Add cr-ctc loss and ctc-decode in aishell (#1980)

egs/aishell/ASR/RESULTS.md

@@ -1,5 +1,63 @@
 ## Results
 
+### Aishell training results (zipformer + CR-CTC)
+
+See <https://github.com/k2-fsa/icefall/pull/1980> for more details.
+
+[zipformer](./zipformer)
+
+#### Non-streaming
+
+##### medium-scale model, number of model parameters: 66218471, i.e., 66.2 M
+
+| decoding method                      | test       | dev        | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| ctc-greedy-search                    | 3.98       | 3.69       | --epoch 60 --avg 28 |
+| ctc-prefix-beam-search               | 3.98       | 3.70       | --epoch 60 --avg 21 |
+
+The training command using 2 32G-V100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+./zipformer/train.py \
+  --world-size 2 \
+  --num-epochs 60 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --context-size 1 \
+  --enable-musan 0 \
+  --exp-dir zipformer/exp \
+  --max-duration 500 \
+  --base-lr 0.045 \
+  --lr-batches 7500 \
+  --lr-epochs 18 \
+  --spec-aug-time-warp-factor 20 \
+  --use-ctc 1 \
+  --use-cr-ctc 1 \
+  --use-transducer 0 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.2
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+for m in ctc-greedy-search ctc-prefix-beam-search; do
+  ./zipformer/ctc_decode.py \
+    --epoch 60 \
+    --avg 28 \
+    --exp-dir zipformer/exp \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --max-duration 600 \
+    --decoding-method $m
+done
+```
+
+Pretrained models, training logs, decoding logs, tensorboard and decoding results
+are available at
+<https://huggingface.co/MistMoon/icefall-asr-aishell-zipformer-medium-cr-ctc-20250702>
+
 ### Aishell training results (Fine-tuning Pretrained Models)
 #### Whisper
 [./whisper](./whisper)

egs/aishell/ASR/zipformer/ctc_decode.py

@@ -0,0 +1,540 @@
+#!/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 60 \
+    --avg 28 \
+    --exp-dir ./zipformer/exp \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --max-duration 600 \
+    --decoding-method ctc-greedy-search
+(2) ctc-prefix-beam-search (with cr-ctc)
+./zipformer/ctc_decode.py \
+    --epoch 60 \
+    --avg 21 \
+    --exp-dir zipformer/exp \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --max-duration 600 \
+    --decoding-method ctc-prefix-beam-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 lhotse.cut import Cut
+from train import add_model_arguments, get_model, get_params
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.decode import (
+    ctc_greedy_search,
+    ctc_prefix_beam_search,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+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.
+          (2) ctc-prefix-beam-search. Extract n paths with the given beam, the best
+          path of the n paths is the decoding result.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def get_decoding_params() -> AttributeDict:
+    """Parameters for decoding."""
+    params = AttributeDict(
+        {
+            "beam": 4,  # for prefix-beam-search
+        }
+    )
+    return params
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    batch: dict,
+) -> 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`.
+    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)
+
+    hyp_tokens = []
+    hyps = []
+
+    if params.decoding_method == "ctc-greedy-search":
+        hyp_tokens = ctc_greedy_search(
+            ctc_output=ctc_output,
+            encoder_out_lens=encoder_out_lens,
+        )
+    elif params.decoding_method == "ctc-prefix-beam-search":
+        hyp_tokens = ctc_prefix_beam_search(
+            ctc_output=ctc_output,
+            encoder_out_lens=encoder_out_lens,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported decoding method: {params.decoding_method}"
+        )
+    
+    for i in range(encoder_out.size(0)):
+        hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+
+    if params.decoding_method == "ctc-greedy-search":
+        return {"ctc-greedy-search" : hyps}
+    elif params.decoding_method == "ctc-prefix-beam-search":
+        return {"ctc-prefix-beam-search" : hyps}
+    else:
+        assert False, f"Unsupported decoding method: {params.decoding_method}"
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+    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 3 elements:
+      Respectively, they are cut_id, the reference transcript, and the predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    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,
+            batch=batch,
+        )
+        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]]]],
+):
+    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(filename=recog_path, texts=results, char_level = True)
+        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"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                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",
+        "ctc-prefix-beam-search",
+    ) # support ctc-greedy-search and ctc-prefix-beam-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 "prefix-beam-search" in params.decoding_method:
+        params.suffix += f"_beam-{params.beam}"
+
+    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}")
+
+    lexicon = Lexicon(params.lang_dir)
+    
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    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()
+
+    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)
+
+    dev_cuts = aishell.valid_cuts()
+    dev_dl = aishell.valid_dataloaders(dev_cuts)
+
+    test_cuts = aishell.test_cuts()
+    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,
+        )
+
+        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
@@ -240,6 +241,27 @@ 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(
@@ -380,6 +402,27 @@ 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,
@@ -583,8 +626,13 @@ 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,
@@ -594,9 +642,27 @@ 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,
@@ -723,6 +789,7 @@ 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.
@@ -739,6 +806,8 @@ 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.
     """
@@ -758,6 +827,21 @@ 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,
@@ -766,25 +850,40 @@ 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]
+        if params.use_ctc:
+            simple_loss, pruned_loss, ctc_loss, _, cr_loss = losses[:5]
+        else:
+            simple_loss, pruned_loss = losses[:2]
 
-        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()
@@ -794,8 +893,13 @@ 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
 
@@ -843,6 +947,7 @@ 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,
@@ -869,6 +974,8 @@ 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:
@@ -918,6 +1025,7 @@ 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
@@ -1083,6 +1191,9 @@ 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")
@@ -1091,6 +1202,12 @@ 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:
@@ -1200,6 +1317,7 @@ 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)
@@ -1227,6 +1345,7 @@ 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,
@@ -1293,6 +1412,7 @@ 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
 
@@ -1310,6 +1430,7 @@ def scan_pessimistic_batches_for_oom(
                     graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
+                    spec_augment=spec_augment,
                 )
             loss.backward()
             optimizer.zero_grad()