add sliding window

egs/librilight/SSL/local/extract_kmeans.py

@@ -17,6 +17,7 @@
 
 import argparse
 import logging
+import math
 from pathlib import Path
 from typing import Optional
 
@@ -98,44 +99,90 @@ def get_args():
         help="Stop processing pieces until this number (exclusive).",
     )
 
+    parser.add_argument(
+        "--window-duration",
+        type=float,
+        default=300.0,
+    )
+
+    parser.add_argument(
+        "--shift-duration",
+        type=float,
+        default=250.0,
+    )
+
     return parser.parse_args()
 
 
+@torch.no_grad()
 def extract_and_save_one_cuts(
-    raw_cuts_path, cuts_path, model, apply_kmeans, do_normalize, device
+    raw_cuts_path,
+    cuts_path,
+    model,
+    apply_kmeans,
+    do_normalize,
+    window_duration,
+    shift_duration,
 ):
     logging.info(f"Loading {raw_cuts_path}")
     cut_set = CutSet.from_file(raw_cuts_path)
 
     logging.info("Extracting kmeans")
     cuts = []
+
+    assert window_duration >= shift_duration
+    window_size = int(window_duration * 16000)
+    shift_size = int(shift_duration * 16000)
+    overlap_size = window_size - shift_size
+    out_overlap_size = get_out_length(overlap_size)
+
     for cut in tqdm(cut_set):
         assert cut.sampling_rate == 16000, f"Sampling rate: {cut.sampling_rate}"
+
         audio = cut.load_audio()
 
-        offsets = 0
-        if True:
-            x = torch.from_numpy(audio).float().to(device)
+        T = audio.shape[1]
+        start = 0
+        kmeans = []
+        while start < T:
+            real_window_size = min(window_size, T - start)
+            audio_window = audio[:, start : start + real_window_size]
 
-            with torch.no_grad():
-                if do_normalize:
-                    x = torch.nn.functional.layer_norm(x, x.shape)
-
-                feature, _ = model.extract_features(
-                    source=x,
-                    padding_mask=None,
-                    mask=False,
-                    output_layer=9,
-                )
-                feature = feature.squeeze(0)
-
-            kmeans = " ".join(map(str, apply_kmeans(feature).tolist()))
-
-            cut_with_kmeans = fastcopy(
-                cut,
-                custom={"kmeans": kmeans},
+            x = (
+                torch.from_numpy(audio_window)
+                .float()
+                .to(next(model.parameters()).device)
             )
-            cuts.append(cut_with_kmeans)
+            if do_normalize:
+                x = torch.nn.functional.layer_norm(x, x.shape)
+
+            feature, _ = model.extract_features(
+                source=x,
+                padding_mask=None,
+                mask=False,
+                output_layer=9,
+            )
+            feature = feature.squeeze(0)
+
+            current_kmeans = apply_kmeans(feature).tolist()
+
+            if start == 0:
+                kmeans.extend(current_kmeans)
+            else:
+                kmeans.extend(current_kmeans[out_overlap_size:])
+
+            if T - start <= window_size:
+                break
+
+            start += shift_size
+
+        kmeans = " ".join(map(str, kmeans))
+
+        cut_with_kmeans = fastcopy(
+            cut,
+            custom={"kmeans": kmeans},
+        )
+        cuts.append(cut_with_kmeans)
 
     cuts = CutSet(cuts)
 
@@ -166,6 +213,9 @@ def extract_kmeans(args):
     model = model[0].eval().to(device)
     do_normalize = task.cfg.normalize
 
+    window_duration = args.window_duration
+    shift_duration = args.shift_duration
+
     if args.subset == "small":
         cuts_path = output_dir / f"{prefix}_cuts_{args.subset}.jsonl.gz"
         if cuts_path.is_file():
@@ -183,7 +233,8 @@ def extract_kmeans(args):
             model,
             apply_kmeans,
             do_normalize,
-            device,
+            window_duration,
+            shift_duration,
         )
     else:
         num_digits = 8  # num_digits is fixed by lhotse split-lazy
@@ -213,10 +264,19 @@ def extract_kmeans(args):
                 model,
                 apply_kmeans,
                 do_normalize,
-                device,
+                window_duration,
+                shift_duration,
             )
 
 
+def get_out_length(T):
+    conv_layers = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+    for i, (out_channels, kernel_size, stride) in enumerate(conv_layers):
+        T = math.floor((T - kernel_size) / stride) + 1
+
+    return max(0, T)
+
+
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
 

egs/librilight/SSL/prepare.sh

@@ -86,15 +86,15 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
     wget https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960_L9_km500.bin -P download
   fi
   if [ ! -e data/kmeans/.extract_small.done ]; then
-    ./local/extract_kmeans_from_hubert_base.py --subset small
+    ./local/extract_kmeans.py --subset small
     touch data/kmeans/.extract_small.done
   fi
   if [ ! -e data/kmeans/.extract_medium.done ]; then
-    ./local/extract_kmeans_from_hubert_base.py --subset medium
+    ./local/extract_kmeans.py --subset medium
     touch data/kmeans/.extract_medium.done
   fi
   if [ ! -e data/kmeans/.extract_large.done ]; then
-    ./local/extract_kmeans_from_hubert_base.py --subset large
+    ./local/extract_kmeans.py --subset large
     touch data/kmeans/.extract_large.done
   fi
 fi