Revert "Apply new Black style changes"

.git-blame-ignore-revs

@@ -1,2 +0,0 @@
-# Migrate to 88 characters per line (see: https://github.com/lhotse-speech/lhotse/issues/890)
-d110b04ad389134c82fa314e3aafc7b40043efb0

.github/workflows/style_check.yml

@@ -45,18 +45,17 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip black==22.3.0 flake8==5.0.4 click==8.1.0
-          # Click issue fixed in https://github.com/psf/black/pull/2966
+          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
         working-directory: ${{github.workspace}}
         run: |
           # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 \
-            --statistics --extend-ignore=E203,E266,E501,F401,E402,F403,F841,W503
+          flake8 . --count --show-source --statistics
+          flake8 .
 
       - name: Run black
         shell: bash

.pre-commit-config.yaml

@@ -1,38 +1,26 @@
 repos:
   - repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 21.6b0
     hooks:
       - id: black
-        args: ["--line-length=88"]
-        additional_dependencies: ['click==8.1.0']
+        args: [--line-length=80]
+        additional_dependencies: ['click==8.0.1']
         exclude: icefall\/__init__\.py
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 3.9.2
     hooks:
       - id: flake8
-        args: ["--max-line-length=88", "--extend-ignore=E203,E266,E501,F401,E402,F403,F841,W503"]
-
-      # What are we ignoring here?
-      # E203: whitespace before ':'
-      # E266: too many leading '#' for block comment
-      # E501: line too long
-      # F401: module imported but unused
-      # E402: module level import not at top of file
-      # F403: 'from module import *' used; unable to detect undefined names
-      # F841: local variable is assigned to but never used
-      # W503: line break before binary operator
-      # In addition, the default ignore list is:
-      # E121,E123,E126,E226,E24,E704,W503,W504
+        args: [--max-line-length=80]
 
   - repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.9.2
     hooks:
       - id: isort
-        args: ["--profile=black"]
+        args: [--profile=black, --line-length=80]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.2.0
+    rev: v4.0.1
     hooks:
       - id: check-executables-have-shebangs
       - id: end-of-file-fixer

docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile

@@ -88,3 +88,4 @@ RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
 ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
 
 WORKDIR /workspace/icefall
+

docs/source/recipes/aishell/index.rst

@@ -19,3 +19,4 @@ It can be downloaded from `<https://www.openslr.org/33/>`_
    tdnn_lstm_ctc
    conformer_ctc
    stateless_transducer
+

docs/source/recipes/timit/index.rst

@@ -6,3 +6,4 @@ TIMIT
 
    tdnn_ligru_ctc
    tdnn_lstm_ctc
+

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -87,7 +87,9 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cut_set = cut_set.compute_and_store_features(
                 extractor=extractor,
@@ -114,7 +116,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/aidatatang_200zh/ASR/local/prepare_char.py

@@ -86,7 +86,9 @@ def lexicon_to_fst_no_sil(
         cur_state = loop_state
 
         word = word2id[word]
-        pieces = [token2id[i] if i in token2id else token2id["<unk>"] for i in pieces]
+        pieces = [
+            token2id[i] if i in token2id else token2id["<unk>"] for i in pieces
+        ]
 
         for i in range(len(pieces) - 1):
             w = word if i == 0 else eps
@@ -140,7 +142,9 @@ def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
     return False
 
 
-def generate_lexicon(token_sym_table: Dict[str, int], words: List[str]) -> Lexicon:
+def generate_lexicon(
+    token_sym_table: Dict[str, int], words: List[str]
+) -> Lexicon:
     """Generate a lexicon from a word list and token_sym_table.
 
     Args:

egs/aidatatang_200zh/ASR/local/prepare_lang.py

@@ -317,7 +317,9 @@ def lexicon_to_fst(
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--lang-dir", type=str, help="The lang dir, data/lang_phone")
+    parser.add_argument(
+        "--lang-dir", type=str, help="The lang dir, data/lang_phone"
+    )
     return parser.parse_args()
 
 

egs/aidatatang_200zh/ASR/local/test_prepare_lang.py

@@ -88,7 +88,9 @@ def test_read_lexicon(filename: str):
     fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa.draw("L.pdf", title="L")
 
-    fsa_disambig = lexicon_to_fst(lexicon_disambig, phone2id=phone2id, word2id=word2id)
+    fsa_disambig = lexicon_to_fst(
+        lexicon_disambig, phone2id=phone2id, word2id=word2id
+    )
     fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt")
     fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa_disambig.draw("L_disambig.pdf", title="L_disambig")

egs/aidatatang_200zh/ASR/local/text2token.py

@@ -50,15 +50,15 @@ def get_parser():
         "-n",
         default=1,
         type=int,
-        help=(
-            "number of characters to split, i.e.,                         aabb -> a a b"
-            " b with -n 1 and aa bb with -n 2"
-        ),
+        help="number of characters to split, i.e., \
+                        aabb -> a a b b with -n 1 and aa bb with -n 2",
     )
     parser.add_argument(
         "--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
     )
-    parser.add_argument("--space", default="<space>", type=str, help="space symbol")
+    parser.add_argument(
+        "--space", default="<space>", type=str, help="space symbol"
+    )
     parser.add_argument(
         "--non-lang-syms",
         "-l",
@@ -66,7 +66,9 @@ def get_parser():
         type=str,
         help="list of non-linguistic symobles, e.g., <NOISE> etc.",
     )
-    parser.add_argument("text", type=str, default=False, nargs="?", help="input text")
+    parser.add_argument(
+        "text", type=str, default=False, nargs="?", help="input text"
+    )
     parser.add_argument(
         "--trans_type",
         "-t",
@@ -106,7 +108,8 @@ def token2id(
             if token_type == "lazy_pinyin":
                 text = lazy_pinyin(chars_list)
                 sub_ids = [
-                    token_table[txt] if txt in token_table else oov_id for txt in text
+                    token_table[txt] if txt in token_table else oov_id
+                    for txt in text
                 ]
                 ids.append(sub_ids)
             else:  # token_type = "pinyin"
@@ -132,7 +135,9 @@ def main():
     if args.text:
         f = codecs.open(args.text, encoding="utf-8")
     else:
-        f = codecs.getreader("utf-8")(sys.stdin if is_python2 else sys.stdin.buffer)
+        f = codecs.getreader("utf-8")(
+            sys.stdin if is_python2 else sys.stdin.buffer
+        )
 
     sys.stdout = codecs.getwriter("utf-8")(
         sys.stdout if is_python2 else sys.stdout.buffer

egs/aidatatang_200zh/ASR/prepare.sh

@@ -113,3 +113,4 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
     ./local/prepare_char.py
   fi
 fi
+

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -81,12 +81,10 @@ class Aidatatang_200zhAsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
         group.add_argument(
             "--manifest-dir",
@@ -98,91 +96,75 @@ class Aidatatang_200zhAsrDataModule:
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
         group.add_argument(
             "--num-buckets",
             type=int,
             default=300,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler"
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
         )
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, utterances (cuts) will be concatenated "
-                "to minimize the amount of padding."
-            ),
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
         )
         group.add_argument(
             "--duration-factor",
             type=float,
             default=1.0,
-            help=(
-                "Determines the maximum duration of a concatenated cut "
-                "relative to the duration of the longest cut in a batch."
-            ),
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
         )
         group.add_argument(
             "--gap",
             type=float,
             default=1.0,
-            help=(
-                "The amount of padding (in seconds) inserted between "
+            help="The amount of padding (in seconds) inserted between "
             "concatenated cuts. This padding is filled with noise when "
-                "noise augmentation is used."
-            ),
+            "noise augmentation is used.",
         )
         group.add_argument(
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available."
-            ),
+            "if available.",
         )
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
         group.add_argument(
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -196,22 +178,18 @@ class Aidatatang_200zhAsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
     def train_dataloaders(
@@ -227,20 +205,24 @@ class Aidatatang_200zhAsrDataModule:
             The state dict for the training sampler.
         """
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            self.args.manifest_dir / "musan_cuts.jsonl.gz"
+        )
 
         transforms = []
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
 
         if self.args.concatenate_cuts:
             logging.info(
-                "Using cut concatenation with duration factor "
+                f"Using cut concatenation with duration factor "
                 f"{self.args.duration_factor} and gap {self.args.gap}."
             )
             # Cut concatenation should be the first transform in the list,
@@ -255,7 +237,9 @@ class Aidatatang_200zhAsrDataModule:
         input_transforms = []
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -298,7 +282,9 @@ class Aidatatang_200zhAsrDataModule:
             # Drop feats to be on the safe side.
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -354,7 +340,9 @@ class Aidatatang_200zhAsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py

@@ -69,7 +69,11 @@ from beam_search import (
 )
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -88,30 +92,25 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--batch",
         type=int,
         default=None,
-        help=(
-            "It specifies the batch checkpoint to use for decoding."
-            "Note: Epoch counts from 0."
-        ),
+        help="It specifies the batch checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -193,7 +192,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -249,7 +249,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -264,7 +266,10 @@ def decode_one_batch(
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -310,7 +315,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -381,7 +390,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -414,7 +425,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py

@@ -62,20 +62,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -106,7 +103,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     return parser
@@ -175,7 +173,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/pretrained.py

@@ -85,11 +85,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -114,12 +112,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -166,7 +162,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -196,9 +193,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -259,7 +257,9 @@ def main():
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
@@ -284,7 +284,10 @@ def main():
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -336,7 +339,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py

@@ -81,7 +81,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
 
@@ -185,45 +187,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -543,15 +542,22 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -705,7 +711,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")

egs/aishell/ASR/conformer_ctc/conformer.py

@@ -157,7 +157,9 @@ class ConformerEncoderLayer(nn.Module):
         normalize_before: bool = True,
     ) -> None:
         super(ConformerEncoderLayer, self).__init__()
-        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)
+        self.self_attn = RelPositionMultiheadAttention(
+            d_model, nhead, dropout=0.0
+        )
 
         self.feed_forward = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
@@ -175,14 +177,18 @@ class ConformerEncoderLayer(nn.Module):
 
         self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
 
-        self.norm_ff_macaron = nn.LayerNorm(d_model)  # for the macaron style FNN module
+        self.norm_ff_macaron = nn.LayerNorm(
+            d_model
+        )  # for the macaron style FNN module
         self.norm_ff = nn.LayerNorm(d_model)  # for the FNN module
         self.norm_mha = nn.LayerNorm(d_model)  # for the MHA module
 
         self.ff_scale = 0.5
 
         self.norm_conv = nn.LayerNorm(d_model)  # for the CNN module
-        self.norm_final = nn.LayerNorm(d_model)  # for the final output of the block
+        self.norm_final = nn.LayerNorm(
+            d_model
+        )  # for the final output of the block
 
         self.dropout = nn.Dropout(dropout)
 
@@ -216,7 +222,9 @@ class ConformerEncoderLayer(nn.Module):
         residual = src
         if self.normalize_before:
             src = self.norm_ff_macaron(src)
-        src = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(src))
+        src = residual + self.ff_scale * self.dropout(
+            self.feed_forward_macaron(src)
+        )
         if not self.normalize_before:
             src = self.norm_ff_macaron(src)
 
@@ -335,7 +343,9 @@ class RelPositionalEncoding(torch.nn.Module):
 
     """
 
-    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
+    def __init__(
+        self, d_model: int, dropout_rate: float, max_len: int = 5000
+    ) -> None:
         """Construct an PositionalEncoding object."""
         super(RelPositionalEncoding, self).__init__()
         self.d_model = d_model
@@ -351,7 +361,9 @@ class RelPositionalEncoding(torch.nn.Module):
             # the length of self.pe is 2 * input_len - 1
             if self.pe.size(1) >= x.size(1) * 2 - 1:
                 # Note: TorchScript doesn't implement operator== for torch.Device
-                if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device):
+                if self.pe.dtype != x.dtype or str(self.pe.device) != str(
+                    x.device
+                ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
         # Suppose `i` means to the position of query vector and `j` means the
@@ -621,9 +633,9 @@ class RelPositionMultiheadAttention(nn.Module):
 
         if torch.equal(query, key) and torch.equal(key, value):
             # self-attention
-            q, k, v = nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk(
-                3, dim=-1
-            )
+            q, k, v = nn.functional.linear(
+                query, in_proj_weight, in_proj_bias
+            ).chunk(3, dim=-1)
 
         elif torch.equal(key, value):
             # encoder-decoder attention
@@ -691,25 +703,33 @@ class RelPositionMultiheadAttention(nn.Module):
             if attn_mask.dim() == 2:
                 attn_mask = attn_mask.unsqueeze(0)
                 if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
-                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 2D attn_mask is not correct."
+                    )
             elif attn_mask.dim() == 3:
                 if list(attn_mask.size()) != [
                     bsz * num_heads,
                     query.size(0),
                     key.size(0),
                 ]:
-                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 3D attn_mask is not correct."
+                    )
             else:
                 raise RuntimeError(
-                    "attn_mask's dimension {} is not supported".format(attn_mask.dim())
+                    "attn_mask's dimension {} is not supported".format(
+                        attn_mask.dim()
+                    )
                 )
             # attn_mask's dim is 3 now.
 
         # convert ByteTensor key_padding_mask to bool
-        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+        if (
+            key_padding_mask is not None
+            and key_padding_mask.dtype == torch.uint8
+        ):
             warnings.warn(
-                "Byte tensor for key_padding_mask is deprecated. Use bool tensor"
-                " instead."
+                "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead."
             )
             key_padding_mask = key_padding_mask.to(torch.bool)
 
@@ -746,7 +766,9 @@ class RelPositionMultiheadAttention(nn.Module):
         # first compute matrix a and matrix c
         # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
         k = k.permute(1, 2, 3, 0)  # (batch, head, d_k, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k)  # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(
+            q_with_bias_u, k
+        )  # (batch, head, time1, time2)
 
         # compute matrix b and matrix d
         matrix_bd = torch.matmul(
@@ -758,7 +780,9 @@ class RelPositionMultiheadAttention(nn.Module):
             matrix_ac + matrix_bd
         ) * scaling  # (batch, head, time1, time2)
 
-        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1)
+        attn_output_weights = attn_output_weights.view(
+            bsz * num_heads, tgt_len, -1
+        )
 
         assert list(attn_output_weights.size()) == [
             bsz * num_heads,
@@ -792,9 +816,13 @@ class RelPositionMultiheadAttention(nn.Module):
         attn_output = torch.bmm(attn_output_weights, v)
         assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
         attn_output = (
-            attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+            attn_output.transpose(0, 1)
+            .contiguous()
+            .view(tgt_len, bsz, embed_dim)
+        )
+        attn_output = nn.functional.linear(
+            attn_output, out_proj_weight, out_proj_bias
         )
-        attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)
 
         if need_weights:
             # average attention weights over heads
@@ -817,7 +845,9 @@ class ConvolutionModule(nn.Module):
 
     """
 
-    def __init__(self, channels: int, kernel_size: int, bias: bool = True) -> None:
+    def __init__(
+        self, channels: int, kernel_size: int, bias: bool = True
+    ) -> None:
         """Construct an ConvolutionModule object."""
         super(ConvolutionModule, self).__init__()
         # kernerl_size should be a odd number for 'SAME' padding

egs/aishell/ASR/conformer_ctc/decode.py

@@ -58,19 +58,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=49,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=20,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -404,7 +401,9 @@ def decode_dataset(
         if batch_idx % 100 == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -432,7 +431,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
@@ -440,7 +441,9 @@ def save_results(
             test_set_wers[key] = wer
 
         if enable_log:
-            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+            logging.info(
+                "Wrote detailed error stats to {}".format(errs_filename)
+            )
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = params.exp_dir / f"cer-summary-{test_set_name}.txt"
@@ -559,7 +562,9 @@ def main():
             eos_id=eos_id,
         )
 
-        save_results(params=params, test_set_name=test_set, results_dict=results_dict)
+        save_results(
+            params=params, test_set_name=test_set, results_dict=results_dict
+        )
 
     logging.info("Done!")
 

egs/aishell/ASR/conformer_ctc/export.py

@@ -40,20 +40,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=84,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=25,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -160,7 +157,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/conformer_ctc/pretrained.py

@@ -46,29 +46,27 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
         "--tokens-file",
         type=str,
-        help="Path to tokens.txtUsed only when method is ctc-decoding",
+        help="Path to tokens.txt" "Used only when method is ctc-decoding",
     )
 
     parser.add_argument(
         "--words-file",
         type=str,
-        help="Path to words.txtUsed when method is NOT ctc-decoding",
+        help="Path to words.txt" "Used when method is NOT ctc-decoding",
     )
 
     parser.add_argument(
         "--HLG",
         type=str,
-        help="Path to HLG.pt.Used when method is NOT ctc-decoding",
+        help="Path to HLG.pt." "Used when method is NOT ctc-decoding",
     )
 
     parser.add_argument(
@@ -165,12 +163,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     return parser
@@ -214,9 +210,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -277,7 +274,9 @@ def main():
     logging.info("Decoding started")
     features = fbank(waves)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     # Note: We don't use key padding mask for attention during decoding
     with torch.no_grad():
@@ -372,7 +371,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/conformer_ctc/subsampling.py

@@ -42,9 +42,13 @@ class Conv2dSubsampling(nn.Module):
         assert idim >= 7
         super().__init__()
         self.conv = nn.Sequential(
-            nn.Conv2d(in_channels=1, out_channels=odim, kernel_size=3, stride=2),
+            nn.Conv2d(
+                in_channels=1, out_channels=odim, kernel_size=3, stride=2
+            ),
             nn.ReLU(),
-            nn.Conv2d(in_channels=odim, out_channels=odim, kernel_size=3, stride=2),
+            nn.Conv2d(
+                in_channels=odim, out_channels=odim, kernel_size=3, stride=2
+            ),
             nn.ReLU(),
         )
         self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
@@ -128,13 +132,17 @@ class VggSubsampling(nn.Module):
                 )
             )
             layers.append(
-                torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+                torch.nn.MaxPool2d(
+                    kernel_size=2, stride=2, padding=0, ceil_mode=True
+                )
             )
             cur_channels = block_dim
 
         self.layers = nn.Sequential(*layers)
 
-        self.out = nn.Linear(block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim)
+        self.out = nn.Linear(
+            block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Subsample x.

egs/aishell/ASR/conformer_ctc/test_subsampling.py

@@ -16,8 +16,9 @@
 # limitations under the License.
 
 
+from subsampling import Conv2dSubsampling
+from subsampling import VggSubsampling
 import torch
-from subsampling import Conv2dSubsampling, VggSubsampling
 
 
 def test_conv2d_subsampling():

egs/aishell/ASR/conformer_ctc/train.py

@@ -382,7 +382,9 @@ def compute_loss(
             #
             # See https://github.com/k2-fsa/icefall/issues/97
             # for more details
-            unsorted_token_ids = graph_compiler.texts_to_ids(supervisions["text"])
+            unsorted_token_ids = graph_compiler.texts_to_ids(
+                supervisions["text"]
+            )
             att_loss = mmodel.decoder_forward(
                 encoder_memory,
                 memory_mask,
@@ -518,7 +520,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -626,7 +630,9 @@ def run(rank, world_size, args):
 
         cur_lr = optimizer._rate
         if tb_writer is not None:
-            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
         if rank == 0:

egs/aishell/ASR/conformer_ctc/transformer.py

@@ -149,7 +149,9 @@ class Transformer(nn.Module):
                 norm=decoder_norm,
             )
 
-            self.decoder_output_layer = torch.nn.Linear(d_model, self.decoder_num_class)
+            self.decoder_output_layer = torch.nn.Linear(
+                d_model, self.decoder_num_class
+            )
 
             self.decoder_criterion = LabelSmoothingLoss()
         else:
@@ -181,7 +183,9 @@ class Transformer(nn.Module):
             x = x.permute(0, 2, 1)  # (N, T, C) -> (N, C, T)
             x = self.feat_batchnorm(x)
             x = x.permute(0, 2, 1)  # (N, C, T) -> (N, T, C)
-        encoder_memory, memory_key_padding_mask = self.run_encoder(x, supervision)
+        encoder_memory, memory_key_padding_mask = self.run_encoder(
+            x, supervision
+        )
         x = self.ctc_output(encoder_memory)
         return x, encoder_memory, memory_key_padding_mask
 
@@ -262,17 +266,23 @@ class Transformer(nn.Module):
         """
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
+        ys_in_pad = pad_sequence(
+            ys_in, batch_first=True, padding_value=float(eos_id)
+        )
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
+        ys_out_pad = pad_sequence(
+            ys_out, batch_first=True, padding_value=float(-1)
+        )
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device)
         ys_out_pad = ys_out_pad.to(device)
 
-        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
+        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(
+            device
+        )
 
         tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
         # TODO: Use length information to create the decoder padding mask
@@ -333,17 +343,23 @@ class Transformer(nn.Module):
 
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
+        ys_in_pad = pad_sequence(
+            ys_in, batch_first=True, padding_value=float(eos_id)
+        )
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
+        ys_out_pad = pad_sequence(
+            ys_out, batch_first=True, padding_value=float(-1)
+        )
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device, dtype=torch.int64)
         ys_out_pad = ys_out_pad.to(device, dtype=torch.int64)
 
-        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
+        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(
+            device
+        )
 
         tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
         # TODO: Use length information to create the decoder padding mask
@@ -616,7 +632,9 @@ def _get_activation_fn(activation: str):
     elif activation == "gelu":
         return nn.functional.gelu
 
-    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
+    raise RuntimeError(
+        "activation should be relu/gelu, not {}".format(activation)
+    )
 
 
 class PositionalEncoding(nn.Module):
@@ -818,7 +836,9 @@ def encoder_padding_mask(
         1,
     ).to(torch.int32)
 
-    lengths = [0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)]
+    lengths = [
+        0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)
+    ]
     for idx in range(supervision_segments.size(0)):
         # Note: TorchScript doesn't allow to unpack tensors as tuples
         sequence_idx = supervision_segments[idx, 0].item()
@@ -839,7 +859,9 @@ def encoder_padding_mask(
     return mask
 
 
-def decoder_padding_mask(ys_pad: torch.Tensor, ignore_id: int = -1) -> torch.Tensor:
+def decoder_padding_mask(
+    ys_pad: torch.Tensor, ignore_id: int = -1
+) -> torch.Tensor:
     """Generate a length mask for input.
 
     The masked position are filled with True,

egs/aishell/ASR/conformer_mmi/conformer.py

@@ -157,7 +157,9 @@ class ConformerEncoderLayer(nn.Module):
         normalize_before: bool = True,
     ) -> None:
         super(ConformerEncoderLayer, self).__init__()
-        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)
+        self.self_attn = RelPositionMultiheadAttention(
+            d_model, nhead, dropout=0.0
+        )
 
         self.feed_forward = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
@@ -175,14 +177,18 @@ class ConformerEncoderLayer(nn.Module):
 
         self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
 
-        self.norm_ff_macaron = nn.LayerNorm(d_model)  # for the macaron style FNN module
+        self.norm_ff_macaron = nn.LayerNorm(
+            d_model
+        )  # for the macaron style FNN module
         self.norm_ff = nn.LayerNorm(d_model)  # for the FNN module
         self.norm_mha = nn.LayerNorm(d_model)  # for the MHA module
 
         self.ff_scale = 0.5
 
         self.norm_conv = nn.LayerNorm(d_model)  # for the CNN module
-        self.norm_final = nn.LayerNorm(d_model)  # for the final output of the block
+        self.norm_final = nn.LayerNorm(
+            d_model
+        )  # for the final output of the block
 
         self.dropout = nn.Dropout(dropout)
 
@@ -216,7 +222,9 @@ class ConformerEncoderLayer(nn.Module):
         residual = src
         if self.normalize_before:
             src = self.norm_ff_macaron(src)
-        src = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(src))
+        src = residual + self.ff_scale * self.dropout(
+            self.feed_forward_macaron(src)
+        )
         if not self.normalize_before:
             src = self.norm_ff_macaron(src)
 
@@ -335,7 +343,9 @@ class RelPositionalEncoding(torch.nn.Module):
 
     """
 
-    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
+    def __init__(
+        self, d_model: int, dropout_rate: float, max_len: int = 5000
+    ) -> None:
         """Construct an PositionalEncoding object."""
         super(RelPositionalEncoding, self).__init__()
         self.d_model = d_model
@@ -351,7 +361,9 @@ class RelPositionalEncoding(torch.nn.Module):
             # the length of self.pe is 2 * input_len - 1
             if self.pe.size(1) >= x.size(1) * 2 - 1:
                 # Note: TorchScript doesn't implement operator== for torch.Device
-                if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device):
+                if self.pe.dtype != x.dtype or str(self.pe.device) != str(
+                    x.device
+                ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
         # Suppose `i` means to the position of query vector and `j` means the
@@ -621,9 +633,9 @@ class RelPositionMultiheadAttention(nn.Module):
 
         if torch.equal(query, key) and torch.equal(key, value):
             # self-attention
-            q, k, v = nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk(
-                3, dim=-1
-            )
+            q, k, v = nn.functional.linear(
+                query, in_proj_weight, in_proj_bias
+            ).chunk(3, dim=-1)
 
         elif torch.equal(key, value):
             # encoder-decoder attention
@@ -691,25 +703,33 @@ class RelPositionMultiheadAttention(nn.Module):
             if attn_mask.dim() == 2:
                 attn_mask = attn_mask.unsqueeze(0)
                 if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
-                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 2D attn_mask is not correct."
+                    )
             elif attn_mask.dim() == 3:
                 if list(attn_mask.size()) != [
                     bsz * num_heads,
                     query.size(0),
                     key.size(0),
                 ]:
-                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 3D attn_mask is not correct."
+                    )
             else:
                 raise RuntimeError(
-                    "attn_mask's dimension {} is not supported".format(attn_mask.dim())
+                    "attn_mask's dimension {} is not supported".format(
+                        attn_mask.dim()
+                    )
                 )
             # attn_mask's dim is 3 now.
 
         # convert ByteTensor key_padding_mask to bool
-        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+        if (
+            key_padding_mask is not None
+            and key_padding_mask.dtype == torch.uint8
+        ):
             warnings.warn(
-                "Byte tensor for key_padding_mask is deprecated. Use bool tensor"
-                " instead."
+                "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead."
             )
             key_padding_mask = key_padding_mask.to(torch.bool)
 
@@ -746,7 +766,9 @@ class RelPositionMultiheadAttention(nn.Module):
         # first compute matrix a and matrix c
         # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
         k = k.permute(1, 2, 3, 0)  # (batch, head, d_k, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k)  # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(
+            q_with_bias_u, k
+        )  # (batch, head, time1, time2)
 
         # compute matrix b and matrix d
         matrix_bd = torch.matmul(
@@ -758,7 +780,9 @@ class RelPositionMultiheadAttention(nn.Module):
             matrix_ac + matrix_bd
         ) * scaling  # (batch, head, time1, time2)
 
-        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1)
+        attn_output_weights = attn_output_weights.view(
+            bsz * num_heads, tgt_len, -1
+        )
 
         assert list(attn_output_weights.size()) == [
             bsz * num_heads,
@@ -792,9 +816,13 @@ class RelPositionMultiheadAttention(nn.Module):
         attn_output = torch.bmm(attn_output_weights, v)
         assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
         attn_output = (
-            attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+            attn_output.transpose(0, 1)
+            .contiguous()
+            .view(tgt_len, bsz, embed_dim)
+        )
+        attn_output = nn.functional.linear(
+            attn_output, out_proj_weight, out_proj_bias
         )
-        attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)
 
         if need_weights:
             # average attention weights over heads
@@ -817,7 +845,9 @@ class ConvolutionModule(nn.Module):
 
     """
 
-    def __init__(self, channels: int, kernel_size: int, bias: bool = True) -> None:
+    def __init__(
+        self, channels: int, kernel_size: int, bias: bool = True
+    ) -> None:
         """Construct an ConvolutionModule object."""
         super(ConvolutionModule, self).__init__()
         # kernerl_size should be a odd number for 'SAME' padding

egs/aishell/ASR/conformer_mmi/decode.py

@@ -59,19 +59,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=49,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=20,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -416,7 +413,9 @@ def decode_dataset(
         if batch_idx % 100 == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -444,7 +443,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
@@ -452,7 +453,9 @@ def save_results(
             test_set_wers[key] = wer
 
         if enable_log:
-            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+            logging.info(
+                "Wrote detailed error stats to {}".format(errs_filename)
+            )
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = params.exp_dir / f"cer-summary-{test_set_name}.txt"
@@ -547,7 +550,9 @@ def main():
 
     if params.export:
         logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
-        torch.save({"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
         return
 
     model.to(device)
@@ -576,7 +581,9 @@ def main():
             eos_id=eos_id,
         )
 
-        save_results(params=params, test_set_name=test_set, results_dict=results_dict)
+        save_results(
+            params=params, test_set_name=test_set, results_dict=results_dict
+        )
 
     logging.info("Done!")
 

egs/aishell/ASR/conformer_mmi/subsampling.py

@@ -42,9 +42,13 @@ class Conv2dSubsampling(nn.Module):
         assert idim >= 7
         super().__init__()
         self.conv = nn.Sequential(
-            nn.Conv2d(in_channels=1, out_channels=odim, kernel_size=3, stride=2),
+            nn.Conv2d(
+                in_channels=1, out_channels=odim, kernel_size=3, stride=2
+            ),
             nn.ReLU(),
-            nn.Conv2d(in_channels=odim, out_channels=odim, kernel_size=3, stride=2),
+            nn.Conv2d(
+                in_channels=odim, out_channels=odim, kernel_size=3, stride=2
+            ),
             nn.ReLU(),
         )
         self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
@@ -128,13 +132,17 @@ class VggSubsampling(nn.Module):
                 )
             )
             layers.append(
-                torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+                torch.nn.MaxPool2d(
+                    kernel_size=2, stride=2, padding=0, ceil_mode=True
+                )
             )
             cur_channels = block_dim
 
         self.layers = nn.Sequential(*layers)
 
-        self.out = nn.Linear(block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim)
+        self.out = nn.Linear(
+            block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim
+        )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Subsample x.

egs/aishell/ASR/conformer_mmi/train.py

@@ -511,7 +511,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -623,7 +625,9 @@ def run(rank, world_size, args):
 
         cur_lr = optimizer._rate
         if tb_writer is not None:
-            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
         if rank == 0:

egs/aishell/ASR/conformer_mmi/transformer.py

@@ -149,7 +149,9 @@ class Transformer(nn.Module):
                 norm=decoder_norm,
             )
 
-            self.decoder_output_layer = torch.nn.Linear(d_model, self.decoder_num_class)
+            self.decoder_output_layer = torch.nn.Linear(
+                d_model, self.decoder_num_class
+            )
 
             self.decoder_criterion = LabelSmoothingLoss()
         else:
@@ -181,7 +183,9 @@ class Transformer(nn.Module):
             x = x.permute(0, 2, 1)  # (N, T, C) -> (N, C, T)
             x = self.feat_batchnorm(x)
             x = x.permute(0, 2, 1)  # (N, C, T) -> (N, T, C)
-        encoder_memory, memory_key_padding_mask = self.run_encoder(x, supervision)
+        encoder_memory, memory_key_padding_mask = self.run_encoder(
+            x, supervision
+        )
         x = self.ctc_output(encoder_memory)
         return x, encoder_memory, memory_key_padding_mask
 
@@ -262,17 +266,23 @@ class Transformer(nn.Module):
         """
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
+        ys_in_pad = pad_sequence(
+            ys_in, batch_first=True, padding_value=float(eos_id)
+        )
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
+        ys_out_pad = pad_sequence(
+            ys_out, batch_first=True, padding_value=float(-1)
+        )
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device)
         ys_out_pad = ys_out_pad.to(device)
 
-        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
+        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(
+            device
+        )
 
         tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
         # TODO: Use length information to create the decoder padding mask
@@ -333,17 +343,23 @@ class Transformer(nn.Module):
 
         ys_in = add_sos(token_ids, sos_id=sos_id)
         ys_in = [torch.tensor(y) for y in ys_in]
-        ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id))
+        ys_in_pad = pad_sequence(
+            ys_in, batch_first=True, padding_value=float(eos_id)
+        )
 
         ys_out = add_eos(token_ids, eos_id=eos_id)
         ys_out = [torch.tensor(y) for y in ys_out]
-        ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1))
+        ys_out_pad = pad_sequence(
+            ys_out, batch_first=True, padding_value=float(-1)
+        )
 
         device = memory.device
         ys_in_pad = ys_in_pad.to(device, dtype=torch.int64)
         ys_out_pad = ys_out_pad.to(device, dtype=torch.int64)
 
-        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device)
+        tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(
+            device
+        )
 
         tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id)
         # TODO: Use length information to create the decoder padding mask
@@ -616,7 +632,9 @@ def _get_activation_fn(activation: str):
     elif activation == "gelu":
         return nn.functional.gelu
 
-    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
+    raise RuntimeError(
+        "activation should be relu/gelu, not {}".format(activation)
+    )
 
 
 class PositionalEncoding(nn.Module):
@@ -818,7 +836,9 @@ def encoder_padding_mask(
         1,
     ).to(torch.int32)
 
-    lengths = [0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)]
+    lengths = [
+        0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)
+    ]
     for idx in range(supervision_segments.size(0)):
         # Note: TorchScript doesn't allow to unpack tensors as tuples
         sequence_idx = supervision_segments[idx, 0].item()
@@ -839,7 +859,9 @@ def encoder_padding_mask(
     return mask
 
 
-def decoder_padding_mask(ys_pad: torch.Tensor, ignore_id: int = -1) -> torch.Tensor:
+def decoder_padding_mask(
+    ys_pad: torch.Tensor, ignore_id: int = -1
+) -> torch.Tensor:
     """Generate a length mask for input.
 
     The masked position are filled with True,

egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -87,7 +87,9 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cut_set = cut_set.compute_and_store_features(
                 extractor=extractor,
@@ -114,7 +116,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/aishell/ASR/local/compute_fbank_aishell.py

@@ -83,7 +83,9 @@ def compute_fbank_aishell(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cut_set = cut_set.compute_and_store_features(
                 extractor=extractor,
@@ -109,7 +111,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/aishell/ASR/local/prepare_char.py

@@ -86,7 +86,9 @@ def lexicon_to_fst_no_sil(
         cur_state = loop_state
 
         word = word2id[word]
-        pieces = [token2id[i] if i in token2id else token2id["<unk>"] for i in pieces]
+        pieces = [
+            token2id[i] if i in token2id else token2id["<unk>"] for i in pieces
+        ]
 
         for i in range(len(pieces) - 1):
             w = word if i == 0 else eps
@@ -140,7 +142,9 @@ def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
     return False
 
 
-def generate_lexicon(token_sym_table: Dict[str, int], words: List[str]) -> Lexicon:
+def generate_lexicon(
+    token_sym_table: Dict[str, int], words: List[str]
+) -> Lexicon:
     """Generate a lexicon from a word list and token_sym_table.
 
     Args:

egs/aishell/ASR/local/prepare_lang.py

@@ -317,7 +317,9 @@ def lexicon_to_fst(
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--lang-dir", type=str, help="The lang dir, data/lang_phone")
+    parser.add_argument(
+        "--lang-dir", type=str, help="The lang dir, data/lang_phone"
+    )
     return parser.parse_args()
 
 

egs/aishell/ASR/local/test_prepare_lang.py

@@ -88,7 +88,9 @@ def test_read_lexicon(filename: str):
     fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa.draw("L.pdf", title="L")
 
-    fsa_disambig = lexicon_to_fst(lexicon_disambig, phone2id=phone2id, word2id=word2id)
+    fsa_disambig = lexicon_to_fst(
+        lexicon_disambig, phone2id=phone2id, word2id=word2id
+    )
     fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt")
     fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa_disambig.draw("L_disambig.pdf", title="L_disambig")

egs/aishell/ASR/pruned_transducer_stateless2/decode.py

@@ -76,7 +76,11 @@ from beam_search import (
 )
 from train import add_model_arguments, get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -114,11 +118,9 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
@@ -186,7 +188,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -246,7 +249,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
 
     if params.decoding_method == "fast_beam_search":
         hyp_tokens = fast_beam_search_one_best(
@@ -258,7 +263,10 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -302,7 +310,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -375,7 +387,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -401,7 +415,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -412,7 +428,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
@@ -456,7 +473,9 @@ def main():
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -485,7 +504,8 @@ def main():
         ]
         if len(filenames) == 0:
             raise ValueError(
-                f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                f"No checkpoints found for"
+                f" --iter {params.iter}, --avg {params.avg}"
             )
         elif len(filenames) < params.avg:
             raise ValueError(

egs/aishell/ASR/pruned_transducer_stateless2/export.py

@@ -50,7 +50,11 @@ from pathlib import Path
 import torch
 from train import add_model_arguments, get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.lexicon import Lexicon
 from icefall.utils import str2bool
 
@@ -83,11 +87,9 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
@@ -118,7 +120,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     add_model_arguments(parser)
@@ -154,7 +157,8 @@ def main():
         ]
         if len(filenames) == 0:
             raise ValueError(
-                f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                f"No checkpoints found for"
+                f" --iter {params.iter}, --avg {params.avg}"
             )
         elif len(filenames) < params.avg:
             raise ValueError(
@@ -187,7 +191,9 @@ def main():
         model.__class__.forward = torch.jit.ignore(model.__class__.forward)
         logging.info("Using torch.jit.script")
         model = torch.jit.script(model)
-        filename = params.exp_dir / f"cpu_jit-epoch-{params.epoch}-avg-{params.avg}.pt"
+        filename = (
+            params.exp_dir / f"cpu_jit-epoch-{params.epoch}-avg-{params.avg}.pt"
+        )
         model.save(str(filename))
         logging.info(f"Saved to {filename}")
     else:
@@ -195,14 +201,17 @@ def main():
         # Save it using a format so that it can be loaded
         # by :func:`load_checkpoint`
         filename = (
-            params.exp_dir / f"pretrained-epoch-{params.epoch}-avg-{params.avg}.pt"
+            params.exp_dir
+            / f"pretrained-epoch-{params.epoch}-avg-{params.avg}.pt"
         )
         torch.save({"model": model.state_dict()}, str(filename))
         logging.info(f"Saved to {filename}")
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/pruned_transducer_stateless2/pretrained.py

@@ -87,11 +87,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -117,12 +115,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -169,16 +165,15 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
         default=1,
-        help=(
-            "Maximum number of symbols per frame. "
-            "Use only when --method is greedy_search"
-        ),
+        help="Maximum number of symbols per frame. "
+        "Use only when --method is greedy_search",
     )
 
     add_model_arguments(parser)
@@ -201,9 +196,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -260,9 +256,13 @@ def main():
     feature_lens = [f.size(0) for f in features]
     feature_lens = torch.tensor(feature_lens, device=device)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lens
+    )
 
     num_waves = encoder_out.size(0)
     hyp_list = []
@@ -310,7 +310,9 @@ def main():
                     beam=params.beam_size,
                 )
             else:
-                raise ValueError(f"Unsupported decoding method: {params.method}")
+                raise ValueError(
+                    f"Unsupported decoding method: {params.method}"
+                )
             hyp_list.append(hyp)
 
     hyps = []
@@ -327,7 +329,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/pruned_transducer_stateless2/train.py

@@ -49,6 +49,7 @@ import optim
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
+
 from asr_datamodule import AishellAsrDataModule
 from conformer import Conformer
 from decoder import Decoder
@@ -74,7 +75,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
@@ -200,7 +203,8 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need "
+        "to be changed.",
     )
 
     parser.add_argument(
@@ -223,45 +227,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -560,7 +561,11 @@ def compute_loss(
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    device = (
+        model.device
+        if isinstance(model, DDP)
+        else next(model.parameters()).device
+    )
     feature = batch["inputs"]
     # at entry, feature is (N, T, C)
     assert feature.ndim == 3
@@ -588,16 +593,23 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
 
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -713,7 +725,9 @@ def train_one_epoch(
             scaler.update()
             optimizer.zero_grad()
         except:  # noqa
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -1015,7 +1029,9 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
 

egs/aishell/ASR/pruned_transducer_stateless3/decode.py

@@ -121,24 +121,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=False,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -206,7 +202,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -266,7 +263,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
 
     if params.decoding_method == "fast_beam_search":
         hyp_tokens = fast_beam_search_one_best(
@@ -278,7 +277,10 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -322,7 +324,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -395,7 +401,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -421,7 +429,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -432,7 +442,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
@@ -477,7 +488,9 @@ def main():
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -505,12 +518,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -537,12 +551,13 @@ def main():
             )
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -571,7 +586,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)

egs/aishell/ASR/pruned_transducer_stateless3/export.py

@@ -88,24 +88,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=True,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -136,7 +132,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     add_model_arguments(parser)
@@ -169,12 +166,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -197,12 +195,13 @@ def main():
             model.load_state_dict(average_checkpoints(filenames, device=device))
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -230,7 +229,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)
@@ -253,7 +252,9 @@ def main():
         model.__class__.forward = torch.jit.ignore(model.__class__.forward)
         logging.info("Using torch.jit.script")
         model = torch.jit.script(model)
-        filename = params.exp_dir / f"cpu_jit-epoch-{params.epoch}-avg-{params.avg}.pt"
+        filename = (
+            params.exp_dir / f"cpu_jit-epoch-{params.epoch}-avg-{params.avg}.pt"
+        )
         model.save(str(filename))
         logging.info(f"Saved to {filename}")
     else:
@@ -261,14 +262,17 @@ def main():
         # Save it using a format so that it can be loaded
         # by :func:`load_checkpoint`
         filename = (
-            params.exp_dir / f"pretrained-epoch-{params.epoch}-avg-{params.avg}.pt"
+            params.exp_dir
+            / f"pretrained-epoch-{params.epoch}-avg-{params.avg}.pt"
         )
         torch.save({"model": model.state_dict()}, str(filename))
         logging.info(f"Saved to {filename}")
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/pruned_transducer_stateless3/model.py

@@ -84,7 +84,9 @@ class Transducer(nn.Module):
         self.decoder_datatang = decoder_datatang
         self.joiner_datatang = joiner_datatang
 
-        self.simple_am_proj = ScaledLinear(encoder_dim, vocab_size, initial_speed=0.5)
+        self.simple_am_proj = ScaledLinear(
+            encoder_dim, vocab_size, initial_speed=0.5
+        )
         self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size)
 
         if decoder_datatang is not None:
@@ -177,7 +179,9 @@ class Transducer(nn.Module):
         y_padded = y.pad(mode="constant", padding_value=0)
 
         y_padded = y_padded.to(torch.int64)
-        boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)
+        boundary = torch.zeros(
+            (x.size(0), 4), dtype=torch.int64, device=x.device
+        )
         boundary[:, 2] = y_lens
         boundary[:, 3] = encoder_out_lens
 

egs/aishell/ASR/pruned_transducer_stateless3/pretrained.py

@@ -87,11 +87,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -117,12 +115,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -169,16 +165,15 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
         default=1,
-        help=(
-            "Maximum number of symbols per frame. "
-            "Use only when --method is greedy_search"
-        ),
+        help="Maximum number of symbols per frame. "
+        "Use only when --method is greedy_search",
     )
 
     add_model_arguments(parser)
@@ -201,9 +196,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -261,9 +257,13 @@ def main():
     feature_lens = [f.size(0) for f in features]
     feature_lens = torch.tensor(feature_lens, device=device)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lens
+    )
 
     num_waves = encoder_out.size(0)
     hyp_list = []
@@ -311,7 +311,9 @@ def main():
                     beam=params.beam_size,
                 )
             else:
-                raise ValueError(f"Unsupported decoding method: {params.method}")
+                raise ValueError(
+                    f"Unsupported decoding method: {params.method}"
+                )
             hyp_list.append(hyp)
 
     hyps = []
@@ -328,7 +330,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/pruned_transducer_stateless3/train.py

@@ -96,7 +96,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
@@ -222,7 +224,8 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need "
+        "to be changed.",
     )
 
     parser.add_argument(
@@ -245,45 +248,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=1,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -635,7 +635,11 @@ def compute_loss(
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    device = (
+        model.device
+        if isinstance(model, DDP)
+        else next(model.parameters()).device
+    )
     feature = batch["inputs"]
     # at entry, feature is (N, T, C)
     assert feature.ndim == 3
@@ -666,16 +670,23 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
 
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -813,7 +824,9 @@ def train_one_epoch(
                 )
             # summary stats
             if datatang_train_dl is not None:
-                tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+                tot_loss = (
+                    tot_loss * (1 - 1 / params.reset_interval)
+                ) + loss_info
 
             if aishell:
                 aishell_tot_loss = (
@@ -834,7 +847,9 @@ def train_one_epoch(
             scaler.update()
             optimizer.zero_grad()
         except:  # noqa
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -877,7 +892,9 @@ def train_one_epoch(
             cur_lr = scheduler.get_last_lr()[0]
             if datatang_train_dl is not None:
                 datatang_str = f"datatang_tot_loss[{datatang_tot_loss}], "
-                tot_loss_str = f"tot_loss[{tot_loss}], batch size: {batch_size}, "
+                tot_loss_str = (
+                    f"tot_loss[{tot_loss}], batch size: {batch_size}, "
+                )
             else:
                 tot_loss_str = ""
                 datatang_str = ""
@@ -1059,7 +1076,9 @@ def run(rank, world_size, args):
     train_cuts = filter_short_and_long_utterances(train_cuts)
 
     if args.enable_musan:
-        cuts_musan = load_manifest(Path(args.manifest_dir) / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            Path(args.manifest_dir) / "musan_cuts.jsonl.gz"
+        )
     else:
         cuts_musan = None
 
@@ -1074,7 +1093,9 @@ def run(rank, world_size, args):
     if params.datatang_prob > 0:
         datatang = AIDatatang200zh(manifest_dir=args.manifest_dir)
         train_datatang_cuts = datatang.train_cuts()
-        train_datatang_cuts = filter_short_and_long_utterances(train_datatang_cuts)
+        train_datatang_cuts = filter_short_and_long_utterances(
+            train_datatang_cuts
+        )
         train_datatang_cuts = train_datatang_cuts.repeat(times=None)
         datatang_train_dl = asr_datamodule.train_dataloaders(
             train_datatang_cuts,
@@ -1228,7 +1249,9 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
 

egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -64,12 +64,10 @@ class AishellAsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
         group.add_argument(
             "--manifest-dir",
@@ -81,74 +79,59 @@ class AishellAsrDataModule:
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
         group.add_argument(
             "--num-buckets",
             type=int,
             default=30,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler"
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
         )
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, utterances (cuts) will be concatenated "
-                "to minimize the amount of padding."
-            ),
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
         )
         group.add_argument(
             "--duration-factor",
             type=float,
             default=1.0,
-            help=(
-                "Determines the maximum duration of a concatenated cut "
-                "relative to the duration of the longest cut in a batch."
-            ),
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
         )
         group.add_argument(
             "--gap",
             type=float,
             default=1.0,
-            help=(
-                "The amount of padding (in seconds) inserted between "
+            help="The amount of padding (in seconds) inserted between "
             "concatenated cuts. This padding is filled with noise when "
-                "noise augmentation is used."
-            ),
+            "noise augmentation is used.",
         )
         group.add_argument(
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available."
-            ),
+            "if available.",
         )
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
         group.add_argument(
             "--drop-last",
@@ -160,18 +143,17 @@ class AishellAsrDataModule:
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -185,40 +167,40 @@ class AishellAsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
     def train_dataloaders(self, cuts_train: CutSet) -> DataLoader:
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            self.args.manifest_dir / "musan_cuts.jsonl.gz"
+        )
 
         transforms = []
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
 
         if self.args.concatenate_cuts:
             logging.info(
-                "Using cut concatenation with duration factor "
+                f"Using cut concatenation with duration factor "
                 f"{self.args.duration_factor} and gap {self.args.gap}."
             )
             # Cut concatenation should be the first transform in the list,
@@ -233,7 +215,9 @@ class AishellAsrDataModule:
         input_transforms = []
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -276,7 +260,9 @@ class AishellAsrDataModule:
             # Drop feats to be on the safe side.
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -322,7 +308,9 @@ class AishellAsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:
@@ -378,9 +366,13 @@ class AishellAsrDataModule:
     @lru_cache()
     def valid_cuts(self) -> CutSet:
         logging.info("About to get dev cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "aishell_cuts_dev.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell_cuts_dev.jsonl.gz"
+        )
 
     @lru_cache()
     def test_cuts(self) -> List[CutSet]:
         logging.info("About to get test cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "aishell_cuts_test.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell_cuts_test.jsonl.gz"
+        )

egs/aishell/ASR/tdnn_lstm_ctc/decode.py

@@ -49,19 +49,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=19,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=5,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
     parser.add_argument(
         "--method",
@@ -268,7 +265,9 @@ def decode_dataset(
         if batch_idx % 100 == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -290,7 +289,9 @@ def save_results(
         # We compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(f, f"{test_set_name}-{key}", results_char)
             test_set_wers[key] = wer
@@ -334,7 +335,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -359,7 +362,9 @@ def main():
 
     if params.export:
         logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
-        torch.save({"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
 
     model.to(device)
     model.eval()
@@ -387,7 +392,9 @@ def main():
             lexicon=lexicon,
         )
 
-        save_results(params=params, test_set_name=test_set, results_dict=results_dict)
+        save_results(
+            params=params, test_set_name=test_set, results_dict=results_dict
+        )
 
     logging.info("Done!")
 

