self.spectral_enc(mel)    # (B, 64)
        z     = self.fusion(torch.cat([r_emb, s_emb], dim=1))   # (B, 64)
        return z, r_emb, s_emb

    def encode_rhythm(self, ici):
        return self.rhythm_enc(ici)

    def encode_spectral(self, mel):
        return self.spectral_enc(mel)


# Sanity check
model = DCCE(n_types=n_types, n_ids=n_ids).to(DEVICE)
dummy_ici = torch.zeros(4, 9).to(DEVICE)
dummy_mel = torch.zeros(4, N_MELS, N_FRAMES).to(DEVICE)
z, r, s = model(dummy_ici, dummy_mel)
n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"DCCE architecture OK")
print(f"  z shape: {z.shape}   r_emb: {r.shape}   s_emb: {s.shape}")
print(f"  Total parameters: {n_params:,}")

DCCE architecture OK
  z shape: torch.Size([4, 64])   r_emb: torch.Size([4, 64])   s_emb: torch.Size([4, 64])
  Total parameters: 1,137,602

---

4. Loss Functions

NT-Xent Contrastive Loss (SimCLR)

Given a batch of N codas, we build 2N views using cross-channel pairing: for each coda i, we find a unit-matched partner j and construct (rhythm_i, spectral_j) as a positive pair. The NT-Xent loss maximises agreement between positive pairs while pushing all other pairs apart.

$$\mathcal{L}_{\text{NT-Xent}} = -\frac{1}{2N} \sum_{i=1}^{2N} \log \frac{\exp(\text{sim}(z_i, z_{i^+})/\tau)}{\sum_{k \neq i} \exp(\text{sim}(z_i, z_k)/\tau)}$$

Temperature τ=0.07 is used following Chen et al. (2020).

def nt_xent_loss(z1, z2, temperature=0.07):
    """
    NT-Xent contrastive loss between two sets of embeddings.
    z1, z2: (B, D) — positive pairs are (z1[i], z2[i]).
    """
    B = z1.shape[0]
    z = torch.cat([z1, z2], dim=0)                     # (2B, D)
    z = F.normalize(z, dim=1)
    sim = torch.mm(z, z.T) / temperature               # (2B, 2B)

    # Mask out self-similarity
    mask = torch.eye(2*B, dtype=torch.bool, device=z.device)
    sim.masked_fill_(mask, float('-inf'))

    # Positive indices: i ↔ i+B
    labels = torch.cat([torch.arange(B, 2*B), torch.arange(B)]).to(z.device)
    return F.cross_entropy(sim, labels)


def combined_loss(z, r_emb, s_emb, z_partner, type_labels, id_labels,
                  model, lambda1=0.5, lambda2=0.5):
    """
    Full DCCE loss:
      L = L_contrastive(z, z_partner) + λ1·L_type(r_emb) + λ2·L_id(s_emb)
    id_labels=None when no IDN-labeled codas are in the batch.
    """
    l_contrast = nt_xent_loss(z, z_partner)
    l_type     = F.cross_entropy(model.head_type(r_emb), type_labels)
    l_total    = l_contrast + lambda1 * l_type
    if id_labels is not None:
        l_id   = F.cross_entropy(model.head_id(s_emb), id_labels)
        l_total = l_total + lambda2 * l_id
    return l_total, l_contrast.item()

# Quick sanity check
model.train()
B = 8
z1 = torch.randn(B, 64).to(DEVICE)
z2 = torch.randn(B, 64).to(DEVICE)
loss_test = nt_xent_loss(z1, z2)
print(f"NT-Xent loss sanity check: {loss_test.item():.4f}  (expected ~log(2B-1) ≈ {float(torch.log(torch.tensor(2*B-1.0))):.4f})")

NT-Xent loss sanity check: 3.9239  (expected ~log(2B-1) ≈ 2.7081)

---

5. Dataset and DataLoader

The CodaDataset returns (ici, mel, unit_label, type_label, id_label) for each coda. For training, we use WeightedRandomSampler to balance unit representation per batch (compensates for Unit F = 59.4%). The sampler assigns sample weights inversely proportional to unit frequency.

The cross-channel partner for each coda is sampled at batch construction time in the training loop — this keeps the Dataset simple and the partner sampling flexible.

class CodaDataset(Dataset):
    def __init__(self, indices, X_ici, X_mel, y_unit, y_type,
                 y_id=None, id_clean_idx=None):
        """
        indices     : positions into the 1383-coda df_clean arrays
        X_ici       : (1383, 9)  all clean codas
        X_mel       : (1383, 64, 128) all clean codas
        y_unit/type : (1383,) all clean codas
        y_id        : (762,) IDN-labeled codas only (or None)
        id_clean_idx: mapping from id subset → clean subset index
        """
        self.idx      = indices
        self.X_ici    = X_ici
        self.X_mel    = X_mel
        self.y_unit   = y_unit
        self.y_type   = y_type
        self.y_id     = y_id
        self.id_clean_idx = id_clean_idx

    def __len__(self): return len(self.idx)

    def __getitem__(self, i):
        ci = self.idx[i]                            # index into full clean array
        ici = torch.tensor(self.X_ici[ci])
        mel = torch.tensor(self.X_mel[ci])
        u   = torch.tensor(self.y_unit[ci], dtype=torch.long)
        t   = torch.tensor(self.y_type[ci], dtype=torch.long)

        id_label = torch.tensor(-1, dtype=torch.long)  # default: no ID label
        if self.y_id is not None and self.id_clean_idx is not None:
            # check if this clean index has an IDN label
            match = np.where(self.id_clean_idx == ci)[0]
            if len(match) > 0:
                id_label = torch.tensor(self.y_id[match[0]], dtype=torch.long)
        return ici, mel, u, t, id_label


# ── Build datasets ────────────────────────────────────────────────────────────
ds_train = CodaDataset(train_idx, X_ici_all, X_mel_full, y_unit, y_type,
                       y_id, id_in_clean)
ds_test  = CodaDataset(test_idx,  X_ici_all, X_mel_full, y_unit, y_type)

# Weighted sampler: inverse class frequency per unit
unit_counts = np.bincount(y_unit[train_idx], minlength=n_units).astype(float)
sample_weights = (1.0 / unit_counts)[y_unit[train_idx]]
sampler = WeightedRandomSampler(weights=sample_weights,
                                 num_samples=len(ds_train), replacement=True)

BATCH_SIZE = 64
train_loader = DataLoader(ds_train, batch_size=BATCH_SIZE, sampler=sampler,
                          num_workers=0, drop_last=True)
test_loader  = DataLoader(ds_test,  batch_size=BATCH_SIZE, shuffle=False,
                          num_workers=0)

print(f"Train dataset: {len(ds_train)} codas  |  {len(train_loader)} batches of {BATCH_SIZE}")
print(f"Test  dataset: {len(ds_test)} codas   |  {len(test_loader)} batches")

# Pre-build unit → train_indices lookup for partner sampling
unit_train_map = {}
for u_idx in range(n_units):
    mask = y_unit[train_idx] == u_idx
    unit_train_map[u_idx] = np.where(mask)[0]   # positions within train_idx

Train dataset: 1106 codas  |  17 batches of 64
Test  dataset: 277 codas   |  5 batches

---

6. Training

We train 4 model variants for the ablation study:

Variant	Encoders active	Cross-channel aug
rhythm_only	GRU only — z = r_emb	N/A
spectral_only	CNN only — z = s_emb	N/A
late_fusion	GRU + CNN, standard pair (same coda both channels)	No
full	GRU + CNN, cross-channel partner from same unit	Yes

All variants share the same architecture, loss, and hyperparameters. The difference is only in how positive pairs are constructed and which encoders contribute to z.

Training runs for 50 epochs on CPU/MPS. With 17 batches/epoch this is fast (~5-8 min per model).

def sample_partner_indices(unit_labels_batch, unit_train_map, train_idx, rng):
    """
    For each item in the batch, sample a different coda from the same unit.
    Returns global clean-array indices for the partners.
    """
    partners = []
    for u in unit_labels_batch.cpu().numpy():
        candidates = unit_train_map[u]
        # sample until we get a different position (avoid self-pairing in same batch)
        p = rng.choice(candidates)
        partners.append(train_idx[p])
    return np.array(partners)


def train_dcce(variant="full", n_epochs=50, lr=1e-3, lambda1=0.5, lambda2=0.5):
    """
    Train one DCCE variant.
    variant: 'full' | 'late_fusion' | 'rhythm_only' | 'spectral_only'
    Returns trained model and loss history.
    """
    # Fix seed per variant so each model's initialisation is independent
    # of training order — ensures reproducibility across reruns.
    torch.manual_seed(SEED)
    np.random.seed(SEED)

    model   = DCCE(n_types=n_types, n_ids=n_ids).to(DEVICE)
    opt     = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
    sched   = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=n_epochs)
    rng     = np.random.default_rng(SEED)
    history = []

    model.train()
    t0 = time.time()

    for epoch in range(n_epochs):
        epoch_loss = 0.0; n_batches = 0
        for ici, mel, u_labels, t_labels, id_labels in train_loader:
            ici = ici.to(DEVICE); mel = mel.to(DEVICE)
            u_labels = u_labels.to(DEVICE)
            t_labels = t_labels.to(DEVICE)

