Compute word starting time from framewise token alignment.

egs/librispeech/ASR/transducer_stateless/compute_ali.py

@@ -32,7 +32,6 @@ import logging
 from pathlib import Path
 from typing import List
 
-import k2
 import numpy as np
 import sentencepiece as spm
 import torch
@@ -88,19 +87,14 @@ def get_parser():
         help="""Output directory.
         It contains 3 generated files:
 
-        - labels_xxx.h5
-        - aux_labels_xxx.h5
+        - token_ali_xxx.h5
         - cuts_xxx.json.gz
 
         where xxx is the value of `--dataset`. For instance, if
-        `--dataset` is `train-clean-100`, it will contain 3 files:
+        `--dataset` is `train-clean-100`, it will contain 2 files:
 
-        - `labels_train-clean-100.h5`
-        - `aux_labels_train-clean-100.h5`
+        - `token_ali_train-clean-100.h5`
         - `cuts_train-clean-100.json.gz`
-
-        Note: Both labels_xxx.h5 and aux_labels_xxx.h5 contain framewise
-        alignment. The difference is that labels_xxx.h5 contains repeats.
         """,
     )
 
@@ -179,7 +173,6 @@ def compute_alignments(
         ys_list: List[List[int]] = sp.encode(texts, out_type=int)
 
         ali_list = []
-        word_begin_time_list = []
         for i in range(batch_size):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
@@ -208,7 +201,7 @@ def compute_alignments(
 
         num_cuts += len(cut_list)
 
-        if batch_idx % 100 == 0:
+        if batch_idx % 2 == 0:
             batch_str = f"{batch_idx}/{num_batches}"
 
             logging.info(
@@ -255,13 +248,10 @@ def main():
     out_ali_filename = out_dir / f"token_ali_{params.dataset}.h5"
     out_manifest_filename = out_dir / f"cuts_{params.dataset}.json.gz"
 
-    for f in (
-        out_ali_filename,
-        out_manifest_filename,
-    ):
-        if f.exists():
-            logging.info(f"{f} exists - skipping")
-            return
+    done_file = out_dir / f".{params.dataset}.done"
+    if done_file.is_file():
+        logging.info(f"{done_file} exists - skipping")
+        exit()
 
     logging.info("About to create model")
     model = get_transducer_model(params)
@@ -329,6 +319,7 @@ def main():
         f"saved to {out_ali_filename} and the cut manifest "
         f"file is {out_manifest_filename}. Number of cuts: {len(cut_set)}"
     )
+    done_file.touch()
 
 
 #  torch.set_num_threads(1)

egs/librispeech/ASR/transducer_stateless/test_compute_ali.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+# Copyright    2022  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.
+
+
+"""
+This script shows how to get word starting time
+from framewise token alignment.
+
+Usage:
+    ./transducer_stateless/compute_ali.py \
+            --exp-dir ./transducer_stateless/exp \
+            --bpe-model ./data/lang_bpe_500/bpe.model \
+            --epoch 20 \
+            --avg 10 \
+            --max-duration 300 \
+            --dataset train-clean-100 \
+            --out-dir data/ali
+
+And the you can run:
+
+    ./transducer_stateless/test_compute_ali.py \
+            --bpe-model ./data/lang_bpe_500/bpe.model \
+            --ali-dir data/ali \
+            --dataset train-clean-100
+"""
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from alignment import get_word_begin_frame
+from lhotse import CutSet, load_manifest
+from lhotse.dataset import K2SpeechRecognitionDataset, SingleCutSampler
+from lhotse.dataset.collation import collate_custom_field
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--ali-dir",
+        type=Path,
+        default="./data/ali",
+        help="It specifies the directory where alignments can be found.",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=True,
+        help="""The name of the dataset:
+        Possible values are:
+            - test-clean.
+            - test-other
+            - train-clean-100
+            - train-clean-360
+            - train-other-500
+            - dev-clean
+            - dev-other
+        """,
+    )
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(args.bpe_model)
+
+    cuts_json = args.ali_dir / f"cuts_{args.dataset}.json.gz"
+
+    logging.info(f"Loading {cuts_json}")
+    cuts = load_manifest(cuts_json)
+
+    sampler = SingleCutSampler(
+        cuts,
+        max_duration=30,
+        shuffle=False,
+    )
+
+    dataset = K2SpeechRecognitionDataset(return_cuts=True)
+
+    dl = torch.utils.data.DataLoader(
+        dataset,
+        sampler=sampler,
+        batch_size=None,
+        num_workers=1,
+        persistent_workers=False,
+    )
+
+    frame_shift = 10  # ms
+    subsampling_factor = 4
+
+    frame_shift_in_second = frame_shift * subsampling_factor / 1000.0
+
+    # key: cut.id
+    # value: a list of pairs (word, time_in_second)
+    word_begin_time_dict = {}
+    for batch in dl:
+        supervisions = batch["supervisions"]
+        cuts = supervisions["cut"]
+
+        token_alignment, token_alignment_length = collate_custom_field(
+            CutSet.from_cuts(cuts), "token_alignment"
+        )
+
+        for i in range(len(cuts)):
+            assert (
+                (cuts[i].features.num_frames - 1) // 2 - 1
+            ) // 2 == token_alignment_length[i]
+
+            word_begin_frame = get_word_begin_frame(
+                token_alignment[i, : token_alignment_length[i]].tolist(), sp=sp
+            )
+            word_begin_time = [
+                "{:.2f}".format(i * frame_shift_in_second)
+                for i in word_begin_frame
+            ]
+
+            words = supervisions["text"][i].split()
+
+            assert len(word_begin_frame) == len(words)
+            word_begin_time_dict[cuts[i].id] = list(zip(words, word_begin_time))
+
+        # This is a demo script and we exit here after processing
+        # one batch.
+        # You can find word starting time in the dict "word_begin_time_dict"
+        for cut_id, word_time in word_begin_time_dict.items():
+            print(f"{cut_id}\n{word_time}\n")
+        break
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()