add timestamps for ctc-decode in aishell

egs/aishell/ASR/zipformer/ctc_decode.py

@@ -0,0 +1,736 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Liyong Guo,
+#                                                 Quandong Wang,
+#                                                 Zengwei Yao,
+#                                                 Zhifeng Han,)
+#
+# 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.
+"""
+Usage:
+
+(1) ctc-greedy-search (with cr-ctc)
+./zipformer/ctc_decode.py \
+    --epoch 50 \
+    --avg 24 \
+    --exp-dir ./zipformer/exp \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --max-duration 600 \
+    --decoding-method ctc-greedy-search
+"""
+
+
+import argparse
+import logging
+import math
+import os
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from asr_datamodule import AishellAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from lhotse.cut import Cut
+from train import add_model_arguments, get_model, get_params
+
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.context_graph import ContextGraph, ContextState
+from icefall.decode import (
+    ctc_greedy_search,
+    ctc_prefix_beam_search,
+    ctc_prefix_beam_search_attention_decoder_rescoring,
+    ctc_prefix_beam_search_shallow_fussion,
+    get_lattice,
+    nbest_decoding,
+    nbest_oracle,
+    one_best_decoding,
+    rescore_with_attention_decoder_no_ngram,
+    rescore_with_attention_decoder_with_ngram,
+    rescore_with_n_best_list,
+    rescore_with_whole_lattice,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    DecodingResults,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts_and_timestamps_withoutref,
+    str2bool,
+    write_error_stats,
+    parse_hyp_and_timestamp_ch,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        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. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipformer/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_char",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="ctc-greedy-search",
+        help="""Decoding method.
+        Supported values are:
+        - (1) ctc-greedy-search. Use CTC greedy search. It uses a sentence piece
+          model, i.e., lang_dir/bpe.model, to convert word pieces to words.
+          It needs neither a lexicon nor an n-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search, fast_beam_search_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--ilme-scale",
+        type=float,
+        default=0.2,
+        help="""
+        Used only when --decoding_method is fast_beam_search_LG.
+        It specifies the scale for the internal language model estimation.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search, fast_beam_search_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search, fast_beam_search_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        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.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--blank-penalty",
+        type=float,
+        default=0.0,
+        help="""
+        The penalty applied on blank symbol during decoding.
+        Note: It is a positive value that would be applied to logits like
+        this `logits[:, 0] -= blank_penalty` (suppose logits.shape is
+        [batch_size, vocab] and blank id is 0).
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
+    batch: dict,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, Tuple[List[List[str]], List[List[Tuple[float, float]]]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    if params.causal:
+        # this seems to cause insertions at the end of the utterance if used with zipformer.
+        pad_len = 30
+        feature_lens += pad_len
+        feature = torch.nn.functional.pad(
+            feature,
+            pad=(0, 0, 0, pad_len),
+            value=LOG_EPS,
+        )
+
+    x, x_lens = model.encoder_embed(feature, feature_lens)
+
+    src_key_padding_mask = make_pad_mask(x_lens)
+    x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+    encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask)
+    encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+    ctc_output = model.ctc_output(encoder_out)  # (N, T, C)
+
+    hyps = []
+
+    if params.decoding_method == "ctc-greedy-search" and params.max_sym_per_frame == 1:
+        res = ctc_greedy_search(
+            ctc_output=ctc_output,
+            encoder_out_lens=encoder_out_lens,
+            return_timestamps = True,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported decoding method: {params.decoding_method}"
+        )
+    
+    hyps, timestamps = parse_hyp_and_timestamp_ch(
+        res=res,
+        subsampling_factor=params.subsampling_factor,
+        word_table = lexicon.token_table,
+        # frame_shift_ms=params.frame_shift_ms,
+    )
+
+    key = f"blank_penalty_{params.blank_penalty}"
+    if params.decoding_method == "ctc-greedy-search":
+        return {"ctc-greedy-search_" + key: (hyps, timestamps)}
+    elif "fast_beam_search" in params.decoding_method:
+        key += f"_beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+        if "LG" in params.decoding_method:
+            key += f"_ilme_scale_{params.ilme_scale}"
+            key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: (hyps, timestamps)}
+    else:
+        return {f"beam_size_{params.beam_size}_" + key: (hyps, timestamps)}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
+    decoding_graph: Optional[k2.Fsa] = None,
+    with_timestamp: bool = False,
+) -> Dict[str, List[Tuple[str, List[str], List[str], List[Tuple[float, float]]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+      with_timestamp:
+        Whether to decode with timestamp.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains 4 elements:
+      Respectively, they are cut_id, the reference transcript, the predicted result and the decoded_timestamps.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "ctc-greedy-search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        texts = [list("".join(text.split())) for text in texts]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            graph_compiler=graph_compiler,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+        if with_timestamp:
+
+            for name, (hyps, timestamps_hyp) in hyps_dict.items():
+                this_batch = []
+                assert len(hyps) == len(texts) and len(timestamps_hyp) == len(hyps)
+                for cut_id, hyp_words, ref_text, time_hyp in zip(
+                    cut_ids, hyps, texts, timestamps_hyp
+                ):
+                    this_batch.append((cut_id, ref_text, hyp_words, time_hyp))
+
+                results[name].extend(this_batch)
+        else:
+
+            for name, hyps in hyps_dict.items():
+                this_batch = []
+                assert len(hyps) == len(texts)
+                for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                    this_batch.append((cut_id, ref_text, hyp_words))
+
+                results[name].extend(this_batch)
+                
+        num_cuts += len(texts)
+
+        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}")
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str], List[Tuple[float, float]]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts_and_timestamps_withoutref(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        result_without_timestamp = [(res[0], res[1], res[2]) for res in results]
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f,
+                f"{test_set_name}-{key}",
+                result_without_timestamp,
+                enable_log=True,
+                compute_CER=True,
+            )
+            test_set_wers[key] = wer
+
+        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.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    AishellAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+
+    params = get_params()
+    # add decoding params
+    # params.update(get_decoding_params())
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "ctc-greedy-search",
+    ) # only support ctc-greedy-search
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.causal:
+        assert (
+            "," not in params.chunk_size
+        ), "chunk_size should be one value in decoding."
+        assert (
+            "," not in params.left_context_frames
+        ), "left_context_frames should be one value in decoding."
+        params.suffix += f"-chunk-{params.chunk_size}"
+        params.suffix += f"-left-context-{params.left_context_frames}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+        if "LG" in params.decoding_method:
+            params.suffix += f"_ilme_scale_{params.ilme_scale}"
+            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}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+    params.suffix += f"-blank-penalty-{params.blank_penalty}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    params.device = device
+
+    logging.info(f"Device: {device}")
+    logging.info(params)
+
+    lexicon = Lexicon(params.lang_dir)
+    
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif 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 i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            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
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    if "fast_beam_search" in params.decoding_method:
+        if "LG" in params.decoding_method:
+            lexicon = Lexicon(params.lang_dir)
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    aishell = AishellAsrDataModule(args)
+
+    def remove_short_utt(c: Cut):
+        T = ((c.num_frames - 7) // 2 + 1) // 2
+        if T <= 0:
+            logging.warning(
+                f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
+            )
+        return T > 0
+
+    dev_cuts = aishell.valid_cuts()
+    dev_cuts = dev_cuts.filter(remove_short_utt)
+    dev_dl = aishell.valid_dataloaders(dev_cuts)
+
+    test_cuts = aishell.test_cuts()
+    test_cuts = test_cuts.filter(remove_short_utt)
+    test_dl = aishell.test_dataloaders(test_cuts)
+
+    test_sets = ["dev", "test"]
+    test_dls = [dev_dl, test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dls):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            graph_compiler=graph_compiler,
+            decoding_graph=decoding_graph,
+            with_timestamp=True,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/aishell/ASR/zipformer/train.py

@@ -64,6 +64,7 @@ from asr_datamodule import AishellAsrDataModule
 from decoder import Decoder
 from joiner import Joiner
 from lhotse.cut import Cut
+from lhotse.dataset import SpecAugment
 from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
 from model import AsrModel
@@ -239,6 +240,27 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         chunk left-context frames will be chosen randomly from this list; else not relevant.""",
     )
 
+    parser.add_argument(
+        "--use-transducer",
+        type=str2bool,
+        default=True,
+        help="If True, use Transducer head.",
+    )
+
+    parser.add_argument(
+        "--use-ctc",
+        type=str2bool,
+        default=False,
+        help="If True, use CTC head.",
+    )
+
+    parser.add_argument(
+        "--use-cr-ctc",
+        type=str2bool,
+        default=False,
+        help="If True, use consistency-regularized CTC.",
+    )
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -379,6 +401,27 @@ def get_parser():
         with this parameter before adding to the final loss.""",
     )
 
+    parser.add_argument(
+        "--ctc-loss-scale",
+        type=float,
+        default=0.2,
+        help="Scale for CTC loss.",
+    )
+
+    parser.add_argument(
+        "--cr-loss-scale",
+        type=float,
+        default=0.2,
+        help="Scale for consistency-regularization loss.",
+    )
+
+    parser.add_argument(
+        "--time-mask-ratio",
+        type=float,
+        default=2.5,
+        help="When using cr-ctc, we increase the amount of time-masking in SpecAugment.",
+    )
+
     parser.add_argument(
         "--seed",
         type=int,
@@ -582,8 +625,13 @@ def get_joiner_model(params: AttributeDict) -> nn.Module:
 def get_model(params: AttributeDict) -> nn.Module:
     encoder_embed = get_encoder_embed(params)
     encoder = get_encoder_model(params)
-    decoder = get_decoder_model(params)
-    joiner = get_joiner_model(params)
+
+    if params.use_transducer:
+        decoder = get_decoder_model(params)
+        joiner = get_joiner_model(params)
+    else:
+        decoder = None
+        joiner = None
 
     model = AsrModel(
         encoder_embed=encoder_embed,
@@ -593,9 +641,27 @@ def get_model(params: AttributeDict) -> nn.Module:
         encoder_dim=int(max(params.encoder_dim.split(","))),
         decoder_dim=params.decoder_dim,
         vocab_size=params.vocab_size,
+        use_transducer=params.use_transducer,
+        use_ctc=params.use_ctc,
     )
     return model
 
+def get_spec_augment(params: AttributeDict) -> SpecAugment:
+    num_frame_masks = int(10 * params.time_mask_ratio)
+    max_frames_mask_fraction = 0.15 * params.time_mask_ratio
+    logging.info(
+        f"num_frame_masks: {num_frame_masks}, "
+        f"max_frames_mask_fraction: {max_frames_mask_fraction}"
+    )
+    spec_augment = SpecAugment(
+        time_warp_factor=0,  # Do time warping in model.py
+        num_frame_masks=num_frame_masks,  # default: 10
+        features_mask_size=27,
+        num_feature_masks=2,
+        frames_mask_size=100,
+        max_frames_mask_fraction=max_frames_mask_fraction,  # default: 0.15
+    )
+    return spec_augment
 
 def load_checkpoint_if_available(
     params: AttributeDict,
@@ -722,6 +788,7 @@ def compute_loss(
     graph_compiler: CharCtcTrainingGraphCompiler,
     batch: dict,
     is_training: bool,
+    spec_augment: Optional[SpecAugment] = None,
 ) -> Tuple[Tensor, MetricsTracker]:
     """
     Compute CTC loss given the model and its inputs.
@@ -738,6 +805,8 @@ def compute_loss(
         True for training. False for validation. When it is True, this
         function enables autograd during computation; when it is False, it
         disables autograd.
+      spec_augment:
+        The SpecAugment instance used only when use_cr_ctc is True.
      warmup: a floating point value which increases throughout training;
         values >= 1.0 are fully warmed up and have all modules present.
     """
@@ -757,6 +826,21 @@ def compute_loss(
     y = graph_compiler.texts_to_ids(texts)
     y = k2.RaggedTensor(y).to(device)
 
+    use_cr_ctc = params.use_cr_ctc
+    use_spec_aug = use_cr_ctc and is_training
+    if use_spec_aug:
+        supervision_intervals = batch["supervisions"]
+        supervision_segments = torch.stack(
+            [
+                supervision_intervals["sequence_idx"],
+                supervision_intervals["start_frame"],
+                supervision_intervals["num_frames"],
+            ],
+            dim=1,
+        )  # shape: (S, 3)
+    else:
+        supervision_segments = None
+
     with torch.set_grad_enabled(is_training):
         losses = model(
             x=feature,
@@ -765,25 +849,37 @@ def compute_loss(
             prune_range=params.prune_range,
             am_scale=params.am_scale,
             lm_scale=params.lm_scale,
+            use_cr_ctc=use_cr_ctc,
+            use_spec_aug=use_spec_aug,
+            spec_augment=spec_augment,
+            supervision_segments=supervision_segments,
+            time_warp_factor=params.spec_aug_time_warp_factor,
         )
-        simple_loss, pruned_loss = losses[:2]
+        simple_loss, pruned_loss, ctc_loss, _, cr_loss = losses[:5]
 
-        s = params.simple_loss_scale
-        # take down the scale on the simple loss from 1.0 at the start
-        # to params.simple_loss scale by warm_step.
-        simple_loss_scale = (
-            s
-            if batch_idx_train >= warm_step
-            else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
-        )
-        pruned_loss_scale = (
-            1.0
-            if batch_idx_train >= warm_step
-            else 0.1 + 0.9 * (batch_idx_train / warm_step)
-        )
+        loss = 0.0
 
-        loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+        if params.use_transducer:
+            s = params.simple_loss_scale
+            # take down the scale on the simple loss from 1.0 at the start
+            # to params.simple_loss scale by warm_step.
+            simple_loss_scale = (
+                s
+                if batch_idx_train >= warm_step
+                else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
+            )
+            pruned_loss_scale = (
+                1.0
+                if batch_idx_train >= warm_step
+                else 0.1 + 0.9 * (batch_idx_train / warm_step)
+            )
+            loss += simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
 
+        if params.use_ctc:
+            loss += params.ctc_loss_scale * ctc_loss
+            if use_cr_ctc:
+                loss += params.cr_loss_scale * cr_loss
+    
     assert loss.requires_grad == is_training
 
     info = MetricsTracker()
@@ -793,8 +889,13 @@ def compute_loss(
 
     # Note: We use reduction=sum while computing the loss.
     info["loss"] = loss.detach().cpu().item()
-    info["simple_loss"] = simple_loss.detach().cpu().item()
-    info["pruned_loss"] = pruned_loss.detach().cpu().item()
+    if params.use_transducer:
+        info["simple_loss"] = simple_loss.detach().cpu().item()
+        info["pruned_loss"] = pruned_loss.detach().cpu().item()
+    if params.use_ctc:
+        info["ctc_loss"] = ctc_loss.detach().cpu().item()
+        if params.use_cr_ctc:
+            info["cr_loss"] = cr_loss.detach().cpu().item()
 
     return loss, info
 
@@ -842,6 +943,7 @@ def train_one_epoch(
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     scaler: GradScaler,
+    spec_augment: Optional[SpecAugment] = None,
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -868,6 +970,8 @@ def train_one_epoch(
         Dataloader for the validation dataset.
       scaler:
         The scaler used for mix precision training.
+      spec_augment:
+        The SpecAugment instance used only when use_cr_ctc is True.
       model_avg:
         The stored model averaged from the start of training.
       tb_writer:
@@ -917,6 +1021,7 @@ def train_one_epoch(
                     graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
+                    spec_augment=spec_augment,
                 )
             # summary stats
             tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
@@ -1082,6 +1187,9 @@ def run(rank, world_size, args):
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    if not params.use_transducer:
+        params.ctc_loss_scale = 1.0
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -1090,6 +1198,12 @@ def run(rank, world_size, args):
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
+    if params.use_cr_ctc:
+        assert not params.enable_spec_aug  # we will do spec_augment in model.py
+        spec_augment = get_spec_augment(params)
+    else:
+        spec_augment = None
+
     assert params.save_every_n >= params.average_period
     model_avg: Optional[nn.Module] = None
     if rank == 0:
@@ -1199,6 +1313,7 @@ def run(rank, world_size, args):
             optimizer=optimizer,
             graph_compiler=graph_compiler,
             params=params,
+            spec_augment=spec_augment,
         )
 
     scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
@@ -1226,6 +1341,7 @@ def run(rank, world_size, args):
             train_dl=train_dl,
             valid_dl=valid_dl,
             scaler=scaler,
+            spec_augment=spec_augment,
             tb_writer=tb_writer,
             world_size=world_size,
             rank=rank,
@@ -1292,6 +1408,7 @@ def scan_pessimistic_batches_for_oom(
     optimizer: torch.optim.Optimizer,
     graph_compiler: CharCtcTrainingGraphCompiler,
     params: AttributeDict,
+    spec_augment: Optional[SpecAugment] = None,
 ):
     from lhotse.dataset import find_pessimistic_batches
 
@@ -1309,6 +1426,7 @@ def scan_pessimistic_batches_for_oom(
                     graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
+                    spec_augment=spec_augment,
                 )
             loss.backward()
             optimizer.zero_grad()

icefall/decode.py

@@ -26,7 +26,7 @@ import torch
 from icefall.context_graph import ContextGraph, ContextState
 from icefall.lm_wrapper import LmScorer
 from icefall.ngram_lm import NgramLm, NgramLmStateCost
-from icefall.utils import add_eos, add_sos, get_texts
+from icefall.utils import DecodingResults, add_eos, add_sos, get_texts
 
 DEFAULT_LM_SCALE = [
     0.01,
@@ -1485,25 +1485,52 @@ def ctc_greedy_search(
     ctc_output: torch.Tensor,
     encoder_out_lens: torch.Tensor,
     blank_id: int = 0,
-) -> List[List[int]]:
+    return_timestamps: bool = False,
+) -> Union[List[List[int]], DecodingResults]:
     """CTC greedy search.
 
     Args:
          ctc_output: (batch, seq_len, vocab_size)
          encoder_out_lens: (batch,)
     Returns:
-         List[List[int]]: greedy search result
+         Union[List[List[int]], DecodingResults]: greedy search result
     """
     batch = ctc_output.shape[0]
     index = ctc_output.argmax(dim=-1)  # (batch, seq_len)
-    hyps = [
-        torch.unique_consecutive(index[i, : encoder_out_lens[i]]) for i in range(batch)
-    ]
-
-    hyps = [h[h != blank_id].tolist() for h in hyps]
-
-    return hyps
-
+    
+    hyps = [[] for _ in range(batch)]
+    # timestamps[n][i] is the frame index after subsampling
+    # on which hyp[n][i] is decoded
+    timestamps = [[] for _ in range(batch)]
+    # scores[n][i] is the logits on which hyp[n][i] is decoded
+    scores = [[] for _ in range(batch)]
+    
+    for i in range(batch):
+      cur_pos = -1
+      last = -1
+      next_other = torch.where(index[i,0 : encoder_out_lens[i]] != last)[0]
+      flag = False # whether decoding words
+      while next_other.size(0) > 0:
+        cur_pos = cur_pos + 1 + next_other[0].item()
+        last = index[i,cur_pos].item()
+        if flag: # last word decoding finished
+          timestamps[i][-1] = timestamps[i][-1] + (cur_pos - 1, )
+        if last != blank_id:
+          hyps[i].append(last)
+          timestamps[i].append((cur_pos,))
+          scores[i].append(ctc_output[i, cur_pos, last].item())
+          flag = True # Decoding words
+        else:
+          flag = False
+        
+        next_other = torch.where(index[i,cur_pos + 1 : encoder_out_lens[i]] != last)[0]
+      if flag:
+        timestamps[i][-1] = timestamps[i][-1] + (encoder_out_lens[i].item() - 1, )
+        
+    if not return_timestamps:
+      return hyps
+    else:
+      return DecodingResults(hyps = hyps, timestamps = timestamps, scores = scores)
 
 @dataclass
 class Hypothesis:

icefall/utils.py

@@ -360,7 +360,7 @@ class KeywordResult:
 class DecodingResults:
     # timestamps[i][k] contains the frame number on which tokens[i][k]
     # is decoded
-    timestamps: List[List[int]]
+    timestamps: List[List[Union[int, Tuple[int, int]]]]
 
     # hyps[i] is the recognition results, i.e., word IDs or token IDs
     # for the i-th utterance with fast_beam_search_nbest_LG.
@@ -583,6 +583,40 @@ def store_transcripts_and_timestamps(
                     s = "[" + ", ".join(["%0.3f" % i for i in time_hyp]) + "]"
                 print(f"{cut_id}:\ttimestamp_hyp={s}", file=f)
 
+def store_transcripts_and_timestamps_withoutref(
+    filename: Pathlike,
+    texts: Iterable[Tuple[str, List[str], List[str], List[Tuple[float]]]],
+) -> None:
+    """Save predicted results and reference transcripts as well as their timestamps
+    to a file.
+
+    Args:
+      filename:
+        File to save the results to.
+      texts:
+        An iterable of tuples. The first element is the cur_id, the second is
+        the reference transcript and the third element is the predicted result.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf8") as f:
+        for cut_id, ref, hyp, time_hyp in texts:
+            print(f"{cut_id}:\tref={ref}", file=f)
+            print(f"{cut_id}:\thyp={hyp}", file=f)
+
+            if len(time_hyp) > 0:
+                if isinstance(time_hyp[0], tuple):
+                    # each element is <start, end> pair
+                    s = (
+                        "["
+                        + ", ".join(["(%0.3f, %.03f)" % (i, j) for (i, j) in time_hyp])
+                        + "]"
+                    )
+                else:
+                    # each element is a float number
+                    s = "[" + ", ".join(["%0.3f" % i for i in time_hyp]) + "]"
+                print(f"{cut_id}:\ttimestamp_hyp={s}", file=f)
+
 
 def write_error_stats(
     f: TextIO,
@@ -1839,6 +1873,30 @@ def convert_timestamp(
 
     return time
 
+def convert_timestamp_duration(
+    frames: List[Tuple[int, int]],
+    subsampling_factor: int,
+    frame_shift_ms: float = 10,
+) -> List[Tuple[float, float]]:
+    """Convert frame numbers to time (in seconds) given subsampling factor
+    and frame shift (in milliseconds).
+
+    Args:
+      frames:
+        A list of frame numbers after subsampling.
+      subsampling_factor:
+        The subsampling factor of the model.
+      frame_shift_ms:
+        Frame shift in milliseconds between two contiguous frames.
+    Return:
+      Return the time in seconds corresponding to each given frame.
+    """
+    frame_shift = frame_shift_ms / 1000.0
+    time = []
+    for f_start, f_end in frames:
+        time.append((round(f_start * subsampling_factor * frame_shift, ndigits=3), round(f_end * subsampling_factor * frame_shift, ndigits=3)))
+
+    return time
 
 def parse_timestamp(tokens: List[str], timestamp: List[float]) -> List[float]:
     """
@@ -1924,6 +1982,43 @@ def parse_hyp_and_timestamp(
 
     return hyps, timestamps
 
+def parse_hyp_and_timestamp_ch(
+    res: DecodingResults,
+    subsampling_factor: int,
+    word_table: k2.SymbolTable,
+    frame_shift_ms: float = 10,
+) -> Tuple[List[List[str]], List[List[float]]]:
+    """Parse hypothesis and timestamps of Chinese characters.
+
+    Args:
+      res:
+        A DecodingResults object.
+      subsampling_factor:
+        The integer subsampling factor.
+      word_table:
+        The word symbol table.
+      frame_shift_ms:
+        The float frame shift used for feature extraction.
+
+    Returns:
+       Return a list of hypothesis and timestamp.
+    """
+    hyps = []
+    timestamps = []
+
+    N = len(res.hyps)
+    assert len(res.timestamps) == N, (len(res.timestamps), N)
+    assert word_table is not None
+
+    for i in range(N):
+        time = convert_timestamp_duration(res.timestamps[i], subsampling_factor, frame_shift_ms)
+        words_decoded = [word_table[idx] for idx in res.hyps[i]]
+        hyps.append(words_decoded)
+        timestamps.append(time)
+        assert len(time) == len(words_decoded), (len(time), len(words_decoded))
+
+    return hyps, timestamps
+
 
 # `is_module_available` is copied from
 # https://github.com/pytorch/audio/blob/6bad3a66a7a1c7cc05755e9ee5931b7391d2b94c/torchaudio/_internal/module_utils.py#L9