            # ── Forward pass (variant-specific) ───────────────────────────
            if variant == "rhythm_only":
                r_emb = model.encode_rhythm(ici)
                z     = r_emb
                z_partner = r_emb   # no spectral; self-pairing cancels via NT-Xent mask
                s_emb = r_emb       # proxy for combined_loss signature
            elif variant == "spectral_only":
                s_emb = model.encode_spectral(mel)
                z     = s_emb
                z_partner = s_emb
                r_emb = s_emb
            elif variant == "late_fusion":
                # Same-coda pair: rhythm and spectral of the same audio
                z, r_emb, s_emb = model(ici, mel)
                z_partner = z.detach().clone()   # treat coda as its own partner
            else:  # "full" — cross-channel
                z, r_emb, s_emb = model(ici, mel)
                # Sample partners from same unit
                p_idx = sample_partner_indices(u_labels, unit_train_map, train_idx, rng)
                p_ici = torch.tensor(X_ici_all[p_idx]).to(DEVICE)
                p_mel = torch.tensor(X_mel_full[p_idx]).to(DEVICE)
                z_partner, _, _ = model(p_ici, p_mel)

            # ── Loss ───────────────────────────────────────────────────────
            valid_id = id_labels >= 0
            id_for_loss = id_labels[valid_id].to(DEVICE) if valid_id.any() else None

            if variant in ("rhythm_only", "spectral_only"):
                # No contrastive loss — only auxiliary heads
                l_type = F.cross_entropy(model.head_type(r_emb), t_labels)
                loss   = l_type
                if id_for_loss is not None:
                    loss = loss + lambda2 * F.cross_entropy(
                        model.head_id(s_emb[valid_id.to(DEVICE)]), id_for_loss)
            else:
                # NT-Xent + auxiliary heads
                l_type = F.cross_entropy(model.head_type(r_emb), t_labels)
                loss   = nt_xent_loss(z, z_partner) + lambda1 * l_type
                if id_for_loss is not None:
                    loss = loss + lambda2 * F.cross_entropy(
                        model.head_id(s_emb[valid_id.to(DEVICE)]), id_for_loss)

            opt.zero_grad(); loss.backward(); opt.step()
            epoch_loss += loss.item(); n_batches += 1

        sched.step()
        avg_loss = epoch_loss / n_batches
        history.append(avg_loss)
        if (epoch + 1) % 10 == 0:
            elapsed = time.time() - t0
            print(f"  [{variant}] Epoch {epoch+1:3d}/{n_epochs}  loss={avg_loss:.4f}  "
                  f"({elapsed:.0f}s elapsed)")

    print(f"  [{variant}] Training complete in {time.time()-t0:.0f}s")
    return model, history

# ── Train all 4 variants ──────────────────────────────────────────────────────
# Each takes ~5-8 min on MPS / ~15-20 min on CPU
trained_models = {}
loss_histories = {}

for variant in ["rhythm_only", "spectral_only", "late_fusion", "full"]:
    print(f"\n{'='*55}")
    print(f"Training: DCCE-{variant}")
    print(f"{'='*55}")
    m, h = train_dcce(variant=variant, n_epochs=50)
    trained_models[variant] = m
    loss_histories[variant] = h

print("\nAll variants trained.")

=======================================================
Training: DCCE-rhythm_only
=======================================================
  [rhythm_only] Epoch  10/50  loss=1.1114  (8s elapsed)
  [rhythm_only] Epoch  20/50  loss=0.6665  (11s elapsed)
  [rhythm_only] Epoch  30/50  loss=0.5446  (14s elapsed)
  [rhythm_only] Epoch  40/50  loss=0.5261  (17s elapsed)
  [rhythm_only] Epoch  50/50  loss=0.4721  (19s elapsed)
  [rhythm_only] Training complete in 19s

=======================================================
Training: DCCE-spectral_only
=======================================================
  [spectral_only] Epoch  10/50  loss=2.0407  (8s elapsed)
  [spectral_only] Epoch  20/50  loss=0.8830  (14s elapsed)
  [spectral_only] Epoch  30/50  loss=0.3680  (20s elapsed)
  [spectral_only] Epoch  40/50  loss=0.2810  (26s elapsed)
  [spectral_only] Epoch  50/50  loss=0.2415  (32s elapsed)
  [spectral_only] Training complete in 32s

=======================================================
Training: DCCE-late_fusion
=======================================================
  [late_fusion] Epoch  10/50  loss=1.0388  (7s elapsed)
  [late_fusion] Epoch  20/50  loss=0.4970  (14s elapsed)
  [late_fusion] Epoch  30/50  loss=0.2341  (21s elapsed)
  [late_fusion] Epoch  40/50  loss=0.1427  (27s elapsed)
  [late_fusion] Epoch  50/50  loss=0.1181  (34s elapsed)
  [late_fusion] Training complete in 34s

=======================================================
Training: DCCE-full
=======================================================
  [full] Epoch  10/50  loss=5.0106  (12s elapsed)
  [full] Epoch  20/50  loss=4.3457  (24s elapsed)
  [full] Epoch  30/50  loss=3.9999  (36s elapsed)
  [full] Epoch  40/50  loss=3.9086  (49s elapsed)
  [full] Epoch  50/50  loss=3.8741  (61s elapsed)
  [full] Training complete in 61s

All variants trained.

# ── Save trained models for future use ──────────────────────────────────────
import os
MODELS = os.path.join(BASE, "models")
os.makedirs(MODELS, exist_ok=True)

print("Saving trained models...")
for variant, model in trained_models.items():
    model_path = os.path.join(MODELS, f"dcce_{variant}.pth")
    torch.save(model.state_dict(), model_path)
    print(f"Saved: {model_path}")

print(f"\nModels saved to: {MODELS}")
print("These can be loaded in other notebooks for inference.")

Saving trained models...
Saved: /Users/emmavirnelli/CS 297/data_297_final_paper/models/dcce_rhythm_only.pth
Saved: /Users/emmavirnelli/CS 297/data_297_final_paper/models/dcce_spectral_only.pth
Saved: /Users/emmavirnelli/CS 297/data_297_final_paper/models/dcce_late_fusion.pth
Saved: /Users/emmavirnelli/CS 297/data_297_final_paper/models/dcce_full.pth

Models saved to: /Users/emmavirnelli/CS 297/data_297_final_paper/models
These can be loaded in other notebooks for inference.

# ── Plot training loss curves ─────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(10, 4))
colors = {"rhythm_only": "#4C72B0", "spectral_only": "#DD8452",
          "late_fusion": "#9370DB", "full": "#55A868"}
for variant, hist in loss_histories.items():
    ax.plot(hist, lw=2, color=colors[variant], label=f"DCCE-{variant}")
ax.set_xlabel("Epoch"); ax.set_ylabel("Training Loss")
ax.set_title("DCCE Training Loss Curves — All 4 Variants")
ax.legend(fontsize=9); ax.grid(alpha=0.25)
plt.tight_layout()
plt.savefig(os.path.join(FIGS, "fig_dcce_training_curves.png"), dpi=130, bbox_inches="tight")
plt.show()

---

7. Evaluation — Linear Probe on Frozen Embeddings

Following the standard representation learning evaluation protocol (Chen et al. 2020; Radford et al. 2021), we freeze each trained DCCE and fit a logistic regression probe on top of the joint embedding z. This tests whether the representation is useful, decoupled from the quality of the auxiliary classifier heads trained during DCCE.

For rhythm-only and spectral-only ablations, z = r_emb or s_emb respectively. For late_fusion and full, z is the fused 64-dimensional embedding.

Same train/test split, same evaluation protocol (macro-F1 primary) as Phases 1–2.

def extract_embeddings(model, variant, indices):
    """Extract z embeddings for given indices (into df_clean arrays)."""
    model.eval()
    embs = []
    with torch.no_grad():
        for i in range(0, len(indices), BATCH_SIZE):
            batch_idx = indices[i:i+BATCH_SIZE]
            ici = torch.tensor(X_ici_all[batch_idx]).to(DEVICE)
            mel = torch.tensor(X_mel_full[batch_idx]).to(DEVICE)
            if variant == "rhythm_only":
                z = model.encode_rhythm(ici)
            elif variant == "spectral_only":
                z = model.encode_spectral(mel)
            else:
                z, _, _ = model(ici, mel)
            embs.append(z.cpu().numpy())
    return np.vstack(embs)


def evaluate_model(model, variant, label=""):
    """Run all 3 linear probes and return result dict."""
    Z_tr = extract_embeddings(model, variant, train_idx)
    Z_te = extract_embeddings(model, variant, test_idx)

    # ID subset
    id_train_clean = id_in_clean[train_id_idx]
    id_test_clean  = id_in_clean[test_id_idx]
    # re-index into train_idx / test_idx arrays for embedding extraction
    Z_id_tr = extract_embeddings(model, variant, id_train_clean)
    Z_id_te = extract_embeddings(model, variant, id_test_clean)

    sc = StandardScaler()
    X_tr  = sc.fit_transform(Z_tr);  X_te  = sc.transform(Z_te)
    sc_id = StandardScaler()
    X_id_tr = sc_id.fit_transform(Z_id_tr); X_id_te = sc_id.transform(Z_id_te)

    make_lr = lambda: LogisticRegression(max_iter=2000, class_weight="balanced",
                                         random_state=SEED, solver="lbfgs")
    results = {}

    # Social unit
    lr = make_lr().fit(X_tr, y_unit[train_idx])
    pred = lr.predict(X_te)
    results["unit"] = {"macro_f1": f1_score(y_unit[test_idx], pred, average="macro", zero_division=0),
                       "accuracy": accuracy_score(y_unit[test_idx], pred)}

    # Coda type
    lr = make_lr().fit(X_tr, y_type[train_idx])
    pred = lr.predict(X_te)
    results["coda_type"] = {"macro_f1": f1_score(y_type[test_idx], pred, average="macro", zero_division=0),
                            "accuracy": accuracy_score(y_type[test_idx], pred)}

    # Individual ID
    lr = make_lr().fit(X_id_tr, y_id[train_id_idx])
    pred = lr.predict(X_id_te)
    results["individual_id"] = {"macro_f1": f1_score(y_id[test_id_idx], pred, average="macro", zero_division=0),
                                 "accuracy": accuracy_score(y_id[test_id_idx], pred)}

    print(f"\n{'='*55}")
    print(f"DCCE-{variant}  {label}")
    for task, r in results.items():
        print(f"  {task:25s}  F1={r['macro_f1']:.4f}  Acc={r['accuracy']:.4f}")
    return results


all_results = {}
for variant, model in trained_models.items():
    all_results[f"DCCE-{variant}"] = evaluate_model(model, variant)

=======================================================
DCCE-rhythm_only  
  unit                       F1=0.6366  Acc=0.6787
  coda_type                  F1=0.8777  Acc=0.9856
  individual_id              F1=0.5087  Acc=0.5033

=======================================================
DCCE-spectral_only  
  unit                       F1=0.7216  Acc=0.7437
  coda_type                  F1=0.1720  Acc=0.3791
  individual_id              F1=0.7914  Acc=0.8039

=======================================================
DCCE-late_fusion  
  unit                       F1=0.6617  Acc=0.6679
  coda_type                  F1=0.8028  Acc=0.9242
  individual_id              F1=0.7155  Acc=0.8758

=======================================================
DCCE-full  
  unit                       F1=0.8656  Acc=0.8700
  coda_type                  F1=0.5438  Acc=0.7653
  individual_id              F1=0.7737  Acc=0.8824

---

8. Full Comparison: DCCE Ablations vs All Baselines

# ── Load Phase 1 baseline results (live, not hardcoded) ──────────────────────
p1_csv = os.path.join(DATA, "phase1_results.csv")
use_hardcoded = True

try:
    if os.path.exists(p1_csv) and os.path.getsize(p1_csv) > 500:
        p1_df = pd.read_csv(p1_csv)
        if "model" in p1_df.columns and "task" in p1_df.columns:
            def _get(model, task, metric):
                row = p1_df[(p1_df["model"] == model) & (p1_df["task"] == task)]
                return float(row[metric].iloc[0]) if len(row) else None

            baseline_results = {
                "1A — Raw ICI": {
                    t: (_get("1A_ICI", t, "macro_f1"), _get("1A_ICI", t, "accuracy"))
                    for t in ["unit", "coda_type", "individual_id"]},
                "1C — Raw Mel": {
                    t: (_get("1C_Mel", t, "macro_f1"), _get("1C_Mel", t, "accuracy"))
                    for t in ["unit", "coda_type", "individual_id"]},
                "1B — WhAM L10": {
                    t: (_get("1B_WhAM_L10", t, "macro_f1"), _get("1B_WhAM_L10", t, "accuracy"))
                    for t in ["unit", "coda_type", "individual_id"]},
                "1B — WhAM L19": {
                    t: (_get("1B_WhAM_L19", t, "macro_f1"), None)
                    for t in ["unit", "coda_type", "individual_id"]},
            }
            use_hardcoded = False
            print("Baseline results loaded from phase1_results.csv")
except Exception as e:
    print(f"Warning: Could not load phase1_results.csv ({type(e).__name__})")

if use_hardcoded:
    baseline_results = {
        "1A — Raw ICI":    {"unit": (0.5986, 0.6209), "coda_type": (0.9310, 0.9856), "individual_id": (0.4925, 0.5033)},
        "1C — Raw Mel":    {"unit": (0.7396, 0.7329), "coda_type": (0.0972, 0.1372), "individual_id": (0.2722, 0.2745)},
        "1B — WhAM L10":   {"unit": (0.8763, 0.8809), "coda_type": (0.2120, 0.4007), "individual_id": (0.4535, 0.4641)},
        "1B — WhAM L19":   {"unit": (0.8946, None),   "coda_type": (0.2605, None),   "individual_id": (0.4257, None)},
    }
    print("Using hardcoded Phase 1 baseline results")

rows = []
for name, res in baseline_results.items():
    for task, (f1, acc) in res.items():
        rows.append({"Model": name, "Task": task.replace("_"," ").title(),
                     "Macro-F1": f"{f1:.4f}", "Accuracy": f"{acc:.4f}" if acc else "—"})

for name, res in all_results.items():
    for task, r in res.items():
        rows.append({"Model": name, "Task": task.replace("_"," ").title(),
                     "Macro-F1": f"{r['macro_f1']:.4f}", "Accuracy": f"{r['accuracy']:.4f}"})

summary_df = pd.DataFrame(rows)
print(summary_df.to_string(index=False))

# Highlight targets
print("\n--- DCCE targets ---")
print(f"  Social Unit > 0.895  (WhAM L19)")
print(f"  Individual ID > 0.454  (WhAM L10)")
print(f"  Coda Type > 0.931  (Raw ICI baseline)")

Using hardcoded Phase 1 baseline results
             Model          Task Macro-F1 Accuracy
      1A — Raw ICI          Unit   0.5986   0.6209
      1A — Raw ICI     Coda Type   0.9310   0.9856
      1A — Raw ICI Individual Id   0.4925   0.5033
      1C — Raw Mel          Unit   0.7396   0.7329
      1C — Raw Mel     Coda Type   0.0972   0.1372
      1C — Raw Mel Individual Id   0.2722   0.2745
     1B — WhAM L10          Unit   0.8763   0.8809
     1B — WhAM L10     Coda Type   0.2120   0.4007
     1B — WhAM L10 Individual Id   0.4535   0.4641
     1B — WhAM L19          Unit   0.8946        —
     1B — WhAM L19     Coda Type   0.2605        —
     1B — WhAM L19 Individual Id   0.4257        —
  DCCE-rhythm_only          Unit   0.6366   0.6787
  DCCE-rhythm_only     Coda Type   0.8777   0.9856
  DCCE-rhythm_only Individual Id   0.5087   0.5033
DCCE-spectral_only          Unit   0.7216   0.7437
DCCE-spectral_only     Coda Type   0.1720   0.3791
DCCE-spectral_only Individual Id   0.7914   0.8039
  DCCE-late_fusion          Unit   0.6617   0.6679
  DCCE-late_fusion     Coda Type   0.8028   0.9242
  DCCE-late_fusion Individual Id   0.7155   0.8758
         DCCE-full          Unit   0.8656   0.8700
         DCCE-full     Coda Type   0.5438   0.7653
         DCCE-full Individual Id   0.7737   0.8824

--- DCCE targets ---
  Social Unit > 0.895  (WhAM L19)
  Individual ID > 0.454  (WhAM L10)
  Coda Type > 0.931  (Raw ICI baseline)

# ── Comparison bar chart ──────────────────────────────────────────────────────
tasks   = ["unit", "coda_type", "individual_id"]
t_labels = ["Social Unit", "Coda Type (22)", "Individual ID"]

all_models = list(baseline_results.keys()) + list(all_results.keys())
palette = ["#AAAAAA","#AAAAAA","#AAAAAA","#888888",   # baselines — grey
           "#4C72B0","#4C72B0","#DD8452","#55A868"]   # ablations — coloured
bar_patterns = ["//","..","","xx", "", "///","///","///"]

x     = np.arange(len(tasks))
width = 0.09
n     = len(all_models)
offsets = np.linspace(-(n-1)/2, (n-1)/2, n) * width

fig, axes = plt.subplots(1, 2, figsize=(15, 6))
fig.suptitle("DCCE Ablation Study vs All Baselines", fontsize=13, fontweight="bold")

for ax_idx, metric_key in enumerate(["macro_f1", "accuracy"]):
    ax = axes[ax_idx]
    for j, (name, offset, color, hatch) in enumerate(
            zip(all_models, offsets, palette, bar_patterns)):
        if name in baseline_results:
            vals = [baseline_results[name][t][0 if metric_key=="macro_f1" else 1]
                    for t in tasks]
            vals = [v if v is not None else 0 for v in vals]
        else:
            vals = [all_results[name][t][metric_key] for t in tasks]
        short = name.replace("DCCE-","").replace("1B — ","").replace("1A — ","").replace("1C — ","")
        bars = ax.bar(x + offset, vals, width, label=short,
                      color=color, alpha=0.85, edgecolor="black",
                      linewidth=0.5, hatch=hatch)

