seame zipformer-hat-lid recipe

egs/seame/ASR/README.md

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+# Introduction
+
+This recipe includes ASR models (zipformer, zipformer-hat, zipformer-hat-lid) trained and evaluated on SEAME dataset.
+The SEAME corpora is Singaporean Codeswitched English and Mandarin.
+
+This corpus comes defined with a training split and two development splits:
+
+train -- A mix of codeswitched, Mandarin and Singaporean English
+dev_sge -- A set of primarily Singaporean English though there is codeswitching  
+dev_man -- A set of primarily Mandarin though there is also some codeswitching
+
+
+[./RESULTS.md](./RESULTS.md) contains the latest results.
+
+# Zipformer-hat
+
+Zipformer with hybrid autoregressive transducer (HAT) loss https://arxiv.org/abs/2003.07705
+see https://github.com/k2-fsa/icefall/pull/1291
+
+# Zipformer-hat-lid
+
+Zipformer-hat with auxiliary LID encoder and blank sharing for synchronization between ASR and LID as described here (will be shared soon)
+

egs/seame/ASR/RESULTS.md

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+## Results
+
+#### Zipformer
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+| modified beam search               | 21.87      | 29.04      | --epoch 25, --avg 5, --max-duration 500 |
+
+The training command:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./zipformer/train.py \
+  --world-size 4 \
+  --num-epochs 25 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-asr-seame \
+  --causal 0 \
+  --num-encoder-layers 2,2,2,2,2,2 \
+  --feedforward-dim 512,768,1024,1024,1024,768 \
+  --encoder-dim 192,256,256,256,256,256 \
+  --encoder-unmasked-dim 192,192,192,192,192,192 \
+  --prune-range 10 \
+  --max-duration 500
+```
+
+The decoding command:
+
+```
+ ./zipformer/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --beam-size 10
+    --exp-dir ./zipformer/exp-asr-seame \
+    --max-duration 800 \
+    --num-encoder-layers 2,2,2,2,2,2 \
+    --feedforward-dim 512,768,1024,1024,1024,768 \
+    --encoder-dim 192,256,256,256,256,256 \
+    --encoder-unmasked-dim 192,192,192,192,192,192 \
+    --decoding-method modified_beam_search
+```
+
+The pretrained model is available at:  <https://huggingface.co/AmirHussein/zipformer-seame>
+
+
+### Zipformer-HAT
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+| modified beam search               | 22.00      | 29.92      | --epoch 20, --avg 5, --max-duration 500 |
+
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
+
+./zipformer_hat/train.py \
+  --world-size 4 \
+  --num-epochs 25 \
+  --start-epoch 1 \
+  --base-lr 0.045 \
+  --lr-epochs 6 \
+  --use-fp16 1 \
+  --exp-dir zipformer_hat/exp \
+  --causal 0 \
+  --num-encoder-layers 2,2,2,2,2,2 \
+  --feedforward-dim 512,768,1024,1024,1024,768 \
+  --encoder-dim 192,256,256,256,256,256 \
+  --encoder-unmasked-dim 192,192,192,192,192,192 \
+  --prune-range 10 \
+  --max-duration 500 
+```
+
+The decoding command is:
+```
+## modified beam search
+./zipformer_hat/decode.py \
+      --epoch 25 --avg 5 --use-averaged-model True \
+      --beam-size 10 \
+      --causal 0 \
+      --exp-dir zipformer_hat/exp \
+      --bpe-model data_seame/lang_bpe_4000/bpe.model \
+      --max-duration 1000 \
+      --num-encoder-layers 2,2,2,2,2,2 \
+      --feedforward-dim 512,768,1024,1024,1024,768 \
+      --encoder-dim 192,256,256,256,256,256 \
+      --encoder-unmasked-dim 192,192,192,192,192,192 \
+      --decoding-method modified_beam_search 
+```
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/AmirHussein/zipformer-hat-seame>
+
+### Zipformer-HAT-LID
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+| modified beam search               | 20.04      | 26.91      | --epoch 15, --avg 5, --max-duration 500 |
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
+
+./zipformer_hat_lid/train.py \
+  --world-size 4 \
+  --lid True \
+  --num-epochs 25 \
+  --start-epoch 1 \
+  --base-lr 0.045 \
+  --use-fp16 1 \
+  --lid-loss-scale 0.3 \
+  --exp-dir zipformer_hat_lid/exp \
+  --causal 0 \
+  --lid-output-layer 3 \
+  --num-encoder-layers 2,2,2,2,2,2 \
+  --feedforward-dim 512,768,1024,1024,1024,768 \
+  --encoder-dim 192,256,256,256,256,256 \
+  --encoder-unmasked-dim 192,192,192,192,192,192 \
+  --lids "<en>,<zh>" \
+  --prune-range 10 \
+  --freeze-main-model False \
+  --use-lid-encoder True \
+  --use-lid-joiner True \
+  --lid-num-encoder-layers 2,2,2 \
+  --lid-downsampling-factor 2,4,2 \
+  --lid-feedforward-dim 256,256,256 \
+  --lid-num-heads 4,4,4 \
+  --lid-encoder-dim 256,256,256 \
+  --lid-encoder-unmasked-dim 128,128,128 \
+  --lid-cnn-module-kernel 31,15,31 \
+  --max-duration 500
+
+```
+
+The decoding command is:
+```
+## modified beam search
+python zipformer_hat_lid/decode.py \
+      --epoch $epoch --avg 5 --use-averaged-model True \
+      --beam-size 10 \
+      --lid False \
+      --lids "<en>,<zh>" \
+      --exp-dir zipformer_hat_lid/exp \
+      --bpe-model data_seame/lang_bpe_4000/bpe.model \
+      --max-duration 800 \
+      --num-encoder-layers 2,2,2,2,2,2 \
+      --feedforward-dim 512,768,1024,1024,1024,768 \
+      --encoder-dim 192,256,256,256,256,256 \
+      --encoder-unmasked-dim 192,192,192,192,192,192 \
+      --decoding-method modified_beam_search \
+      --lid-output-layer 3 \
+      --use-lid-encoder True \
+      --use-lid-joiner True \
+      --lid-num-encoder-layers 2,2,2 \
+      --lid-downsampling-factor 2,4,2 \
+      --lid-feedforward-dim 256,256,256 \
+      --lid-num-heads 4,4,4 \
+      --lid-encoder-dim 256,256,256 \
+      --lid-encoder-unmasked-dim 128,128,128 \
+      --lid-cnn-module-kernel 31,15,31 
+```
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/AmirHussein/zipformer-hat-lid-seame>
+
+

egs/seame/ASR/local/cer.py

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+#!/usr/bin/env python3
+# Johns Hopkins University  (authors: Amir Hussein)
+
+
+"""
+This file cer from icefall decoded "recogs" file:
+    id [ref] xxx
+    id [hyp] yxy 
+"""
+
+import argparse 
+import jiwer
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dec-file",
+        type=str,
+        help="Decoded icefall recogs file"
+                
+    )
+
+    return parser
+
+
+def cer_(file):
+    hyp = []
+    ref = []
+    cer_results = 0
+    ref_lens = 0
+    with open(file, 'r', encoding='utf-8') as dec:
+        for line in dec:
+            id, target = line.split('\t')
+            id = id[0:-2]
+            target, txt = target.split("=")
+            if target == 'ref':
+                words = txt.strip().strip('[]').split(', ')
+                word_list = [word.strip("'") for word in words]
+                ref.append(" ".join(word_list))
+            elif target == 'hyp':
+                words = txt.strip().strip('[]').split(', ')
+                word_list = [word.strip("'") for word in words]
+                hyp.append(" ".join(word_list))
+        for h, r in zip(hyp, ref):
+            if r:
+                cer_results += (jiwer.cer(r, h)*len(r))
+                
+                ref_lens += len(r)
+    print(cer_results / ref_lens)
+
+
+def main():
+    parse = get_args()
+    args = parse.parse_args() 
+    cer_(args.dec_file)
+    
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/compute_fbank_gpu_seame.py

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+#!/usr/bin/env python3
+# Johns Hopkins University  (authors: Amir Hussein)
+#
+# 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 file computes fbank features.
+It looks for manifests in the directory data_seame/manifests.
+
+The generated fbank features are saved in data_seame/fbank.
+"""
+
+import logging
+import os
+from pathlib import Path
+import argparse
+
+from lhotse import CutSet, LilcomChunkyWriter
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-splits",
+        type=int,
+        default=5,
+        help="Number of splits for the train set.",
+    )
+    parser.add_argument(
+        "--start",
+        type=int,
+        default=0,
+        help="Start index of the train set split.",
+    )
+    parser.add_argument(
+        "--stop",
+        type=int,
+        default=-1,
+        help="Stop index of the train set split.",
+    )
+    parser.add_argument(
+        "--test",
+        action="store_true",
+        help="If set, only compute features for the dev and val set.",
+    )
+
+    return parser.parse_args()
+
+
+def compute_fbank_gpu(args):
+    src_dir = Path("data_seame/manifests")
+    output_dir = Path("data_seame/fbank")
+    num_jobs = min(os.cpu_count(),10)
+    num_mel_bins = 80
+    sampling_rate = 16000
+    sr = 16000
+
+    logging.info(f"Cpus {num_jobs}")
+
+    dataset_parts = (
+        "valid",
+        "dev_man",
+        "train",
+        "dev_sge",
+    )
+    prefix = ""
+    suffix = "jsonl.gz"
+    breakpoint
+    manifests = read_manifests_if_cached(
+        prefix=prefix, dataset_parts=dataset_parts, output_dir=src_dir,suffix=suffix,
+    )
+    assert manifests is not None
+
+    extractor = KaldifeatFbank(
+        KaldifeatFbankConfig(
+            frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
+            mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
+            device="cuda",
+        )
+    )
+
+    for partition, m in manifests.items():
+        cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
+        if (output_dir / f"{cuts_filename}").is_file():
+            logging.info(f"{partition} already exists - skipping.")
+            continue
+        logging.info(f"Processing {partition}")
+        cut_set = CutSet.from_manifests(
+            recordings=m["recordings"],
+            supervisions=m["supervisions"],
+        )
+
+        logging.info("About to split cuts into smaller chunks.")
+        if sr != None:
+            logging.info(f"Resampling to {sr}")
+            cut_set = cut_set.resample(sr)
+
+        cut_set = cut_set.trim_to_supervisions(
+                    keep_overlapping=False, 
+                    keep_all_channels=False)
+        cut_set = cut_set.filter(lambda c: c.duration >= .2 and c.duration <= 30)
+        if "train" in partition:
+            cut_set = (
+                cut_set
+                + cut_set.perturb_speed(0.9)
+                + cut_set.perturb_speed(1.1)
+            )
+            cut_set = cut_set.compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+                manifest_path=f"{src_dir}/{cuts_filename}",
+                batch_duration=2000,
+                num_workers=num_jobs,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+                )
+            cut_set.to_file(output_dir / f"cuts_{partition}.jsonl.gz")
+        else:
+            logging.info(f"Processing {partition}")
+            cut_set = cut_set.compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+                batch_duration=2000,
+                num_workers=num_jobs,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+                )
+            cut_set.to_file(output_dir / f"cuts_{partition}.jsonl.gz")
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+
+    compute_fbank_gpu(args)

egs/seame/ASR/local/compute_fbank_gpu_seame_sample.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+# Johns Hopkins University  (authors: Amir Hussein)
+#
+# 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 file computes fbank features for seame with different data size.
+It looks for manifests in the directory data_seame/manifests.
+
+The generated fbank features are saved in data_seame/fbank.
+"""
+
+import logging
+import os
+from pathlib import Path
+import argparse
+
+from lhotse import CutSet, LilcomChunkyWriter
+
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-splits",
+        type=int,
+        default=5,
+        help="Number of splits for the train set.",
+    )
+    parser.add_argument(
+        "--start",
+        type=int,
+        default=0,
+        help="Start index of the train set split.",
+    )
+    parser.add_argument(
+        "--stop",
+        type=int,
+        default=-1,
+        help="Stop index of the train set split.",
+    )
+    parser.add_argument(
+        "--test",
+        action="store_true",
+        help="If set, only compute features for the dev and val set.",
+    )
+
+    return parser.parse_args()
+
+
+def compute_fbank_gpu(args):
+    src_dir = Path("data_seame/manifests")
+    output_dir = Path("data_seame/fbank")
+    num_jobs = min(os.cpu_count(),10)
+    num_mel_bins = 80
+    sampling_rate = 16000
+    sr = 16000
+
+    logging.info(f"Cpus {num_jobs}")
+
+    dataset_parts = (
+        "train10",
+        "train50",
+        "train30",
+        
+    )
+    prefix = ""
+    suffix = "jsonl.gz"
+
+    extractor = KaldifeatFbank(
+        KaldifeatFbankConfig(
+            frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
+            mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
+            device="cuda",
+        )
+    )
+
+    for part in dataset_parts:
+        cuts_filename = f"cuts_{part}.{suffix}"
+        cut_set = CutSet.from_file(src_dir / cuts_filename)
+        logging.info(f"Processing {part}")
+        logging.info("About to split cuts into smaller chunks.")
+        if sr != None:
+            logging.info(f"Resampling to {sr}")
+            cut_set = cut_set.resample(sr)
+
+        cut_set = cut_set.trim_to_supervisions(
+                    keep_overlapping=False, 
+                    keep_all_channels=False)
+        cut_set = cut_set.filter(lambda c: c.duration >= .5 and c.duration <= 30)
+        if "train" in part:
+            cut_set = (
+                cut_set
+                + cut_set.perturb_speed(0.9)
+                + cut_set.perturb_speed(1.1)
+            )
+            cut_set = cut_set.compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{part}",
+                batch_duration=2000,
+                num_workers=num_jobs,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+                )
+            cut_set.to_file(output_dir / f"cuts_{part}.jsonl.gz")
+        else:
+            logging.info(f"Processing {part}")
+            cut_set = cut_set.compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{part}",
+                manifest_path=f"{src_dir}/{cuts_filename}",
+                batch_duration=2000,
+                num_workers=num_jobs,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+                )
+            cut_set.to_file(output_dir / f"cuts_{part}.jsonl.gz")
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+
+    compute_fbank_gpu(args)

egs/seame/ASR/local/compute_fbank_musan.py

@@ -0,0 +1,109 @@
+#!/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.
+
+
+"""
+This file computes fbank features of the musan dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter, MonoCut, combine
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from icefall.utils import get_executor
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def is_cut_long(c: MonoCut) -> bool:
+    return c.duration > 5
+
+
+def compute_fbank_musan():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+    num_jobs = min(30, os.cpu_count())
+    num_mel_bins = 80
+
+    dataset_parts = (
+        "music",
+        "speech",
+        "noise",
+    )
+    prefix = "musan"
+    suffix = "jsonl.gz"
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts,
+        output_dir=src_dir,
+        prefix=prefix,
+        suffix=suffix,
+    )
+    assert manifests is not None
+
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
+    musan_cuts_path = output_dir / "musan_cuts.jsonl.gz"
+
+    if musan_cuts_path.is_file():
+        logging.info(f"{musan_cuts_path} already exists - skipping")
+        return
+
+    logging.info("Extracting features for Musan")
+
+    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        # create chunks of Musan with duration 5 - 10 seconds
+        musan_cuts = (
+            CutSet.from_manifests(
+                recordings=combine(part["recordings"] for part in manifests.values())
+            )
+            .cut_into_windows(10.0)
+            .filter(is_cut_long)
+            .compute_and_store_features(
+                extractor=extractor,
+                storage_path=f"{output_dir}/musan_feats",
+                num_jobs=num_jobs if ex is None else 80,
+                executor=ex,
+                storage_type=LilcomChunkyWriter,
+            )
+        )
+        musan_cuts.to_file(musan_cuts_path)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    compute_fbank_musan()

egs/seame/ASR/local/cuts_validate.py

@@ -0,0 +1,97 @@
+#!/usr/bin/python
+
+from lhotse import RecordingSet, SupervisionSet, CutSet
+import argparse
+import logging
+from lhotse.qa import fix_manifests, validate_recordings_and_supervisions
+import pdb
+
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--sup",
+        type=str,
+        default="",
+        help="Supervisions file",
+    )
+
+    parser.add_argument(
+        "--rec",
+        type=str,
+        default="",
+        help="Recordings file",
+    )
+    parser.add_argument(
+        "--cut",
+        type=str,
+        default="",
+        help="Cutset file",
+    )
+    parser.add_argument(
+        "--savecut",
+        type=str,
+        default="",
+        help="name of the cutset to be saved",
+    )
+
+    return parser
+
+
+def valid_asr(cut):
+    tol = 2e-3
+    i=0
+    total_dur = 0
+    for c in cut:
+        if c.supervisions != []:
+            if c.supervisions[0].end > c.duration + tol:
+
+                logging.info(f"Supervision beyond the cut. Cut number: {i}")
+                total_dur += c.duration
+                logging.info(f"id: {c.id}, sup_end: {c.supervisions[0].end},  dur: {c.duration}, source {c.recording.sources[0].source}")
+            elif c.supervisions[0].start < -tol:
+                logging.info(f"Supervision starts before the cut. Cut number: {i}")
+                logging.info(f"id: {c.id}, sup_start: {c.supervisions[0].start},  dur: {c.duration}, source {c.recording.sources[0].source}")
+            else:
+                continue
+        else:
+            logging.info("Empty supervision")
+            logging.info(f"id: {c.id}")
+        i += 1
+    logging.info(f"filtered duration: {total_dur}")
+     
+
+def main():
+
+    parser = get_parser()
+    args = parser.parse_args()
+    if args.cut != "":
+        cuts = CutSet.from_file(args.cut)
+    else:
+        recordings = RecordingSet.from_file(args.rec)
+        supervisions = SupervisionSet.from_file(args.sup)
+       # breakpoint()
+        logging.info("Example from supervisions:")
+        logging.info(supervisions[0])
+        logging.info("Example from recordings")
+        logging.info("Fixing manifests")
+        recordings, supervisions = fix_manifests(recordings, supervisions)
+        logging.info("Validating manifests")
+        validate_recordings_and_supervisions(recordings, supervisions)
+    
+        cuts = CutSet.from_manifests(recordings= recordings, supervisions=supervisions,)
+    cuts = cuts.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
+    cuts.describe()
+    logging.info("Example from cut:")
+    logging.info(cuts[100])
+    logging.info("Validating manifests for ASR")
+    valid_asr(cuts)
+    if args.savecut != "":
+        cuts.to_file(args.savecut)
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/prepare_lang.py

@@ -0,0 +1,414 @@
+#!/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.
+
+
+"""
+This script takes as input a lexicon file "data/lang_phone/lexicon.txt"
+consisting of words and tokens (i.e., phones) and does the following:
+
+1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt
+
+2. Generate tokens.txt, the token table mapping a token to a unique integer.
+
+3. Generate words.txt, the word table mapping a word to a unique integer.
+
+4. Generate L.pt, in k2 format. It can be loaded by
+
+        d = torch.load("L.pt")
+        lexicon = k2.Fsa.from_dict(d)
+
+5. Generate L_disambig.pt, in k2 format.
+"""
+import argparse
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import k2
+import torch
+
+from icefall.lexicon import read_lexicon, write_lexicon
+from icefall.utils import str2bool
+
+Lexicon = List[Tuple[str, List[str]]]
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        It should contain a file lexicon.txt.
+        Generated files by this script are saved into this directory.
+        """,
+    )
+
+    parser.add_argument(
+        "--debug",
+        type=str2bool,
+        default=False,
+        help="""True for debugging, which will generate
+        a visualization of the lexicon FST.
+
+        Caution: If your lexicon contains hundreds of thousands
+        of lines, please set it to False!
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
+    """Write a symbol to ID mapping to a file.
+
+    Note:
+      No need to implement `read_mapping` as it can be done
+      through :func:`k2.SymbolTable.from_file`.
+
+    Args:
+      filename:
+        Filename to save the mapping.
+      sym2id:
+        A dict mapping symbols to IDs.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf-8") as f:
+        for sym, i in sym2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def get_tokens(lexicon: Lexicon) -> List[str]:
+    """Get tokens from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique tokens.
+    """
+    ans = set()
+    for _, tokens in lexicon:
+        ans.update(tokens)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def get_words(lexicon: Lexicon) -> List[str]:
+    """Get words from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique words.
+    """
+    ans = set()
+    for word, _ in lexicon:
+        ans.add(word)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]:
+    """It adds pseudo-token disambiguation symbols #1, #2 and so on
+    at the ends of tokens to ensure that all pronunciations are different,
+    and that none is a prefix of another.
+
+    See also add_lex_disambig.pl from kaldi.
+
+    Args:
+      lexicon:
+        It is returned by :func:`read_lexicon`.
+    Returns:
+      Return a tuple with two elements:
+
+        - The output lexicon with disambiguation symbols
+        - The ID of the max disambiguation symbol that appears
+          in the lexicon
+    """
+
+    # (1) Work out the count of each token-sequence in the
+    # lexicon.
+    count = defaultdict(int)
+    for _, tokens in lexicon:
+        count[" ".join(tokens)] += 1
+
+    # (2) For each left sub-sequence of each token-sequence, note down
+    # that it exists (for identifying prefixes of longer strings).
+    issubseq = defaultdict(int)
+    for _, tokens in lexicon:
+        tokens = tokens.copy()
+        tokens.pop()
+        while tokens:
+            issubseq[" ".join(tokens)] = 1
+            tokens.pop()
+
+    # (3) For each entry in the lexicon:
+    # if the token sequence is unique and is not a
+    # prefix of another word, no disambig symbol.
+    # Else output #1, or #2, #3, ... if the same token-seq
+    # has already been assigned a disambig symbol.
+    ans = []
+
+    # We start with #1 since #0 has its own purpose
+    first_allowed_disambig = 1
+    max_disambig = first_allowed_disambig - 1
+    last_used_disambig_symbol_of = defaultdict(int)
+
+    for word, tokens in lexicon:
+        tokenseq = " ".join(tokens)
+        assert tokenseq != ""
+        if issubseq[tokenseq] == 0 and count[tokenseq] == 1:
+            ans.append((word, tokens))
+            continue
+
+        cur_disambig = last_used_disambig_symbol_of[tokenseq]
+        if cur_disambig == 0:
+            cur_disambig = first_allowed_disambig
+        else:
+            cur_disambig += 1
+
+        if cur_disambig > max_disambig:
+            max_disambig = cur_disambig
+        last_used_disambig_symbol_of[tokenseq] = cur_disambig
+        tokenseq += f" #{cur_disambig}"
+        ans.append((word, tokenseq.split()))
+    return ans, max_disambig
+
+
+def generate_id_map(symbols: List[str]) -> Dict[str, int]:
+    """Generate ID maps, i.e., map a symbol to a unique ID.
+
+    Args:
+      symbols:
+        A list of unique symbols.
+    Returns:
+      A dict containing the mapping between symbols and IDs.
+    """
+    return {sym: i for i, sym in enumerate(symbols)}
+
+
+def add_self_loops(
+    arcs: List[List[Any]], disambig_token: int, disambig_word: int
+) -> List[List[Any]]:
+    """Adds self-loops to states of an FST to propagate disambiguation symbols
+    through it. They are added on each state with non-epsilon output symbols
+    on at least one arc out of the state.
+
+    See also fstaddselfloops.pl from Kaldi. One difference is that
+    Kaldi uses OpenFst style FSTs and it has multiple final states.
+    This function uses k2 style FSTs and it does not need to add self-loops
+    to the final state.
+
+    The input label of a self-loop is `disambig_token`, while the output
+    label is `disambig_word`.
+
+    Args:
+      arcs:
+        A list-of-list. The sublist contains
+        `[src_state, dest_state, label, aux_label, score]`
+      disambig_token:
+        It is the token ID of the symbol `#0`.
+      disambig_word:
+        It is the word ID of the symbol `#0`.
+
+    Return:
+      Return new `arcs` containing self-loops.
+    """
+    states_needs_self_loops = set()
+    for arc in arcs:
+        src, dst, ilabel, olabel, score = arc
+        if olabel != 0:
+            states_needs_self_loops.add(src)
+
+    ans = []
+    for s in states_needs_self_loops:
+        ans.append([s, s, disambig_token, disambig_word, 0])
+
+    return arcs + ans
+
+
+def lexicon_to_fst(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    sil_token: str = "SIL",
+    sil_prob: float = 0.5,
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format) with optional silence at
+    the beginning and end of each word.
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      sil_token:
+        The silence token.
+      sil_prob:
+        The probability for adding a silence at the beginning and end
+        of the word.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    assert sil_prob > 0.0 and sil_prob < 1.0
+    # CAUTION: we use score, i.e, negative cost.
+    sil_score = math.log(sil_prob)
+    no_sil_score = math.log(1.0 - sil_prob)
+
+    start_state = 0
+    loop_state = 1  # words enter and leave from here
+    sil_state = 2  # words terminate here when followed by silence; this state
+    # has a silence transition to loop_state.
+    # the next un-allocated state, will be incremented as we go.
+    next_state = 3
+    arcs = []
+
+    assert token2id["<eps>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    sil_token = token2id[sil_token]
+
+    arcs.append([start_state, loop_state, eps, eps, no_sil_score])
+    arcs.append([start_state, sil_state, eps, eps, sil_score])
+    arcs.append([sil_state, loop_state, sil_token, eps, 0])
+
+    for word, tokens in lexicon:
+        assert len(tokens) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        tokens = [token2id[i] for i in tokens]
+
+        for i in range(len(tokens) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, tokens[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last token of this word
+        # It has two out-going arcs, one to the loop state,
+        # the other one to the sil_state.
+        i = len(tokens) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score])
+        arcs.append([cur_state, sil_state, tokens[i], w, sil_score])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+    lexicon_filename = lang_dir / "lexicon.txt"
+    sil_token = "SIL"
+    sil_prob = 0.5
+
+    lexicon = read_lexicon(lexicon_filename)
+    tokens = get_tokens(lexicon)
+    words = get_words(lexicon)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in tokens
+        tokens.append(f"#{i}")
+
+    assert "<eps>" not in tokens
+    tokens = ["<eps>"] + tokens
+
+    assert "<eps>" not in words
+    assert "#0" not in words
+    assert "<s>" not in words
+    assert "</s>" not in words
+
+    words = ["<eps>"] + words + ["#0", "<s>", "</s>"]
+
+    token2id = generate_id_map(tokens)
+    word2id = generate_id_map(words)
+
+    write_mapping(lang_dir / "tokens.txt", token2id)
+    write_mapping(lang_dir / "words.txt", word2id)
+    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst(
+        lexicon,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+    )
+
+    L_disambig = lexicon_to_fst(
+        lexicon_disambig,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), lang_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")
+
+    if args.debug:
+        labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt")
+        aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+        L.labels_sym = labels_sym
+        L.aux_labels_sym = aux_labels_sym
+        L.draw(f"{lang_dir / 'L.svg'}", title="L.pt")
+
+        L_disambig.labels_sym = labels_sym
+        L_disambig.aux_labels_sym = aux_labels_sym
+        L_disambig.draw(f"{lang_dir / 'L_disambig.svg'}", title="L_disambig.pt")
+
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/prepare_lang_bpe.py

@@ -0,0 +1,255 @@
+#!/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.
+
+
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+"""
+
+This script takes as input `lang_dir`, which should contain::
+
+    - lang_dir/bpe.model,
+    - lang_dir/words.txt
+
+and generates the following files in the directory `lang_dir`:
+
+    - lexicon.txt
+    - lexicon_disambig.txt
+    - L.pt
+    - L_disambig.pt
+    - tokens.txt
+"""
+
+import argparse
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+from prepare_lang import (
+    Lexicon,
+    add_disambig_symbols,
+    add_self_loops,
+    write_lexicon,
+    write_mapping,
+)
+
+from icefall.utils import str2bool
+import pdb
+
+
+def lexicon_to_fst_no_sil(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format).
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    loop_state = 0  # words enter and leave from here
+    next_state = 1  # the next un-allocated state, will be incremented as we go
+
+    arcs = []
+
+    # The blank symbol <blk> is defined in local/train_bpe_model.py
+    assert token2id["<blk>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    for word, pieces in lexicon:
+        assert len(pieces) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        pieces = [token2id[i] for i in pieces]
+
+        for i in range(len(pieces) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, pieces[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last piece of this word
+        i = len(pieces) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, pieces[i], w, 0])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def generate_lexicon(
+    model_file: str, words: List[str]
+) -> Tuple[Lexicon, Dict[str, int]]:
+    """Generate a lexicon from a BPE model.
+
+    Args:
+      model_file:
+        Path to a sentencepiece model.
+      words:
+        A list of strings representing words.
+    Returns:
+      Return a tuple with two elements:
+        - A dict whose keys are words and values are the corresponding
+          word pieces.
+        - A dict representing the token symbol, mapping from tokens to IDs.
+    """
+    sp = spm.SentencePieceProcessor()
+    sp.load(str(model_file))
+
+    words_pieces: List[List[str]] = sp.encode(words, out_type=str)
+
+    lexicon = []
+    for word, pieces in zip(words, words_pieces):
+        lexicon.append((word, pieces))
+
+    # The OOV word is <UNK>
+    lexicon.append(("<UNK>", [sp.id_to_piece(sp.unk_id())]))
+
+    token2id: Dict[str, int] = dict()
+    for i in range(sp.vocab_size()):
+        token2id[sp.id_to_piece(i)] = i
+
+    return lexicon, token2id
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        It should contain the bpe.model and words.txt
+        """,
+    )
+
+    parser.add_argument(
+        "--debug",
+        type=str2bool,
+        default=False,
+        help="""True for debugging, which will generate
+        a visualization of the lexicon FST.
+
+        Caution: If your lexicon contains hundreds of thousands
+        of lines, please set it to False!
+
+        See "test/test_bpe_lexicon.py" for usage.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+    model_file = lang_dir / "bpe.model"
+
+    word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+    words = word_sym_table.symbols
+
+    excluded = ["<eps>", "!SIL", "<SPOKEN_NOISE>", "<UNK>", "#0", "<s>", "</s>"]
+    for w in excluded:
+        if w in words:
+            words.remove(w)
+
+    lexicon, token_sym_table = generate_lexicon(model_file, words)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    next_token_id = max(token_sym_table.values()) + 1
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in token_sym_table
+        token_sym_table[disambig] = next_token_id
+        next_token_id += 1
+
+    word_sym_table.add("#0")
+    word_sym_table.add("<s>")
+    word_sym_table.add("</s>")
+
+    write_mapping(lang_dir / "tokens.txt", token_sym_table)
+
+    write_lexicon(lang_dir / "lexicon.txt", lexicon)
+    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst_no_sil(
+        lexicon,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+    )
+
+    L_disambig = lexicon_to_fst_no_sil(
+        lexicon_disambig,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), lang_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")
+
+    if args.debug:
+        labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt")
+        aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+        L.labels_sym = labels_sym
+        L.aux_labels_sym = aux_labels_sym
+        L.draw(f"{lang_dir / 'L.svg'}", title="L.pt")
+
+        L_disambig.labels_sym = labels_sym
+        L_disambig.aux_labels_sym = aux_labels_sym
+        L_disambig.draw(f"{lang_dir / 'L_disambig.svg'}", title="L_disambig.pt")
+
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/prepare_lexicon.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+
+# Copyright 2023 Johns Hopkins University  (Amir Hussein)
+# Apache 2.0
+
+# This script prepares givel a column of words lexicon.
+
+import argparse
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        description="""Creates the list of characters and words in lexicon"""
+    )
+    parser.add_argument("input", type=str, help="""Input list of words file""")
+    parser.add_argument("output", type=str, help="""output graphemic lexicon""")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    lex = {}
+    args = get_args()
+    with open(args.input, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            characters = list(line)
+            characters = " ".join(
+                ["V" if char == "*" else char for char in characters]
+            )
+            lex[line] = characters
+
+    with open(args.output, "w", encoding="utf-8") as fp:
+        for key in sorted(lex):
+            fp.write(key + "  " + lex[key] + "\n")
+
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/prepare_transcripts.py

@@ -0,0 +1,54 @@
+# Copyright 2023 Johns Hopkins University  (Amir Hussein)
+
+#!/usr/bin/python
+"""
+This script prepares transcript_words.txt from cutset
+"""
+
+from lhotse import CutSet
+import argparse
+import logging
+import pdb
+from pathlib import Path
+import os
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--cut",
+        type=str,
+        default="",
+        help="Cutset file",
+    )
+    parser.add_argument(
+        "--langdir",
+        type=str,
+        default="",
+        help="name of the lang-dir",
+    )
+    return parser
+     
+
+def main():
+
+    parser = get_parser()
+    args = parser.parse_args()
+
+    logging.info("Reading the cuts")
+    cuts = CutSet.from_file(args.cut)
+    langdir = Path(args.langdir)
+    
+    if not os.path.exists(langdir):
+        os.makedirs(langdir)
+    
+    with open(langdir / "transcript_words.txt", 'w') as txt:
+        for c in cuts:
+            #breakpoint()
+            text = c.supervisions[0].text
+            txt.write(text + '\n')
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/sample_hours.py

@@ -0,0 +1,90 @@
+#!/usr/bin/python
+# Johns Hopkins University  (authors: Amir Hussein)
+
+"""
+Sample data given duration in seconds.
+"""
+
+from lhotse import RecordingSet, SupervisionSet, CutSet
+import argparse
+import logging
+import os
+from pathlib import Path
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--sup",
+        type=str,
+        default="",
+        help="Supervisions file",
+    )
+
+    parser.add_argument(
+        "--rec",
+        type=str,
+        default="",
+        help="Recordings file",
+    )
+    parser.add_argument(
+        "--cut",
+        type=str,
+        default="",
+        help="Cutset file",
+    )
+    parser.add_argument(
+        "--outcut",
+        type=str,
+        default="",
+        help="name of the cutset to be saved",
+    )
+    parser.add_argument(
+        "--dur",
+        type=float,
+        default=10.0,
+        help="duration of the cut in seconds",
+    )
+
+    return parser
+     
+
+def main():
+
+    parser = get_parser()
+    args = parser.parse_args()
+
+    if args.cut != "":
+        logging.info(f"Loading {args.cut}")
+        cuts = CutSet.from_file(args.cut)
+        outdir = Path(os.path.dirname(args.cut))
+       
+    else:
+        outdir = Path(os.path.dirname(args.sup))
+        logging.info(f"Loading supervisions")
+        recordings = RecordingSet.from_file(args.rec)
+        supervisions = SupervisionSet.from_file(args.sup)
+        logging.info("Fixing manifests")
+        cuts = CutSet.from_manifests(recordings= recordings, supervisions=supervisions,)
+        cuts = cuts.trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
+
+    shuffled = cuts.shuffle()
+    total_dur = 0
+    cuts_list = []
+    for c in shuffled:
+        if total_dur < args.dur and "_sp" not in c.id:
+            total_dur += c.duration
+            cuts_list.append(c.id)
+        else:
+            break
+    cuts = cuts.filter(lambda c: c.id in cuts_list)
+    cuts.describe()
+    
+    logging.info(f"Saving {args.outcut}")
+    cuts.to_file(outdir / args.outcut)
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/train_bpe_model.py

@@ -0,0 +1,120 @@
+#!/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.
+
+
+# You can install sentencepiece via:
+#
+#  pip install sentencepiece
+#
+# Due to an issue reported in
+# https://github.com/google/sentencepiece/pull/642#issuecomment-857972030
+#
+# Please install a version >=0.1.96
+
+import argparse
+import shutil
+from pathlib import Path
+from typing import Dict
+import sentencepiece as spm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        It should contain the training corpus: transcript_words.txt.
+        The generated bpe.model is saved to this directory.
+        """,
+    )
+
+    parser.add_argument(
+        "--transcript",
+        type=str,
+        help="Training transcript.",
+    )
+
+    parser.add_argument(
+        "--vocab-size",
+        type=int,
+        help="Vocabulary size for BPE training",
+    )
+
+    parser.add_argument(
+        "--predef-symbols",
+        type=str,
+        help="User predefined symbols",
+        default=None,
+    )
+    return parser.parse_args()
+
+
+def generate_tokens(lang_dir: Path):
+    """
+    Generate the tokens.txt from a bpe model.
+    """
+    sp = spm.SentencePieceProcessor()
+    sp.load(str(lang_dir / "bpe.model"))
+    token2id: Dict[str, int] = {sp.id_to_piece(i): i for i in range(sp.vocab_size())}
+    with open(lang_dir / "tokens.txt", "w", encoding="utf-8") as f:
+        for sym, i in token2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def main():
+    args = get_args()
+    vocab_size = args.vocab_size
+    lang_dir = Path(args.lang_dir)
+    predef_sym = args.predef_symbols
+    model_type = "unigram"
+
+    model_prefix = f"{lang_dir}/{model_type}_{vocab_size}"
+    train_text = args.transcript
+    character_coverage = 1.0
+    input_sentence_size = 100000000
+
+    user_defined_symbols = ["<blk>", "<sos/eos>"]
+    unk_id = len(user_defined_symbols)
+    if predef_sym:
+        syms = predef_sym.split(',')
+        for i in syms:
+            user_defined_symbols.append(i)
+    # Note: unk_id is fixed to 2.
+    # If you change it, you should also change other
+    # places that are using it.
+
+    model_file = Path(model_prefix + ".model")
+    if not model_file.is_file():
+        spm.SentencePieceTrainer.train(
+            input=train_text,
+            vocab_size=vocab_size,
+            model_type=model_type,
+            model_prefix=model_prefix,
+            input_sentence_size=input_sentence_size,
+            character_coverage=character_coverage,
+            user_defined_symbols=user_defined_symbols,
+            unk_id=unk_id,
+            bos_id=-1,
+            eos_id=-1,
+        )
+
+    shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
+    generate_tokens(lang_dir)
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/local/wer_lang.py

@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+# Johns Hopkins University  (authors: Amir Hussein)
+
+"""
+Compute WER per language
+"""
+
+import sys, codecs, math, pickle, unicodedata, re
+from collections import Counter
+import argparse
+from kaldialign import align
+from collections import defaultdict
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--rec",
+        type=str,
+        default="",
+        help="Cut ref file",
+    )
+    return parser
+
+lids = "en,zh"
+lids_dict = {lid:id+1 for id, lid in enumerate(lids.split(","))}
+id2lang = {id+1: lid for id, lid in enumerate(lids.split(","))}
+bad_id = []
+
+def extract_info(line, info):
+    # Split the line at the first colon to separate the ID
+    id_part, rest = line.split(':', 1)
+    
+    # Extract 'ref' by finding its start and end
+    ref_start = rest.find(info)
+    ref_end = rest.find(']', ref_start)
+    ref = rest[ref_start+len(info):ref_end].replace("'", "").split(', ')
+  
+    # Extract 'lid'
+    if 'lid=' in rest:
+        lid_start = rest.find('lid=[')
+        lid_end = rest.find(']', lid_start)
+        lid = rest[lid_start+len('lid=['):lid_end].split(', ')
+    else:
+        lid = ['']
+
+    if lid[0]=='':
+        bad_id.append(id_part)
+    if " ".join(lid):
+        lid = [int(i) for i in lid]  # Convert each element to integer
+    return id_part.strip(), ref, lid
+
+
+def is_English(c):
+    """check character is in English"""
+    return ord(c.lower()) >= ord("a") and ord(c.lower()) <= ord("z")
+
+def get_en(text):
+    res = []
+    for w in text:
+        if w:
+            if is_English(w[0]):
+                res.append(w)
+            else:
+                continue
+    return res
+
+def get_zh(text):
+    res = []
+    for w in text:
+        if w:
+            if is_English(w[0]):
+                continue
+            else:
+                res.append(w)
+    return res
+
+
+
+def extract_info_lid(line, tag):
+    # Split the line at the first colon to separate the ID
+    id_part, rest = line.split(':', 1)
+    
+    # Extract 'ref' by finding its start and end
+    
+    ref_start = rest.find(tag)
+    ref_end = rest.find(']', ref_start)
+    ref = rest[ref_start+len(tag):ref_end].replace("'", "").split(', ')
+  
+    return id_part.strip(), ref
+
+
+def align_lid2(labels_a, labels_b, a, b):
+  # Alignment
+  EPS = '*'
+  ali = align(a, b, EPS, sclite_mode=True)
+
+  a2idx = {(i,idx):j for idx,(i,j) in enumerate(zip(a,labels_a))}
+  b2idx = {(i,idx):j for idx,(i,j) in enumerate(zip(b,labels_b))}
+  # Comparing labels of aligned elements
+  idx_a = 0
+  idx_b = 0
+  ali_idx=0
+  aligned_a = []
+  aligned_b = []
+  while idx_a <len(a) and idx_b <len(b) and ali_idx < len(ali):
+    elem_a, elem_b = ali[ali_idx]
+    if elem_a == EPS:
+      idx_b += 1
+    elif elem_b == EPS:
+      idx_a += 1
+    elif elem_a != EPS and elem_b != EPS:
+
+      label_a = a2idx[(elem_a,idx_a)]
+      label_b = b2idx[(elem_b,idx_b)]
+      aligned_a.append(label_a)
+      aligned_b.append(label_b)
+      idx_b += 1
+      idx_a += 1
+
+    ali_idx += 1
+  return aligned_a, aligned_b
+
+
+def align_lid(labels_a, labels_b):
+  # Alignment
+  res_a, res_b = [], []
+  EPS = '*'
+  ali = align(labels_a, labels_b, EPS, sclite_mode=True)
+
+  # Comparing labels of aligned elements
+  for val_a, val_b in ali:
+    res_a.append(val_a)
+    res_b.append(val_b)
+  return res_a, res_b
+
+
+def read_file(infile, tag):
+    """"returns list of dict (id, lid, text)"""
+    res = []
+    with open(infile, 'r') as file:
+        for line in file:
+            _, rest = line.split(':', 1)
+            if tag in rest:
+                _id, text = extract_info_lid(line, tag)
+                
+                res.append((_id, text))
+    return res
+
+def wer(results, sclite_mode=False):
+    subs = defaultdict(int)
+    ins = defaultdict(int)
+    dels = defaultdict(int)
+    # `words` stores counts per word, as follows:
+    #   corr, ref_sub, hyp_sub, ins, dels
+    words =  defaultdict(lambda: [0, 0, 0, 0, 0])
+    num_corr = 0
+    ERR = "*"
+    for cut_id, ref, hyp in results:
+        ali = align(ref, hyp, ERR, sclite_mode=sclite_mode)
+        for ref_word, hyp_word in ali:
+            if ref_word == ERR:
+                ins[hyp_word] += 1
+                words[hyp_word][3] += 1
+            elif hyp_word == ERR:
+                dels[ref_word] += 1
+                words[ref_word][4] += 1
+            elif hyp_word != ref_word:
+                subs[(ref_word, hyp_word)] += 1
+                words[ref_word][1] += 1
+                words[hyp_word][2] += 1
+            else:
+                words[ref_word][0] += 1
+                num_corr += 1
+    ref_len = sum([len(r) for _, r, _ in results])
+    sub_errs = sum(subs.values())
+    ins_errs = sum(ins.values())
+    del_errs = sum(dels.values())
+    tot_errs = sub_errs + ins_errs + del_errs
+    tot_err_rate = "%.2f" % (100.0 * tot_errs / ref_len)
+    print(f"%WER = {tot_err_rate}")
+    return tot_err_rate
+
+
+if __name__ == '__main__': 
+    parser = get_parser()
+    args = parser.parse_args() 
+    ref_data = read_file(args.rec, 'ref=[')
+    ref_data = sorted(ref_data)
+    hyp_data = read_file(args.rec, 'hyp=[')
+    hyp_data = sorted(hyp_data)
+    results = defaultdict(list)
+    
+    for (ref, hyp) in zip(ref_data, hyp_data):
+        assert ref[0] == hyp[0], f"ref_id: {ref[0]} != hyp_id: {hyp[0]}"
+        _, text_ref = ref
+        _, hyp_text = hyp
+        if ref:
+            ref_text_en = get_en(text_ref)
+            ref_text_zh = get_zh(text_ref)
+        if hyp:
+            hyp_text_en = get_en(hyp_text)
+            hyp_text_zh = get_zh(hyp_text)
+
+
+        results['en'].append((ref[0],ref_text_en, hyp_text_en))
+        results['zh'].append((ref[0],ref_text_zh, hyp_text_zh))
+
+    for key, val in results.items():
+        print(key)
+        res = wer(val)
+
+
+   
+       
+
+

egs/seame/ASR/prepare.sh

@@ -0,0 +1,115 @@
+#!/usr/bin/env bash
+# Copyright 2024 Johns Hopkins University  (Amir Hussein)
+# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+set -eou pipefail
+
+nj=20
+stage=-1
+stop_stage=6
+
+# We assume dl_dir (download dir) contains the following
+# directories and files.
+#
+#  - $dl_dir/mgb2
+#
+#      You can download the data from
+#
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+#
+
+dl_dir=download
+. shared/parse_options.sh || exit 1
+
+# vocab size for sentence piece models.
+# It will generate data/lang_bpe_xxx,
+# data/lang_bpe_yyy if the array contains xxx, yyy
+vocab_sizes=(
+  4000
+)
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage 0: Download data"
+  
+  # Note: Download SEAME from https://catalog.ldc.upenn.edu/LDC2015S04
+  #
+  # downlaod the splits https://github.com/zengzp0912/SEAME-dev-set.git to $dl_dir
+  # 
+  # If you have pre-downloaded it to /path/to/seame,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/seame $dl_dir/seame
+
+  # If you have pre-downloaded it to /path/to/musan,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/musan $dl_dir/
+  #
+  if [ ! -d $dl_dir/musan ]; then
+    lhotse download musan $dl_dir
+  fi
+fi
+
+fbank=data_seame/fbank
+manifests=data_seame/manifests
+mkdir -p $manifests
+
+if [ $stage -le 0] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Prepare seame manifest"
+  # We assume that you have downloaded the corpus
+  # to $dl_dir/seame
+    lhotse prepare seame $dl_dir/seame $dl_dir/SEAME-dev-set data/manifests
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  mkdir -p data/manifests
+  lhotse prepare musan $dl_dir/musan data/manifests
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Compute fbank features"
+  mkdir -p ${fbank}
+  python local/compute_fbank_gpu_seame.py
+  gunzip -c $fbank/cuts_train.jsonl.gz | shuf | gzip -c > ${fbank}/cuts_train_shuf.jsonl.gz
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare BPE based lang"
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data_seame/lang_bpe_${vocab_size}
+    mkdir -p ${lang_dir}
+    if [ ! -f $lang_dir/transcript_words.txt ]; then
+      log "Generate text for BPE training from data_seame/fbank/cuts_train_shuf.jsonl.gz"
+      python local/prepare_transcripts.py --cut ${fbank}/cuts_train_shuf.jsonl.gz --langdir ${lang_dir} 
+    fi
+    source data_seame/manifests/token.man.1
+    ./local/train_bpe_model.py \
+      --lang-dir $lang_dir \
+      --vocab-size $vocab_size \
+      --transcript $lang_dir/transcript_words.txt \
+      --predef-symbols "$bpe_nlsyms"
+  done
+fi
+

egs/seame/ASR/shared

@@ -0,0 +1 @@
+../../../icefall/shared/

egs/seame/ASR/zipformer/asr_datamodule.py

@@ -0,0 +1,423 @@
+# Copyright      Johns Hopkins University  (Amir Hussein)
+#
+# 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 inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class SeameAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    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 "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data_seame/fbank"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        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.",
+        )
+        group.add_argument(
+            "--lid",
+            type=str2bool,
+            default=False,
+            help="Whether to add LID",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            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).",
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            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.",
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=1.0,
+            help="The amount of padding (in seconds) inserted between "
+            "concatenated cuts. This padding is filled with noise when "
+            "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 "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=10,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--enable-spec-aug",
+            type=str2bool,
+            default=True,
+            help="When enabled, use SpecAugment for training dataset.",
+        )
+
+        group.add_argument(
+            "--spec-aug-time-warp-factor",
+            type=int,
+            default=80,
+            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.",
+        )
+
+        group.add_argument(
+            "--enable-musan",
+            type=str2bool,
+            default=True,
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        transforms = []
+        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")
+            transforms.append(
+                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
+            )
+        else:
+            logging.info("Disable MUSAN")
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                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,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        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}")
+            # 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.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        train = K2SpeechRecognitionDataset(
+            input_strategy=eval(self.args.input_strategy)(),
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+            lid=self.args.lid,
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+                lid=self.args.lid,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                return_cuts=self.args.return_cuts,
+                lid=self.args.lid,
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+                lid=self.args.lid,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.debug("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+            lid=self.args.lid,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("Train data: About to get training cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cuts_train_shuf.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("Dev data: About to get develop cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cuts_valid.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_man(self) -> CutSet:
+        logging.info("About to get dev_man cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cuts_dev_man.jsonl.gz"
+        )
+    def dev_sge(self) -> CutSet:
+        logging.info("About to get dev_sge cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cuts_dev_sge.jsonl.gz"
+        )

