Removed unused k2 dependencies from the AT recipe (#1633)

egs/audioset/AT/zipformer/at_datamodule.py

@@ -373,9 +373,11 @@ def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = AudioTaggingDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            if self.args.on_the_fly_feats
-            else eval(self.args.input_strategy)(),
+            input_strategy=(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+                if self.args.on_the_fly_feats
+                else eval(self.args.input_strategy)()
+            ),
             return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(

egs/audioset/AT/zipformer/evaluate.py

@@ -29,27 +29,18 @@
 """
 
 import argparse
-import csv
 import logging
-import math
-import os
-from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import Dict
 
-import k2
-import numpy as np
-import sentencepiece as spm
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from at_datamodule import AudioSetATDatamodule
-from lhotse import load_manifest
 
 try:
     from sklearn.metrics import average_precision_score
-except Exception as ex:
-    raise RuntimeError(f"{ex}\nPlease run\n" "pip3 install -U scikit-learn")
+except:
+    raise ImportError(f"Please run\n" "pip3 install -U scikit-learn")
 from train import add_model_arguments, get_model, get_params, str2multihot
 
 from icefall.checkpoint import (
@@ -58,15 +49,7 @@
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import Lexicon
-from icefall.utils import (
-    AttributeDict,
-    make_pad_mask,
-    setup_logger,
-    store_transcripts,
-    str2bool,
-    write_error_stats,
-)
+from icefall.utils import AttributeDict, setup_logger, str2bool
 
 
 def get_parser():

egs/audioset/AT/zipformer/export-onnx.py

@@ -36,7 +36,6 @@
 from pathlib import Path
 from typing import Dict
 
-import k2
 import onnx
 import onnxoptimizer
 import torch
@@ -53,7 +52,7 @@
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import make_pad_mask, num_tokens, str2bool
+from icefall.utils import make_pad_mask, str2bool
 
 
 def get_parser():

egs/audioset/AT/zipformer/jit_pretrained.py

@@ -50,7 +50,6 @@
 import math
 from typing import List
 
-import k2
 import kaldifeat
 import torch
 import torchaudio

egs/audioset/AT/zipformer/model.py

@@ -14,17 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-import random
-from typing import List, Optional, Tuple
+from typing import Tuple
 
-import k2
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from encoder_interface import EncoderInterface
 
-from icefall.utils import AttributeDict, make_pad_mask
+from icefall.utils import make_pad_mask
 
 
 class AudioTaggingModel(nn.Module):

egs/audioset/AT/zipformer/onnx_pretrained.py

@@ -42,9 +42,8 @@
 import csv
 import logging
 import math
-from typing import List, Tuple
+from typing import List
 
-import k2
 import kaldifeat
 import onnxruntime as ort
 import torch

egs/audioset/AT/zipformer/train.py

@@ -41,7 +41,6 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import optim
-import sentencepiece as spm
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
@@ -632,7 +631,7 @@ def compute_loss(
       model:
         The model for training. It is an instance of Zipformer in our case.
       batch:
-        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        A batch of data. See `lhotse.dataset.AudioTaggingDataset()`
         for the content in it.
       is_training:
         True for training. False for validation. When it is True, this
@@ -1108,7 +1107,7 @@ def display_and_save_batch(
 
     Args:
       batch:
-        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        A batch of data. See `lhotse.dataset.AudioTaggingDataset()`
         for the content in it.
       params:
         Parameters for training. See :func:`get_params`.