use lr hours in librilight ssl

egs/librilight/SSL/run_multi_node_multi_gpu.sh

@@ -97,20 +97,20 @@ torchrun \
   zipformer/pretrain.py \
     --use-multi-node 1 \
     --master-port $master_port \
-    --num-epochs 30 \
+    --num-epochs 20 \
     --start-epoch 1 \
     --use-fp16 1 \
     --exp-dir zipformer/exp_pretrain \
-    --max-duration 600 \
+    --max-duration 350 \
     --quadratic-duration 1024 \
     --accum-grad 1 \
     --do-normalize 1 \
     --mask-prob 0.8 \
-    --dropout-input 0.0 \
-    --dropout-features 0.0 \
+    --dropout-input 0.1 \
+    --dropout-features 0.1 \
     --feature-grad-mult 1.0 \
-    --num-encoder-layers 2,2,3,4,3,2 \
-    --feedforward-dim 512,768,1024,1536,1024,768 \
-    --encoder-dim 192,256,448,768,448,192 \
-    --encoder-unmasked-dim 192,192,256,256,256,192 \
-    --base-lr 0.045
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 768,1536,2048,3072,2048,1536 \
+    --encoder-dim 256,512,768,1024,768,512 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --base-lr 0.045

egs/librilight/SSL/zipformer/pretrain.py

@@ -472,10 +472,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lr-epochs",
+        "--lr-hours",
         type=float,
-        default=0.2,
-        help="""Number of epochs that affects how rapidly the learning rate decreases.
+        default=30000,
+        help="""Number of hours that affects how rapidly the learning rate decreases.
         """,
     )
 
@@ -951,6 +951,10 @@ def train_one_epoch(
             if sub_batch_idx % params.accum_grad == params.accum_grad - 1:
                 params.batch_idx_train += 1
                 scheduler.step_batch(params.batch_idx_train)
+                # Use the number of hours of speech to adjust the learning rate
+                scheduler.step_epoch(
+                    params.batch_idx_train * params.max_duration * params.world_size / 3600
+                )
 
                 scaler.step(optimizer)
                 scaler.update()
@@ -1142,7 +1146,7 @@ def run(rank, world_size, args):
     scheduler = Eden(
         optimizer,
         params.lr_batches,
-        params.lr_epochs,
+        params.lr_hours,
         params.warmup_batches,
         params.warmup_start,
     )
@@ -1246,7 +1250,6 @@ def run(rank, world_size, args):
         scaler.load_state_dict(checkpoints["grad_scaler"])
 
     for epoch in range(params.start_epoch, params.num_epochs + 1):
-        scheduler.step_epoch(epoch - 1)
         fix_random_seed(params.seed + epoch - 1)
         train_dl.sampler.set_epoch(epoch - 1)