egs/seame/ASR/zipformer/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/beam_search.py

egs/seame/ASR/zipformer/decode_stream.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decode_stream.py

egs/seame/ASR/zipformer/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decoder.py

egs/seame/ASR/zipformer/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/encoder_interface.py

egs/seame/ASR/zipformer/export.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export.py

egs/seame/ASR/zipformer/generate_averaged_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/generate_averaged_model.py

egs/seame/ASR/zipformer/jit_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/jit_pretrained.py

egs/seame/ASR/zipformer/jit_pretrained_ctc.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/jit_pretrained_ctc.py

egs/seame/ASR/zipformer/jit_pretrained_streaming.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/jit_pretrained_streaming.py

egs/seame/ASR/zipformer/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/joiner.py

egs/seame/ASR/zipformer/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/model.py

egs/seame/ASR/zipformer/onnx_check.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_check.py

egs/seame/ASR/zipformer/onnx_decode.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_decode.py

egs/seame/ASR/zipformer/onnx_pretrained-streaming.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_pretrained-streaming.py

egs/seame/ASR/zipformer/onnx_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_pretrained.py

egs/seame/ASR/zipformer/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/optim.py

egs/seame/ASR/zipformer/pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/pretrained.py

egs/seame/ASR/zipformer/profile.py

@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+#
+# Copyright 2023 Xiaomi Corporation     (Author: Zengwei Yao)
+#
+# 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: ./zipformer/profile.py
+"""
+
+import argparse
+import logging
+import sentencepiece as spm
+import torch
+
+from typing import Tuple
+from torch import Tensor, nn
+
+from icefall.utils import make_pad_mask
+from icefall.profiler import get_model_profile
+from scaling import BiasNorm
+from train import (
+    get_encoder_embed,
+    get_encoder_model,
+    get_joiner_model,
+    add_model_arguments,
+    get_params,
+)
+from zipformer import BypassModule
+
+
+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",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def _bias_norm_flops_compute(module, input, output):
+    assert len(input) == 1, len(input)
+    # estimate as layer_norm, see icefall/profiler.py
+    flops = input[0].numel() * 5
+    module.__flops__ += int(flops)
+
+
+def _swoosh_module_flops_compute(module, input, output):
+    # For SwooshL and SwooshR modules
+    assert len(input) == 1, len(input)
+    # estimate as swish/silu, see icefall/profiler.py
+    flops = input[0].numel()
+    module.__flops__ += int(flops)
+
+
+def _bypass_module_flops_compute(module, input, output):
+    # For Bypass module
+    assert len(input) == 2, len(input)
+    flops = input[0].numel() * 2
+    module.__flops__ += int(flops)
+
+
+MODULE_HOOK_MAPPING = {
+    BiasNorm: _bias_norm_flops_compute,
+    BypassModule: _bypass_module_flops_compute,
+}
+
+
+class Model(nn.Module):
+    """A Wrapper for encoder, encoder_embed, and encoder_proj"""
+
+    def __init__(
+        self,
+        encoder: nn.Module,
+        encoder_embed: nn.Module,
+        encoder_proj: nn.Module,
+    ) -> None:
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_embed = encoder_embed
+        self.encoder_proj = encoder_proj
+
+    def forward(self, feature: Tensor, feature_lens: Tensor) -> Tuple[Tensor, Tensor]:
+        x, x_lens = self.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 = self.encoder(x, x_lens, src_key_padding_mask)
+
+        encoder_out = encoder_out.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+        logits = self.encoder_proj(encoder_out)
+
+        return logits, encoder_out_lens
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+
+    # We only profile the encoder part
+    model = Model(
+        encoder=get_encoder_model(params),
+        encoder_embed=get_encoder_embed(params),
+        encoder_proj=get_joiner_model(params).encoder_proj,
+    )
+    model.eval()
+    model.to(device)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # for 30-second input
+    B, T, D = 1, 3000, 80
+    feature = torch.ones(B, T, D, dtype=torch.float32).to(device)
+    feature_lens = torch.full((B,), T, dtype=torch.int64).to(device)
+
+    flops, params = get_model_profile(
+        model=model,
+        args=(feature, feature_lens),
+        module_hoop_mapping=MODULE_HOOK_MAPPING,
+    )
+    logging.info(f"For the encoder part, params: {params}, flops: {flops}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/seame/ASR/zipformer/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling.py

egs/seame/ASR/zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling_converter.py

egs/seame/ASR/zipformer/streaming_beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/streaming_beam_search.py

egs/seame/ASR/zipformer/streaming_decode.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/streaming_decode.py

egs/seame/ASR/zipformer/subsampling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/subsampling.py

egs/seame/ASR/zipformer/test_scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/test_scaling.py

egs/seame/ASR/zipformer/test_subsampling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/test_subsampling.py

egs/seame/ASR/zipformer/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/zipformer.py

egs/seame/ASR/zipformer_hat/asr_datamodule.py

@@ -0,0 +1 @@
+../zipformer/asr_datamodule.py

egs/seame/ASR/zipformer_hat/decode.py

@@ -0,0 +1,933 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# 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) greedy search
+    ./zipformer_hat/decode.py \
+      --epoch 25 --avg 5 --use-averaged-model True \
+      --beam-size 10 \
+      --causal 0 \
+      --exp-dir zipformer_hat_seame/exp \
+      --bpe-model data_seame/lang_bpe_4000/bpe.model \
+      --max-duration 1000 \
+      --num-encoder-layers 2,2,2,2,2,2 \
+      --feedforward-dim 512,768,1024,1024,1024,768 \
+      --encoder-dim 192,256,256,256,256,256 \
+      --encoder-unmasked-dim 192,192,192,192,192,192 \
+      --decoding-method greedy_search 
+
+(3) modified beam search
+    ./zipformer_hat/decode.py \
+      --epoch 25 --avg 5 --use-averaged-model True \
+      --beam-size 10 \
+      --causal 0 \
+      --exp-dir zipformer_hat/exp \
+      --bpe-model data_seame/lang_bpe_4000/bpe.model \
+      --max-duration 1000 \
+      --num-encoder-layers 2,2,2,2,2,2 \
+      --feedforward-dim 512,768,1024,1024,1024,768 \
+      --encoder-dim 192,256,256,256,256,256 \
+      --encoder-unmasked-dim 192,192,192,192,192,192 \
+      --decoding-method modified_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 sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import SeameAsrDataModule
+from beam_search import (
+    greedy_search_batch,
+    modified_beam_search,
+    modified_beam_search_lm_shallow_fusion,
+    modified_beam_search_lm_rescore_LODR,
+    modified_beam_search_LODR,
+)
+from train import add_model_arguments, get_model, get_params
+
+from icefall import ContextGraph, LmScorer, NgramLm
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+import string
+import re
+
+LOG_EPS = math.log(1e-10)
+
+def remove_punc(text):
+    """This function removes all English punctuations except the single quote (verbatim)."""
+
+    english_punctuations = string.punctuation + "¿¡"
+    # # Remove the single quote from the punctuations as it is verbatim
+    # english_punctuations = english_punctuations.replace("'", "")
+
+    # Create a translation table that maps each punctuation to a space.
+    translator = str.maketrans(english_punctuations, ' ' * len(english_punctuations))
+    
+    # Translate the text using the translation table
+    text = text.translate(translator)
+    
+    return text
+
+def clean(text):
+    text = remove_punc(text)
+    text = text.lower()
+    text = re.sub(r'\s+', ' ', text)
+    text = text.rstrip()
+    return text
+
+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(
+        "--bpe-model",
+        type=str,
+        default="data_seame/lang_bpe_4000/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data_seame/lang_bpe_4000",
+        help="The lang dir containing word table and LG graph",
+    )
+    parser.add_argument(
+        "--clean",
+        type=bool,
+        default=True,
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - modified_beam_search_LODR
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    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_nbest, fast_beam_search_nbest_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_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_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_nbest, fast_beam_search_nbest_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,
+        fast_beam_search_nbest_LG, and 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 fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--use-shallow-fusion",
+        type=str2bool,
+        default=False,
+        help="""Use neural network LM for shallow fusion.
+        If you want to use LODR, you will also need to set this to true
+        """,
+    )
+
+    parser.add_argument(
+        "--lm-type",
+        type=str,
+        default="rnn",
+        help="Type of NN lm",
+        choices=["rnn", "transformer"],
+    )
+
+    parser.add_argument(
+        "--lm-scale",
+        type=float,
+        default=0.3,
+        help="""The scale of the neural network LM
+        Used only when `--use-shallow-fusion` is set to True.
+        """,
+    )
+
+    parser.add_argument(
+        "--subtract-ilm",
+        type=str2bool,
+        default=False,
+        help="""Subtract the ILME LM score from the NN LM score.
+        Used only when `--use-shallow-fusion` is set to True.
+        """,
+    )
+
+    parser.add_argument(
+        "--ilm-scale",
+        type=float,
+        default=0.1,
+        help="""The scale of the ILME LM that will be subtracted.""",
+    )
+
+    parser.add_argument(
+        "--tokens-ngram",
+        type=int,
+        default=2,
+        help="""The order of the ngram lm.
+        """,
+    )
+
+    parser.add_argument(
+        "--backoff-id",
+        type=int,
+        default=500,
+        help="ID of the backoff symbol in the ngram LM",
+    )
+
+    parser.add_argument(
+        "--context-score",
+        type=float,
+        default=2,
+        help="""
+        The bonus score of each token for the context biasing words/phrases.
+        Used only when --decoding-method is modified_beam_search and
+        modified_beam_search_LODR.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-file",
+        type=str,
+        default="",
+        help="""
+        The path of the context biasing lists, one word/phrase each line
+        Used only when --decoding-method is modified_beam_search and
+        modified_beam_search_LODR.
+        """,
+    )
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+    context_graph: Optional[ContextGraph] = None,
+    LM: Optional[LmScorer] = None,
+    ngram_lm=None,
+    ngram_lm_scale: float = 0.0,
+) -> Dict[str, List[List[str]]]:
+    """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.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding-method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+      LM:
+        A neural network language model.
+      ngram_lm:
+        A ngram language model
+      ngram_lm_scale:
+        The scale for the ngram language model.
+    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,
+        )
+
+    encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
+
+    hyps = []
+
+    if params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            # context_graph=context_graph,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search_LODR":
+        hyp_tokens = modified_beam_search_LODR(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            LODR_lm=ngram_lm,
+            LODR_lm_scale=ngram_lm_scale,
+            LM=LM,
+            context_graph=context_graph,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search_lm_shallow_fusion":
+        hyp_tokens = modified_beam_search_lm_shallow_fusion(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            LM=LM,
+            subtract_ilm=params.subtract_ilm,
+            ilm_scale=params.ilm_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+
+    elif params.decoding_method == "modified_beam_search_lm_rescore_LODR":
+       lm_scale_list = [0.05 * i for i in range(4, 10)]
+       hyp_tokens = modified_beam_search_lm_rescore_LODR(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            LM=LM,
+            LODR_lm=ngram_lm,
+            sp=sp,
+            lm_scale_list=lm_scale_list,
+        )
+       for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif "modified_beam_search" in params.decoding_method:
+        prefix = f"beam_size_{params.beam_size}"
+        if params.has_contexts:
+            prefix += f"-context-score-{params.context_score}"
+        return {prefix: hyps}
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+    context_graph: Optional[ContextGraph] = None,
+    LM: Optional[LmScorer] = None,
+    ngram_lm=None,
+    ngram_lm_scale: float = 0.0,
+) -> 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.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, 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 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 two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            context_graph=context_graph,
+            word_table=word_table,
+            batch=batch,
+            LM=LM,
+            ngram_lm=ngram_lm,
+            ngram_lm_scale=ngram_lm_scale,
+        )
+
+        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):
+                
+                if params.clean:
+                    tmp_hyp = " ".join(hyp_words)
+                    tmp_hyp = clean(tmp_hyp)
+                    ref_text = clean(ref_text)
+                    hyp_words = tmp_hyp.split()
+                ref_words = ref_text.split()
+                this_batch.append((cut_id, ref_words, 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)
+        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
+            )
+            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()
+    SeameAsrDataModule.add_arguments(parser)
+    LmScorer.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "modified_beam_search",
+        "modified_beam_search_lm_shallow_fusion",
+        "modified_beam_search_lm_rescore_LODR",
+        "modified_beam_search_LODR",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if os.path.exists(params.context_file):
+        params.has_contexts = True
+    else:
+        params.has_contexts = False
+
+    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 "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        if params.decoding_method in (
+            "modified_beam_search",
+            "modified_beam_search_LODR",
+        ):
+            if params.has_contexts:
+                params.suffix += f"-context-score-{params.context_score}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_shallow_fusion:
+        params.suffix += f"-{params.lm_type}-lm-scale-{params.lm_scale}"
+
+        if "LODR" in params.decoding_method:
+            params.suffix += (
+                f"-LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
+            )
+
+    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)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    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()
+
+    # only load the neural network LM if required
+    if (
+        params.use_shallow_fusion
+        or params.decoding_method in (
+            "modified_beam_search_lm_shallow_fusion",
+            "modified_beam_search_LODR",
+            "modified_beam_search_lm_rescore_LODR",)
+    ):
+        LM = LmScorer(
+            lm_type=params.lm_type,
+            params=params,
+            device=device,
+            lm_scale=params.lm_scale,
+        )
+        LM.to(device)
+        LM.eval()
+    else:
+        LM = None
+
+    # only load N-gram LM when needed
+    if params.decoding_method == "modified_beam_search_lm_rescore_LODR":
+        try:
+            import kenlm
+        except ImportError:
+            print("Please install kenlm first. You can use")
+            print(" pip install https://github.com/kpu/kenlm/archive/master.zip")
+            print("to install it")
+            import sys
+
+            sys.exit(-1)
+        ngram_file_name = str(params.lang_dir / f"{params.tokens_ngram}gram.arpa")
+        logging.info(f"lm filename: {ngram_file_name}")
+        ngram_lm = kenlm.Model(ngram_file_name)
+        ngram_lm_scale = None  # use a list to search
+
+    elif params.decoding_method == "modified_beam_search_LODR":
+        lm_filename = f"{params.tokens_ngram}gram.fst.txt"
+        logging.info(f"Loading token level lm: {lm_filename}")
+        ngram_lm = NgramLm(
+            str(params.lang_dir / lm_filename),
+            backoff_id=params.backoff_id,
+            is_binary=False,
+        )
+        logging.info(f"num states: {ngram_lm.lm.num_states}")
+        ngram_lm_scale = params.ngram_lm_scale
+    else:
+        ngram_lm = None
+        ngram_lm_scale = None
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            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:
+            word_table = None
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+        word_table = None
+
+    if "modified_beam_search" in params.decoding_method:
+        if os.path.exists(params.context_file):
+            contexts = []
+            for line in open(params.context_file).readlines():
+                contexts.append(line.strip())
+            context_graph = ContextGraph(params.context_score)
+            context_graph.build(sp.encode(contexts))
+        else:
+            context_graph = None
+    else:
+        context_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
+    seame = SeameAsrDataModule(args)
+
+    dev_man = seame.dev_man()
+    dev_sge = seame.dev_sge()
+
+    dev_man_dl = seame.test_dataloaders(dev_man)
+    dev_sge_dl = seame.test_dataloaders(dev_sge)
+
+    test_sets = ["dev_man", "dev_sge"]
+    test_dl = [dev_man_dl, dev_sge_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+            context_graph=context_graph,
+            LM=LM,
+            ngram_lm=ngram_lm,
+            ngram_lm_scale=ngram_lm_scale,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/seame/ASR/zipformer_hat/decoder.py

@@ -0,0 +1 @@
+../zipformer/decoder.py

egs/seame/ASR/zipformer_hat/encoder_interface.py

@@ -0,0 +1 @@
+../zipformer/encoder_interface.py

egs/seame/ASR/zipformer_hat/export.py

@@ -0,0 +1 @@
+../zipformer/export.py

egs/seame/ASR/zipformer_hat/generate_averaged_model.py

@@ -0,0 +1 @@
+../zipformer/generate_averaged_model.py

egs/seame/ASR/zipformer_hat/jit_pretrained.py

@@ -0,0 +1 @@
+../zipformer/jit_pretrained.py

egs/seame/ASR/zipformer_hat/jit_pretrained_streaming.py

@@ -0,0 +1 @@
+../zipformer/jit_pretrained_streaming.py

egs/seame/ASR/zipformer_hat/joiner.py

@@ -0,0 +1 @@
+../zipformer/joiner.py

egs/seame/ASR/zipformer_hat/model.py

@@ -0,0 +1,358 @@
+# Copyright    2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Wei Kang,
+#                                                       Zengwei Yao)
+#
+# 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.
+
+from typing import Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+
+from icefall.utils import add_sos, make_pad_mask
+from scaling import ScaledLinear
+
+
+class AsrModel(nn.Module):
+    def __init__(
+        self,
+        encoder_embed: nn.Module,
+        encoder: EncoderInterface,
+        decoder: Optional[nn.Module] = None,
+        joiner: Optional[nn.Module] = None,
+        encoder_dim: int = 384,
+        decoder_dim: int = 512,
+        vocab_size: int = 500,
+        use_transducer: bool = True,
+        use_ctc: bool = False,
+    ):
+        """A joint CTC & Transducer ASR model.
+
+        - Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (http://imagine.enpc.fr/~obozinsg/teaching/mva_gm/papers/ctc.pdf)
+        - Sequence Transduction with Recurrent Neural Networks (https://arxiv.org/pdf/1211.3711.pdf)
+        - Pruned RNN-T for fast, memory-efficient ASR training (https://arxiv.org/pdf/2206.13236.pdf)
+
+        Args:
+          encoder_embed:
+            It is a Convolutional 2D subsampling module. It converts
+            an input of shape (N, T, idim) to an output of of shape
+            (N, T', odim), where T' = (T-3)//2-2 = (T-7)//2.
+          encoder:
+            It is the transcription network in the paper. Its accepts
+            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
+            It returns two tensors: `logits` of shape (N, T, encoder_dim) and
+            `logit_lens` of shape (N,).
+          decoder:
+            It is the prediction network in the paper. Its input shape
+            is (N, U) and its output shape is (N, U, decoder_dim).
+            It should contain one attribute: `blank_id`.
+            It is used when use_transducer is True.
+          joiner:
+            It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim).
+            Its output shape is (N, T, U, vocab_size). Note that its output contains
+            unnormalized probs, i.e., not processed by log-softmax.
+            It is used when use_transducer is True.
+          use_transducer:
+            Whether use transducer head. Default: True.
+          use_ctc:
+            Whether use CTC head. Default: False.
+        """
+        super().__init__()
+
+        assert (
+            use_transducer or use_ctc
+        ), f"At least one of them should be True, but got use_transducer={use_transducer}, use_ctc={use_ctc}"
+
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+
+        self.encoder_embed = encoder_embed
+        self.encoder = encoder
+
+        self.use_transducer = use_transducer
+        if use_transducer:
+            # Modules for Transducer head
+            assert decoder is not None
+            assert hasattr(decoder, "blank_id")
+            assert joiner is not None
+
+            self.decoder = decoder
+            self.joiner = joiner
+            self.simple_am_proj = ScaledLinear(
+                encoder_dim, vocab_size, initial_scale=0.25
+            )
+            self.simple_lm_proj = ScaledLinear(
+                decoder_dim, vocab_size, initial_scale=0.25
+            )
+        else:
+            assert decoder is None
+            assert joiner is None
+
+        self.use_ctc = use_ctc
+        if use_ctc:
+            # Modules for CTC head
+            self.ctc_output = nn.Sequential(
+                nn.Dropout(p=0.1),
+                nn.Linear(encoder_dim, vocab_size),
+                nn.LogSoftmax(dim=-1),
+            )
+
+    def forward_encoder(
+        self, x: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute encoder outputs.
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+
+        Returns:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+        """
+        # logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
+        x, x_lens = self.encoder_embed(x, x_lens)
+        # logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")
+
+        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 = self.encoder(x, x_lens, src_key_padding_mask)
+
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+        assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens)
+
+        return encoder_out, encoder_out_lens
+
+    def forward_ctc(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        targets: torch.Tensor,
+        target_lengths: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute CTC loss.
+        Args:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+          targets:
+            Target Tensor of shape (sum(target_lengths)). The targets are assumed
+            to be un-padded and concatenated within 1 dimension.
+        """
+        # Compute CTC log-prob
+        ctc_output = self.ctc_output(encoder_out)  # (N, T, C)
+
+        ctc_loss = torch.nn.functional.ctc_loss(
+            log_probs=ctc_output.permute(1, 0, 2),  # (T, N, C)
+            targets=targets,
+            input_lengths=encoder_out_lens,
+            target_lengths=target_lengths,
+            reduction="sum",
+        )
+        return ctc_loss
+
+    def forward_transducer(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        y_lens: torch.Tensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute Transducer loss.
+        Args:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+          y:
+            A ragged tensor with 2 axes [utt][label]. It contains labels of each
+            utterance.
+          prune_range:
+            The prune range for rnnt loss, it means how many symbols(context)
+            we are considering for each frame to compute the loss.
+          am_scale:
+            The scale to smooth the loss with am (output of encoder network)
+            part
+          lm_scale:
+            The scale to smooth the loss with lm (output of predictor network)
+            part
+        """
+        # Now for the decoder, i.e., the prediction network
+        blank_id = self.decoder.blank_id
+        sos_y = add_sos(y, sos_id=blank_id)
+
+        # sos_y_padded: [B, S + 1], start with SOS.
+        sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
+
+        # decoder_out: [B, S + 1, decoder_dim]
+        decoder_out = self.decoder(sos_y_padded)
+
+        # Note: y does not start with SOS
+        # y_padded : [B, S]
+        y_padded = y.pad(mode="constant", padding_value=0)
+
+        y_padded = y_padded.to(torch.int64)
+        boundary = torch.zeros(
+            (encoder_out.size(0), 4),
+            dtype=torch.int64,
+            device=encoder_out.device,
+        )
+        boundary[:, 2] = y_lens
+        boundary[:, 3] = encoder_out_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        # if self.training and random.random() < 0.25:
+        #    lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04)
+        # if self.training and random.random() < 0.25:
+        #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
+                lm=lm.float(),
+                am=am.float(),
+                symbols=y_padded,
+                termination_symbol=blank_id,
+                lm_only_scale=lm_scale,
+                am_only_scale=am_scale,
+                boundary=boundary,
+                reduction="sum",
+                return_grad=True,
+            )
+
+        # ranges : [B, T, prune_range]
+        ranges = k2.get_rnnt_prune_ranges(
+            px_grad=px_grad,
+            py_grad=py_grad,
+            boundary=boundary,
+            s_range=prune_range,
+        )
+
+        # am_pruned : [B, T, prune_range, encoder_dim]
+        # lm_pruned : [B, T, prune_range, decoder_dim]
+        am_pruned, lm_pruned = k2.do_rnnt_pruning(
+            am=self.joiner.encoder_proj(encoder_out),
+            lm=self.joiner.decoder_proj(decoder_out),
+            ranges=ranges,
+        )
+
+        # logits : [B, T, prune_range, vocab_size]
+
+        # project_input=False since we applied the decoder's input projections
+        # prior to do_rnnt_pruning (this is an optimization for speed).
+        logits = self.joiner(am_pruned, lm_pruned, project_input=False)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            pruned_loss = k2.rnnt_loss_pruned(
+                logits=logits.float(),
+                symbols=y_padded,
+                ranges=ranges,
+                termination_symbol=blank_id,
+                boundary=boundary,
+                reduction="sum",
+                use_hat_loss=True,
+            )
+
+        return simple_loss, pruned_loss
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          y:
+            A ragged tensor with 2 axes [utt][label]. It contains labels of each
+            utterance.
+          prune_range:
+            The prune range for rnnt loss, it means how many symbols(context)
+            we are considering for each frame to compute the loss.
+          am_scale:
+            The scale to smooth the loss with am (output of encoder network)
+            part
+          lm_scale:
+            The scale to smooth the loss with lm (output of predictor network)
+            part
+        Returns:
+          Return the transducer losses and CTC loss,
+          in form of (simple_loss, pruned_loss, ctc_loss)
+
+        Note:
+           Regarding am_scale & lm_scale, it will make the loss-function one of
+           the form:
+              lm_scale * lm_probs + am_scale * am_probs +
+              (1-lm_scale-am_scale) * combined_probs
+        """
+        assert x.ndim == 3, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.num_axes == 2, y.num_axes
+
+        assert x.size(0) == x_lens.size(0) == y.dim0, (x.shape, x_lens.shape, y.dim0)
+
+        # Compute encoder outputs
+        encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens)
+
+        row_splits = y.shape.row_splits(1)
+        y_lens = row_splits[1:] - row_splits[:-1]
+
+        if self.use_transducer:
+            # Compute transducer loss
+            simple_loss, pruned_loss = self.forward_transducer(
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+                y=y.to(x.device),
+                y_lens=y_lens,
+                prune_range=prune_range,
+                am_scale=am_scale,
+                lm_scale=lm_scale,
+            )
+        else:
+            simple_loss = torch.empty(0)
+            pruned_loss = torch.empty(0)
+
+        if self.use_ctc:
+            # Compute CTC loss
+            targets = y.values
+            ctc_loss = self.forward_ctc(
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+                targets=targets,
+                target_lengths=y_lens,
+            )
+        else:
+            ctc_loss = torch.empty(0)
+
+        return simple_loss, pruned_loss, ctc_loss

egs/seame/ASR/zipformer_hat/optim.py

@@ -0,0 +1 @@
+../zipformer/optim.py

egs/seame/ASR/zipformer_hat/pretrained.py

@@ -0,0 +1 @@
+../zipformer/pretrained.py

egs/seame/ASR/zipformer_hat/pretrained_ctc.py

@@ -0,0 +1 @@
+../zipformer/pretrained_ctc.py

egs/seame/ASR/zipformer_hat/profile.py

@@ -0,0 +1 @@
+../zipformer/profile.py

egs/seame/ASR/zipformer_hat/scaling.py

@@ -0,0 +1 @@
+../zipformer/scaling.py

egs/seame/ASR/zipformer_hat/scaling_converter.py

@@ -0,0 +1 @@
+../zipformer/scaling_converter.py

egs/seame/ASR/zipformer_hat/subsampling.py

@@ -0,0 +1 @@
+../zipformer/subsampling.py

egs/seame/ASR/zipformer_hat/test_scaling.py

@@ -0,0 +1 @@
+../zipformer/test_scaling.py

egs/seame/ASR/zipformer_hat/test_subsampling.py

@@ -0,0 +1 @@
+../zipformer/test_subsampling.py

egs/seame/ASR/zipformer_hat/zipformer.py

@@ -0,0 +1 @@
+../zipformer/zipformer.py

egs/seame/ASR/zipformer_hat_lid/asr_datamodule.py

@@ -0,0 +1 @@
+../zipformer/asr_datamodule.py

egs/seame/ASR/zipformer_hat_lid/decoder.py

@@ -0,0 +1 @@
+../zipformer/decoder.py

egs/seame/ASR/zipformer_hat_lid/export.py

@@ -0,0 +1,526 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Wei Kang)
+#
+# 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 converts several saved checkpoints
+# to a single one using model averaging.
+"""
+
+Usage:
+
+Note: This is a example for librispeech dataset, if you are using different
+dataset, you should change the argument values according to your dataset.
+
+(1) Export to torchscript model using torch.jit.script()
+
+- For non-streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9 \
+  --jit 1
+
+It will generate a file `jit_script.pt` in the given `exp_dir`. You can later
+load it by `torch.jit.load("jit_script.pt")`.
+
+Check ./jit_pretrained.py for its usage.
+
+Check https://github.com/k2-fsa/sherpa
+for how to use the exported models outside of icefall.
+
+- For streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --causal 1 \
+  --chunk-size 16 \
+  --left-context-frames 128 \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9 \
+  --jit 1
+
+It will generate a file `jit_script_chunk_16_left_128.pt` in the given `exp_dir`.
+You can later load it by `torch.jit.load("jit_script_chunk_16_left_128.pt")`.
+
+Check ./jit_pretrained_streaming.py for its usage.
+
+Check https://github.com/k2-fsa/sherpa
+for how to use the exported models outside of icefall.
+
+(2) Export `model.state_dict()`
+
+- For non-streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+- For streaming model:
+
+./zipformer/export.py \
+  --exp-dir ./zipformer/exp \
+  --causal 1 \
+  --tokens data/lang_bpe_500/tokens.txt \
+  --epoch 30 \
+  --avg 9
+
+It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
+load it by `icefall.checkpoint.load_checkpoint()`.
+
+- For non-streaming model:
+
+To use the generated file with `zipformer/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./zipformer/decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+- For streaming model:
+
+To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+
+    # simulated streaming decoding
+    ./zipformer/decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --causal 1 \
+        --chunk-size 16 \
+        --left-context-frames 128 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+    # chunk-wise streaming decoding
+    ./zipformer/streaming_decode.py \
+        --exp-dir ./zipformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --causal 1 \
+        --chunk-size 16 \
+        --left-context-frames 128 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+Check ./pretrained.py for its usage.
+
+Note: If you don't want to train a model from scratch, we have
+provided one for you. You can get it at
+
+- non-streaming model:
+https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+
+- streaming model:
+https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
+
+with the following commands:
+
+    sudo apt-get install git-lfs
+    git lfs install
+    git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+    git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
+    # You will find the pre-trained models in exp dir
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import List, Tuple
+
+import k2
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from torch import Tensor, nn
+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.utils import make_pad_mask, num_tokens, str2bool
+
+
+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=9,
+        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="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data_libri/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        It will generate a file named jit_script.pt.
+        Check ./jit_pretrained.py for how to use it.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+class EncoderModel(nn.Module):
+    """A wrapper for encoder and encoder_embed"""
+
+    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_embed = encoder_embed
+
+    def forward(
+        self, features: Tensor, feature_lengths: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            features: (N, T, C)
+            feature_lengths: (N,)
+        """
+        x, x_lens = self.encoder_embed(features, feature_lengths)
+
+        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 = self.encoder(x, x_lens, src_key_padding_mask)
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        return encoder_out, encoder_out_lens
+
+
+class StreamingEncoderModel(nn.Module):
+    """A wrapper for encoder and encoder_embed"""
+
+    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
+        super().__init__()
+        assert len(encoder.chunk_size) == 1, encoder.chunk_size
+        assert len(encoder.left_context_frames) == 1, encoder.left_context_frames
+        self.chunk_size = encoder.chunk_size[0]
+        self.left_context_len = encoder.left_context_frames[0]
+
+        # The encoder_embed subsample features (T - 7) // 2
+        # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
+        self.pad_length = 7 + 2 * 3
+
+        self.encoder = encoder
+        self.encoder_embed = encoder_embed
+
+    def forward(
+        self, features: Tensor, feature_lengths: Tensor, states: List[Tensor]
+    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
+        """Streaming forward for encoder_embed and encoder.
+
+        Args:
+            features: (N, T, C)
+            feature_lengths: (N,)
+            states: a list of Tensors
+
+        Returns encoder outputs, output lengths, and updated states.
+        """
+        chunk_size = self.chunk_size
+        left_context_len = self.left_context_len
+
+        cached_embed_left_pad = states[-2]
+        x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward(
+            x=features,
+            x_lens=feature_lengths,
+            cached_left_pad=cached_embed_left_pad,
+        )
+        assert x.size(1) == chunk_size, (x.size(1), chunk_size)
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+
+        # processed_mask is used to mask out initial states
+        processed_mask = torch.arange(left_context_len, device=x.device).expand(
+            x.size(0), left_context_len
+        )
+        processed_lens = states[-1]  # (batch,)
+        # (batch, left_context_size)
+        processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
+        # Update processed lengths
+        new_processed_lens = processed_lens + x_lens
+
+        # (batch, left_context_size + chunk_size)
+        src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
+
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+        encoder_states = states[:-2]
+
+        (
+            encoder_out,
+            encoder_out_lens,
+            new_encoder_states,
+        ) = self.encoder.streaming_forward(
+            x=x,
+            x_lens=x_lens,
+            states=encoder_states,
+            src_key_padding_mask=src_key_padding_mask,
+        )
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        new_states = new_encoder_states + [
+            new_cached_embed_left_pad,
+            new_processed_lens,
+        ]
+        return encoder_out, encoder_out_lens, new_states
+
+    @torch.jit.export
+    def get_init_states(
+        self,
+        batch_size: int = 1,
+        device: torch.device = torch.device("cpu"),
+    ) -> List[torch.Tensor]:
+        """
+        Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
+        is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
+        states[-2] is the cached left padding for ConvNeXt module,
+        of shape (batch_size, num_channels, left_pad, num_freqs)
+        states[-1] is processed_lens of shape (batch,), which records the number
+        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+        """
+        states = self.encoder.get_init_states(batch_size, device)
+
+        embed_states = self.encoder_embed.get_init_states(batch_size, device)
+        states.append(embed_states)
+
+        processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device)
+        states.append(processed_lens)
+
+        return states
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    # if torch.cuda.is_available():
+    #     device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    token_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 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.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.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.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        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.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.eval()
+
+    if params.jit is True:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        # We won't use the forward() method of the model in C++, so just ignore
+        # it here.
+        # Otherwise, one of its arguments is a ragged tensor and is not
+        # torch scriptabe.
+        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
+
+        # Wrap encoder and encoder_embed as a module
+        if params.causal:
+            model.encoder = StreamingEncoderModel(model.encoder, model.encoder_embed)
+            chunk_size = model.encoder.chunk_size
+            left_context_len = model.encoder.left_context_len
+            filename = f"jit_script_chunk_{chunk_size}_left_{left_context_len}.pt"
+        else:
+            model.encoder = EncoderModel(model.encoder, model.encoder_embed)
+            filename = "jit_script.pt"
+
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        model.save(str(params.exp_dir / filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torchscript. Export model.state_dict()")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.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"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/seame/ASR/zipformer_hat_lid/generate_averaged_model.py

@@ -0,0 +1 @@
+../zipformer/generate_averaged_model.py

egs/seame/ASR/zipformer_hat_lid/joiner.py

@@ -0,0 +1,72 @@
+# 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 torch
+import torch.nn as nn
+from scaling import ScaledLinear
+from typing import Optional
+
+class Joiner(nn.Module):
+    def __init__(
+        self,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+        encoder_lid: Optional[int] = None,
+    ):
+        super().__init__()
+        self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim, initial_scale=0.25)
+        self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim, initial_scale=0.25)
+        if encoder_lid:
+            self.lid_proj = ScaledLinear(encoder_lid, joiner_dim, initial_scale=0.25)
+        self.output_linear = nn.Linear(joiner_dim, vocab_size) 
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+        project_input: bool = True,
+        lid_out: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            Output from the encoder. Its shape is (N, T, s_range, C).
+          decoder_out:
+            Output from the decoder. Its shape is (N, T, s_range, C).
+           project_input:
+            If true, apply input projections encoder_proj and decoder_proj.
+            If this is false, it is the user's responsibility to do this
+            manually.
+        Returns:
+          Return a tensor of shape (N, T, s_range, C).
+        """
+        assert encoder_out.ndim == decoder_out.ndim, (
+            encoder_out.shape,
+            decoder_out.shape,
+        )
+
+        if project_input:
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
+        elif lid_out is not None:
+            logit = encoder_out + decoder_out + lid_out
+        else:
+            logit = encoder_out + decoder_out
+
+        logit = self.output_linear(torch.tanh(logit))
+
+        return logit

egs/seame/ASR/zipformer_hat_lid/model.py

@@ -0,0 +1,426 @@
+# Copyright    2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Wei Kang,
+#                                                       Zengwei Yao)
+#              2023  Johns Hopkins University (author: Amir Hussein)
+#
+# 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.
+
+from typing import Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+
+from icefall.utils import add_sos, make_pad_mask
+from scaling import ScaledLinear
+
+
+class AsrModel(nn.Module):
+    def __init__(
+        self,
+        encoder_embed: nn.Module,
+        encoder: EncoderInterface,
+        decoder: Optional[nn.Module] = None,
+        joiner: Optional[nn.Module] = None,
+        lid_joiner: Optional[nn.Module] = None,
+        lid_encoder: Optional[nn.Module] = None,
+        encoder_dim: int = 384,
+        decoder_dim: int = 512,
+        vocab_size: int = 500,
+        use_transducer: bool = True,
+        use_ctc: bool = False,
+    ):
+        """A joint CTC & Transducer ASR model.
+
+        - Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (http://imagine.enpc.fr/~obozinsg/teaching/mva_gm/papers/ctc.pdf)
+        - Sequence Transduction with Recurrent Neural Networks (https://arxiv.org/pdf/1211.3711.pdf)
+        - Pruned RNN-T for fast, memory-efficient ASR training (https://arxiv.org/pdf/2206.13236.pdf)
+
+        Args:
+          encoder_embed:
+            It is a Convolutional 2D subsampling module. It converts
+            an input of shape (N, T, idim) to an output of of shape
+            (N, T', odim), where T' = (T-3)//2-2 = (T-7)//2.
+          encoder:
+            It is the transcription network in the paper. Its accepts
+            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
+            It returns two tensors: `logits` of shape (N, T, encoder_dim) and
+            `logit_lens` of shape (N,).
+          decoder:
+            It is the prediction network in the paper. Its input shape
+            is (N, U) and its output shape is (N, U, decoder_dim).
+            It should contain one attribute: `blank_id`.
+            It is used when use_transducer is True.
+          joiner:
+            It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim).
+            Its output shape is (N, T, U, vocab_size). Note that its output contains
+            unnormalized probs, i.e., not processed by log-softmax.
+            It is used when use_transducer is True.
+          use_transducer:
+            Whether use transducer head. Default: True.
+          use_ctc:
+            Whether use CTC head. Default: False.
+        """
+        super().__init__()
+
+        assert (
+            use_transducer or use_ctc
+        ), f"At least one of them should be True, but got use_transducer={use_transducer}, use_ctc={use_ctc}"
+
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+
+        self.encoder_embed = encoder_embed
+        self.encoder = encoder
+
+        self.use_transducer = use_transducer
+        if use_transducer:
+            # Modules for Transducer head
+            assert decoder is not None
+            assert hasattr(decoder, "blank_id")
+            assert joiner is not None
+
+            self.decoder = decoder
+            self.joiner = joiner
+            
+            self.lid_joiner = lid_joiner
+            self.lid_encoder = lid_encoder
+
+            self.simple_am_proj = ScaledLinear(
+                encoder_dim, vocab_size, initial_scale=0.25
+            )
+            self.simple_lm_proj = ScaledLinear(
+                decoder_dim, vocab_size, initial_scale=0.25
+            )
+        else:
+            assert decoder is None
+            assert joiner is None
+
+        self.use_ctc = use_ctc
+        if use_ctc:
+            # Modules for CTC head
+            self.ctc_output = nn.Sequential(
+                nn.Dropout(p=0.1),
+                nn.Linear(encoder_dim, vocab_size),
+                nn.LogSoftmax(dim=-1),
+            )
+
+    def forward_encoder(
+        self, x: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute encoder outputs.
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+
+        Returns:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+        """
+        # logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
+        x, x_lens = self.encoder_embed(x, x_lens)
+        # logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")
+
+        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, lid_output = self.encoder(x, x_lens, src_key_padding_mask)
+
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+        assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens)
+        if self.lid_encoder is not None:
+            lid_encoder_out, lid_encoder_out_lens = self.lid_encoder(
+                lid_output, x_lens, src_key_padding_mask
+            )
+            assert encoder_out_lens.eq(lid_encoder_out_lens).all()
+            lid_encoder_out = lid_encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+        else:
+            lid_encoder_out = None
+        return encoder_out, encoder_out_lens, lid_encoder_out
+
+    def forward_ctc(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        targets: torch.Tensor,
+        target_lengths: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute CTC loss.
+        Args:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+          targets:
+            Target Tensor of shape (sum(target_lengths)). The targets are assumed
+            to be un-padded and concatenated within 1 dimension.
+        """
+        # Compute CTC log-prob
+        ctc_output = self.ctc_output(encoder_out)  # (N, T, C)
+
+        ctc_loss = torch.nn.functional.ctc_loss(
+            log_probs=ctc_output.permute(1, 0, 2),  # (T, N, C)
+            targets=targets,
+            input_lengths=encoder_out_lens,
+            target_lengths=target_lengths,
+            reduction="sum",
+        )
+        return ctc_loss
+
+    def forward_transducer(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_out_lens: torch.Tensor,
+        lid_encoder_out: torch.Tensor,
+        y: k2.RaggedTensor,
+        y_lens: torch.Tensor,
+        y_lid: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute Transducer loss.
+        Args:
+          encoder_out:
+            Encoder output, of shape (N, T, C).
+          encoder_out_lens:
+            Encoder output lengths, of shape (N,).
+          y:
+            A ragged tensor with 2 axes [utt][label]. It contains labels of each
+            utterance.
+          prune_range:
+            The prune range for rnnt loss, it means how many symbols(context)
+            we are considering for each frame to compute the loss.
+          am_scale:
+            The scale to smooth the loss with am (output of encoder network)
+            part
+          lm_scale:
+            The scale to smooth the loss with lm (output of predictor network)
+            part
+        """
+        # Now for the decoder, i.e., the prediction network
+        
+
+        blank_id = self.decoder.blank_id
+        sos_y = add_sos(y, sos_id=blank_id)
+
+        # sos_y_padded: [B, S + 1], start with SOS.
+        sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
+
+        # decoder_out: [B, S + 1, decoder_dim]
+        decoder_out = self.decoder(sos_y_padded)
+
+        # Note: y does not start with SOS
+        # y_padded : [B, S]
+        y_padded = y.pad(mode="constant", padding_value=0)
+
+        y_padded = y_padded.to(torch.int64)
+        boundary = torch.zeros(
+            (encoder_out.size(0), 4),
+            dtype=torch.int64,
+            device=encoder_out.device,
+        )
+        boundary[:, 2] = y_lens
+        boundary[:, 3] = encoder_out_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        # if self.training and random.random() < 0.25:
+        #    lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04)
+        # if self.training and random.random() < 0.25:
+        #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
+                lm=lm.float(),
+                am=am.float(),
+                symbols=y_padded,
+                termination_symbol=blank_id,
+                lm_only_scale=lm_scale,
+                am_only_scale=am_scale,
+                boundary=boundary,
+                reduction="sum",
+                return_grad=True,
+            )
+
+        # ranges : [B, T, prune_range]
+        ranges = k2.get_rnnt_prune_ranges(
+            px_grad=px_grad,
+            py_grad=py_grad,
+            boundary=boundary,
+            s_range=prune_range,
+        )
+
+        # am_pruned : [B, T, prune_range, encoder_dim]
+        # lm_pruned : [B, T, prune_range, decoder_dim]
+        am_pruned, lm_pruned = k2.do_rnnt_pruning(
+            am=self.joiner.encoder_proj(encoder_out),
+            lm=self.joiner.decoder_proj(decoder_out),
+            ranges=ranges,
+        )
+
+        lid_pruned, _ = k2.do_rnnt_pruning(
+            am=self.joiner.lid_proj(lid_encoder_out),
+            lm=self.joiner.decoder_proj(decoder_out),
+            ranges=ranges,
+        )
+
+        # logits : [B, T, prune_range, vocab_size]
+        if self.lid_joiner is not None:
+          lid_am_pruned, lid_lm_pruned = k2.do_rnnt_pruning(
+                am=self.lid_joiner.encoder_proj(lid_encoder_out),
+                lm=self.lid_joiner.decoder_proj(decoder_out),
+                ranges=ranges,
+            )
+          lid_logits = self.lid_joiner(
+              lid_am_pruned, lid_lm_pruned, project_input=False)
+          
+        # project_input=False since we applied the decoder's input projections
+        # prior to do_rnnt_pruning (this is an optimization for speed).
+        logits = self.joiner(am_pruned, lm_pruned, project_input=False, lid_out=lid_pruned)
+        # Add blank logits to lid_logits
+        logits = torch.cat((lid_logits[..., 0].unsqueeze(-1), logits), dim=-1)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            pruned_loss = k2.rnnt_loss_pruned(
+                logits=logits.float(),
+                symbols=y_padded,
+                ranges=ranges,
+                termination_symbol=blank_id,
+                boundary=boundary,
+                reduction="sum",
+                use_hat_loss=True,
+            )
+
+          # Compute HAT loss for auxiliary lm joiner
+        if self.lid_joiner is not None:
+          with torch.cuda.amp.autocast(enabled=False):
+            pruned_lid_loss = k2.rnnt_loss_pruned(
+                logits=lid_logits.float(),
+                symbols=y_lid.pad(mode="constant", padding_value=blank_id).to(torch.int64),
+                ranges=ranges,
+                termination_symbol=blank_id,
+                boundary=boundary,
+                reduction="sum",
+                use_hat_loss=True,
+            )
+          return simple_loss, pruned_loss, pruned_lid_loss
+        else:
+          return simple_loss, pruned_loss
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        y_lid: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          y:
+            A ragged tensor with 2 axes [utt][label]. It contains labels of each
+            utterance.
+          prune_range:
+            The prune range for rnnt loss, it means how many symbols(context)
+            we are considering for each frame to compute the loss.
+          am_scale:
+            The scale to smooth the loss with am (output of encoder network)
+            part
+          lm_scale:
+            The scale to smooth the loss with lm (output of predictor network)
+            part
+        Returns:
+          Return the transducer losses and CTC loss,
+          in form of (simple_loss, pruned_loss, ctc_loss)
+
+        Note:
+           Regarding am_scale & lm_scale, it will make the loss-function one of
+           the form:
+              lm_scale * lm_probs + am_scale * am_probs +
+              (1-lm_scale-am_scale) * combined_probs
+        """
+        assert x.ndim == 3, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.num_axes == 2, y.num_axes
+        assert y_lid.num_axes == 2, y_lid.num_axes
+
+        assert x.size(0) == x_lens.size(0) == y.dim0, (x.shape, x_lens.shape, y.dim0)
+
+        # Compute encoder outputs
+        if self.lid_joiner != None:
+          encoder_out, encoder_out_lens, lid_encoder_out = self.forward_encoder(x, x_lens)
+        else:
+            encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens)
+
+        row_splits = y.shape.row_splits(1)
+        y_lens = row_splits[1:] - row_splits[:-1]
+
+        if self.use_transducer:
+            
+            # Compute transducer loss
+            if self.lid_joiner != None:
+              simple_loss, pruned_loss, pruned_loss_lm = self.forward_transducer(
+                  encoder_out=encoder_out,
+                  encoder_out_lens=encoder_out_lens,
+                  lid_encoder_out=lid_encoder_out,
+                  y=y.to(x.device),
+                  y_lens=y_lens,
+                  y_lid=y_lid,
+                  prune_range=prune_range,
+                  am_scale=am_scale,
+                  lm_scale=lm_scale,
+              )
+            else:
+                simple_loss, pruned_loss = self.forward_transducer(
+                  encoder_out=encoder_out,
+                  encoder_out_lens=encoder_out_lens,
+                  y=y.to(x.device),
+                  y_lens=y_lens,
+                  prune_range=prune_range,
+                  am_scale=am_scale,
+                  lm_scale=lm_scale,
+              )
+        else:
+            simple_loss = torch.empty(0)
+            pruned_loss = torch.empty(0)
+            pruned_loss_lm = torch.empty(0)
+
+        if self.use_ctc:
+            # Compute CTC loss
+            targets = y.values
+            ctc_loss = self.forward_ctc(
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+                targets=targets,
+                target_lengths=y_lens,
+            )
+        else:
+            ctc_loss = torch.empty(0)
+
+        return simple_loss, pruned_loss, ctc_loss, pruned_loss_lm

egs/seame/ASR/zipformer_hat_lid/optim.py

@@ -0,0 +1 @@
+../zipformer/optim.py

egs/seame/ASR/zipformer_hat_lid/scaling.py

@@ -0,0 +1 @@
+../zipformer/scaling.py

egs/seame/ASR/zipformer_hat_lid/scaling_converter.py

@@ -0,0 +1 @@
+../zipformer/scaling_converter.py

egs/seame/ASR/zipformer_hat_lid/subsampling.py

@@ -0,0 +1 @@
+../zipformer/subsampling.py

egs/seame/ASR/zipformer_hat_lid/test_scaling.py

@@ -0,0 +1 @@
+../zipformer/test_scaling.py

egs/seame/ASR/zipformer_hat_lid/test_subsampling.py

@@ -0,0 +1 @@
+../zipformer/test_subsampling.py