egs/aishell/ASR/tdnn_lstm_ctc/model.py

@@ -66,7 +66,10 @@ class TdnnLstm(nn.Module):
             nn.BatchNorm1d(num_features=500, affine=False),
         )
         self.lstms = nn.ModuleList(
-            [nn.LSTM(input_size=500, hidden_size=500, num_layers=1) for _ in range(5)]
+            [
+                nn.LSTM(input_size=500, hidden_size=500, num_layers=1)
+                for _ in range(5)
+            ]
         )
         self.lstm_bnorms = nn.ModuleList(
             [nn.BatchNorm1d(num_features=500, affine=False) for _ in range(5)]

egs/aishell/ASR/tdnn_lstm_ctc/pretrained.py

@@ -41,11 +41,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -55,7 +53,9 @@ def get_parser():
         help="Path to words.txt",
     )
 
-    parser.add_argument("--HLG", type=str, required=True, help="Path to HLG.pt.")
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
 
     parser.add_argument(
         "--method",
@@ -71,12 +71,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     return parser
@@ -114,9 +112,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -174,7 +173,9 @@ def main():
     logging.info("Decoding started")
     features = fbank(waves)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
     features = features.permute(0, 2, 1)  # now features is [N, C, T]
 
     with torch.no_grad():
@@ -218,7 +219,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/tdnn_lstm_ctc/train.py

@@ -49,7 +49,12 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, encode_supervisions, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    encode_supervisions,
+    setup_logger,
+    str2bool,
+)
 
 
 def get_parser():

egs/aishell/ASR/transducer_stateless/beam_search.py

@@ -47,9 +47,9 @@ def greedy_search(
 
     device = model.device
 
-    decoder_input = torch.tensor([blank_id] * context_size, device=device).reshape(
-        1, context_size
-    )
+    decoder_input = torch.tensor(
+        [blank_id] * context_size, device=device
+    ).reshape(1, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
 
@@ -81,9 +81,9 @@ def greedy_search(
         y = logits.argmax().item()
         if y != blank_id:
             hyp.append(y)
-            decoder_input = torch.tensor([hyp[-context_size:]], device=device).reshape(
-                1, context_size
-            )
+            decoder_input = torch.tensor(
+                [hyp[-context_size:]], device=device
+            ).reshape(1, context_size)
 
             decoder_out = model.decoder(decoder_input, need_pad=False)
 
@@ -157,7 +157,9 @@ class HypothesisList(object):
 
         """
         if length_norm:
-            return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys))
+            return max(
+                self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys)
+            )
         else:
             return max(self._data.values(), key=lambda hyp: hyp.log_prob)
 
@@ -244,9 +246,9 @@ def beam_search(
 
     device = model.device
 
-    decoder_input = torch.tensor([blank_id] * context_size, device=device).reshape(
-        1, context_size
-    )
+    decoder_input = torch.tensor(
+        [blank_id] * context_size, device=device
+    ).reshape(1, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
 

egs/aishell/ASR/transducer_stateless/conformer.py

@@ -155,7 +155,9 @@ class ConformerEncoderLayer(nn.Module):
         normalize_before: bool = True,
     ) -> None:
         super(ConformerEncoderLayer, self).__init__()
-        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)
+        self.self_attn = RelPositionMultiheadAttention(
+            d_model, nhead, dropout=0.0
+        )
 
         self.feed_forward = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
@@ -173,14 +175,18 @@ class ConformerEncoderLayer(nn.Module):
 
         self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
 
-        self.norm_ff_macaron = nn.LayerNorm(d_model)  # for the macaron style FNN module
+        self.norm_ff_macaron = nn.LayerNorm(
+            d_model
+        )  # for the macaron style FNN module
         self.norm_ff = nn.LayerNorm(d_model)  # for the FNN module
         self.norm_mha = nn.LayerNorm(d_model)  # for the MHA module
 
         self.ff_scale = 0.5
 
         self.norm_conv = nn.LayerNorm(d_model)  # for the CNN module
-        self.norm_final = nn.LayerNorm(d_model)  # for the final output of the block
+        self.norm_final = nn.LayerNorm(
+            d_model
+        )  # for the final output of the block
 
         self.dropout = nn.Dropout(dropout)
 
@@ -214,7 +220,9 @@ class ConformerEncoderLayer(nn.Module):
         residual = src
         if self.normalize_before:
             src = self.norm_ff_macaron(src)
-        src = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(src))
+        src = residual + self.ff_scale * self.dropout(
+            self.feed_forward_macaron(src)
+        )
         if not self.normalize_before:
             src = self.norm_ff_macaron(src)
 
@@ -333,7 +341,9 @@ class RelPositionalEncoding(torch.nn.Module):
 
     """
 
-    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
+    def __init__(
+        self, d_model: int, dropout_rate: float, max_len: int = 5000
+    ) -> None:
         """Construct an PositionalEncoding object."""
         super(RelPositionalEncoding, self).__init__()
         self.d_model = d_model
@@ -349,7 +359,9 @@ class RelPositionalEncoding(torch.nn.Module):
             # the length of self.pe is 2 * input_len - 1
             if self.pe.size(1) >= x.size(1) * 2 - 1:
                 # Note: TorchScript doesn't implement operator== for torch.Device
-                if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device):
+                if self.pe.dtype != x.dtype or str(self.pe.device) != str(
+                    x.device
+                ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
         # Suppose `i` means to the position of query vector and `j` means the
@@ -619,9 +631,9 @@ class RelPositionMultiheadAttention(nn.Module):
 
         if torch.equal(query, key) and torch.equal(key, value):
             # self-attention
-            q, k, v = nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk(
-                3, dim=-1
-            )
+            q, k, v = nn.functional.linear(
+                query, in_proj_weight, in_proj_bias
+            ).chunk(3, dim=-1)
 
         elif torch.equal(key, value):
             # encoder-decoder attention
@@ -689,25 +701,33 @@ class RelPositionMultiheadAttention(nn.Module):
             if attn_mask.dim() == 2:
                 attn_mask = attn_mask.unsqueeze(0)
                 if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
-                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 2D attn_mask is not correct."
+                    )
             elif attn_mask.dim() == 3:
                 if list(attn_mask.size()) != [
                     bsz * num_heads,
                     query.size(0),
                     key.size(0),
                 ]:
-                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
+                    raise RuntimeError(
+                        "The size of the 3D attn_mask is not correct."
+                    )
             else:
                 raise RuntimeError(
-                    "attn_mask's dimension {} is not supported".format(attn_mask.dim())
+                    "attn_mask's dimension {} is not supported".format(
+                        attn_mask.dim()
+                    )
                 )
             # attn_mask's dim is 3 now.
 
         # convert ByteTensor key_padding_mask to bool
-        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+        if (
+            key_padding_mask is not None
+            and key_padding_mask.dtype == torch.uint8
+        ):
             warnings.warn(
-                "Byte tensor for key_padding_mask is deprecated. Use bool tensor"
-                " instead."
+                "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead."
             )
             key_padding_mask = key_padding_mask.to(torch.bool)
 
@@ -744,7 +764,9 @@ class RelPositionMultiheadAttention(nn.Module):
         # first compute matrix a and matrix c
         # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
         k = k.permute(1, 2, 3, 0)  # (batch, head, d_k, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k)  # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(
+            q_with_bias_u, k
+        )  # (batch, head, time1, time2)
 
         # compute matrix b and matrix d
         matrix_bd = torch.matmul(
@@ -756,7 +778,9 @@ class RelPositionMultiheadAttention(nn.Module):
             matrix_ac + matrix_bd
         ) * scaling  # (batch, head, time1, time2)
 
-        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1)
+        attn_output_weights = attn_output_weights.view(
+            bsz * num_heads, tgt_len, -1
+        )
 
         assert list(attn_output_weights.size()) == [
             bsz * num_heads,
@@ -790,9 +814,13 @@ class RelPositionMultiheadAttention(nn.Module):
         attn_output = torch.bmm(attn_output_weights, v)
         assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
         attn_output = (
-            attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+            attn_output.transpose(0, 1)
+            .contiguous()
+            .view(tgt_len, bsz, embed_dim)
+        )
+        attn_output = nn.functional.linear(
+            attn_output, out_proj_weight, out_proj_bias
         )
-        attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)
 
         if need_weights:
             # average attention weights over heads
@@ -815,7 +843,9 @@ class ConvolutionModule(nn.Module):
 
     """
 
-    def __init__(self, channels: int, kernel_size: int, bias: bool = True) -> None:
+    def __init__(
+        self, channels: int, kernel_size: int, bias: bool = True
+    ) -> None:
         """Construct an ConvolutionModule object."""
         super(ConvolutionModule, self).__init__()
         # kernerl_size should be a odd number for 'SAME' padding

egs/aishell/ASR/transducer_stateless/decode.py

@@ -52,19 +52,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=30,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -102,7 +99,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -229,7 +227,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
     hyps = []
     batch_size = encoder_out.size(0)
 
@@ -248,7 +248,9 @@ def decode_one_batch(
                 model=model, encoder_out=encoder_out_i, beam=params.beam_size
             )
         else:
-            raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
+            raise ValueError(
+                f"Unsupported decoding method: {params.decoding_method}"
+            )
         hyps.append([lexicon.token_table[i] for i in hyp])
 
     if params.decoding_method == "greedy_search":
@@ -317,7 +319,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -342,7 +346,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -353,7 +359,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
@@ -423,7 +430,9 @@ def main():
 
     if params.export:
         logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
-        torch.save({"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
         return
 
     model.to(device)

egs/aishell/ASR/transducer_stateless/decoder.py

@@ -86,7 +86,9 @@ class Decoder(nn.Module):
         if self.context_size > 1:
             embedding_out = embedding_out.permute(0, 2, 1)
             if need_pad is True:
-                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
+                embedding_out = F.pad(
+                    embedding_out, pad=(self.context_size - 1, 0)
+                )
             else:
                 # During inference time, there is no need to do extra padding
                 # as we only need one output

egs/aishell/ASR/transducer_stateless/export.py

@@ -69,20 +69,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=20,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -113,7 +110,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     return parser
@@ -245,7 +243,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless/model.py

@@ -103,7 +103,9 @@ class Transducer(nn.Module):
         y_padded = y.pad(mode="constant", padding_value=0)
 
         y_padded = y_padded.to(torch.int64)
-        boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)
+        boundary = torch.zeros(
+            (x.size(0), 4), dtype=torch.int64, device=x.device
+        )
         boundary[:, 2] = y_lens
         boundary[:, 3] = x_lens
 

egs/aishell/ASR/transducer_stateless/pretrained.py

@@ -73,11 +73,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -102,12 +100,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -121,7 +117,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -214,9 +211,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -275,7 +273,9 @@ def main():
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
@@ -319,7 +319,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless/train.py

@@ -126,7 +126,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -388,7 +389,9 @@ def compute_loss(
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -501,7 +504,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -620,7 +625,9 @@ def run(rank, world_size, args):
 
         cur_lr = optimizer._rate
         if tb_writer is not None:
-            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
         if rank == 0:

egs/aishell/ASR/transducer_stateless/transformer.py

@@ -250,7 +250,9 @@ def _get_activation_fn(activation: str):
     elif activation == "gelu":
         return nn.functional.gelu
 
-    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
+    raise RuntimeError(
+        "activation should be relu/gelu, not {}".format(activation)
+    )
 
 
 class PositionalEncoding(nn.Module):

egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py

@@ -29,7 +29,10 @@ from lhotse.dataset import (
     K2SpeechRecognitionDataset,
     SpecAugment,
 )
-from lhotse.dataset.input_strategies import OnTheFlyFeatures, PrecomputedFeatures
+from lhotse.dataset.input_strategies import (
+    OnTheFlyFeatures,
+    PrecomputedFeatures,
+)
 from torch.utils.data import DataLoader
 
 from icefall.utils import str2bool
@@ -43,69 +46,59 @@ class AsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
 
         group.add_argument(
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
 
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
 
         group.add_argument(
             "--num-buckets",
             type=int,
             default=30,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler "
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler "
+            "(you might want to increase it for larger datasets).",
         )
 
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
 
         group.add_argument(
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -119,22 +112,18 @@ class AsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
         group.add_argument(
@@ -148,11 +137,9 @@ class AsrDataModule:
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available. Used only in dev/test CutSet"
-            ),
+            "if available. Used only in dev/test CutSet",
         )
 
     def train_dataloaders(
@@ -175,7 +162,9 @@ class AsrDataModule:
         if cuts_musan is not None:
             logging.info("Enable MUSAN")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
@@ -184,7 +173,9 @@ class AsrDataModule:
 
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -261,7 +252,9 @@ class AsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:

egs/aishell/ASR/transducer_stateless_modified-2/decode.py

@@ -93,19 +93,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=30,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -173,7 +170,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -229,7 +227,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
 
     if params.decoding_method == "fast_beam_search":
         hyp_tokens = fast_beam_search_one_best(
@@ -241,7 +241,10 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -285,7 +288,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -358,7 +365,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -384,7 +393,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -395,7 +406,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
@@ -436,7 +448,9 @@ def main():
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"

egs/aishell/ASR/transducer_stateless_modified-2/export.py

@@ -68,20 +68,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=20,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -112,7 +109,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     return parser
@@ -243,7 +241,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless_modified-2/pretrained.py

@@ -87,11 +87,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -117,12 +115,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -169,16 +165,15 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
         default=1,
-        help=(
-            "Maximum number of symbols per frame. "
-            "Use only when --method is greedy_search"
-        ),
+        help="Maximum number of symbols per frame. "
+        "Use only when --method is greedy_search",
     )
 
     return parser
@@ -199,9 +194,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -258,9 +254,13 @@ def main():
     feature_lens = [f.size(0) for f in features]
     feature_lens = torch.tensor(feature_lens, device=device)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lens
+    )
 
     num_waves = encoder_out.size(0)
     hyp_list = []
@@ -308,7 +308,9 @@ def main():
                     beam=params.beam_size,
                 )
             else:
-                raise ValueError(f"Unsupported decoding method: {params.method}")
+                raise ValueError(
+                    f"Unsupported decoding method: {params.method}"
+                )
             hyp_list.append(hyp)
 
     hyps = []
@@ -325,7 +327,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless_modified-2/train.py

@@ -149,7 +149,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -167,7 +168,8 @@ def get_parser():
         "--datatang-prob",
         type=float,
         default=0.2,
-        help="The probability to select a batch from the aidatatang_200zh dataset",
+        help="The probability to select a batch from the "
+        "aidatatang_200zh dataset",
     )
 
     return parser
@@ -447,7 +449,9 @@ def compute_loss(
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -601,7 +605,9 @@ def train_one_epoch(
                     f"train/current_{prefix}_",
                     params.batch_idx_train,
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
                 aishell_tot_loss.write_summary(
                     tb_writer, "train/aishell_tot_", params.batch_idx_train
                 )
@@ -729,7 +735,9 @@ def run(rank, world_size, args):
     train_datatang_cuts = train_datatang_cuts.repeat(times=None)
 
     if args.enable_musan:
-        cuts_musan = load_manifest(Path(args.manifest_dir) / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            Path(args.manifest_dir) / "musan_cuts.jsonl.gz"
+        )
     else:
         cuts_musan = None
 
@@ -768,7 +776,9 @@ def run(rank, world_size, args):
 
         cur_lr = optimizer._rate
         if tb_writer is not None:
-            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
         if rank == 0:

egs/aishell/ASR/transducer_stateless_modified/decode.py

@@ -94,19 +94,16 @@ def get_parser():
         "--epoch",
         type=int,
         default=30,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -174,7 +171,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -233,7 +231,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
 
     if params.decoding_method == "fast_beam_search":
         hyp_tokens = fast_beam_search_one_best(
@@ -245,7 +245,10 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -289,7 +292,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -362,7 +369,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -388,7 +397,9 @@ def save_results(
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+            results_char.append(
+                (res[0], list("".join(res[1])), list("".join(res[2])))
+            )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -399,7 +410,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
@@ -440,7 +452,9 @@ def main():
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"

egs/aishell/ASR/transducer_stateless_modified/export.py

@@ -68,20 +68,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=20,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=10,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -112,7 +109,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     return parser
@@ -243,7 +241,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless_modified/pretrained.py

@@ -87,11 +87,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -117,12 +115,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -169,16 +165,15 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
         default=1,
-        help=(
-            "Maximum number of symbols per frame. "
-            "Use only when --method is greedy_search"
-        ),
+        help="Maximum number of symbols per frame. "
+        "Use only when --method is greedy_search",
     )
 
     return parser
@@ -199,9 +194,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -258,9 +254,13 @@ def main():
     feature_lens = [f.size(0) for f in features]
     feature_lens = torch.tensor(feature_lens, device=device)
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lens
+    )
 
     num_waves = encoder_out.size(0)
     hyp_list = []
@@ -308,7 +308,9 @@ def main():
                     beam=params.beam_size,
                 )
             else:
-                raise ValueError(f"Unsupported decoding method: {params.method}")
+                raise ValueError(
+                    f"Unsupported decoding method: {params.method}"
+                )
             hyp_list.append(hyp)
 
     hyps = []
@@ -325,7 +327,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell/ASR/transducer_stateless_modified/train.py

@@ -142,7 +142,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -413,7 +414,9 @@ def compute_loss(
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -526,7 +529,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -652,7 +657,9 @@ def run(rank, world_size, args):
 
         cur_lr = optimizer._rate
         if tb_writer is not None:
-            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
             tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
 
         if rank == 0:

egs/aishell2/ASR/local/compute_fbank_aishell2.py

@@ -83,7 +83,9 @@ def compute_fbank_aishell2(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cut_set = cut_set.compute_and_store_features(
                 extractor=extractor,
@@ -109,7 +111,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -76,12 +76,10 @@ class AiShell2AsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
         group.add_argument(
             "--manifest-dir",
@@ -93,74 +91,59 @@ class AiShell2AsrDataModule:
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
         group.add_argument(
             "--num-buckets",
             type=int,
             default=30,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler"
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
         )
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, utterances (cuts) will be concatenated "
-                "to minimize the amount of padding."
-            ),
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
         )
         group.add_argument(
             "--duration-factor",
             type=float,
             default=1.0,
-            help=(
-                "Determines the maximum duration of a concatenated cut "
-                "relative to the duration of the longest cut in a batch."
-            ),
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
         )
         group.add_argument(
             "--gap",
             type=float,
             default=1.0,
-            help=(
-                "The amount of padding (in seconds) inserted between "
+            help="The amount of padding (in seconds) inserted between "
             "concatenated cuts. This padding is filled with noise when "
-                "noise augmentation is used."
-            ),
+            "noise augmentation is used.",
         )
         group.add_argument(
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available."
-            ),
+            "if available.",
         )
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
         group.add_argument(
             "--drop-last",
@@ -172,18 +155,17 @@ class AiShell2AsrDataModule:
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -197,22 +179,18 @@ class AiShell2AsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
         group.add_argument(
@@ -238,16 +216,20 @@ class AiShell2AsrDataModule:
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
             logging.info("About to get Musan cuts")
-            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+            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, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
 
         if self.args.concatenate_cuts:
             logging.info(
-                "Using cut concatenation with duration factor "
+                f"Using cut concatenation with duration factor "
                 f"{self.args.duration_factor} and gap {self.args.gap}."
             )
             # Cut concatenation should be the first transform in the list,
@@ -262,7 +244,9 @@ class AiShell2AsrDataModule:
         input_transforms = []
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -306,7 +290,9 @@ class AiShell2AsrDataModule:
             # Drop feats to be on the safe side.
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -362,7 +348,9 @@ class AiShell2AsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:
@@ -418,7 +406,9 @@ class AiShell2AsrDataModule:
     @lru_cache()
     def valid_cuts(self) -> CutSet:
         logging.info("About to gen cuts from aishell2_cuts_dev.jsonl.gz")
-        return load_manifest_lazy(self.args.manifest_dir / "aishell2_cuts_dev.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell2_cuts_dev.jsonl.gz"
+        )
 
     @lru_cache()
     def test_cuts(self) -> CutSet:

egs/aishell2/ASR/pruned_transducer_stateless5/decode.py

@@ -168,24 +168,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=True,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -273,7 +269,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -351,7 +348,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -410,7 +409,10 @@ def decode_one_batch(
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -536,7 +538,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -569,7 +573,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
@@ -620,7 +625,9 @@ def main():
             if "LG" in params.decoding_method:
                 params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -654,12 +661,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -682,12 +690,13 @@ def main():
             model.load_state_dict(average_checkpoints(filenames, device=device))
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -715,7 +724,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)
@@ -740,7 +749,9 @@ def main():
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
-            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+            decoding_graph = k2.trivial_graph(
+                params.vocab_size - 1, device=device
+            )
     else:
         decoding_graph = None
 

egs/aishell2/ASR/pruned_transducer_stateless5/export.py

@@ -89,24 +89,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=False,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -137,7 +133,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     add_model_arguments(parser)
@@ -170,12 +167,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -198,12 +196,13 @@ def main():
             model.load_state_dict(average_checkpoints(filenames, device=device))
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -231,7 +230,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)
@@ -267,7 +266,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py

@@ -81,11 +81,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -111,12 +109,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -163,7 +159,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -194,9 +191,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -256,11 +254,15 @@ def main():
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lengths)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lengths
+    )
 
     num_waves = encoder_out.size(0)
     hyps = []
@@ -332,7 +334,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell2/ASR/pruned_transducer_stateless5/train.py

@@ -92,7 +92,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
@@ -218,7 +220,8 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need "
+        "to be changed.",
     )
 
     parser.add_argument(
@@ -241,45 +244,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -603,7 +603,11 @@ def compute_loss(
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    device = (
+        model.device
+        if isinstance(model, DDP)
+        else next(model.parameters()).device
+    )
     feature = batch["inputs"]
     # at entry, feature is (N, T, C)
     assert feature.ndim == 3
@@ -632,16 +636,23 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
 
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -760,7 +771,9 @@ def train_one_epoch(
             scaler.update()
             optimizer.zero_grad()
         except:  # noqa
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -816,7 +829,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -1089,7 +1104,9 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(
+                batch, params=params, graph_compiler=graph_compiler
+            )
             raise
 
 

egs/aishell4/ASR/local/compute_fbank_aishell4.py

@@ -85,7 +85,9 @@ def compute_fbank_aishell4(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cut_set = cut_set.compute_and_store_features(
                 extractor=extractor,
@@ -118,7 +120,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/aishell4/ASR/local/prepare_char.py

@@ -86,7 +86,9 @@ def lexicon_to_fst_no_sil(
         cur_state = loop_state
 
         word = word2id[word]
-        pieces = [token2id[i] if i in token2id else token2id["<unk>"] for i in pieces]
+        pieces = [
+            token2id[i] if i in token2id else token2id["<unk>"] for i in pieces
+        ]
 
         for i in range(len(pieces) - 1):
             w = word if i == 0 else eps
@@ -140,7 +142,9 @@ def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
     return False
 
 
-def generate_lexicon(token_sym_table: Dict[str, int], words: List[str]) -> Lexicon:
+def generate_lexicon(
+    token_sym_table: Dict[str, int], words: List[str]
+) -> Lexicon:
     """Generate a lexicon from a word list and token_sym_table.
 
     Args:

egs/aishell4/ASR/local/prepare_lang.py

@@ -317,7 +317,9 @@ def lexicon_to_fst(
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--lang-dir", type=str, help="The lang dir, data/lang_phone")
+    parser.add_argument(
+        "--lang-dir", type=str, help="The lang dir, data/lang_phone"
+    )
     return parser.parse_args()
 
 

egs/aishell4/ASR/local/test_prepare_lang.py

@@ -88,7 +88,9 @@ def test_read_lexicon(filename: str):
     fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa.draw("L.pdf", title="L")
 
-    fsa_disambig = lexicon_to_fst(lexicon_disambig, phone2id=phone2id, word2id=word2id)
+    fsa_disambig = lexicon_to_fst(
+        lexicon_disambig, phone2id=phone2id, word2id=word2id
+    )
     fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt")
     fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa_disambig.draw("L_disambig.pdf", title="L_disambig")

egs/aishell4/ASR/local/text2token.py

@@ -50,15 +50,15 @@ def get_parser():
         "-n",
         default=1,
         type=int,
-        help=(
-            "number of characters to split, i.e.,                         aabb -> a a b"
-            " b with -n 1 and aa bb with -n 2"
-        ),
+        help="number of characters to split, i.e., \
+                        aabb -> a a b b with -n 1 and aa bb with -n 2",
     )
     parser.add_argument(
         "--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
     )
-    parser.add_argument("--space", default="<space>", type=str, help="space symbol")
+    parser.add_argument(
+        "--space", default="<space>", type=str, help="space symbol"
+    )
     parser.add_argument(
         "--non-lang-syms",
         "-l",
@@ -66,7 +66,9 @@ def get_parser():
         type=str,
         help="list of non-linguistic symobles, e.g., <NOISE> etc.",
     )
-    parser.add_argument("text", type=str, default=False, nargs="?", help="input text")
+    parser.add_argument(
+        "text", type=str, default=False, nargs="?", help="input text"
+    )
     parser.add_argument(
         "--trans_type",
         "-t",
@@ -106,7 +108,8 @@ def token2id(
             if token_type == "lazy_pinyin":
                 text = lazy_pinyin(chars_list)
                 sub_ids = [
-                    token_table[txt] if txt in token_table else oov_id for txt in text
+                    token_table[txt] if txt in token_table else oov_id
+                    for txt in text
                 ]
                 ids.append(sub_ids)
             else:  # token_type = "pinyin"
@@ -132,7 +135,9 @@ def main():
     if args.text:
         f = codecs.open(args.text, encoding="utf-8")
     else:
-        f = codecs.getreader("utf-8")(sys.stdin if is_python2 else sys.stdin.buffer)
+        f = codecs.getreader("utf-8")(
+            sys.stdin if is_python2 else sys.stdin.buffer
+        )
 
     sys.stdout = codecs.getwriter("utf-8")(
         sys.stdout if is_python2 else sys.stdout.buffer

egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -74,12 +74,10 @@ class Aishell4AsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
 
         group.add_argument(
@@ -93,81 +91,66 @@ class Aishell4AsrDataModule:
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
 