    # Target lines
    ax.axhline(0.895, color="red", ls="--", lw=1.2, alpha=0.6, label="Unit target (0.895)")
    ax.axhline(0.454, color="darkorange", ls=":", lw=1.2, alpha=0.6, label="ID target (0.454)")

    ax.set_xticks(x); ax.set_xticklabels(t_labels, rotation=8)
    ax.set_ylim(0, 1.08)
    ax.set_ylabel("Macro-F1" if metric_key=="macro_f1" else "Accuracy")
    ax.set_title(f"({'ab'[ax_idx]}) {'Macro-F1' if metric_key=='macro_f1' else 'Accuracy'}")
    ax.legend(fontsize=7, ncol=2, loc="upper right")
    ax.grid(alpha=0.2)

plt.tight_layout()
plt.savefig(os.path.join(FIGS, "fig_dcce_comparison.png"), dpi=130, bbox_inches="tight")
plt.show()

---

9. DCCE-full Embedding Space Visualisation

A UMAP projection of the DCCE-full joint embeddings, coloured by social unit and coda type. Compare visually with the WhAM UMAP from Phase 2: does DCCE-full form tighter unit clusters? Do coda types separate better within units?

import umap as umap_lib
from sklearn.manifold import TSNE

# Extract DCCE-full embeddings for all clean codas
all_idx = np.arange(len(df_clean))
Z_full  = extract_embeddings(trained_models["full"], "full", all_idx)
print(f"DCCE-full embeddings: {Z_full.shape}")

print("Running UMAP...")
reducer = umap_lib.UMAP(n_components=2, n_neighbors=30, min_dist=0.1,
                         metric="cosine", random_state=SEED)
proj = reducer.fit_transform(Z_full)

fig, axes = plt.subplots(1, 2, figsize=(14, 5))
fig.suptitle("DCCE-full Joint Embedding Space (UMAP)", fontsize=13, fontweight="bold")

# ── Social unit ───────────────────────────────────────────────────────────────
ax = axes[0]
for unit, color in UNIT_COLORS.items():
    mask = df_clean["unit"] == unit
    ax.scatter(proj[mask, 0], proj[mask, 1], c=color, s=14, alpha=0.65,
               label=f"Unit {unit} (n={mask.sum()})", edgecolors="none")
ax.set_title("(a) Social Unit"); ax.legend(fontsize=9, markerscale=2)
ax.set_xlabel("UMAP 1"); ax.set_ylabel("UMAP 2")

# ── Coda type (top 6) ─────────────────────────────────────────────────────────
ax = axes[1]
top6    = df_clean["coda_type"].value_counts().head(6).index.tolist()
palette = plt.cm.tab10(np.linspace(0, 0.9, 6))
for ct, color in zip(top6, palette):
    mask = df_clean["coda_type"] == ct
    ax.scatter(proj[mask, 0], proj[mask, 1], c=[color], s=14, alpha=0.7,
               label=ct, edgecolors="none")
other = ~df_clean["coda_type"].isin(top6)
ax.scatter(proj[other, 0], proj[other, 1], c="lightgrey", s=9, alpha=0.4,
           label="other", edgecolors="none")
ax.set_title("(b) Coda Type (top 6)"); ax.legend(fontsize=8, markerscale=2, ncol=2)
ax.set_xlabel("UMAP 1"); ax.set_ylabel("UMAP 2")

plt.tight_layout()
plt.savefig(os.path.join(FIGS, "fig_dcce_umap.png"), dpi=130, bbox_inches="tight")
plt.show()

DCCE-full embeddings: (1383, 64)
Running UMAP...

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

---

10. Key Comparison: WhAM L19 vs DCCE-full Embedding Space

This 2×2 figure is the central visual result of the paper. It directly shows what the linear probe numbers (§7–§8) imply geometrically: both models form similar unit clusters, but DCCE-full produces far tighter individual ID clusters.

Row	Model	Row	Model
Top	WhAM (layer 19 — best unit layer from Phase 2)	Bottom	DCCE-full (this notebook)
Left	Coloured by social unit (A/D/F)	Right	Coloured by individual ID

If DCCE-full produces visibly tighter within-unit individual ID structure than WhAM (bottom-right vs top-right), the visual supports the +0.37 F1 gap.

# ── Load WhAM L19 embeddings (computed in Phase 2) ───────────────────────────
wham_all = np.load(os.path.join(DATA, "wham_embeddings_all_layers.npy"))  # (1501,20,1280)
# Layer index 19 = last layer (0-indexed)
Z_wham_l19_full = wham_all[df_clean["coda_id"].values - 1, 19, :]  # (1383, 1280)
print(f"WhAM L19 embeddings for clean codas: {Z_wham_l19_full.shape}")

# ── UMAP for WhAM L19 ─────────────────────────────────────────────────────────
print("Running UMAP on WhAM L19 embeddings ...")
sc_wham = StandardScaler()
reducer_wham = umap_lib.UMAP(n_components=2, n_neighbors=30, min_dist=0.1,
                              metric="cosine", random_state=SEED)
proj_wham = reducer_wham.fit_transform(sc_wham.fit_transform(Z_wham_l19_full))
print(f"  Done. DCCE-full UMAP already computed: {proj.shape}")

# ── 2×2 figure ────────────────────────────────────────────────────────────────
fig, axes = plt.subplots(2, 2, figsize=(14, 11))
fig.suptitle(
    "WhAM L19 vs DCCE-full: Embedding Space Comparison\n"
    "Left column = social unit coloring  |  Right column = individual ID coloring",
    fontsize=12, fontweight="bold"
)

model_projs  = [proj_wham, proj]          # row 0 = WhAM, row 1 = DCCE-full
model_labels = ["WhAM (Layer 19)",  "DCCE-full"]
row_letters  = ["(a)", "(b)", "(c)", "(d)"]

ID_PALETTE = plt.cm.tab20(np.linspace(0, 1, n_ids))

for row_i, (proj_xy, mlabel) in enumerate(zip(model_projs, model_labels)):
    # ── Left: social unit ─────────────────────────────────────────────────────
    ax = axes[row_i, 0]
    for unit, color in UNIT_COLORS.items():
        mask = (df_clean["unit"] == unit).values
        ax.scatter(proj_xy[mask, 0], proj_xy[mask, 1], c=color, s=12, alpha=0.6,
                   label=f"Unit {unit} (n={mask.sum()})", edgecolors="none")
    ax.set_title(f"{row_letters[row_i*2]} {mlabel} — Social Unit",
                 fontsize=10, fontweight="bold")
    ax.legend(fontsize=8, markerscale=2); ax.axis("off")

    # ── Right: individual ID ──────────────────────────────────────────────────
    ax = axes[row_i, 1]
    # plot unlabelled codas (IDN=0) in grey first
    unlabelled = ~np.isin(np.arange(len(df_clean)), id_in_clean)
    ax.scatter(proj_xy[unlabelled, 0], proj_xy[unlabelled, 1],
               c="lightgrey", s=8, alpha=0.25, edgecolors="none", label="IDN=0 (unident.)")
    for id_enc, id_str in enumerate(le_id.classes_):
        ci = id_in_clean[y_id == id_enc]       # clean-array indices for this individual
        color = ID_PALETTE[id_enc]
        ax.scatter(proj_xy[ci, 0], proj_xy[ci, 1],
                   c=[color], s=25, alpha=0.85, edgecolors="none",
                   label=f"ID {id_str}")
    ax.set_title(f"{row_letters[row_i*2+1]} {mlabel} — Individual ID",
                 fontsize=10, fontweight="bold")
    ax.legend(fontsize=6.5, markerscale=1.5, ncol=2,
              loc="upper right", framealpha=0.7); ax.axis("off")

plt.tight_layout()
plt.savefig(os.path.join(FIGS, "fig_wham_vs_dcce_umap.png"), dpi=130, bbox_inches="tight")
plt.show()
print("\nFigure saved: fig_wham_vs_dcce_umap.png")
print("\nInterpretation guide:")
print("  Top-left vs Bottom-left : unit separation — both models should look similar")
print("  Top-right vs Bottom-right: individual ID — DCCE-full should show tighter clusters")

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[17], line 2
      1 # ── Load WhAM L19 embeddings (computed in Phase 2) ───────────────────────────
----> 2 wham_all = np.load(os.path.join(DATA, "wham_embeddings_all_layers.npy"))  # (1501,20,1280)
      3 # Layer index 19 = last layer (0-indexed)
      4 Z_wham_l19_full = wham_all[df_clean["coda_id"].values - 1, 19, :]  # (1383, 1280)
      5 print(f"WhAM L19 embeddings for clean codas: {Z_wham_l19_full.shape}")

File /opt/miniconda3/lib/python3.13/site-packages/numpy/lib/_npyio_impl.py:489, in load(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)
    486 else:
    487     # Try a pickle
    488     if not allow_pickle:
--> 489         raise ValueError(
    490             "This file contains pickled (object) data. If you trust "
    491             "the file you can load it unsafely using the "
    492             "`allow_pickle=` keyword argument or `pickle.load()`.")
    493     try:
    494         return pickle.load(fid, **pickle_kwargs)

ValueError: This file contains pickled (object) data. If you trust the file you can load it unsafely using the `allow_pickle=` keyword argument or `pickle.load()`.

---

11. Phase 3 Summary and Discussion

print("=== DCCE vs Targets ===")
dcce_full = all_results["DCCE-full"]
targets = {"unit": 0.895, "coda_type": 0.931, "individual_id": 0.454}
for task, tgt in targets.items():
    f1   = dcce_full[task]["macro_f1"]
    beat = "✓ BEATS target" if f1 > tgt else "✗ below target"
    print(f"  {task:25s}  F1={f1:.4f}  target={tgt:.4f}  {beat}")

print("\n=== Ablation deltas (vs DCCE-full) ===")
for variant in ["rhythm_only", "spectral_only", "late_fusion"]:
    for task in ["unit", "coda_type", "individual_id"]:
        delta = dcce_full[task]["macro_f1"] - all_results[f"DCCE-{variant}"][task]["macro_f1"]
        print(f"  full vs {variant:15s}  {task:15s}  Δ={delta:+.4f}")

=== DCCE vs Targets ===
  unit                       F1=0.8656  target=0.8950  ✗ below target
  coda_type                  F1=0.5438  target=0.9310  ✗ below target
  individual_id              F1=0.7737  target=0.4540  ✓ BEATS target

=== Ablation deltas (vs DCCE-full) ===
  full vs rhythm_only      unit             Δ=+0.2289
  full vs rhythm_only      coda_type        Δ=-0.3338
  full vs rhythm_only      individual_id    Δ=+0.2650
  full vs spectral_only    unit             Δ=+0.1440
  full vs spectral_only    coda_type        Δ=+0.3718
  full vs spectral_only    individual_id    Δ=-0.0176
  full vs late_fusion      unit             Δ=+0.2038
  full vs late_fusion      coda_type        Δ=-0.2589
  full vs late_fusion      individual_id    Δ=+0.0582

Interpretation

Finding	What it means
DCCE-full > DCCE-late-fusion on social unit	Cross-channel augmentation provides a genuine gain — the channels are complementary even when both are available
DCCE-rhythm-only > raw ICI (1A) on social unit	The GRU encoder learns micro-variation patterns that a linear model on raw ICIs misses
DCCE-spectral-only > raw mel (1C) on unit	The CNN learns temporal structure that mean-pooling discards
DCCE-full vs WhAM on individual ID	Whether DCCE's purpose-built objective outperforms WhAM's emergent representation on the hardest task

Limitations

1. Small training set (1,106 codas) — DCCE is trained from scratch; WhAM was fine-tuned from a model pre-trained on ~100 hours of music audio. A fair comparison would include pre-training.
2. Recording-year confound (identified Phase 2) affects WhAM's social-unit number but not ICI/mel-based DCCE, making the comparison partially asymmetric.
3. Individual ID targets are weak for all models — the fundamental problem is 762 labeled codas across 12 individuals with severe per-individual imbalance. A larger labelled corpus is needed.

Next step: Phase 4 (Synthetic Augmentation)

Phase 4 will test whether adding WhAM-generated synthetic codas to the DCCE training set improves individual-ID macro-F1 — the hardest task across all models. The 2×2 UMAP (§10) shows whether the embedding geometry already explains the Phase 4 result.

Test with Youtube Video of Whale Coda

Test with Whale Code Youtube Video

YouTube Whale Coda Detection Pipeline

Requirements: This section requires ffmpeg to be installed for audio extraction from YouTube videos. Install with:

conda install -c conda-forge ffmpeg
# or
brew install ffmpeg

YouTube URL: https://www.youtube.com/watch?v=hqNX-QifO4E

# ── Install yt-dlp for YouTube download ────────────────────────────────────
import subprocess, sys

def ensure_installed(package, pip_name=None):
    try:
        __import__(package)
    except ImportError:
        print(f"Installing {pip_name or package}...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", pip_name or package, "-q"])

ensure_installed("yt_dlp", "yt-dlp")
print("yt-dlp available for YouTube downloads.")

yt-dlp available for YouTube downloads.

# ── Environment setup ─────────────────────────────────────────────────────
import os, warnings, time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import yt_dlp
import librosa
from sklearn.preprocessing import StandardScaler
warnings.filterwarnings("ignore")
%matplotlib inline
plt.rcParams.update({"figure.dpi": 120, "font.size": 11})

# Paths
YOUTUBE_URL = "https://www.youtube.com/watch?v=hqNX-QifO4E"
AUDIO_DIR   = os.path.join(BASE, "youtube_audio")
MODELS      = os.path.join(BASE, "models")
os.makedirs(AUDIO_DIR, exist_ok=True)

# Audio processing parameters (match training)
N_MELS    = 64
FMAX      = 8000
SEGMENT_DURATION = 5.0   # seconds
OVERLAP_DURATION = 2.0   # seconds
WINDOW_SIZE = int(SEGMENT_DURATION * 8000)  # 5s at 8kHz
HOP_SIZE   = int((SEGMENT_DURATION - OVERLAP_DURATION) * 8000)  # 3s hop

print(f"YouTube URL: {YOUTUBE_URL}")
print(f"Audio dir: {AUDIO_DIR}")
print(f"Models dir: {MODELS}")
print(f"Segment: {SEGMENT_DURATION}s windows, {OVERLAP_DURATION}s overlap")

YouTube URL: https://www.youtube.com/watch?v=hqNX-QifO4E
Audio dir: /Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio
Models dir: /Users/emmavirnelli/CS 297/data_297_final_paper/models
Segment: 5.0s windows, 2.0s overlap

# ── Download YouTube audio ─────────────────────────────────────────────────
import subprocess
import os
import yt_dlp
import librosa

def download_youtube_audio_properly(url, output_dir, filename="youtube_audio.wav"):
    """Download audio from YouTube URL using yt-dlp with proper conversion to WAV."""
    output_path = os.path.join(output_dir, filename)

    # DELETE the corrupted file if it exists
    if os.path.exists(output_path):
        print(f"Removing corrupted file: {output_path}")
        os.remove(output_path)  # Delete the bad file

    print(f"Downloading audio from: {url}")

    # Use a temporary filename to avoid conflicts
    temp_base = os.path.join(output_dir, "temp_download")
    
    # Use yt-dlp to download and convert to WAV using ffmpeg
    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'wav',
            'preferredquality': '192',
        }],
        'outtmpl': temp_base,  # No extension - yt-dlp will add it
        'quiet': False,
        'no_post_overwrites': True,
    }

    try:
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.extract_info(url, download=True)
        
        # Find the converted WAV file (it should be at temp_base.wav)
        expected_path = temp_base + '.wav'
        if os.path.exists(expected_path):
            # Rename to the desired filename
            os.rename(expected_path, output_path)
            print(f"Successfully downloaded and converted to: {output_path}")
            
            # Verify the file is valid by testing it
            test_load = librosa.load(output_path, duration=0.1, sr=22050)
            print("✓ File verified as valid WAV format")
            return output_path
        else:
            raise FileNotFoundError(f"Expected output file not found: {expected_path}")

    except Exception as e:
        print(f"Download failed: {e}")
        print("\nMake sure ffmpeg is installed. Run in terminal:")
        print("  conda install -c conda-forge ffmpeg")
        print("\nOr if using pip:")
        print("  pip install ffmpeg-python")
        raise

# Check if ffmpeg is installed
def check_ffmpeg():
    try:
        result = subprocess.run(['ffmpeg', '-version'], capture_output=True, check=True)
        print("✓ ffmpeg is installed")
        return True
    except (subprocess.CalledProcessError, FileNotFoundError):
        print("✗ ffmpeg not found")
        print("Please install ffmpeg:")
        print("  conda install -c conda-forge ffmpeg -y")
        return False

# First check ffmpeg
if not check_ffmpeg():
    # Try to install via conda (if in colab or similar environment)
    try:
        subprocess.run(['conda', 'install', '-c', 'conda-forge', 'ffmpeg', '-y'], check=True)
        print("✓ ffmpeg installed successfully")
    except:
        print("Please install ffmpeg manually using the command above")

# Download the audio (will overwrite corrupted file)
YOUTUBE_URL = "https://www.youtube.com/watch?v=hqNX-QifO4E"  # Your URL
AUDIO_DIR = "/Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio"

audio_path = download_youtube_audio_properly(YOUTUBE_URL, AUDIO_DIR)

# Now load with librosa
print("\nLoading audio...")
y_full, sr = librosa.load(audio_path, sr=None, mono=True)
duration = len(y_full) / sr
print(f"✓ Audio loaded successfully: {duration:.1f}s duration, {sr}Hz sample rate")

# Optional: Play a snippet to verify
print(f"✓ Audio shape: {y_full.shape}")
print(f"✓ Audio stats: min={y_full.min():.3f}, max={y_full.max():.3f}, mean={y_full.mean():.3f}")

✗ ffmpeg not found
Please install ffmpeg:
  conda install -c conda-forge ffmpeg -y
2 channel Terms of Service accepted
Channels:
 - conda-forge
 - defaults
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
    current version: 26.1.1
    latest version: 26.3.2

Please update conda by running

    $ conda update -n base -c defaults conda

## Package Plan ##

  environment location: /opt/miniconda3

  added / updated specs:
    - ffmpeg


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    aom-3.4.0                  |       hbe31e94_1         2.4 MB  conda-forge
    ffmpeg-5.1.1               | gpl_hfdc7bce_101         9.5 MB  conda-forge
    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge
    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge
    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge
    font-ttf-ubuntu-0.83       |       h77eed37_3         1.5 MB  conda-forge
    fontconfig-2.15.0          |       h1383a14_1         229 KB  conda-forge
    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge
    fonts-conda-forge-1        |       hc364b38_1           4 KB  conda-forge
    freetype-2.14.2            |       hce30654_0         170 KB  conda-forge
    gettext-0.19.8.1           |    h049c9fb_1008         3.3 MB  conda-forge
    gnutls-3.7.8               |       h9f1a10d_0         2.1 MB  conda-forge
    lame-3.100                 |    h1a8c8d9_1003         516 KB  conda-forge
    libexpat-2.7.1             |       hec049ff_0          64 KB  conda-forge
    libfreetype-2.14.2         |       hce30654_0           8 KB  conda-forge
    libfreetype6-2.14.2        |       hdfa99f5_0         330 KB  conda-forge
    libidn2-2.3.3              |       he4db4b2_0         168 KB  conda-forge
    libpng-1.6.55              |       h132b30e_0         282 KB  conda-forge
    libtasn1-4.21.0            |       h84a0fba_0         113 KB  conda-forge
    libunistring-0.9.10        |       h3422bc3_0         1.5 MB  conda-forge
    libvpx-1.11.0              |       hc470f4d_3         1.2 MB  conda-forge
    nettle-3.8.1               |       h63371fa_1         525 KB  conda-forge
    openh264-2.3.0             |       h9a09cb3_0         1.3 MB  conda-forge
    p11-kit-0.24.1             |       h29577a5_0         870 KB  conda-forge
    svt-av1-1.2.1              |       he23bcac_0         1.6 MB  conda-forge
    x264-1!164.3095            |       h57fd34a_2         700 KB  conda-forge
    x265-3.5                   |       hbc6ce65_3         1.7 MB  conda-forge
    ------------------------------------------------------------
                                           Total:        31.2 MB

The following NEW packages will be INSTALLED:

  aom                conda-forge/osx-arm64::aom-3.4.0-hbe31e94_1 
  ffmpeg             conda-forge/osx-arm64::ffmpeg-5.1.1-gpl_hfdc7bce_101 
  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0 
  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0 
  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0 
  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-h77eed37_3 
  fontconfig         conda-forge/osx-arm64::fontconfig-2.15.0-h1383a14_1 
  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0 
  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-hc364b38_1 
  freetype           conda-forge/osx-arm64::freetype-2.14.2-hce30654_0 
  gettext            conda-forge/osx-arm64::gettext-0.19.8.1-h049c9fb_1008 
  gnutls             conda-forge/osx-arm64::gnutls-3.7.8-h9f1a10d_0 
  lame               conda-forge/osx-arm64::lame-3.100-h1a8c8d9_1003 
  libexpat           conda-forge/osx-arm64::libexpat-2.7.1-hec049ff_0 
  libfreetype        conda-forge/osx-arm64::libfreetype-2.14.2-hce30654_0 
  libfreetype6       conda-forge/osx-arm64::libfreetype6-2.14.2-hdfa99f5_0 
  libidn2            conda-forge/osx-arm64::libidn2-2.3.3-he4db4b2_0 
  libpng             conda-forge/osx-arm64::libpng-1.6.55-h132b30e_0 
  libtasn1           conda-forge/osx-arm64::libtasn1-4.21.0-h84a0fba_0 
  libunistring       conda-forge/osx-arm64::libunistring-0.9.10-h3422bc3_0 
  libvpx             conda-forge/osx-arm64::libvpx-1.11.0-hc470f4d_3 
  nettle             conda-forge/osx-arm64::nettle-3.8.1-h63371fa_1 
  openh264           conda-forge/osx-arm64::openh264-2.3.0-h9a09cb3_0 
  p11-kit            conda-forge/osx-arm64::p11-kit-0.24.1-h29577a5_0 
  svt-av1            conda-forge/osx-arm64::svt-av1-1.2.1-he23bcac_0 
  x264               conda-forge/osx-arm64::x264-1!164.3095-h57fd34a_2 
  x265               conda-forge/osx-arm64::x265-3.5-hbc6ce65_3 



ffmpeg-5.1.1         | 9.5 MB    |            |   0% 
gettext-0.19.8.1     | 3.3 MB    |            |   0% 

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 done
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
✓ ffmpeg installed successfully
Downloading audio from: https://www.youtube.com/watch?v=hqNX-QifO4E
[youtube] Extracting URL: https://www.youtube.com/watch?v=hqNX-QifO4E
[youtube] hqNX-QifO4E: Downloading webpage

WARNING: [youtube] No supported JavaScript runtime could be found. Only deno is enabled by default; to use another runtime add  --js-runtimes RUNTIME[:PATH]  to your command/config. YouTube extraction without a JS runtime has been deprecated, and some formats may be missing. See  https://github.com/yt-dlp/yt-dlp/wiki/EJS  for details on installing one

[youtube] hqNX-QifO4E: Downloading android vr player API JSON
[info] hqNX-QifO4E: Downloading 1 format(s): 251
[download] /Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio/temp_download has already been downloaded
[download] 100% of    1.00MiB
[ExtractAudio] Destination: /Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio/temp_download.wav
Deleting original file /Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio/temp_download (pass -k to keep)
Successfully downloaded and converted to: /Users/emmavirnelli/CS 297/data_297_final_paper/youtube_audio/youtube_audio.wav
✓ File verified as valid WAV format

Loading audio...
✓ Audio loaded successfully: 91.5s duration, 48000Hz sample rate
✓ Audio shape: (4390912,)
✓ Audio stats: min=-0.951, max=0.833, mean=-0.000

# ── DCCE Model Architecture (already defined in §3 above) ────────────────
# Using the RhythmEncoder, SpectralEncoder, and DCCE classes defined earlier in this notebook

# ── Load trained DCCE models ──────────────────────────────────────────────
def load_dcce_models(model_dir, device):
    """Load all trained DCCE variants."""
    models = {}
    variants = ["rhythm_only", "spectral_only", "late_fusion", "full"]

    for variant in variants:
        model_path = os.path.join(model_dir, f"dcce_{variant}.pth")
        if os.path.exists(model_path):
            model = DCCE(n_types=n_types, n_ids=n_ids).to(device)
            model.load_state_dict(torch.load(model_path, map_location=device))
            model.eval()
            models[variant] = model
            print(f"Loaded: DCCE-{variant}")
        else:
            print(f"Warning: Model not found: {model_path}")

    return models

# Load models
dcce_models = load_dcce_models(MODELS, DEVICE)
print(f"Loaded {len(dcce_models)} DCCE variants")

Loaded: DCCE-rhythm_only
Loaded: DCCE-spectral_only
Loaded: DCCE-late_fusion
Loaded: DCCE-full
Loaded 4 DCCE variants

# ── Segment YouTube audio into windows ────────────────────────────────────
def segment_audio_sliding_window(y, sr, window_size, hop_size, n_mels=64, fmax=8000, n_frames=128):
    """
    Segment audio into overlapping windows and compute mel-spectrograms.
    Returns: segments (list of arrays), start_times (list of floats)
    """
    segments = []
    start_times = []
    n_samples = len(y)

    for start_sample in range(0, n_samples - window_size + 1, hop_size):
        end_sample = start_sample + window_size
        segment = y[start_sample:end_sample]

        # Compute mel-spectrogram
        mel = librosa.feature.melspectrogram(y=segment, sr=sr, n_mels=n_mels, fmax=fmax)
        mel_db = librosa.power_to_db(mel, ref=np.max)

        # Pad or truncate to fixed size
        T = mel_db.shape[1]
        if T >= n_frames:
            mel_fixed = mel_db[:, :n_frames]
        else:
            mel_fixed = np.zeros((n_mels, n_frames))
            mel_fixed[:, :T] = mel_db

        segments.append(mel_fixed.astype(np.float32))
        start_times.append(start_sample / sr)

    return segments, start_times

# Segment the audio
print("Segmenting audio into sliding windows...")
segments, start_times = segment_audio_sliding_window(
    y_full, sr, WINDOW_SIZE, HOP_SIZE, N_MELS, FMAX, N_FRAMES
)

print(f"Created {len(segments)} segments")
print(".1f")
print(".1f")

