Merge changes from master

.flake8

@@ -13,4 +13,5 @@ per-file-ignores =
 exclude =
   .git,
   **/data/**,
-  icefall/shared/make_kn_lm.py
+  icefall/shared/make_kn_lm.py,
+  icefall/__init__.py

.github/workflows/style_check.yml

@@ -45,7 +45,9 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2
+          python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2 click==8.0.4
+          # See https://github.com/psf/black/issues/2964
+          # The version of click should be selected from 8.0.0, 8.0.1, 8.0.2, 8.0.3, and 8.0.4
 
       - name: Run flake8
         shell: bash

docs/source/installation/index.rst

@@ -27,9 +27,21 @@ Installation
 ``icefall`` depends on `k2 <https://github.com/k2-fsa/k2>`_ and
 `lhotse <https://github.com/lhotse-speech/lhotse>`_.
 
-We recommend you to install ``k2`` first, as ``k2`` is bound to
+We recommend you to use the following steps to install the dependencies.
-a specific version of PyTorch after compilation. Install ``k2`` also
+
-installs its dependency PyTorch, which can be reused by ``lhotse``.
+- (0) Install PyTorch and torchaudio
+- (1) Install k2
+- (2) Install lhotse
+
+.. caution::
+
+  Installation order matters.
+
+(0) Install PyTorch and torchaudio
+----------------------------------
+
+Please refer `<https://pytorch.org/>`_ to install PyTorch
+and torchaudio.
 
 
 (1) Install k2
@@ -54,14 +66,15 @@ to install ``k2``.
 Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
 to install ``lhotse``.
 
-.. HINT::
 
-  Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
+.. hint::
 
-.. CAUTION::
+    We strongly recommend you to use::
+
+      pip install git+https://github.com/lhotse-speech/lhotse
+
+    to install the latest version of lhotse.
 
-  If you have installed ``torchaudio``, please consider uninstalling it before
-  installing ``lhotse``. Otherwise, it may update your already installed PyTorch.
 
 (3) Download icefall
 --------------------

egs/librispeech/ASR/pruned_transducer_stateless/train.py

@@ -33,6 +33,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 import argparse
 import logging
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple
@@ -496,7 +497,11 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -113,8 +113,10 @@ class Conformer(EncoderInterface):
         x, pos_emb = self.encoder_pos(x)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-        # Caution: We assume the subsampling factor is 4!
+        with warnings.catch_warnings():
-        lengths = ((x_lens - 1) // 2 - 1) // 2
+            warnings.simplefilter("ignore")
+            # Caution: We assume the subsampling factor is 4!
+            lengths = ((x_lens - 1) // 2 - 1) // 2
         assert x.size(0) == lengths.max().item()
         mask = make_pad_mask(lengths)
 

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -34,6 +34,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 import argparse
 import logging
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple
@@ -512,7 +513,9 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -25,6 +25,7 @@ from typing import Any, Dict, Optional
 import torch
 from lhotse.utils import fix_random_seed
 
+import torch
 from lhotse import CutSet, Fbank, FbankConfig, load_manifest
 from lhotse.dataset import (
     BucketingSampler,
@@ -36,11 +37,20 @@ from lhotse.dataset import (
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from lhotse.utils import fix_random_seed
 from torch.utils.data import DataLoader
 
 from icefall.utils import str2bool
 
 
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
 class LibriSpeechAsrDataModule:
     """
     DataModule for k2 ASR experiments.
@@ -303,11 +313,10 @@ class LibriSpeechAsrDataModule:
             logging.info("Loading sampler state dict")
             train_sampler.load_state_dict(sampler_state_dict)
 
-        # 'seed' is derived from the current random state, which will have previously been
+        # 'seed' is derived from the current random state, which will have
-        # set in the main process.
+        # previously been set in the main process.
         seed = torch.randint(0, 100000, ()).item()
-        def worker_init_fn(worker_id: int):
+        worker_init_fn = _SeedWorkers(seed)
-            fix_random_seed(seed + worker_id)
 
         train_dl = DataLoader(
             train,

egs/librispeech/ASR/transducer/train.py

@@ -34,6 +34,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 import argparse
 import logging
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Optional, Tuple
@@ -393,7 +394,11 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

egs/librispeech/ASR/transducer_lstm/train.py

@@ -35,6 +35,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2"
 
 import argparse
 import logging
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Optional, Tuple
@@ -397,7 +398,11 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -107,8 +107,11 @@ class Conformer(Transformer):
         x, pos_emb = self.encoder_pos(x)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-        # Caution: We assume the subsampling factor is 4!
+        with warnings.catch_warnings():
-        lengths = ((x_lens - 1) // 2 - 1) // 2
+            warnings.simplefilter("ignore")
+            # Caution: We assume the subsampling factor is 4!
+            lengths = ((x_lens - 1) // 2 - 1) // 2
+
         assert x.size(0) == lengths.max().item()
         mask = make_pad_mask(lengths)
 

egs/librispeech/ASR/transducer_stateless/train.py

@@ -34,7 +34,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 import argparse
 import logging
-import diagnostics # ./diagnostics.py
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Optional, Tuple
@@ -424,7 +424,11 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py

@@ -22,6 +22,7 @@ import logging
 from pathlib import Path
 from typing import Optional
 
+import torch
 from lhotse import CutSet, Fbank, FbankConfig
 from lhotse.dataset import (
     BucketingSampler,
@@ -34,11 +35,20 @@ from lhotse.dataset.input_strategies import (
     OnTheFlyFeatures,
     PrecomputedFeatures,
 )
+from lhotse.utils import fix_random_seed
 from torch.utils.data import DataLoader
 
 from icefall.utils import str2bool
 
 
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
 class AsrDataModule:
     def __init__(self, args: argparse.Namespace):
         self.args = args
@@ -253,12 +263,19 @@ class AsrDataModule:
             )
 
         logging.info("About to create train dataloader")
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
         train_dl = DataLoader(
             train,
             sampler=train_sampler,
             batch_size=None,
             num_workers=self.args.num_workers,
             persistent_workers=False,
+            worker_init_fn=worker_init_fn,
         )
         return train_dl
 

egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py

@@ -58,6 +58,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 import argparse
 import logging
 import random
+import warnings
 from pathlib import Path
 from shutil import copyfile
 from typing import Optional, Tuple
@@ -466,7 +467,11 @@ def compute_loss(
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
-    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()

icefall/__init__.py

@@ -0,0 +1,24 @@
+from .utils import (
+    AttributeDict,
+    MetricsTracker,
+    add_eos,
+    add_sos,
+    concat,
+    encode_supervisions,
+    get_alignments,
+    get_executor,
+    get_texts,
+    l1_norm,
+    l2_norm,
+    linf_norm,
+    load_alignments,
+    make_pad_mask,
+    measure_gradient_norms,
+    measure_weight_norms,
+    optim_step_and_measure_param_change,
+    save_alignments,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)

icefall/diagnostics.py

@@ -138,9 +138,9 @@ def get_diagnostics_for_dim(
         try:
             eigs, _ = torch.symeig(stats)
             stats = eigs.abs().sqrt()
-        except:
+        except:  # noqa
-            print("Error getting eigenvalues, trying another method")
+            print("Error getting eigenvalues, trying another method.")
-            eigs, _ = torch.eigs(stats)
+            eigs = torch.linalg.eigvals(stats)
             stats = eigs.abs().sqrt()
         # sqrt so it reflects data magnitude, like stddev- not variance
     elif sizes_same: