Extract framewise alignment information using CTC decoding.

egs/librispeech/ASR/conformer_ctc/ali.py

@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import torch
+from asr_datamodule import LibriSpeechAsrDataModule
+from conformer import Conformer
+
+from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.decode import one_best_decoding
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    encode_supervisions,
+    get_alignments,
+    setup_logger,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=34,
+        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 "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "exp_dir": Path("conformer_ctc/exp"),
+            "lang_dir": Path("data/lang_bpe"),
+            "lm_dir": Path("data/lm"),
+            "feature_dim": 80,
+            "nhead": 8,
+            "attention_dim": 512,
+            "subsampling_factor": 4,
+            "num_decoder_layers": 6,
+            "vgg_frontend": False,
+            "is_espnet_structure": True,
+            "mmi_loss": False,
+            "use_feat_batchnorm": True,
+            "output_beam": 10,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def compute_alignments(
+    model: torch.nn.Module,
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    graph_compiler: BpeCtcTrainingGraphCompiler,
+    token_table: k2.SymbolTable,
+):
+    device = graph_compiler.device
+    for batch_idx, batch in enumerate(dl):
+        feature = batch["inputs"]
+
+        # at entry, feature is [N, T, C]
+        assert feature.ndim == 3
+        feature = feature.to(device)
+
+        supervisions = batch["supervisions"]
+        nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
+        # nnet_output is [N, T, C]
+        supervision_segments, texts = encode_supervisions(
+            supervisions, subsampling_factor=params.subsampling_factor
+        )
+
+        token_ids = graph_compiler.texts_to_ids(texts)
+        decoding_graph = graph_compiler.compile(token_ids)
+
+        dense_fsa_vec = k2.DenseFsaVec(
+            nnet_output,
+            supervision_segments,
+            allow_truncate=params.subsampling_factor - 1,
+        )
+
+        lattice = k2.intersect_dense(
+            decoding_graph, dense_fsa_vec, params.output_beam
+        )
+
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+
+        ali_ids = get_alignments(best_path)
+        ali_tokens = [[token_table[i] for i in ids] for ids in ali_ids]
+
+        frame_shift = 0.01  # 10ms, i.e., 0.01 seconds
+        for i, ali in enumerate(ali_tokens[0]):
+            print(i * params.subsampling_factor * frame_shift, ali)
+        import sys
+
+        sys.exit(0)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    assert args.return_cuts is True
+
+    params = get_params()
+    params.update(vars(args))
+
+    setup_logger(f"{params.exp_dir}/log/ali")
+    logging.info("Computing alignment - started")
+    logging.info(params)
+
+    lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
+    num_classes = max_token_id + 1  # +1 for the blank
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    graph_compiler = BpeCtcTrainingGraphCompiler(
+        params.lang_dir,
+        device=device,
+        sos_token="<sos/eos>",
+        eos_token="<sos/eos>",
+    )
+
+    logging.info("About to create model")
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.attention_dim,
+        num_classes=num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_decoder_layers=params.num_decoder_layers,
+        vgg_frontend=False,
+        is_espnet_structure=params.is_espnet_structure,
+        mmi_loss=params.mmi_loss,
+        use_feat_batchnorm=params.use_feat_batchnorm,
+    )
+
+    if params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+        logging.info(f"averaging {filenames}")
+        model.load_state_dict(average_checkpoints(filenames))
+
+    model.to(device)
+    model.eval()
+
+    librispeech = LibriSpeechAsrDataModule(args)
+    test_dl = librispeech.test_dataloaders()  # a list
+
+    enabled_datasets = {
+        "test_clean": test_dl[0],
+        "test_other": test_dl[1],
+    }
+
+    compute_alignments(
+        model=model,
+        dl=enabled_datasets["test_clean"],
+        params=params,
+        graph_compiler=graph_compiler,
+        token_table=lexicon.token_table,
+    )
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()

icefall/decode.py

@@ -878,7 +878,7 @@ def rescore_with_attention_decoder(
 
             best_path_indexes = k2.index_select(new2old, argmax_indexes)
 
-            # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
+            # best_path is a k2.RaggedTensor with 2 axes [path][arc_pos]
             best_path, _ = path_2axes.index(
                 indexes=best_path_indexes, axis=0, need_value_indexes=False
             )

icefall/utils.py

@@ -219,6 +219,28 @@ def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
     return aux_labels.tolist()
 
 
+def get_alignments(best_paths: k2.Fsa) -> List[List[int]]:
+    """Extract the token IDs (from best_paths.labels) from the best-path FSAs.
+
+    Args:
+      best_paths:
+        A k2.Fsa with best_paths.arcs.num_axes() == 3, i.e.
+        containing multiple FSAs, which is expected to be the result
+        of k2.shortest_path (otherwise the returned values won't
+        be meaningful).
+    Returns:
+      Returns a list of lists of int, containing the token sequences we
+      decoded. For `ans[i]`, its length equals to the number of frames
+      after subsampling of the i-th utterance in the batch.
+    """
+    # arc.shape() has axes [fsa][state][arc], we remove "state"-axis here
+    label_shape = best_paths.arcs.shape().remove_axis(1)
+    # label_shape has axes [fsa][arc]
+    labels = k2.RaggedTensor(label_shape, best_paths.labels.contiguous())
+    labels = labels.remove_values_eq(-1)
+    return labels.tolist()
+
+
 def store_transcripts(
     filename: Pathlike, texts: Iterable[Tuple[str, str]]
 ) -> None: