update

egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py

@@ -166,6 +166,41 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         help="Number of entries in the memory for the Emformer",
     )
 
+    parser.add_argument(
+        "--enable-distillation",
+        type=str2bool,
+        default=True,
+        help="Whether to eanble distillation.",
+    )
+
+    parser.add_argument(
+        "--distillation-layer",
+        type=int,
+        default=8,
+        help="On which encoder layer to perform KD"
+    )
+    
+    parser.add_argument(
+        "--num-codebooks",
+        type=int,
+        default=16,
+        help="Number of codebooks"
+    )
+
+    # distillation related args
+    parser.add_argument(
+        "--distil-delta",
+        type=int,
+        default=None,
+        help="Offset when doing KD"
+    )
+
+    parser.add_argument(
+        "--codebook-loss-scale",
+        type=float,
+        default=0.1,
+        help="The scale of codebook loss.",
+    )
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -358,41 +393,6 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
-    parser.add_argument(
-        "--enable-distillation",
-        type=str2bool,
-        default=True,
-        help="Whether to eanble distillation.",
-    )
-
-    parser.add_argument(
-        "--distillation-layer",
-        type=int,
-        default=8,
-        help="On which encoder layer to perform KD"
-    )
-    
-    parser.add_argument(
-        "--num-codebooks",
-        type=int,
-        default=16,
-        help="Number of codebooks"
-    )
-
-    parser.add_argument(
-        "--distil-delta",
-        type=int,
-        default=None,
-        help="Offset when doing KD"
-    )
-
-    parser.add_argument(
-        "--codebook-loss-scale",
-        type=float,
-        default=0.1,
-        help="The scale of codebook loss.",
-    )
-
     add_model_arguments(parser)
 
     return parser
@@ -444,6 +444,7 @@ def get_params() -> AttributeDict:
     """
     params = AttributeDict(
         {
+            "frame_shift_ms": 10.0,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
@@ -652,6 +653,9 @@ def extract_codebook_indexes(batch):
     cuts_pre_mixed = [
         c if isinstance(c, MonoCut) else c.tracks[0].cut for c in cuts
     ]
+    for cut in cuts_pre_mixed:
+        cb = cut.codebook_indexes
+    print(f"All cuts have codebook indexes")
     codebook_indexes, codebook_indexes_lens = collate_custom_field(
         cuts_pre_mixed, "codebook_indexes", pad_value=-100
     )
@@ -969,6 +973,11 @@ def run(rank, world_size, args):
     setup_logger(f"{params.exp_dir}/log/log-train")
     logging.info("Training started")
 
+    # Note: it's better to set --spec-aug-time-warpi-factor=-1
+    # when doing distillation with vq.
+    if params.enable_distillation:
+        assert args.spec_aug_time_warp_factor < 1, "You need to disable time warp in MVQ KD"
+
     if args.tensorboard and rank == 0:
         tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
     else:
@@ -1034,10 +1043,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    if params.full_libri:
-        train_cuts = librispeech.train_all_shuf_cuts()
-    else:
     train_cuts = librispeech.train_clean_100_cuts()
+    if params.full_libri:
+        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts += librispeech.train_other_500_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
@@ -1067,14 +1076,14 @@ def run(rank, world_size, args):
     valid_cuts += librispeech.dev_other_cuts()
     valid_dl = librispeech.valid_dataloaders(valid_cuts)
 
-    if not params.print_diagnostics:
+    # if not params.print_diagnostics:
-        scan_pessimistic_batches_for_oom(
+    #     scan_pessimistic_batches_for_oom(
-            model=model,
+    #         model=model,
-            train_dl=train_dl,
+    #         train_dl=train_dl,
-            optimizer=optimizer,
+    #         optimizer=optimizer,
-            sp=sp,
+    #         sp=sp,
-            params=params,
+    #         params=params,
-        )
+    #     )
 
     scaler = GradScaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints: