Add causal person fusion and MuVAP stage-3 baselines

third_party/MuVAP/data/utils.py

@@ -0,0 +1,64 @@
+import os
+from glob import glob
+
+import torch
+
+from data.media import AudioSpec, load_waveform, slice_audio
+
+
+def load_audio(
+    audio_path,
+    target_sr=16000,
+    mono=True,
+    start_time=None,
+    end_time=None,
+    target_samples=None,
+):
+    """Load canonical float PCM while preserving the clip's time origin.
+
+    Short clips are padded on the right and long clips are cropped on the
+    right. No peak, RMS, or dataset-specific volume normalization is applied.
+    """
+    spec = AudioSpec(sample_rate=target_sr, mono=mono)
+    waveform, sr = load_waveform(audio_path, spec)
+    waveform = slice_audio(
+        waveform,
+        sr,
+        start_time=start_time,
+        end_time=end_time,
+        target_samples=target_samples,
+    )
+
+    duration = waveform.shape[1] / sr
+
+    return waveform, duration
+
+
+def load_fisher_vad(vad_path, conv_id, nframes, frame_hz=25):
+
+    pattern = os.path.join(vad_path, f"{conv_id}_*_words.txt")
+    vad_files = sorted(glob(pattern))
+
+    vad_labels = []
+    for vad_file in vad_files:
+        labels = torch.zeros(nframes)
+        with open(vad_file, "r") as f:
+            for line in f:
+                parts = line.strip().split()
+                start = float(parts[0])
+                end = float(parts[1])
+                start_idx = int(round(start * frame_hz))
+                end_idx = int(round(end * frame_hz))
+                start_idx = max(0, min(nframes, start_idx))
+                end_idx = max(0, min(nframes, end_idx))
+                labels[start_idx:end_idx] = 1
+        vad_labels.append(labels)
+
+    return torch.stack(vad_labels, dim=0)
+
+
+def load_lit_state_dict(model_path):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    checkpoint = torch.load(model_path, map_location=device)
+    state_dict = checkpoint.get("state_dict", checkpoint)
+    return {k.replace("model.", ""): v for k, v in state_dict.items()}