added scripts for streaming related stuff

egs/mdcc/ASR/zipformer/decode_stream.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decode_stream.py

egs/mdcc/ASR/zipformer/onnx_decode.py

@@ -31,7 +31,7 @@ from typing import List, Tuple
 import k2
 import torch
 import torch.nn as nn
-from asr_datamodule import AishellAsrDataModule
+from asr_datamodule import MdccAsrDataModule
 from lhotse.cut import Cut
 from onnx_pretrained import OnnxModel, greedy_search
 
@@ -212,7 +212,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    AishellAsrDataModule.add_arguments(parser)
+    MdccAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
 
     assert (
@@ -241,7 +241,7 @@ def main():
     # we need cut ids to display recognition results.
     args.return_cuts = True
 
-    aishell = AishellAsrDataModule(args)
+    mdcc = MdccAsrDataModule(args)
 
     def remove_short_utt(c: Cut):
         T = ((c.num_frames - 7) // 2 + 1) // 2
@@ -251,16 +251,16 @@ def main():
             )
         return T > 0
 
-    dev_cuts = aishell.valid_cuts()
+    valid_cuts = mdcc.valid_cuts()
-    dev_cuts = dev_cuts.filter(remove_short_utt)
+    valid_cuts = valid_cuts.filter(remove_short_utt)
-    dev_dl = aishell.valid_dataloaders(dev_cuts)
+    valid_dl = mdcc.valid_dataloaders(valid_cuts)
 
-    test_cuts = aishell.test_net_cuts()
+    test_cuts = mdcc.test_net_cuts()
     test_cuts = test_cuts.filter(remove_short_utt)
-    test_dl = aishell.test_dataloaders(test_cuts)
+    test_dl = mdcc.test_dataloaders(test_cuts)
 
-    test_sets = ["dev", "test"]
+    test_sets = ["valid", "test"]
-    test_dl = [dev_dl, test_dl]
+    test_dl = [valid_dl, test_dl]
 
     for test_set, test_dl in zip(test_sets, test_dl):
         start_time = time.time()

egs/mdcc/ASR/zipformer/streaming_beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/streaming_beam_search.py

egs/mdcc/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 AishellAsrDataModule
+from asr_datamodule import MdccAsrDataModule
 from decode_stream import DecodeStream
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
@@ -177,7 +177,7 @@ def get_parser():
     parser.add_argument(
         "--context-size",
         type=int,
-        default=2,
+        default=1,
         help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
     )
 
@@ -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,
@@ -714,7 +718,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    AishellAsrDataModule.add_arguments(parser)
+    MdccAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -852,13 +856,13 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    aishell = AishellAsrDataModule(args)
+    mdcc = MdccAsrDataModule(args)
 
-    dev_cuts = aishell.valid_cuts()
+    valid_cuts = mdcc.valid_cuts()
-    test_cuts = aishell.test_cuts()
+    test_cuts = mdcc.test_cuts()
 
-    test_sets = ["dev", "test"]
+    test_sets = ["valid", "test"]
-    test_cuts = [dev_cuts, test_cuts]
+    test_cuts = [valid_cuts, test_cuts]
 
     for test_set, test_cut in zip(test_sets, test_cuts):
         results_dict = decode_dataset(