combine the training data and decode without webdataset

egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -209,13 +209,6 @@ class Aishell4AsrDataModule:
             help="AudioSamples or PrecomputedFeatures",
         )
 
-        group.add_argument(
-            "--training-subset",
-            type=str,
-            default="L",
-            help="The training subset for using",
-        )
-
     def train_dataloaders(
         self,
         cuts_train: CutSet,
@@ -379,18 +372,14 @@ class Aishell4AsrDataModule:
         valid_sampler = DynamicBucketingSampler(
             cuts_valid,
             max_duration=self.args.max_duration,
+            rank=0,
+            world_size=1,
             shuffle=False,
         )
         logging.info("About to create dev dataloader")
-
-        from lhotse.dataset.iterable_dataset import IterableDatasetWrapper
-
-        dev_iter_dataset = IterableDatasetWrapper(
-            dataset=validate,
-            sampler=valid_sampler,
-        )
         valid_dl = DataLoader(
-            dev_iter_dataset,
+            validate,
+            sampler=valid_sampler,
             batch_size=None,
             num_workers=self.args.num_workers,
             persistent_workers=False,
@@ -409,27 +398,38 @@ class Aishell4AsrDataModule:
         sampler = DynamicBucketingSampler(
             cuts,
             max_duration=self.args.max_duration,
+            rank=0,
+            world_size=1,
             shuffle=False,
         )
-        from lhotse.dataset.iterable_dataset import IterableDatasetWrapper
+        logging.info("About to create test dataloader")
-
-        test_iter_dataset = IterableDatasetWrapper(
-            dataset=test,
-            sampler=sampler,
-        )
         test_dl = DataLoader(
-            test_iter_dataset,
+            test,
             batch_size=None,
+            sampler=sampler,
             num_workers=self.args.num_workers,
         )
         return test_dl
 
     @lru_cache()
-    def train_cuts(self) -> CutSet:
+    def train_S_cuts(self) -> CutSet:
-        logging.info("About to get train cuts")
+        logging.info("About to get S train cuts")
         return load_manifest_lazy(
-            self.args.manifest_dir
+            self.args.manifest_dir / "aishell4_cuts_train_S.jsonl.gz"
-            / f"aishell4_cuts_train_{self.args.training_subset}.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_M_cuts(self) -> CutSet:
+        logging.info("About to get M train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell4_cuts_train_M.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_L_cuts(self) -> CutSet:
+        logging.info("About to get L train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell4_cuts_train_L.jsonl.gz"
         )
 
     @lru_cache()

egs/aishell4/ASR/pruned_transducer_stateless5/decode.py

@@ -74,6 +74,8 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
+from lhotse.cut import Cut
+from local.text_normalize import text_normalize
 from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
@@ -380,6 +382,7 @@ def decode_dataset(
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
         texts = batch["supervisions"]["text"]
+        texts = [list(str(text).replace(" ", "")) for text in texts]
 
         hyps_dict = decode_one_batch(
             params=params,
@@ -393,8 +396,7 @@ def decode_dataset(
             this_batch = []
             assert len(hyps) == len(texts)
             for hyp_words, ref_text in zip(hyps, texts):
-                ref_words = ref_text.split()
+                this_batch.append((ref_text, hyp_words))
-                this_batch.append((ref_words, hyp_words))
 
             results[name].extend(this_batch)
 
@@ -597,38 +599,17 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    # Note: Please use "pip install webdataset==0.1.103"
+    def text_normalize_for_cut(c: Cut):
-    # for installing the webdataset.
+        # Text normalize for each sample
-    import glob
+        text = c.supervisions[0].text
-    import os
+        text = text.strip("\n").strip("\t")
-
+        c.supervisions[0].text = text_normalize(text)
-    from lhotse import CutSet
+        return c
-    from lhotse.dataset.webdataset import export_to_webdataset
 
     aishell4 = Aishell4AsrDataModule(args)
-
-    test = "test"
-    if not os.path.exists(f"{test}/shared-0.tar"):
-        os.makedirs(test)
     test_cuts = aishell4.test_cuts()
-        export_to_webdataset(
+    test_cuts = test_cuts.map(text_normalize_for_cut)
-            test_cuts,
+    test_dl = aishell4.test_dataloaders(test_cuts)
-            output_path=f"{test}/shared-%d.tar",
-            shard_size=300,
-        )
-
-    test_shards = [
-        str(path)
-        for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
-    ]
-    cuts_test_webdataset = CutSet.from_webdataset(
-        test_shards,
-        split_by_worker=True,
-        split_by_node=True,
-        shuffle_shards=True,
-    )
-
-    test_dl = aishell4.test_dataloaders(cuts_test_webdataset)
 
     test_sets = ["test"]
     test_dl = [test_dl]

egs/aishell4/ASR/pruned_transducer_stateless5/train.py

@@ -389,14 +389,14 @@ def get_params() -> AttributeDict:
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
             "batch_idx_train": 0,
-            "log_interval": 1,
+            "log_interval": 50,
-            "reset_interval": 200,
+            "reset_interval": 100,
-            "valid_interval": 3000,  # For the 100h subset, use 800
+            "valid_interval": 200,
             # parameters for conformer
             "feature_dim": 80,
             "subsampling_factor": 4,
             # parameters for Noam
-            "model_warm_step": 3000,  # arg given to model, not for lrate
+            "model_warm_step": 50,  # arg given to model, not for lrate
             "env_info": get_env_info(),
         }
     )
@@ -942,8 +942,10 @@ def run(rank, world_size, args):
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 
     aishell4 = Aishell4AsrDataModule(args)
-
+    # Combine all of the training data
-    train_cuts = aishell4.train_cuts()
+    train_cuts = aishell4.train_S_cuts()
+    train_cuts += aishell4.train_M_cuts()
+    train_cuts += aishell4.train_L_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds