Minor refinements for some stale but recently merged PRs (#1354)

* incorporate #1269

* incorporate #1301

* black formatted

* incorporate #1162

* black formatted

egs/aishell/ASR/zipformer/train.py

@@ -1128,7 +1128,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/gigaspeech/ASR/zipformer/asr_datamodule.py

@@ -233,7 +233,7 @@ def train_dataloaders(
             logging.info("About to get Musan cuts")
             cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
             )
         else:
             logging.info("Disable MUSAN")

egs/gigaspeech/ASR/zipformer/train.py

@@ -1164,7 +1164,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/libriheavy/ASR/zipformer_prompt_asr/optim.py

@@ -116,7 +116,7 @@ def batched_params(self, param_group, group_params_names):
 
         yield tuples  # <-- calling code will do the actual optimization here!
 
-        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+        for (stacked_params, _state, _names), batch in zip(tuples, batches):
             for i, p in enumerate(batch):  # batch is list of Parameter
                 p.copy_(stacked_params[i])
 
@@ -181,7 +181,6 @@ def __init__(
         size_update_period=4,
         clipping_update_period=100,
     ):
-
         defaults = dict(
             lr=lr,
             clipping_scale=clipping_scale,
@@ -327,9 +326,7 @@ def step(self, closure=None):
         batch = True
 
         for group, group_params_names in zip(self.param_groups, self.parameters_names):
-
             with self.batched_params(group["params"], group_params_names) as batches:
-
                 # batches is list of pairs (stacked_param, state).  stacked_param is like
                 # a regular parameter, and will have a .grad, but the 1st dim corresponds to
                 # a stacking dim, it is not a real dim.
@@ -429,7 +426,7 @@ def _get_clipping_scale(
         clipping_update_period = group["clipping_update_period"]
 
         tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for (p, state, param_names) in tuples:
+        for p, state, param_names in tuples:
             grad = p.grad
             if grad.is_sparse:
                 raise RuntimeError(
@@ -514,7 +511,7 @@ def _show_gradient_dominating_parameter(
                 from tuples, we still pass it to save some time.
         """
         all_sumsq_orig = {}
-        for (p, state, batch_param_names) in tuples:
+        for p, state, batch_param_names in tuples:
             # p is a stacked batch parameters.
             batch_grad = p.grad
             if p.numel() == p.shape[0]:  # a batch of scalars
@@ -530,7 +527,6 @@ def _show_gradient_dominating_parameter(
             for name, sumsq_orig, rms, grad in zip(
                 batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
             ):
-
                 proportion_orig = sumsq_orig / tot_sumsq
                 all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
 
@@ -1106,7 +1102,7 @@ def _test_scaled_adam(hidden_dim: int):
 
             # if epoch == 130:
             #    opts = diagnostics.TensorDiagnosticOptions(
-            #        2 ** 22
+            #        512
             #    )  # allow 4 megabytes per sub-module
             #    diagnostic = diagnostics.attach_diagnostics(m, opts)
 

egs/libriheavy/ASR/zipformer_prompt_asr/train_baseline.py

@@ -1194,7 +1194,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/libriheavy/ASR/zipformer_prompt_asr/train_bert_encoder.py

@@ -1565,7 +1565,7 @@ def run(rank, world_size, args):
     if params.print_diagnostics:
         args.max_duration = 100
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/tiny_transducer_ctc/asr_datamodule.py

@@ -31,7 +31,7 @@
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
@@ -225,7 +225,7 @@ def train_dataloaders(
             logging.info("About to get Musan cuts")
             cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
             )
         else:
             logging.info("Disable MUSAN")
@@ -307,8 +307,8 @@ def train_dataloaders(
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
-            train_sampler = SingleCutSampler(
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/librispeech/ASR/tiny_transducer_ctc/ctc_decode.py

@@ -22,10 +22,11 @@
 import argparse
 import logging
 import math
+import pprint
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
-import pprint
+
 import k2
 import sentencepiece as spm
 import torch

egs/librispeech/ASR/tiny_transducer_ctc/encoder.py

@@ -253,7 +253,6 @@ def moving_avg(self, x: Tensor) -> Tensor:
         return y
 
     def forward(self, x: Tensor) -> Tensor:
-
         assert len(x.shape) == 3, "Input is not a 3D tensor!"
         y = self.exponential_moving_avg(x)
         y = y.permute(0, 2, 1)  # make channel last for squeeze op

egs/librispeech/ASR/tiny_transducer_ctc/export.py

@@ -76,17 +76,17 @@
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
+from train import add_model_arguments, get_params, get_transducer_model
+
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import UniqLexicon
-from icefall.utils import str2bool
-from train import add_model_arguments, get_params, get_transducer_model
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -143,13 +143,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500",
-        help="""The lang dir
-        It contains language related input files such as
-        "lexicon.txt"
-        """,
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -189,17 +186,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    if "lang_bpe" in str(params.lang_dir):
-        sp = spm.SentencePieceProcessor()
-        sp.load(params.lang_dir + "/bpe.model")
-        # <blk> is defined in local/train_bpe_model.py
-        params.blank_id = sp.piece_to_id("<blk>")
-        params.vocab_size = sp.get_piece_size()
-    else:
-        assert "lang_phone" in str(params.lang_dir)
-        phone_lexicon = UniqLexicon(params.lang_dir)
-        params.blank_id = 0
-        params.vocab_size = max(phone_lexicon.tokens) + 1
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/tiny_transducer_ctc/train.py

@@ -89,7 +89,6 @@ def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
-
     parser.add_argument(
         "--encoder-dim",
         type=int,
@@ -405,7 +404,6 @@ def get_params() -> AttributeDict:
 
 
 def get_encoder_model(params: AttributeDict) -> nn.Module:
-
     encoder = Conv1dNet(
         output_dim=params.encoder_dim,
         input_dim=params.feature_dim,
@@ -1043,7 +1041,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/zipformer_ctc/export.py

@@ -23,6 +23,7 @@
 import logging
 from pathlib import Path
 
+import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_ctc_model, get_params
@@ -33,8 +34,7 @@
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.lexicon import Lexicon
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -90,11 +90,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500",
-        help="""It contains language related input files such as "lexicon.txt"
-        """,
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -113,17 +112,15 @@ def get_parser():
 def main():
     args = get_parser().parse_args()
     args.exp_dir = Path(args.exp_dir)
-    args.lang_dir = Path(args.lang_dir)
 
     params = get_params()
     params.update(vars(args))
 
-    logging.info(params)
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
-    lexicon = Lexicon(params.lang_dir)
-    max_token_id = max(lexicon.tokens)
-    num_classes = max_token_id + 1  # +1 for the blank
-    params.vocab_size = num_classes
+    logging.info(params)
 
     device = torch.device("cpu")
     if torch.cuda.is_available():

egs/librispeech/ASR/zipformer_ctc/train.py

@@ -947,7 +947,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

icefall/diagnostics.py

@@ -244,16 +244,14 @@ def get_count(count):
 
                 if stats_type == "eigs":
                     try:
-                        if hasattr(torch, 'linalg') and hasattr(torch.linalg, 'eigh'):
+                        if hasattr(torch, "linalg") and hasattr(torch.linalg, "eigh"):
                             eigs, _ = torch.linalg.eigh(stats)
                         else:
                             eigs, _ = torch.symeig(stats)
                         stats = eigs.abs().sqrt()
                     except:  # noqa
-                        print(
-                            "Error getting eigenvalues, trying another method."
-                        )
-                        if hasattr(torch, 'linalg') and hasattr(torch.linalg, 'eig'):
+                        print("Error getting eigenvalues, trying another method.")
+                        if hasattr(torch, "linalg") and hasattr(torch.linalg, "eig"):
                             eigs, _ = torch.linalg.eig(stats)
                             eigs = eigs.abs()
                         else:
@@ -579,10 +577,15 @@ def forward_hook(_module, _input, _output, _model_diagnostic=ans, _name=name):
                 )
             elif isinstance(_output, tuple):
                 for i, o in enumerate(_output):
-                    if isinstance(o, Tensor) and o.dtype in ( torch.float32, torch.float16, torch.float64 ):
-                        _model_diagnostic[f"{_name}.output[{i}]"].accumulate(o,
-                                                                             class_name=get_class_name(_module))
-
+                    if isinstance(o, Tensor) and o.dtype in (
+                        torch.float32,
+                        torch.float16,
+                        torch.float64,
+                    ):
+                        _model_diagnostic[f"{_name}.output[{i}]"].accumulate(
+                            o, class_name=get_class_name(_module)
+                        )
+
         def backward_hook(_module, _input, _output, _model_diagnostic=ans, _name=name):
             if isinstance(_output, tuple) and len(_output) == 1:
                 _output = _output[0]
@@ -596,9 +599,15 @@ def backward_hook(_module, _input, _output, _model_diagnostic=ans, _name=name):
                 )
             elif isinstance(_output, tuple):
                 for i, o in enumerate(_output):
-                    if isinstance(o, Tensor) and o.dtype in ( torch.float32, torch.float16, torch.float64 ):
-                        _model_diagnostic[f"{_name}.grad[{i}]"].accumulate(o,
-                                                                           class_name=get_class_name(_module))
+                    if isinstance(o, Tensor) and o.dtype in (
+                        torch.float32,
+                        torch.float16,
+                        torch.float64,
+                    ):
+                        _model_diagnostic[f"{_name}.grad[{i}]"].accumulate(
+                            o, class_name=get_class_name(_module)
+                        )
+
         module.register_forward_hook(forward_hook)
         module.register_backward_hook(backward_hook)