add webdataset for dataload

egs/wenetspeech/ASR/pruned_transducer_stateless/decode.py

@@ -322,12 +322,10 @@ def decode_one_batch(
 
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
-    import time
-    st1 = time.time()
+
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    ed1 = time.time()
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -346,15 +344,12 @@ def decode_one_batch(
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
     ):
-        st2 = time.time()
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
         )
-        ed2 = time.time()
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-        ed3 = time.time()
     else:
         batch_size = encoder_out.size(0)
 
@@ -438,8 +433,6 @@ def decode_dataset(
     else:
         log_interval = 2
 
-    import time
-    ed = time.time()
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
         texts = batch["supervisions"]["text"]
@@ -450,8 +443,6 @@ def decode_dataset(
             texts = [pinyin(text) for text in texts]
             for i in range(len(texts)):
                 texts[i] = [token[0] for token in texts[i]]
-        st = time.time()
-        print(f"loading data time: {st - ed}")
         hyps_dict = decode_one_batch(
             params=params,
             model=model,
@@ -459,7 +450,6 @@ def decode_dataset(
             batch=batch,
             decoding_graph=decoding_graph,
         )
-        ed = time.time()
         for name, hyps in hyps_dict.items():
             this_batch = []
             assert len(hyps) == len(texts)
@@ -469,14 +459,14 @@ def decode_dataset(
             results[name].extend(this_batch)
 
         num_cuts += len(texts)
-        
+
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
             logging.info(
                 f"batch {batch_str}, cuts processed until now is {num_cuts}"
             )
-        
+
     return results
 
 
@@ -594,11 +584,12 @@ 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" 
+
+    # Note: Please use "pip install webdataset==0.1.103"
     # for installing the webdataset.
-    import os
     import glob
+    import os
+
     from lhotse import CutSet
     from lhotse.dataset.webdataset import export_to_webdataset
 
@@ -606,14 +597,15 @@ def main():
 
     test_net = "test_net"
     test_meet = "test_meet"
-    if os.path.exists(f"{test_net}/shared-0.tar"): 
+    if not os.path.exists(f"{test_net}/shared-0.tar"):
         test_net_cuts = wenetspeech.test_net_cuts()
         export_to_webdataset(
             test_net_cuts,
             output_path=f"{test_net}/shared-%d.tar",
             shard_size=300,
         )
-    if os.path.exists(f"{test_meet}/shared-0.tar"):
+
+    if not os.path.exists(f"{test_meet}/shared-0.tar"):
         test_meeting_cuts = wenetspeech.test_meeting_cuts()
         export_to_webdataset(
             test_meeting_cuts,
@@ -621,15 +613,21 @@ def main():
             shard_size=300,
         )
 
-    test_net_shards = [str(path) for path in sorted(glob.glob(os.path.join(test_net, "shared-*.tar")))]
+    test_net_shards = [
+        str(path)
+        for path in sorted(glob.glob(os.path.join(test_net, "shared-*.tar")))
+    ]
     cuts_test_net_webdataset = CutSet.from_webdataset(
         test_net_shards,
         split_by_worker=True,
         split_by_node=True,
         shuffle_shards=True,
-    ) 
+    )
 
-    test_meet_shards = [str(path) for path in sorted(glob.glob(os.path.join(test_meet, "shared-*.tar")))]
+    test_meet_shards = [
+        str(path)
+        for path in sorted(glob.glob(os.path.join(test_meet, "shared-*.tar")))
+    ]
     cuts_test_meet_webdataset = CutSet.from_webdataset(
         test_meet_shards,
         split_by_worker=True,