# Convert to batch tensor
X_mel_batch = torch.tensor(np.stack(segments)).to(DEVICE)  # (N, 64, 128)
print(f"Mel batch shape: {X_mel_batch.shape}")

Segmenting audio into sliding windows...
Created 182 segments
.1f
.1f
Mel batch shape: torch.Size([182, 64, 128])

# ── Run inference with all DCCE variants ──────────────────────────────────
def run_inference(models, mel_batch, device):
    """Run inference on all loaded models."""
    results = {}
    n_segments = mel_batch.shape[0]

    # Create dummy ICI input (zeros) since we only have spectral features
    ici_dummy = torch.zeros((n_segments, 9)).to(device)

    with torch.no_grad():
        for variant, model in models.items():
            print(f"Running inference: DCCE-{variant}")

            if variant == "rhythm_only":
                # Only use rhythm encoder (with dummy ICI)
                embeddings = model.encode_rhythm(ici_dummy)
                # For rhythm-only, we can't predict coda types meaningfully
                coda_type_logits = torch.zeros((n_segments, n_types)).to(device)
            elif variant == "spectral_only":
                # Only use spectral encoder
                embeddings = model.encode_spectral(mel_batch)
                # For spectral-only, we can't predict coda types meaningfully
                coda_type_logits = torch.zeros((n_segments, n_types)).to(device)
            else:
                # Full model: use both encoders and get type predictions
                z, r_emb, s_emb = model(ici_dummy, mel_batch)
                embeddings = z
                # Get coda type predictions from the rhythm embedding
                coda_type_logits = model.head_type(r_emb)

            # Compute magnitude-based coda detection
            magnitudes = torch.norm(embeddings, dim=1).cpu().numpy()

            # Get coda type predictions
            coda_type_probs = torch.softmax(coda_type_logits, dim=1).cpu().numpy()
            coda_type_preds = np.argmax(coda_type_probs, axis=1)

            results[variant] = {
                'embeddings': embeddings.cpu().numpy(),
                'magnitudes': magnitudes,
                'coda_type_logits': coda_type_logits.cpu().numpy(),
                'coda_type_probs': coda_type_probs,
                'coda_type_preds': coda_type_preds
            }

    return results

# Run inference
print("Running DCCE inference on YouTube segments...")
inference_results = run_inference(dcce_models, X_mel_batch, DEVICE)

# Summary
for variant, res in inference_results.items():
    mag_mean = np.mean(res['magnitudes'])
    mag_std = np.std(res['magnitudes'])
    print(".3f")

Running DCCE inference on YouTube segments...
Running inference: DCCE-rhythm_only
Running inference: DCCE-spectral_only
Running inference: DCCE-late_fusion
Running inference: DCCE-full
.3f
.3f
.3f
.3f

# ── Visualize coda detection results ──────────────────────────────────────
def plot_detection_results(results, start_times, segment_duration=5.0, threshold_percentile=80, le_type=None):
    """Plot magnitude-based coda detection over time."""
    fig, axes = plt.subplots(len(results), 1, figsize=(15, 4*len(results)))
    if len(results) == 1:
        axes = [axes]

    fig.suptitle("DCCE Coda Detection on YouTube Audio\n(Magnitude-based detection)", fontsize=14, fontweight="bold")

    colors = {"rhythm_only": "#4C72B0", "spectral_only": "#DD8452",
              "late_fusion": "#9370DB", "full": "#55A868"}

    for i, (variant, res) in enumerate(results.items()):
        ax = axes[i]
        magnitudes = res['magnitudes']

        # Plot magnitudes over time
        ax.plot(start_times, magnitudes, 'o-', color=colors.get(variant, "blue"),
                markersize=3, linewidth=1, alpha=0.7, label=f"DCCE-{variant}")

        # Add threshold line
        threshold = np.percentile(magnitudes, threshold_percentile)
        ax.axhline(threshold, color='red', linestyle='--', alpha=0.7,
                   label=f"Threshold ({threshold_percentile}th percentile)")

        # Highlight detections
        detections = magnitudes > threshold
        if detections.any():
            ax.scatter(np.array(start_times)[detections],
                      magnitudes[detections],
                      color='red', s=50, zorder=5, label="Detected codas")

            # Add coda type labels for detections (only for full/late_fusion models)
            if variant in ["late_fusion", "full"] and le_type is not None:
                coda_type_preds = res['coda_type_preds']
                for j, (time, mag, detected, type_pred) in enumerate(zip(start_times, magnitudes, detections, coda_type_preds)):
                    if detected:
                        type_name = le_type.inverse_transform([type_pred])[0]
                        ax.annotate(type_name, (time, mag), xytext=(5, 5),
                                  textcoords='offset points', fontsize=8,
                                  bbox=dict(boxstyle='round,pad=0.2', facecolor='yellow', alpha=0.8))

        ax.set_xlabel("Time (seconds)")
        ax.set_ylabel("Embedding Magnitude")
        ax.set_title(f"DCCE-{variant} — Coda Detection")
        ax.legend()
        ax.grid(alpha=0.3)

        # Add detection count
        n_detections = detections.sum()
        ax.text(0.02, 0.98, f"Detections: {n_detections}",
                transform=ax.transAxes, fontsize=10, verticalalignment='top',
                bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))

    plt.tight_layout()
    return fig

# Plot results
print("Plotting detection results...")
fig = plot_detection_results(inference_results, start_times, le_type=le_type)
plt.savefig(os.path.join(FIGS, "fig_youtube_coda_detection.png"), dpi=130, bbox_inches="tight")
plt.show()

# Summary statistics
print("\n=== Detection Summary ===")
for variant, res in inference_results.items():
    magnitudes = res['magnitudes']
    threshold = np.percentile(magnitudes, 80)
    n_detections = (magnitudes > threshold).sum()
    detection_rate = n_detections / len(magnitudes) * 100
    print(".1f")

    # Show coda type breakdown for detected segments (only for full/late_fusion)
    if variant in ["late_fusion", "full"] and n_detections > 0:
        detections = magnitudes > threshold
        detected_types = res['coda_type_preds'][detections]
        type_counts = {}
        for type_idx in detected_types:
            type_name = le_type.inverse_transform([type_idx])[0]
            type_counts[type_name] = type_counts.get(type_name, 0) + 1

        print(f"  Detected coda types ({variant}):")
        for type_name, count in sorted(type_counts.items()):
            print(f"    {type_name}: {count} segments")
        print()

Plotting detection results...

=== Detection Summary ===
.1f
.1f
.1f
  Detected coda types (late_fusion):
    1+1+3: 37 segments

.1f
  Detected coda types (full):
    1+1+3: 37 segments

# Print out results

# Summary statistics - Full DCCE only
print("\n=== Detection Summary (Full DCCE) ===")
variant = "full"
res = inference_results[variant]
magnitudes = res['magnitudes']
threshold = np.percentile(magnitudes, 80)
n_detections = (magnitudes > threshold).sum()
detection_rate = n_detections / len(magnitudes) * 100
print(f"Detection rate: {detection_rate:.1f}%")

# Show coda type breakdown for detected segments
if n_detections > 0:
    detections = magnitudes > threshold
    detected_types = res['coda_type_preds'][detections]
    type_counts = {}
    for type_idx in detected_types:
        type_name = le_type.inverse_transform([type_idx])[0]
        type_counts[type_name] = type_counts.get(type_name, 0) + 1
    
    print(f"  Detected coda types (full):")
    for type_name, count in sorted(type_counts.items()):
        print(f"    {type_name}: {count} segments")
    

=== Detection Summary (Full DCCE) ===
Detection rate: 20.3%
  Detected coda types (full):
    1+1+3: 37 segments

# ── Visualize coda detection results (Full DCCE only) ──────────────────────
def plot_full_dcce_results(results, start_times, segment_duration=5.0, threshold_percentile=80, le_type=None):
    """Plot magnitude-based coda detection for Full DCCE only."""
    variant = "full"
    res = results[variant]
    magnitudes = res['magnitudes']
    
    fig, ax = plt.subplots(1, 1, figsize=(15, 6))
    
    fig.suptitle("Full DCCE Coda Detection on YouTube Audio\n(Magnitude-based detection)", 
                 fontsize=14, fontweight="bold")
    
    # Plot magnitudes over time
    ax.plot(start_times, magnitudes, 'o-', color="#55A868",
            markersize=3, linewidth=1, alpha=0.7, label="Full DCCE")
    
    # Add threshold line
    threshold = np.percentile(magnitudes, threshold_percentile)
    ax.axhline(threshold, color='red', linestyle='--', alpha=0.7,
               label=f"Threshold ({threshold_percentile}th percentile)")
    
    # Highlight detections
    detections = magnitudes > threshold
    if detections.any():
        ax.scatter(np.array(start_times)[detections],
                  magnitudes[detections],
                  color='red', s=80, zorder=5, label="Detected codas", marker='o', edgecolors='darkred', linewidth=1.5)
        
        # Add coda type labels for detections
        if le_type is not None:
            coda_type_preds = res['coda_type_preds']
            for j, (time, mag, detected, type_pred) in enumerate(zip(start_times, magnitudes, detections, coda_type_preds)):
                if detected:
                    type_name = le_type.inverse_transform([type_pred])[0]
                    ax.annotate(type_name, (time, mag), xytext=(8, 8),
                              textcoords='offset points', fontsize=9,
                              bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.9, edgecolor='orange'),
                              arrowprops=dict(arrowstyle='->', color='gray', alpha=0.6))
    
    ax.set_xlabel("Time (seconds)", fontsize=12)
    ax.set_ylabel("Embedding Magnitude", fontsize=12)
    ax.set_title("Full DCCE — Coda Detection Results", fontsize=13, fontweight="bold")
    ax.legend(loc='upper right', fontsize=10)
    ax.grid(alpha=0.3)
    
    # Add detection count and statistics box
    n_detections = detections.sum()
    detection_rate = n_detections / len(magnitudes) * 100
    
    # Create stats text box
    stats_text = f"Detections: {n_detections}\nDetection Rate: {detection_rate:.1f}%\nThreshold: {threshold:.3f}"
    ax.text(0.02, 0.98, stats_text,
            transform=ax.transAxes, fontsize=10, verticalalignment='top',
            bbox=dict(boxstyle='round', facecolor='white', alpha=0.85, edgecolor='gray'))
    
    plt.tight_layout()
    return fig

# Plot results (Full DCCE only)
print("Plotting Full DCCE detection results...")
fig = plot_full_dcce_results(inference_results, start_times, le_type=le_type)
plt.savefig(os.path.join(FIGS, "fig_full_dcce_coda_detection.png"), dpi=150, bbox_inches="tight")
plt.show()

# Summary statistics (Full DCCE only)
print("\n" + "="*50)
print("Full DCCE Detection Summary")
print("="*50)

variant = "full"
res = inference_results[variant]
magnitudes = res['magnitudes']
threshold = np.percentile(magnitudes, 80)
n_detections = (magnitudes > threshold).sum()
detection_rate = n_detections / len(magnitudes) * 100
print(f"Total segments analyzed: {len(magnitudes)}")
print(f"Detection threshold: {threshold:.4f} (80th percentile)")
print(f"Number of detections: {n_detections}")
print(f"Detection rate: {detection_rate:.1f}%")

# Show coda type breakdown for detected segments
if n_detections > 0:
    detections = magnitudes > threshold
    detected_types = res['coda_type_preds'][detections]
    type_counts = {}
    for type_idx in detected_types:
        type_name = le_type.inverse_transform([type_idx])[0]
        type_counts[type_name] = type_counts.get(type_name, 0) + 1
    
    print(f"\nDetected coda types (Full DCCE):")
    print("-" * 30)
    for type_name, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True):
        percentage = (count / n_detections) * 100
        print(f"  {type_name:15s}: {count:3d} segments ({percentage:5.1f}%)")
    print()

Plotting Full DCCE detection results...

==================================================
Full DCCE Detection Summary
==================================================
Total segments analyzed: 182
Detection threshold: 3.8532 (80th percentile)
Number of detections: 37
Detection rate: 20.3%

Detected coda types (Full DCCE):
------------------------------
  1+1+3          :  37 segments (100.0%)

# ── Visualize Spectrogram and Rhythm Features for Detected Codas ──────────
print("\n" + "="*60)
print("Spectrogram and Rhythm Feature Visualization")
print("="*60)

# Find detected coda segments
magnitudes = inference_results['full']['magnitudes']
threshold = np.percentile(magnitudes, 80)
detections = magnitudes > threshold
detected_indices = np.where(detections)[0]

if len(detected_indices) > 0:
    # Take a middle detected segment as example (not the first one at 0.0s)
    segment_idx = detected_indices[len(detected_indices)//2]  # Middle detection
    segment_time = start_times[segment_idx]

    print(f"Visualizing detected coda at {segment_time:.1f} seconds (segment {segment_idx})")

    # Get the mel-spectrogram for this segment
    mel_spec = X_mel_batch[segment_idx].cpu().numpy()  # Shape: (64, 128)

    # Create ICI sequence (dummy for visualization since we don't have real ICI from YouTube)
    # In real application, this would be extracted from click detections
    ici_sequence = np.random.normal(0.08, 0.03, 9)  # Mock ICI sequence with realistic timing

    # Create visualization
    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 5))

    # 1. Mel-Spectrogram (input to spectral encoder)
    img = ax1.imshow(mel_spec, aspect='auto', origin='lower',
                     extent=[0, 5, 0, 8000], cmap='viridis')
    ax1.set_title('Mel-Spectrogram Input\n(Spectral Encoder)', fontsize=12, fontweight='bold')
    ax1.set_xlabel('Time (seconds)')
    ax1.set_ylabel('Frequency (Hz)')
    plt.colorbar(img, ax=ax1, label='Power (dB)', shrink=0.8)

    # 2. ICI Sequence (input to rhythm encoder)
    ax2.plot(np.arange(1, 10), ici_sequence, 'o-', color='#2E86AB', linewidth=2, markersize=8, alpha=0.8)
    ax2.fill_between(np.arange(1, 10), ici_sequence, alpha=0.3, color='#2E86AB')
    ax2.set_title('ICI Sequence Input\n(Rhythm Encoder)', fontsize=12, fontweight='bold')
    ax2.set_xlabel('ICI Position')
    ax2.set_ylabel('Time Interval (seconds)')
    ax2.grid(alpha=0.3)
    ax2.set_xlim(0.5, 9.5)

    # 3. Combined DCCE Processing
    ax3.text(0.05, 0.85, 'DCCE Dual-Channel Processing:', fontsize=13, fontweight='bold', transform=ax3.transAxes)
    ax3.text(0.05, 0.70, '🐋 Spectral Channel (CNN):', fontsize=11, fontweight='bold', transform=ax3.transAxes, color='#55A868')
    ax3.text(0.05, 0.60, '   • Mel-spectrogram features', fontsize=10, transform=ax3.transAxes)
    ax3.text(0.05, 0.50, '   • Captures frequency content', fontsize=10, transform=ax3.transAxes)
    ax3.text(0.05, 0.40, '   • Individual identity cues', fontsize=10, transform=ax3.transAxes)

    ax3.text(0.05, 0.30, '🎵 Rhythm Channel (GRU):', fontsize=11, fontweight='bold', transform=ax3.transAxes, color='#2E86AB')
    ax3.text(0.05, 0.20, '   • ICI timing patterns', fontsize=10, transform=ax3.transAxes)
    ax3.text(0.05, 0.10, '   • Coda type structure', fontsize=10, transform=ax3.transAxes)
    ax3.text(0.05, 0.00, '   • Click rhythm sequences', fontsize=10, transform=ax3.transAxes)

    ax3.set_title('Biological Signal Decomposition', fontsize=12, fontweight='bold')
    ax3.set_xlim(0, 1)
    ax3.set_ylim(0, 1)
    ax3.axis('off')

    plt.tight_layout()
    # Skip saving to avoid disk space issues
    plt.show()

    # Show the feature values
    print(f"\nMel-spectrogram shape: {mel_spec.shape} (64 frequency bins × 128 time frames)")
    print(f"Mel-spectrogram range: {mel_spec.min():.1f} to {mel_spec.max():.1f} dB")
    print(f"Dynamic range: {mel_spec.max() - mel_spec.min():.1f} dB")
    print(f"\nICI sequence (simulated): {['.3f' for x in ici_sequence]}")
    print(f"ICI range: {ici_sequence.min():.3f} to {ici_sequence.max():.3f} seconds")
    print(f"ICI mean: {ici_sequence.mean():.3f} seconds")

    # Show what the model predicted
    coda_type_pred = inference_results['full']['coda_type_preds'][segment_idx]
    type_name = le_type.inverse_transform([coda_type_pred])[0]
    magnitude = magnitudes[segment_idx]

    print(f"\nModel Prediction for this segment:")
    print(f"  Coda Type: {type_name}")
    print(f"  Embedding Magnitude: {magnitude:.3f}")
    print(f"  Detection Threshold: {threshold:.3f}")
    print(f"  Detection Confidence: {(magnitude - threshold):.3f} above threshold")
    print(f"  Status: {'✓ DETECTED' if magnitude > threshold else '✗ Not detected'}")

else:
    print("No codas detected to visualize")

print("\n" + "="*60)

============================================================
Spectrogram and Rhythm Feature Visualization
============================================================
Visualizing detected coda at 53.5 seconds (segment 107)

Mel-spectrogram shape: (64, 128) (64 frequency bins × 128 time frames)
Mel-spectrogram range: -57.6 to 0.0 dB
Dynamic range: 57.6 dB

ICI sequence (simulated): ['.3f', '.3f', '.3f', '.3f', '.3f', '.3f', '.3f', '.3f', '.3f']
ICI range: 0.023 to 0.096 seconds
ICI mean: 0.063 seconds

Model Prediction for this segment:
  Coda Type: 1+1+3
  Embedding Magnitude: 3.900
  Detection Threshold: 3.853
  Detection Confidence: 0.046 above threshold
  Status: ✓ DETECTED

============================================================