pruned-transducer-stateless5 recipe for aishell4

egs/aishell4/ASR/local/compute_fbank_aishell4.py

@@ -29,7 +29,7 @@ import os
 from pathlib import Path
 
 import torch
-from lhotse import CutSet, Fbank, FbankConfig, ChunkedLilcomHdf5Writer
+from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig
 from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor
@@ -54,7 +54,7 @@ def compute_fbank_aishell4(num_mel_bins: int = 80):
         "test",
     )
     manifests = read_manifests_if_cached(
-        dataset_parts=dataset_parts, 
+        dataset_parts=dataset_parts,
         output_dir=src_dir,
         prefix="aishell4",
         suffix="jsonl.gz",

egs/aishell4/ASR/pruned_transducer_stateless5/decode.py

@@ -64,7 +64,6 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import Aishell4AsrDataModule
@@ -83,6 +82,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -152,7 +152,7 @@ def get_parser():
         "lexicon.txt"
         """,
     )
-    
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -628,7 +628,7 @@ def main():
         shuffle_shards=True,
     )
 
-    test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
+    test_dl = aishell4.test_dataloaders(cuts_test_webdataset)
 
     test_sets = ["test"]
     test_dl = [test_dl]

egs/aishell4/ASR/pruned_transducer_stateless5/train.py

@@ -81,6 +81,7 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 LRSchedulerType = Union[
@@ -752,29 +753,25 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
-                loss, loss_info = compute_loss(
-                    params=params,
-                    model=model,
-                    graph_compiler=graph_compiler,
-                    batch=batch,
-                    is_training=True,
-                    warmup=(params.batch_idx_train / params.model_warm_step),
-                )
-            # summary stats
-            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            loss, loss_info = compute_loss(
+                params=params,
+                model=model,
+                graph_compiler=graph_compiler,
+                batch=batch,
+                is_training=True,
+                warmup=(params.batch_idx_train / params.model_warm_step),
+            )
+        # summary stats
+        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
 
-            # NOTE: We use reduction==sum and loss is computed over utterances
-            # in the batch and there is no normalization to it so far.
-            scaler.scale(loss).backward()
-            scheduler.step_batch(params.batch_idx_train)
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
-        except:  # noqa
-            display_and_save_batch(batch, params=params, sp=sp)
-            raise
+        # NOTE: We use reduction==sum and loss is computed over utterances
+        # in the batch and there is no normalization to it so far.
+        scaler.scale(loss).backward()
+        scheduler.step_batch(params.batch_idx_train)
+        scaler.step(optimizer)
+        scaler.update()
+        optimizer.zero_grad()
 
         if params.print_diagnostics and batch_idx == 5:
             return
@@ -897,7 +894,7 @@ def run(rank, world_size, args):
         lexicon=lexicon,
         device=device,
     )
-    
+
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
@@ -1039,43 +1036,11 @@ def run(rank, world_size, args):
         cleanup_dist()
 
 
-def display_and_save_batch(
-    batch: dict,
-    params: AttributeDict,
-    sp: spm.SentencePieceProcessor,
-) -> None:
-    """Display the batch statistics and save the batch into disk.
-
-    Args:
-      batch:
-        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
-        for the content in it.
-      params:
-        Parameters for training. See :func:`get_params`.
-      sp:
-        The BPE model.
-    """
-    from lhotse.utils import uuid4
-
-    filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
-    logging.info(f"Saving batch to {filename}")
-    torch.save(batch, filename)
-
-    supervisions = batch["supervisions"]
-    features = batch["inputs"]
-
-    logging.info(f"features shape: {features.shape}")
-
-    y = sp.encode(supervisions["text"], out_type=int)
-    num_tokens = sum(len(i) for i in y)
-    logging.info(f"num tokens: {num_tokens}")
-
-
 def scan_pessimistic_batches_for_oom(
     model: Union[nn.Module, DDP],
     train_dl: torch.utils.data.DataLoader,
     optimizer: torch.optim.Optimizer,
-    sp: spm.SentencePieceProcessor,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     params: AttributeDict,
 ):
     from lhotse.dataset import find_pessimistic_batches
@@ -1094,7 +1059,7 @@ def scan_pessimistic_batches_for_oom(
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
-                    sp=sp,
+                    graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
                     warmup=0.0,
@@ -1111,7 +1076,6 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, sp=sp)
             raise