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
 
         group.add_argument(
             "--num-buckets",
             type=int,
             default=300,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler"
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
         )
 
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, utterances (cuts) will be concatenated "
-                "to minimize the amount of padding."
-            ),
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
         )
 
         group.add_argument(
             "--duration-factor",
             type=float,
             default=1.0,
-            help=(
-                "Determines the maximum duration of a concatenated cut "
-                "relative to the duration of the longest cut in a batch."
-            ),
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
         )
 
         group.add_argument(
             "--gap",
             type=float,
             default=1.0,
-            help=(
-                "The amount of padding (in seconds) inserted between "
+            help="The amount of padding (in seconds) inserted between "
             "concatenated cuts. This padding is filled with noise when "
-                "noise augmentation is used."
-            ),
+            "noise augmentation is used.",
         )
 
         group.add_argument(
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available."
-            ),
+            "if available.",
         )
 
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
 
         group.add_argument(
@@ -181,18 +164,17 @@ class Aishell4AsrDataModule:
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -206,22 +188,18 @@ class Aishell4AsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
         group.add_argument(
@@ -244,20 +222,24 @@ class Aishell4AsrDataModule:
             The state dict for the training sampler.
         """
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            self.args.manifest_dir / "musan_cuts.jsonl.gz"
+        )
 
         transforms = []
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
 
         if self.args.concatenate_cuts:
             logging.info(
-                "Using cut concatenation with duration factor "
+                f"Using cut concatenation with duration factor "
                 f"{self.args.duration_factor} and gap {self.args.gap}."
             )
             # Cut concatenation should be the first transform in the list,
@@ -272,7 +254,9 @@ class Aishell4AsrDataModule:
         input_transforms = []
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -316,7 +300,9 @@ class Aishell4AsrDataModule:
             # Drop feats to be on the safe side.
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -373,7 +359,9 @@ class Aishell4AsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:

egs/aishell4/ASR/pruned_transducer_stateless5/decode.py

@@ -117,24 +117,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=False,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -205,7 +201,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -263,7 +260,9 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -278,7 +277,10 @@ def decode_one_batch(
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -324,7 +326,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -395,7 +401,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -428,7 +436,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
@@ -471,7 +480,9 @@ def main():
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -499,12 +510,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -531,12 +543,13 @@ def main():
             )
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -565,7 +578,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)

egs/aishell4/ASR/pruned_transducer_stateless5/export.py

@@ -89,24 +89,20 @@ def get_parser():
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch' and '--iter'"
-        ),
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
         default=False,
-        help=(
-            "Whether to load averaged model. Currently it only supports "
+        help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
         "Actually only the models with epoch number of `epoch-avg` and "
-            "`epoch` are loaded for averaging. "
-        ),
+        "`epoch` are loaded for averaging. ",
     )
 
     parser.add_argument(
@@ -140,7 +136,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     add_model_arguments(parser)
@@ -172,12 +169,13 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg:
                 raise ValueError(
@@ -204,12 +202,13 @@ def main():
             )
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                : params.avg + 1
-            ]
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
             if len(filenames) == 0:
                 raise ValueError(
-                    f"No checkpoints found for --iter {params.iter}, --avg {params.avg}"
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
                 )
             elif len(filenames) < params.avg + 1:
                 raise ValueError(
@@ -238,7 +237,7 @@ def main():
             filename_start = f"{params.exp_dir}/epoch-{start}.pt"
             filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
             logging.info(
-                "Calculating the averaged model over epoch range from "
+                f"Calculating the averaged model over epoch range from "
                 f"{start} (excluded) to {params.epoch}"
             )
             model.to(device)
@@ -277,7 +276,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell4/ASR/pruned_transducer_stateless5/pretrained.py

@@ -94,11 +94,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -124,12 +122,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -176,7 +172,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -207,9 +204,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -268,11 +266,15 @@ def main():
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
-    encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lengths)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lengths
+    )
 
     num_waves = encoder_out.size(0)
     hyps = []
@@ -304,7 +306,10 @@ def main():
 
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -345,7 +350,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/aishell4/ASR/pruned_transducer_stateless5/train.py

@@ -85,7 +85,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
@@ -211,7 +213,8 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need "
+        "to be changed.",
     )
 
     parser.add_argument(
@@ -234,45 +237,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -599,7 +599,11 @@ def compute_loss(
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    device = (
+        model.device
+        if isinstance(model, DDP)
+        else next(model.parameters()).device
+    )
     feature = batch["inputs"]
     # at entry, feature is (N, T, C)
     assert feature.ndim == 3
@@ -629,15 +633,22 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -816,7 +827,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")

egs/alimeeting/ASR/local/compute_fbank_alimeeting.py

@@ -84,7 +84,9 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80):
             )
             if "train" in partition:
                 cut_set = (
-                    cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
                 )
             cur_num_jobs = num_jobs if ex is None else 80
             cur_num_jobs = min(cur_num_jobs, len(cut_set))
@@ -119,7 +121,9 @@ def get_args():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/alimeeting/ASR/local/prepare_char.py

@@ -86,7 +86,9 @@ def lexicon_to_fst_no_sil(
         cur_state = loop_state
 
         word = word2id[word]
-        pieces = [token2id[i] if i in token2id else token2id["<unk>"] for i in pieces]
+        pieces = [
+            token2id[i] if i in token2id else token2id["<unk>"] for i in pieces
+        ]
 
         for i in range(len(pieces) - 1):
             w = word if i == 0 else eps
@@ -140,7 +142,9 @@ def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
     return False
 
 
-def generate_lexicon(token_sym_table: Dict[str, int], words: List[str]) -> Lexicon:
+def generate_lexicon(
+    token_sym_table: Dict[str, int], words: List[str]
+) -> Lexicon:
     """Generate a lexicon from a word list and token_sym_table.
 
     Args:

egs/alimeeting/ASR/local/prepare_lang.py

@@ -317,7 +317,9 @@ def lexicon_to_fst(
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--lang-dir", type=str, help="The lang dir, data/lang_phone")
+    parser.add_argument(
+        "--lang-dir", type=str, help="The lang dir, data/lang_phone"
+    )
     return parser.parse_args()
 
 

egs/alimeeting/ASR/local/test_prepare_lang.py

@@ -88,7 +88,9 @@ def test_read_lexicon(filename: str):
     fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa.draw("L.pdf", title="L")
 
-    fsa_disambig = lexicon_to_fst(lexicon_disambig, phone2id=phone2id, word2id=word2id)
+    fsa_disambig = lexicon_to_fst(
+        lexicon_disambig, phone2id=phone2id, word2id=word2id
+    )
     fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt")
     fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
     fsa_disambig.draw("L_disambig.pdf", title="L_disambig")

egs/alimeeting/ASR/local/text2segments.py

@@ -30,8 +30,8 @@ with word segmenting:
 
 import argparse
 
-import jieba
 import paddle
+import jieba
 from tqdm import tqdm
 
 paddle.enable_static()

egs/alimeeting/ASR/local/text2token.py

@@ -50,15 +50,15 @@ def get_parser():
         "-n",
         default=1,
         type=int,
-        help=(
-            "number of characters to split, i.e.,                         aabb -> a a b"
-            " b with -n 1 and aa bb with -n 2"
-        ),
+        help="number of characters to split, i.e., \
+                        aabb -> a a b b with -n 1 and aa bb with -n 2",
     )
     parser.add_argument(
         "--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
     )
-    parser.add_argument("--space", default="<space>", type=str, help="space symbol")
+    parser.add_argument(
+        "--space", default="<space>", type=str, help="space symbol"
+    )
     parser.add_argument(
         "--non-lang-syms",
         "-l",
@@ -66,7 +66,9 @@ def get_parser():
         type=str,
         help="list of non-linguistic symobles, e.g., <NOISE> etc.",
     )
-    parser.add_argument("text", type=str, default=False, nargs="?", help="input text")
+    parser.add_argument(
+        "text", type=str, default=False, nargs="?", help="input text"
+    )
     parser.add_argument(
         "--trans_type",
         "-t",
@@ -106,7 +108,8 @@ def token2id(
             if token_type == "lazy_pinyin":
                 text = lazy_pinyin(chars_list)
                 sub_ids = [
-                    token_table[txt] if txt in token_table else oov_id for txt in text
+                    token_table[txt] if txt in token_table else oov_id
+                    for txt in text
                 ]
                 ids.append(sub_ids)
             else:  # token_type = "pinyin"
@@ -132,7 +135,9 @@ def main():
     if args.text:
         f = codecs.open(args.text, encoding="utf-8")
     else:
-        f = codecs.getreader("utf-8")(sys.stdin if is_python2 else sys.stdin.buffer)
+        f = codecs.getreader("utf-8")(
+            sys.stdin if is_python2 else sys.stdin.buffer
+        )
 
     sys.stdout = codecs.getwriter("utf-8")(
         sys.stdout if is_python2 else sys.stdout.buffer

egs/alimeeting/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -81,12 +81,10 @@ class AlimeetingAsrDataModule:
     def add_arguments(cls, parser: argparse.ArgumentParser):
         group = parser.add_argument_group(
             title="ASR data related options",
-            description=(
-                "These options are used for the preparation of "
+            description="These options are used for the preparation of "
             "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
             "effective batch sizes, sampling strategies, applied data "
-                "augmentations, etc."
-            ),
+            "augmentations, etc.",
         )
         group.add_argument(
             "--manifest-dir",
@@ -98,91 +96,75 @@ class AlimeetingAsrDataModule:
             "--max-duration",
             type=int,
             default=200.0,
-            help=(
-                "Maximum pooled recordings duration (seconds) in a "
-                "single batch. You can reduce it if it causes CUDA OOM."
-            ),
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, the batches will come from buckets of "
-                "similar duration (saves padding frames)."
-            ),
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
         )
         group.add_argument(
             "--num-buckets",
             type=int,
             default=300,
-            help=(
-                "The number of buckets for the DynamicBucketingSampler"
-                "(you might want to increase it for larger datasets)."
-            ),
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
         )
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, utterances (cuts) will be concatenated "
-                "to minimize the amount of padding."
-            ),
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
         )
         group.add_argument(
             "--duration-factor",
             type=float,
             default=1.0,
-            help=(
-                "Determines the maximum duration of a concatenated cut "
-                "relative to the duration of the longest cut in a batch."
-            ),
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
         )
         group.add_argument(
             "--gap",
             type=float,
             default=1.0,
-            help=(
-                "The amount of padding (in seconds) inserted between "
+            help="The amount of padding (in seconds) inserted between "
             "concatenated cuts. This padding is filled with noise when "
-                "noise augmentation is used."
-            ),
+            "noise augmentation is used.",
         )
         group.add_argument(
             "--on-the-fly-feats",
             type=str2bool,
             default=False,
-            help=(
-                "When enabled, use on-the-fly cut mixing and feature "
+            help="When enabled, use on-the-fly cut mixing and feature "
             "extraction. Will drop existing precomputed feature manifests "
-                "if available."
-            ),
+            "if available.",
         )
         group.add_argument(
             "--shuffle",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled (=default), the examples will be shuffled for each epoch."
-            ),
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
         )
         group.add_argument(
             "--return-cuts",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, each batch will have the "
+            help="When enabled, each batch will have the "
             "field: batch['supervisions']['cut'] with the cuts that "
-                "were used to construct it."
-            ),
+            "were used to construct it.",
         )
 
         group.add_argument(
             "--num-workers",
             type=int,
             default=2,
-            help="The number of training dataloader workers that collect the batches.",
+            help="The number of training dataloader workers that "
+            "collect the batches.",
         )
 
         group.add_argument(
@@ -196,22 +178,18 @@ class AlimeetingAsrDataModule:
             "--spec-aug-time-warp-factor",
             type=int,
             default=80,
-            help=(
-                "Used only when --enable-spec-aug is True. "
+            help="Used only when --enable-spec-aug is True. "
             "It specifies the factor for time warping in SpecAugment. "
             "Larger values mean more warping. "
-                "A value less than 1 means to disable time warp."
-            ),
+            "A value less than 1 means to disable time warp.",
         )
 
         group.add_argument(
             "--enable-musan",
             type=str2bool,
             default=True,
-            help=(
-                "When enabled, select noise from MUSAN and mix it"
-                "with training dataset. "
-            ),
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
         )
 
     def train_dataloaders(
@@ -227,20 +205,24 @@ class AlimeetingAsrDataModule:
             The state dict for the training sampler.
         """
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+        cuts_musan = load_manifest(
+            self.args.manifest_dir / "musan_cuts.jsonl.gz"
+        )
 
         transforms = []
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
             transforms.append(
-                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
             )
         else:
             logging.info("Disable MUSAN")
 
         if self.args.concatenate_cuts:
             logging.info(
-                "Using cut concatenation with duration factor "
+                f"Using cut concatenation with duration factor "
                 f"{self.args.duration_factor} and gap {self.args.gap}."
             )
             # Cut concatenation should be the first transform in the list,
@@ -255,7 +237,9 @@ class AlimeetingAsrDataModule:
         input_transforms = []
         if self.args.enable_spec_aug:
             logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
             # Set the value of num_frame_masks according to Lhotse's version.
             # In different Lhotse's versions, the default of num_frame_masks is
             # different.
@@ -298,7 +282,9 @@ class AlimeetingAsrDataModule:
             # Drop feats to be on the safe side.
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -355,7 +341,9 @@ class AlimeetingAsrDataModule:
         if self.args.on_the_fly_feats:
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
                 return_cuts=self.args.return_cuts,
             )
         else:

egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py

@@ -70,7 +70,11 @@ from beam_search import (
 from lhotse.cut import Cut
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -89,30 +93,25 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--batch",
         type=int,
         default=None,
-        help=(
-            "It specifies the batch checkpoint to use for decoding."
-            "Note: Epoch counts from 0."
-        ),
+        help="It specifies the batch checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -194,7 +193,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
     parser.add_argument(
         "--max-sym-per-frame",
@@ -249,7 +249,9 @@ def decode_one_batch(
 
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
-    encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -264,7 +266,10 @@ def decode_one_batch(
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -310,7 +315,11 @@ def decode_one_batch(
         return {"greedy_search": hyps}
     elif params.decoding_method == "fast_beam_search":
         return {
-            f"beam_{params.beam}_max_contexts_{params.max_contexts}_max_states_{params.max_states}": hyps
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
         }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
@@ -381,7 +390,9 @@ def decode_dataset(
         if batch_idx % log_interval == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
-            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
     return results
 
 
@@ -414,7 +425,8 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
@@ -551,7 +563,8 @@ def main():
         )
 
     dev_shards = [
-        str(path) for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
+        str(path)
+        for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
     ]
     cuts_dev_webdataset = CutSet.from_webdataset(
         dev_shards,
@@ -561,7 +574,8 @@ def main():
     )
 
     test_shards = [
-        str(path) for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
+        str(path)
+        for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
     ]
     cuts_test_webdataset = CutSet.from_webdataset(
         test_shards,
@@ -574,7 +588,9 @@ def main():
         return 1.0 <= c.duration
 
     cuts_dev_webdataset = cuts_dev_webdataset.filter(remove_short_and_long_utt)
-    cuts_test_webdataset = cuts_test_webdataset.filter(remove_short_and_long_utt)
+    cuts_test_webdataset = cuts_test_webdataset.filter(
+        remove_short_and_long_utt
+    )
 
     dev_dl = alimeeting.valid_dataloaders(cuts_dev_webdataset)
     test_dl = alimeeting.test_dataloaders(cuts_test_webdataset)

egs/alimeeting/ASR/pruned_transducer_stateless2/export.py

@@ -62,20 +62,17 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help=(
-            "It specifies the checkpoint to use for decoding.Note: Epoch counts from 0."
-        ),
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
     )
 
     parser.add_argument(
         "--avg",
         type=int,
         default=15,
-        help=(
-            "Number of checkpoints to average. Automatically select "
+        help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-            "'--epoch'. "
-        ),
+        "'--epoch'. ",
     )
 
     parser.add_argument(
@@ -106,7 +103,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     return parser
@@ -175,7 +173,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/alimeeting/ASR/pruned_transducer_stateless2/pretrained.py

@@ -85,11 +85,9 @@ def get_parser():
         "--checkpoint",
         type=str,
         required=True,
-        help=(
-            "Path to the checkpoint. "
+        help="Path to the checkpoint. "
         "The checkpoint is assumed to be saved by "
-            "icefall.checkpoint.save_checkpoint()."
-        ),
+        "icefall.checkpoint.save_checkpoint().",
     )
 
     parser.add_argument(
@@ -114,12 +112,10 @@ def get_parser():
         "sound_files",
         type=str,
         nargs="+",
-        help=(
-            "The input sound file(s) to transcribe. "
+        help="The input sound file(s) to transcribe. "
         "Supported formats are those supported by torchaudio.load(). "
         "For example, wav and flac are supported. "
-            "The sample rate has to be 16kHz."
-        ),
+        "The sample rate has to be 16kHz.",
     )
 
     parser.add_argument(
@@ -166,7 +162,8 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
@@ -196,9 +193,10 @@ def read_sound_files(
     ans = []
     for f in filenames:
         wave, sample_rate = torchaudio.load(f)
-        assert (
-            sample_rate == expected_sample_rate
-        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
         # We use only the first channel
         ans.append(wave[0])
     return ans
@@ -259,7 +257,9 @@ def main():
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
@@ -284,7 +284,10 @@ def main():
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
@@ -336,7 +339,9 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/alimeeting/ASR/pruned_transducer_stateless2/train.py

@@ -81,7 +81,9 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
 
 os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
 
@@ -185,45 +187,42 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
     )
 
     parser.add_argument(
         "--prune-range",
         type=int,
         default=5,
-        help=(
-            "The prune range for rnnt loss, it means how many symbols(context)"
-            "we are using to compute the loss"
-        ),
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
     )
 
     parser.add_argument(
         "--lm-scale",
         type=float,
         default=0.25,
-        help=(
-            "The scale to smooth the loss with lm (output of prediction network) part."
-        ),
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
     )
 
     parser.add_argument(
         "--am-scale",
         type=float,
         default=0.0,
-        help="The scale to smooth the loss with am (output of encoder network)part.",
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
     )
 
     parser.add_argument(
         "--simple-loss-scale",
         type=float,
         default=0.5,
-        help=(
-            "To get pruning ranges, we will calculate a simple version"
+        help="To get pruning ranges, we will calculate a simple version"
         "loss(joiner is just addition), this simple loss also uses for"
         "training (as a regularization item). We will scale the simple loss"
-            "with this parameter before adding to the final loss."
-        ),
+        "with this parameter before adding to the final loss.",
     )
 
     parser.add_argument(
@@ -543,15 +542,22 @@ def compute_loss(
         # overwhelming the simple_loss and causing it to diverge,
         # in case it had not fully learned the alignment yet.
         pruned_loss_scale = (
-            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
         )
-        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
@@ -705,7 +711,9 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")

egs/csj/ASR/local/compute_fbank_csj.py

@@ -25,10 +25,15 @@ from random import Random
 from typing import List, Tuple
 
 import torch
-from lhotse import (  # fmt: off; See the following for why LilcomChunkyWriter is preferred; https://github.com/k2-fsa/icefall/pull/404; https://github.com/lhotse-speech/lhotse/pull/527; fmt: on
+from lhotse import (
     CutSet,
     Fbank,
     FbankConfig,
+    # fmt: off
+    # See the following for why LilcomChunkyWriter is preferred
+    # https://github.com/k2-fsa/icefall/pull/404
+    # https://github.com/lhotse-speech/lhotse/pull/527
+    # fmt: on
     LilcomChunkyWriter,
     RecordingSet,
     SupervisionSet,
@@ -76,13 +81,17 @@ def make_cutset_blueprints(
         cut_sets.append((f"eval{i}", cut_set))
 
     # Create train and valid cuts
-    logging.info("Loading, trimming, and shuffling the remaining core+noncore cuts.")
+    logging.info(
+        "Loading, trimming, and shuffling the remaining core+noncore cuts."
+    )
     recording_set = RecordingSet.from_file(
         manifest_dir / "csj_recordings_core.jsonl.gz"
     ) + RecordingSet.from_file(manifest_dir / "csj_recordings_noncore.jsonl.gz")
     supervision_set = SupervisionSet.from_file(
         manifest_dir / "csj_supervisions_core.jsonl.gz"
-    ) + SupervisionSet.from_file(manifest_dir / "csj_supervisions_noncore.jsonl.gz")
+    ) + SupervisionSet.from_file(
+        manifest_dir / "csj_supervisions_noncore.jsonl.gz"
+    )
 
     cut_set = CutSet.from_manifests(
         recordings=recording_set,
@@ -92,12 +101,15 @@ def make_cutset_blueprints(
     cut_set = cut_set.shuffle(Random(RNG_SEED))
 
     logging.info(
-        f"Creating valid and train cuts from core and noncore,split at {split}."
+        "Creating valid and train cuts from core and noncore,"
+        f"split at {split}."
     )
     valid_set = CutSet.from_cuts(islice(cut_set, 0, split))
 
     train_set = CutSet.from_cuts(islice(cut_set, split, None))
-    train_set = train_set + train_set.perturb_speed(0.9) + train_set.perturb_speed(1.1)
+    train_set = (
+        train_set + train_set.perturb_speed(0.9) + train_set.perturb_speed(1.1)
+    )
 
     cut_sets.extend([("valid", valid_set), ("train", train_set)])
 
@@ -110,9 +122,15 @@ def get_args():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
 
-    parser.add_argument("--manifest-dir", type=Path, help="Path to save manifests")
-    parser.add_argument("--fbank-dir", type=Path, help="Path to save fbank features")
-    parser.add_argument("--split", type=int, default=4000, help="Split at this index")
+    parser.add_argument(
+        "--manifest-dir", type=Path, help="Path to save manifests"
+    )
+    parser.add_argument(
+        "--fbank-dir", type=Path, help="Path to save fbank features"
+    )
+    parser.add_argument(
+        "--split", type=int, default=4000, help="Split at this index"
+    )
 
     return parser.parse_args()
 
@@ -123,7 +141,9 @@ def main():
     extractor = Fbank(FbankConfig(num_mel_bins=80))
     num_jobs = min(16, os.cpu_count())
 
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
 

egs/csj/ASR/local/compute_fbank_musan.py

@@ -26,6 +26,7 @@ from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor
 
+
 ARGPARSE_DESCRIPTION = """
 This file computes fbank features of the musan dataset.
 It looks for manifests in the directory data/manifests.
@@ -83,7 +84,9 @@ def compute_fbank_musan(manifest_dir: Path, fbank_dir: Path):
         # create chunks of Musan with duration 5 - 10 seconds
         musan_cuts = (
             CutSet.from_manifests(
-                recordings=combine(part["recordings"] for part in manifests.values())
+                recordings=combine(
+                    part["recordings"] for part in manifests.values()
+                )
             )
             .cut_into_windows(10.0)
             .filter(lambda c: c.duration > 5)
@@ -104,15 +107,21 @@ def get_args():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
     )
 
-    parser.add_argument("--manifest-dir", type=Path, help="Path to save manifests")
-    parser.add_argument("--fbank-dir", type=Path, help="Path to save fbank features")
+    parser.add_argument(
+        "--manifest-dir", type=Path, help="Path to save manifests"
+    )
+    parser.add_argument(
+        "--fbank-dir", type=Path, help="Path to save fbank features"
+    )
 
     return parser.parse_args()
 
 
 if __name__ == "__main__":
     args = get_args()
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     compute_fbank_musan(args.manifest_dir, args.fbank_dir)

egs/csj/ASR/local/conf/disfluent.ini

@@ -318,3 +318,4 @@ spk_id = 2
 ャ = ǐa
 ュ = ǐu
 ョ = ǐo
+

egs/csj/ASR/local/conf/fluent.ini

@@ -318,3 +318,4 @@ spk_id = 2
 ャ = ǐa
 ュ = ǐu
 ョ = ǐo
+