minor updates

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -102,7 +102,7 @@ class Aidatatang_200zhAsrDataModule:
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
-            default=True,
+            default=False,
             help="When enabled, the batches will come from buckets of "
             "similar duration (saves padding frames).",
         )
@@ -289,6 +289,7 @@ class Aidatatang_200zhAsrDataModule:
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
                 drop_last=True,
+                buffer_size=50000,
             )
         else:
             logging.info("Using SingleCutSampler.")

egs/aidatatang_200zh/ASR/zipformer/decode.py

@@ -88,7 +88,7 @@ from typing import Dict, List, Optional, Tuple
 import k2
 import torch
 import torch.nn as nn
-from asr_datamodule import WenetSpeechAsrDataModule
+from asr_datamodule import Aidatatang_200zhAsrDataModule
 from beam_search import (
     beam_search,
     fast_beam_search_nbest,
@@ -596,7 +596,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    WenetSpeechAsrDataModule.add_arguments(parser)
+    Aidatatang_200zhAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -770,7 +770,7 @@ def main():
 
     # we need cut ids to display recognition results.
     args.return_cuts = True
-    wenetspeech = WenetSpeechAsrDataModule(args)
+    aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
 
     def remove_short_utt(c: Cut):
         T = ((c.num_frames - 7) // 2 + 1) // 2
@@ -780,20 +780,16 @@ def main():
             )
         return T > 0
 
-    dev_cuts = wenetspeech.valid_cuts()
+    dev_cuts = aidatatang_200zh.valid_cuts()
     dev_cuts = dev_cuts.filter(remove_short_utt)
-    dev_dl = wenetspeech.valid_dataloaders(dev_cuts)
+    dev_dl = aidatatang_200zh.valid_dataloaders(dev_cuts)
 
-    test_net_cuts = wenetspeech.test_net_cuts()
-    test_net_cuts = test_net_cuts.filter(remove_short_utt)
-    test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)
+    test_cuts = aidatatang_200zh.test_cuts()
+    test_cuts = test_cuts.filter(remove_short_utt)
+    test_dl = aidatatang_200zh.test_dataloaders(test_cuts)
 
-    test_meeting_cuts = wenetspeech.test_meeting_cuts()
-    test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt)
-    test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
-
-    test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
-    test_dls = [dev_dl, test_net_dl, test_meeting_dl]
+    test_sets = ["valid_cuts", "test_cuts"]
+    test_dls = [dev_dl, test_dl]
 
     for test_set, test_dl in zip(test_sets, test_dls):
         results_dict = decode_dataset(

egs/aidatatang_200zh/ASR/zipformer/streaming_decode.py

@@ -39,7 +39,7 @@ from typing import Dict, List, Optional, Tuple
 import k2
 import numpy as np
 import torch
-from asr_datamodule import WenetSpeechAsrDataModule
+from asr_datamodule import Aidatatang_200zhAsrDataModule
 from decode_stream import DecodeStream
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
@@ -386,7 +386,11 @@ def streaming_forward(
     Returns encoder outputs, output lengths, and updated states.
     """
     cached_embed_left_pad = states[-2]
-    (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
+    (
+        x,
+        x_lens,
+        new_cached_embed_left_pad,
+    ) = model.encoder_embed.streaming_forward(
         x=features,
         x_lens=feature_lens,
         cached_left_pad=cached_embed_left_pad,
@@ -713,7 +717,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    WenetSpeechAsrDataModule.add_arguments(parser)
+    Aidatatang_200zhAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -851,14 +855,13 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    wenetspeech = WenetSpeechAsrDataModule(args)
+    aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
 
-    dev_cuts = wenetspeech.valid_cuts()
-    test_net_cuts = wenetspeech.test_net_cuts()
-    test_meeting_cuts = wenetspeech.test_meeting_cuts()
+    dev_cuts = aidatatang_200zh.valid_cuts()
+    test_cuts = aidatatang_200zh.test_cuts()
 
-    test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
-    test_cuts = [dev_cuts, test_net_cuts, test_meeting_cuts]
+    test_sets = ["valid_cuts", "test_cuts"]
+    test_cuts = [dev_cuts, test_cuts]
 
     for test_set, test_cut in zip(test_sets, test_cuts):
         results_dict = decode_dataset(