lazy loading and use SingleCutSampler

egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py

@@ -75,14 +75,14 @@ class GigaSpeechAsrDataModule:
         group.add_argument(
             "--max-duration",
             type=int,
-            default=600.0,
+            default=200.0,
             help="Maximum pooled recordings duration (seconds) in a "
             "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
-            default=True,
+            default=False,
             help="When enabled, the batches will come from buckets of "
             "similar duration (saves padding frames).",
         )
@@ -179,6 +179,12 @@ class GigaSpeechAsrDataModule:
             default="XL",
             help="Select the GigaSpeech subset (XS|S|M|L|XL)",
         )
+        group.add_argument(
+            "--lazy-load",
+            type=str2bool,
+            default=True,
+            help="lazily open CutSets to avoid OOM (for L|XL subset)",
+        )
         group.add_argument(
             "--small-dev",
             type=str2bool,
@@ -354,9 +360,12 @@ class GigaSpeechAsrDataModule:
     @lru_cache()
     def train_cuts(self) -> CutSet:
         logging.info(f"About to get train_{self.args.subset} cuts")
-        return load_manifest(
-            self.args.manifest_dir / f"cuts_{self.args.subset}.jsonl.gz"
-        )
+        path = self.args.manifest_dir / f"cuts_{self.args.subset}.jsonl.gz"
+        if self.args.subset in ["L", "XL"] and self.args.lazy_load:
+            cuts_train = CutSet.from_jsonl_lazy(path)
+        else:
+            cuts_train = CutSet.from_file(path)
+        return cuts_train
 
     @lru_cache()
     def dev_cuts(self) -> CutSet:

egs/gigaspeech/ASR/conformer_ctc/train.py

@@ -625,7 +625,7 @@ def run(rank, world_size, args):
     train_cuts = GigaSpeech.train_cuts()
     train_dl = GigaSpeech.train_dataloaders(train_cuts)
 
-    valid_cuts = GigaSpeech.dev_clean_cuts()
+    valid_cuts = GigaSpeech.dev_cuts()
     valid_dl = GigaSpeech.valid_dataloaders(valid_cuts)
 
     scan_pessimistic_batches_for_oom(