WIP: Add doc for the LibriSpeech recipe.

docs/source/index.rst

@@ -3,7 +3,7 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-icefall
+Icefall
 =======
 
 .. image:: _static/logo.png

docs/source/recipes/librispeech.rst

@@ -1,2 +1,10 @@
 LibriSpeech
 ===========
+
+We provide the following models for the LibriSpeech dataset:
+
+.. toctree::
+   :maxdepth: 2
+
+   librispeech/tdnn_lstm_ctc
+   librispeech/conformer_ctc

docs/source/recipes/librispeech/conformer_ctc.rst

@@ -0,0 +1,2 @@
+Confromer CTC
+=============

docs/source/recipes/librispeech/tdnn_lstm_ctc.rst

@@ -0,0 +1,2 @@
+TDNN LSTM CTC
+=============

egs/librispeech/ASR/tdnn_lstm_ctc/train.py

@@ -75,6 +75,23 @@ def get_parser():
         help="Should various information be logged in tensorboard.",
     )
 
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=20,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=0,
+        help="""Resume training from from this epoch.
+        If it is positive, it will load checkpoint from
+        tdnn_lstm_ctc/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
     return parser
 
 
@@ -104,11 +121,6 @@ def get_params() -> AttributeDict:
 
         - subsampling_factor:  The subsampling factor for the model.
 
-        - start_epoch:  If it is not zero, load checkpoint `start_epoch-1`
-                        and continue training from that checkpoint.
-
-        - num_epochs:  Number of epochs to train.
-
         - best_train_loss: Best training loss so far. It is used to select
                            the model that has the lowest training loss. It is
                            updated during the training.
@@ -127,6 +139,8 @@ def get_params() -> AttributeDict:
 
         - log_interval:  Print training loss if batch_idx % log_interval` is 0
 
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
         - valid_interval:  Run validation if batch_idx % valid_interval` is 0
 
         - beam_size: It is used in k2.ctc_loss
@@ -143,14 +157,13 @@ def get_params() -> AttributeDict:
             "feature_dim": 80,
             "weight_decay": 5e-4,
             "subsampling_factor": 3,
-            "start_epoch": 0,
-            "num_epochs": 10,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
             "batch_idx_train": 0,
             "log_interval": 10,
+            "reset_interval": 200,
             "valid_interval": 1000,
             "beam_size": 10,
             "reduction": "sum",
@@ -398,8 +411,12 @@ def train_one_epoch(
     """
     model.train()
 
-    tot_loss = 0.0  # sum of losses over all batches
-    tot_frames = 0.0  # sum of frames over all batches
+    tot_loss = 0.0  # reset after params.reset_interval of batches
+    tot_frames = 0.0  # reset after params.reset_interval of batches
+
+    params.tot_loss = 0.0
+    params.tot_frames = 0.0
+
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
@@ -426,6 +443,9 @@ def train_one_epoch(
         tot_loss += loss_cpu
         tot_avg_loss = tot_loss / tot_frames
 
+        params.tot_frames += params.train_frames
+        params.tot_loss += loss_cpu
+
         if batch_idx % params.log_interval == 0:
             logging.info(
                 f"Epoch {params.cur_epoch}, batch {batch_idx}, "
@@ -433,6 +453,22 @@ def train_one_epoch(
                 f"total avg loss: {tot_avg_loss:.4f}, "
                 f"batch size: {batch_size}"
             )
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/current_loss",
+                    loss_cpu / params.train_frames,
+                    params.batch_idx_train,
+                )
+
+                tb_writer.add_scalar(
+                    "train/tot_avg_loss",
+                    tot_avg_loss,
+                    params.batch_idx_train,
+                )
+
+        if batch_idx > 0 and batch_idx % params.reset_interval == 0:
+            tot_loss = 0
+            tot_frames = 0
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             compute_validation_loss(
@@ -449,7 +485,7 @@ def train_one_epoch(
                 f"best valid epoch: {params.best_valid_epoch}"
             )
 
-    params.train_loss = tot_loss / tot_frames
+    params.train_loss = params.tot_loss / params.tot_frames
 
     if params.train_loss < params.best_train_loss:
         params.best_train_epoch = params.cur_epoch