PromptASR for contextualized ASR with controllable style (#1250)

* Add PromptASR with BERT as text encoder

* Support using word-list based content prompts for context biasing

* Upload the pretrained models to huggingface

* Add usage example

egs/libriheavy/ASR/RESULTS.md

@@ -0,0 +1,205 @@
+## Results
+
+### Zipformer PromptASR (zipformer + PromptASR + BERT text encoder)
+
+#### [zipformer_prompt_asr](./zipformer_prompt_asr)
+
+See <https://github.com/k2-fsa/icefall/pull/1250> for commit history and
+our paper <https://arxiv.org/abs/2309.07414> for more details.
+
+
+
+##### Training on the medium subset, with content & style prompt, **no** context list
+
+You can find a pre-trained model, training logs, decoding logs, and decoding results at: <https://huggingface.co/marcoyang/icefall-promptasr-libriheavy-zipformer-BERT-2023-10-10>
+
+The training command is:
+
+```bash
+causal=0
+subset=medium
+memory_dropout_rate=0.05
+text_encoder_type=BERT
+
+python ./zipformer_prompt_asr/train_bert_encoder.py \
+    --world-size 4 \
+    --start-epoch 1 \
+    --num-epochs 60 \
+    --exp-dir ./zipformer_prompt_asr/exp \
+    --use-fp16 True \
+    --memory-dropout-rate $memory_dropout_rate \
+    --causal $causal \
+    --subset $subset \
+    --manifest-dir data/fbank \
+    --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+    --max-duration 1000 \
+    --text-encoder-type $text_encoder_type \
+    --text-encoder-dim 768 \
+    --use-context-list 0 \
+    --top-k $top_k \
+    --use-style-prompt 1
+```
+
+The decoding results using utterance-level context (epoch-60-avg-10):
+
+| decoding method      | lh-test-clean | lh-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   3.13        |    6.78       |    --use-pre-text False --use-style-prompt False      |
+| modified_beam_search        |   2.86        |    5.93       |    --pre-text-transform upper-no-punc --style-text-transform upper-no-punc       |
+| modified_beam_search        |   2.6        |    5.5       |    --pre-text-transform mixed-punc --style-text-transform mixed-punc       |
+
+
+The decoding command is:
+
+```bash
+for style in mixed-punc upper-no-punc; do
+    python ./zipformer_prompt_asr/decode_bert.py \
+        --epoch 60 \
+        --avg 10 \
+        --use-averaged-model True \
+        --post-normalization True \
+        --causal False \
+        --exp-dir ./zipformer_prompt_asr/exp \
+        --manifest-dir data/fbank \
+        --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+        --max-duration 1000 \
+        --decoding-method modified_beam_search \
+        --beam-size 4 \
+        --text-encoder-type BERT \
+        --text-encoder-dim 768 \
+        --memory-layer 0 \
+        --use-ls-test-set False \
+        --use-ls-context-list False \
+        --max-prompt-lens 1000 \
+        --use-pre-text True \
+        --use-style-prompt True \
+        --style-text-transform $style \
+        --pre-text-transform $style \
+        --compute-CER 0
+done
+```
+
+##### Training on the medium subset, with content & style prompt, **with** context list
+
+You can find a pre-trained model, training logs, decoding logs, and decoding results at: <https://huggingface.co/marcoyang/icefall-promptasr-with-context-libriheavy-zipformer-BERT-2023-10-10>
+
+This model is trained with an extra type of content prompt (context words), thus it does better
+on **word-level** context biasing. Note that to train this model, please first run `prepare_prompt_asr.sh`
+to prepare a manifest containing context words.
+
+The training command is:
+
+```bash
+
+causal=0
+subset=medium
+memory_dropout_rate=0.05
+text_encoder_type=BERT
+use_context_list=True
+
+# prepare the required data for context biasing
+./prepare_prompt_asr.sh --stage 0 --stop_stage 1
+
+python ./zipformer_prompt_asr/train_bert_encoder.py \
+    --world-size 4 \
+    --start-epoch 1 \
+    --num-epochs 50 \
+    --exp-dir ./zipformer_prompt_asr/exp \
+    --use-fp16 True \
+    --memory-dropout-rate $memory_dropout_rate \
+    --causal $causal \
+    --subset $subset \
+    --manifest-dir data/fbank \
+    --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+    --max-duration 1000 \
+    --text-encoder-type $text_encoder_type \
+    --text-encoder-dim 768 \
+    --use-context-list $use_context_list \
+    --top-k 10000 \
+    --use-style-prompt 1
+```
+
+*Utterance-level biasing:*
+
+| decoding method      | lh-test-clean | lh-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   3.17        |    6.72       |    --use-pre-text 0 --use-style-prompt 0    |
+| modified_beam_search        |   2.91        |    6.24       |    --pre-text-transform upper-no-punc --style-text-transform upper-no-punc       |
+| modified_beam_search        |   2.72        |    5.72       |    --pre-text-transform mixed-punc --style-text-transform mixed-punc       |
+
+
+The decoding command for the table above is:
+
+```bash
+for style in mixed-punc upper-no-punc; do
+    python ./zipformer_prompt_asr/decode_bert.py \
+        --epoch 50 \
+        --avg 10 \
+        --use-averaged-model True \
+        --post-normalization True \
+        --causal False \
+        --exp-dir ./zipformer_prompt_asr/exp \
+        --manifest-dir data/fbank \
+        --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+        --max-duration 1000 \
+        --decoding-method modified_beam_search \
+        --beam-size 4 \
+        --text-encoder-type BERT \
+        --text-encoder-dim 768 \
+        --memory-layer 0 \
+        --use-ls-test-set False \
+        --use-ls-context-list False \
+        --max-prompt-lens 1000 \
+        --use-pre-text True \
+        --use-style-prompt True \
+        --style-text-transform $style \
+        --pre-text-transform $style \
+        --compute-CER 0
+done
+```
+
+*Word-level biasing:*
+
+The results are reported on LibriSpeech test-sets using the biasing list provided from <https://arxiv.org/abs/2104.02194>.
+You need to set `--use-ls-test-set True` so that the LibriSpeech test sets are used.
+
+| decoding method      | ls-test-clean | ls-test-other | comment             |
+|----------------------|---------------|---------------|---------------------|
+| modified_beam_search        |   2.4        |    5.08       |    --use-pre-text 0 --use-style-prompt 0    |
+| modified_beam_search        |   2.14        |    4.62       |    --use-ls-context-list 1 --pre-text-transform mixed-punc --style-text-transform mixed-punc --ls-distractors 0       |
+| modified_beam_search        |   2.14        |    4.64       |    --use-ls-context-list 1 --pre-text-transform mixed-punc --style-text-transform mixed-punc --ls-distractors 100       |
+
+The decoding command is for the table above is:
+
+```bash
+use_ls_test_set=1
+use_ls_context_list=1
+
+for ls_distractors in 0 100; do
+    python ./zipformer_prompt_asr/decode_bert.py \
+        --epoch 50 \
+        --avg 10 \
+        --use-averaged-model True \
+        --post-normalization True \
+        --causal False \
+        --exp-dir ./zipformer_prompt_asr/exp \
+        --manifest-dir data/fbank \
+        --bpe-model data/lang_bpe_500_fallback_coverage_0.99/bpe.model \
+        --max-duration 1000 \
+        --decoding-method modified_beam_search \
+        --beam-size 4 \
+        --text-encoder-type BERT \
+        --text-encoder-dim 768 \
+        --memory-layer 0 \
+        --use-ls-test-set $use_ls_test_setse \
+        --use-ls-context-list $use_ls_context_list \
+        --ls-distractors $ls_distractors \
+        --max-prompt-lens 1000 \
+        --use-pre-text True \
+        --use-style-prompt True \
+        --style-text-transform mixed-punc \
+        --pre-text-transform mixed-punc \
+        --compute-CER 0
+done
+
+```

egs/libriheavy/ASR/prepare_prompt_asr.sh

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+#!/usr/bin/env bash
+
+set -eou pipefail
+
+# This is the preparation recipe for PromptASR: https://arxiv.org/pdf/2309.07414
+
+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]}) $*"
+}
+
+stage=-1
+stop_stage=100
+manifest_dir=data/fbank
+subset=medium
+topk=10000
+
+. shared/parse_options.sh || exit 1
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+    log "Stage 0: Download the meta biasing list for LibriSpeech"
+    mkdir -p data/context_biasing
+    cd data/context_biasing
+    git clone https://github.com/facebookresearch/fbai-speech.git
+    cd ../..
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+    log "Stage 1: Add rare-words for context biasing to the manifest"
+    python zipformer_prompt_asr/utils.py \
+        --manifest-dir $manifest_dir \
+        --subset $subset \
+        --top-k $topk
+
+fi

egs/libriheavy/ASR/shared

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

egs/libriheavy/ASR/zipformer_prompt_asr/asr_datamodule.py

@@ -0,0 +1,520 @@
+# Copyright      2021  Piotr Żelasko
+#
+# 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, Callable, Dict, List, Optional
+
+import torch
+from dataset import PromptASRDataset
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.dataset import (  # SingleCutSampler,
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    ExtraPadding,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import 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 LibriHeavyAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    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
+
+        if args.use_context_list:
+            assert args.rare_word_file is not None
+            with open(args.rare_word_file, "r") as f:
+                self.rare_word_list = (
+                    f.read().lower().split()
+                )  # Use lower-cased for easier style transform
+        else:
+            self.rare_word_list = None
+
+    @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/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(
+            "--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(
+            "--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=2,
+            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. ",
+        )
+
+        # Libriheavy specific arguments
+        group.add_argument(
+            "--subset",
+            type=str,
+            default="small",
+            help="Select the Libriheavy subset (small|medium|large)",
+        )
+
+        group.add_argument(
+            "--use-context-list",
+            type=str2bool,
+            default=False,
+            help="Use the context list of libri heavy",
+        )
+
+        group.add_argument(
+            "--top-k",
+            type=int,
+            default=10000,
+            help="""The top-k words are identified as common words,
+            the rest as rare words""",
+        )
+
+        group.add_argument(
+            "--with-decoding",
+            type=str2bool,
+            default=False,
+            help="If the texts field contain decoding",
+        )
+
+        group.add_argument(
+            "--random-left-padding",
+            type=str2bool,
+        )
+
+        group.add_argument(
+            "--rare-word-file",
+            type=str,
+        )
+
+        group.add_argument(
+            "--long-audio-cuts",
+            type=str,
+            default="data/manifest_npr/npr1_cuts_all_guids_0.jsonl.gz",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+        text_sampling_func: Callable[[List[str]], str] = 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 = PromptASRDataset(
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+            text_sampling_func=text_sampling_func,
+            rare_word_list=self.rare_word_list,
+        )
+
+        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 = PromptASRDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+                text_sampling_func=text_sampling_func,
+                rare_word_list=self.rare_word_list,
+            )
+
+        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=True,
+            )
+        else:
+            raise NotImplementedError(
+                "SingleCutSampler is no longer supported by lhotse"
+            )
+        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,
+        text_sampling_func: Callable[[List[str]], str] = None,
+    ) -> DataLoader:
+        transforms = []
+        if self.args.random_left_padding:
+            logging.info("Enable random left padding")
+            transforms.append(
+                ExtraPadding(extra_frames=16, randomized=True, direction="left")
+            )
+
+        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 = PromptASRDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                return_cuts=self.args.return_cuts,
+                text_sampling_func=text_sampling_func,
+                rare_word_list=self.rare_word_list,
+            )
+        else:
+            validate = PromptASRDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+                text_sampling_func=text_sampling_func,
+                rare_word_list=self.rare_word_list,
+            )
+        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=2,
+            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 PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
+        )
+        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(f"About to get {self.args.subset} cuts")
+
+        if self.args.use_context_list:
+            path = (
+                self.args.manifest_dir
+                / f"libriheavy_cuts_{self.args.subset}_with_context_list_topk_{self.args.top_k}.jsonl.gz"
+            )
+        elif self.args.with_decoding:
+            path = (
+                self.args.manifest_dir
+                / f"libriheavy_cuts_{self.args.subset}_with_decoding.jsonl.gz"
+            )
+        else:
+            path = (
+                self.args.manifest_dir / f"libriheavy_cuts_{self.args.subset}.jsonl.gz"
+            )
+
+        logging.info(f"Loading manifest from {path}.")
+        cuts_train = CutSet.from_jsonl_lazy(path)
+        return cuts_train
+
+    @lru_cache()
+    def dev_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        cuts_valid = load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_dev.jsonl.gz"
+        )
+        return cuts_valid
+
+    @lru_cache()
+    def test_clean_cuts(self) -> CutSet:
+        logging.info("About to get test-clean cuts")
+        cuts_valid = load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_test-clean_official.jsonl.gz"
+        )
+        return cuts_valid
+
+    @lru_cache()
+    def test_other_cuts(self) -> CutSet:
+        logging.info("About to get test-other cuts")
+        cuts_valid = load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_test-other_official.jsonl.gz"
+        )
+        return cuts_valid
+
+    @lru_cache()
+    def librispeech_test_clean_cuts(self) -> CutSet:
+        logging.info("About to get test-clean cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
+        )
+
+    @lru_cache()
+    def librispeech_test_other_cuts(self) -> CutSet:
+        logging.info("About to get test-other cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz"
+        )
+
+    @lru_cache()
+    def long_audio_cuts(self) -> CutSet:
+        logging.info("About to get long audio cuts")
+        cuts = load_manifest_lazy(
+            self.args.long_audio_cuts,
+        )
+        return cuts
+
+    @lru_cache()
+    def test_dev_cuts(self) -> CutSet:
+        logging.info("About to get test dev cuts")
+        cuts = load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_test_dev.jsonl.gz"
+        )
+        return cuts

egs/libriheavy/ASR/zipformer_prompt_asr/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py

egs/libriheavy/ASR/zipformer_prompt_asr/dataset.py

@@ -0,0 +1,586 @@
+#      Copyright      2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# 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 random
+from typing import Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from lhotse import validate
+from lhotse.cut import CutSet
+from lhotse.dataset import K2SpeechRecognitionDataset
+from lhotse.dataset.input_strategies import BatchIO, PrecomputedFeatures
+from lhotse.utils import compute_num_frames, ifnone
+from text_normalization import (
+    lower_all_char,
+    lower_only_alpha,
+    remove_non_alphabetic,
+    train_text_normalization,
+    upper_all_char,
+    upper_only_alpha,
+)
+from torch.utils.data.dataloader import DataLoader, default_collate
+
+
+class PromptASRDataset(torch.utils.data.Dataset):
+    """This is a dataset for Prompt ASR. It supports the following features:
+    1. Select a tuple of (text, pre_text, style_text) randomly from a
+    list of texts as supervisions.
+
+    """
+
+    def __init__(
+        self,
+        return_cuts: bool = False,
+        cut_transforms: List[Callable[[CutSet], CutSet]] = None,
+        input_transforms: List[Callable[[torch.Tensor], torch.Tensor]] = None,
+        input_strategy: BatchIO = PrecomputedFeatures(),
+        text_sampling_func: Optional[Callable[[List[str]], str]] = None,
+        rare_word_list: Optional[List[str]] = None,
+    ):
+        """
+        Icefall ASR IterableDataset constructor. See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py
+        for more details.
+
+        :param return_cuts: When ``True``, will additionally return a "cut" field in each batch with the Cut
+            objects used to create that batch.
+        :param cut_transforms: A list of transforms to be applied on each sampled batch,
+            before converting cuts to an input representation (audio/features).
+            Examples: cut concatenation, noise cuts mixing, etc.
+        :param input_transforms: A list of transforms to be applied on each sampled batch,
+            after the cuts are converted to audio/features.
+            Examples: normalization, SpecAugment, etc.
+        :param input_strategy: Converts cuts into a collated batch of audio/features.
+            By default, reads pre-computed features from disk.
+        :param text_sampling_func: Sampling a text as transcription from a list of texts.
+        """
+        super().__init__()
+        # Initialize the fields
+        self.return_cuts = return_cuts
+        self.cut_transforms = ifnone(cut_transforms, [])
+        self.input_transforms = ifnone(input_transforms, [])
+        self.input_strategy = input_strategy
+
+        # a text sampling function
+        self.text_sampling_func = text_sampling_func
+        self.rare_word_list = rare_word_list
+
+    def __getitem__(self, cuts: CutSet) -> Dict[str, Union[torch.Tensor, List[str]]]:
+        """
+        Return a new batch, with the batch size automatically determined using the constraints
+        of max_frames and max_cuts.
+        """
+        validate_for_asr(cuts)
+
+        # Sort the cuts by duration so that the first one determines the batch time dimensions.
+        cuts = cuts.sort_by_duration(ascending=False)
+
+        # Optional CutSet transforms - e.g. padding, or speed perturbation that adjusts
+        # the supervision boundaries.
+        for tnfm in self.cut_transforms:
+            cuts = tnfm(cuts)
+
+        # Sort the cuts again after transforms
+        cuts = cuts.sort_by_duration(ascending=False)
+
+        # Get a tensor with batched feature matrices, shape (B, T, F)
+        # Collation performs auto-padding, if necessary.
+        input_tpl = self.input_strategy(cuts)
+        if len(input_tpl) == 3:
+            # An input strategy with fault tolerant audio reading mode.
+            # "cuts" may be a subset of the original "cuts" variable,
+            # that only has cuts for which we succesfully read the audio.
+            inputs, _, cuts = input_tpl
+        else:
+            inputs, _ = input_tpl
+
+        # Get a dict of tensors that encode the positional information about supervisions
+        # in the batch of feature matrices. The tensors are named "sequence_idx",
+        # "start_frame/sample" and "num_frames/samples".
+        supervision_intervals = self.input_strategy.supervision_intervals(cuts)
+
+        # Apply all available transforms on the inputs, i.e. either audio or features.
+        # This could be feature extraction, global MVN, SpecAugment, etc.
+        segments = torch.stack(list(supervision_intervals.values()), dim=1)
+        for tnfm in self.input_transforms:
+            inputs = tnfm(inputs, supervision_segments=segments)
+
+        batch = {
+            "inputs": inputs,
+            "supervisions": default_collate(
+                [
+                    self.text_sampling_func(
+                        texts=supervision.texts,
+                        pre_texts=supervision.pre_texts,
+                        context_list=supervision.context_list
+                        if "context_list" in supervision.custom
+                        else None,
+                        rare_word_list=self.rare_word_list,
+                    )
+                    if self.text_sampling_func is not None
+                    else {
+                        "text": train_text_normalization(supervision.texts[0]),
+                        "pre_text": train_text_normalization(supervision.pre_texts[0]),
+                        "style_text": train_text_normalization(
+                            supervision.pre_texts[0]
+                        ),
+                        "transform_ids": 0,
+                    }
+                    for sequence_idx, cut in enumerate(cuts)
+                    for supervision in cut.supervisions
+                ]
+            ),
+        }
+        # Update the 'supervisions' field with sequence_idx and start/num frames/samples
+        batch["supervisions"].update(supervision_intervals)
+        if self.return_cuts:
+            batch["supervisions"]["cut"] = [
+                cut for cut in cuts for sup in cut.supervisions
+            ]
+
+        has_word_alignments = all(
+            s.alignment is not None and "word" in s.alignment
+            for c in cuts
+            for s in c.supervisions
+        )
+
+        return batch
+
+
+def validate_for_asr(cuts: CutSet) -> None:
+    validate(cuts)
+    tol = 2e-3  # 1ms
+    for cut in cuts:
+        for supervision in cut.supervisions:
+            assert supervision.start >= -tol, (
+                f"Supervisions starting before the cut are not supported for ASR"
+                f" (sup id: {supervision.id}, cut id: {cut.id})"
+            )
+
+            # Supervision start time is relative to Cut ...
+            # https://lhotse.readthedocs.io/en/v0.10_e/cuts.html
+            #
+            # 'supervision.end' is end of supervision inside the Cut
+            assert supervision.end <= cut.duration + tol, (
+                f"Supervisions ending after the cut "
+                f"are not supported for ASR"
+                f" (sup id: {supervision.id}, cut id: {cut.id})"
+            )
+
+
+def get_substring(s: str, min_len: int = 40, max_len: int = 250) -> str:
+    """A helper function that generates a random substring from a given string
+
+    Args:
+        s (str): Input string
+
+    Returns:
+        str: Returned substring
+    """
+    min_len = min(len(s), min_len)
+
+    start = random.randint(0, len(s) - min_len)
+    end = min(start + max_len, random.randint(start + min_len, len(s)))
+
+    return s[start:end]
+
+
+def triplet_text_sampling(
+    texts: List[str],
+    pre_texts: List[str],
+    context_list: Optional[str] = None,
+    rare_word_list: Optional[List[str]] = None,
+    transforms: Optional[List[Callable[[str], str]]] = None,
+    min_len_style: Optional[int] = 80,
+) -> Dict[str, str]:
+    """This function generates a triplet of
+    (pre_text, style_text, ref_text). The style of style_text and ref_text
+    should **always** match, whereas the style of pre_text is arbitrary.
+    Suppose we have 2 different transforms A,B, and the preceding text is
+    referred to as pre_text. The following three tuples are all valid:
+
+    (A(pre_text), A(style_text), A(ref_text))
+    (A(pre_text), B(style_text), B(ref_text))
+    (A(pre_text), A(style_text), A(ref_text))
+    (B(pre_text), B(style_text), B(ref_text))
+
+    If transforms is not given, the following pre-defined transforms
+    are available:
+    0: original (mixed-cased, with punc)
+    1: upper_only_alpha (upper-cased, no punc)
+
+    When the transform of text and pre_text match, we can use the whole
+    pre_text as the prompt text.
+
+    Args:
+        texts (List[str]):
+            A list of ref_texts whose first item is the ground truth
+            text from books.
+        pre_texts (List[str]):
+            A list of pre_texts, whose first item is the groundtruth
+            pre_text from books.
+        context_list: Optional[str] = None,
+            A list of biasing words separated by space
+        rare_word_list: Optional[str] = None,
+            A list of rare-words separated by space (used as distractors)
+        transforms (List[Callable[[str], str]]): A list of possible transforms to be applied
+
+    Returns:
+        A dictionary of ref_text, pre_text, style_text
+    """
+    assert len(texts) == len(pre_texts)
+    assert len(texts) == 2
+
+    # we assume the first item to be ground truth
+    gt_text = texts[0]
+    gt_pre_text = pre_texts[0]
+
+    if transforms is None:
+        transforms = [
+            lambda x: x,  # return it self
+            upper_only_alpha,
+            lower_only_alpha,
+            lower_all_char,
+        ]
+
+    sampling_weight = [
+        0.7,
+        0.3,
+        0.0,
+        0.0,
+    ]  # Mixed-punc should have the largest sampling prob
+
+    total_transforms = len(transforms)  # do not use the recognized trans
+
+    # Randomly sample transforms
+    i_text, i_pre_text = np.random.choice(total_transforms, 2, p=sampling_weight)
+
+    # get the normalized text and pre_text
+    text = transforms[i_text](gt_text)
+    pre_text = transforms[i_pre_text](gt_pre_text)
+
+    if i_text == i_pre_text:
+        style_text = get_substring(pre_text, min_len=min_len_style, max_len=150)
+    else:
+        # get the pre_text of same style as text
+        # For now, **don't** do transform to the style text, because we do it after the dataloader
+        style_text = gt_pre_text
+        # style_text = pre_texts[i_text] if i_text <= 1 else transforms[i_text-2](gt_pre_text)
+        style_text = get_substring(style_text, min_len=min_len_style, max_len=150)
+
+    return {
+        "text": train_text_normalization(text),
+        "pre_text": train_text_normalization(pre_text),
+        "style_text": train_text_normalization(style_text),
+        "transform_ids": i_text,
+    }
+
+
+def triplet_text_sampling_with_context_list(
+    texts: List[str],
+    pre_texts: List[str],
+    context_list: str,
+    rare_word_list: List[str],
+    transforms: Optional[List[Callable[[str], str]]] = None,
+    min_len_style: Optional[int] = 80,
+) -> Dict[str, str]:
+    """This function generates a triplet of
+    (pre_text, style_text, ref_text). The pre_text is either the preceding text
+    or a list of words (context words + distractors).
+    The style of style_text and ref_text should **always** match, whereas
+    the style of pre_text is arbitrary.
+    Suppose we have 2 different transforms A,B, and the preceding text is
+    referred to as pre_text. The following three tuples are all valid:
+
+    (A(pre_text), A(style_text), A(ref_text))
+    (A(pre_text), B(style_text), B(ref_text))
+    (A(pre_text), A(style_text), A(ref_text))
+    (B(pre_text), B(style_text), B(ref_text))
+
+    If transforms is not given, the following pre-defined transforms
+    are available:
+    0: original (mixed-cased, with punc)
+    1: upper_only_alpha (upper-cased, no punc)
+
+    When the transform of text and pre_text match, we can use the whole
+    pre_text as the prompt text.
+
+    Args:
+        texts (List[str]):
+            A list of ref_texts whose first item is the ground truth
+            text from books.
+        pre_texts (List[str]):
+            A list of pre_texts, whose first item is the groundtruth
+            pre_text from books.
+        context_list: Optional[str] = None,
+            A list of biasing words separated by space
+        rare_word_list: Optional[str] = None,
+            A list of rare-words separated by space (used as distractors)
+        transforms (List[Callable[[str], str]]): A list of possible transforms to be applied
+
+    Returns:
+        A dictionary of ref_text, pre_text, style_text
+    Returns:
+        str: A dictionary
+    """
+    # import pdb; pdb.set_trace()
+    assert len(texts) == len(pre_texts)
+    assert len(texts) == 2
+
+    if context_list is not None:
+        context_list = context_list.lower()
+
+    # we assume the first item to be ground truth
+    gt_text = texts[0]
+    gt_pre_text = pre_texts[0]
+
+    if transforms is None:
+        transforms = [
+            lambda x: x,  # return it self
+            upper_only_alpha,
+            lower_only_alpha,
+            lower_all_char,
+        ]
+
+    sampling_weight = [
+        0.7,
+        0.3,
+        0.0,
+        0.0,
+    ]  # Mixed-punc should have the largest sampling prob
+
+    total_transforms = len(transforms)  # do not use the recognized trans
+
+    # Select a transformation randomly
+    i_text, i_pre_text = np.random.choice(total_transforms, 2, p=sampling_weight)
+
+    # get the normalized text and pre_text
+    text = transforms[i_text](gt_text)
+    pre_text = get_pre_text_with_context_list2(
+        text=gt_text,
+        pre_text=gt_pre_text,
+        context_list=context_list,
+        rare_words_list=rare_word_list,
+    )
+    pre_text = transforms[i_pre_text](pre_text)
+
+    if i_text == i_pre_text:
+        style_text = get_substring(pre_text, min_len=min_len_style, max_len=150)
+    else:
+        # get the pre_text of same style as text
+        # For now, **don't** do transform to the style text
+        style_text = gt_pre_text
+        # style_text = pre_texts[i_text] if i_text <= 1 else transforms[i_text-2](gt_pre_text)
+        style_text = get_substring(style_text, min_len=min_len_style, max_len=150)
+
+    return {
+        "text": train_text_normalization(text),
+        "pre_text": train_text_normalization(pre_text),
+        "style_text": train_text_normalization(style_text),
+        "transform_ids": i_text,
+    }
+
+
+def get_pre_text_with_context_list(
+    text: str,
+    pre_text: str,
+    context_list: str,
+    rare_words_list: List[str] = None,
+) -> str:
+    # Always get the first one, which is the gt (mixed-cased trans), but with upper_only_alpha
+    # By a small proportion of time, use the substring of ref_text as pre_text
+
+    if context_list != "" and context_list is not None:
+        v = random.random()
+        if v < 0.5:
+            # correct + distractors
+            # sample distractors
+            num_distractors = random.randint(0, 50)
+            distractors = random.sample(rare_words_list, num_distractors)
+            # sample correct
+            correct = context_list.split()
+            i = random.randint(1, len(correct))
+            correct = random.sample(correct, i)
+            # combine correct and distractors
+            pre_text = distractors + correct
+            random.shuffle(pre_text)
+            pre_text = " ".join(pre_text)
+        elif v < 0.7:
+            splitted = text.split()
+            sampling_weights = [len(w) ** 1.2 for w in splitted]
+            sampling_weights = [p / sum(sampling_weights) for p in sampling_weights]
+            i = random.randint(1, min(len(splitted), 20))
+            splitted = list(np.random.choice(splitted, i, p=sampling_weights))
+            num_distractors = random.randint(0, 70)
+            distractors = random.sample(rare_words_list, num_distractors)
+            splitted += distractors
+            random.shuffle(splitted)  # shuffle the list
+            pre_text = " ".join(splitted)
+        else:
+            pre_text = pre_text
+    else:
+        v = random.random()
+        if v < 0.1:
+            splitted = text.split()
+            sampling_weights = [len(w) ** 1.2 for w in splitted]
+            sampling_weights = [p / sum(sampling_weights) for p in sampling_weights]
+            i = random.randint(1, min(len(splitted), 20))
+            splitted = list(np.random.choice(splitted, i, p=sampling_weights))
+            pre_text = " ".join(splitted)
+            num_distractors = random.randint(0, 70)
+            distractors = random.sample(rare_words_list, num_distractors)
+            splitted += distractors
+            random.shuffle(splitted)  # shuffle the list
+        elif v < 0.2:
+            # full distractors
+            num_distractors = random.randint(5, 100)
+            distractors = random.sample(rare_words_list, num_distractors)
+            pre_text = " ".join(distractors)
+
+        elif v < 0.3:
+            pre_text = get_substring(text, min_len=15, max_len=150)
+        else:
+            pre_text = pre_text
+
+    return pre_text
+
+
+def get_pre_text_with_context_list2(
+    text: str,
+    pre_text: str,
+    context_list: str,
+    rare_words_list: List[str] = None,
+) -> str:
+    # Get the pre_text, either the ground truth preceding text or
+    # a list of words consisting of biasing words and distrators
+    # By a small proportion of time, use the substring of ref_text as pre_text
+
+    if context_list != "" and context_list is not None:
+        v = random.random()
+        if v < 0.4:
+            # sample distractors
+            num_distractors = random.randint(50, 100)
+            distractors = random.sample(rare_words_list, num_distractors)
+            # sample correct
+            correct = context_list.split()
+            i = random.randint(1, len(correct))
+            correct = random.sample(correct, i)
+            # combine correct and distractors
+            pre_text = distractors + correct
+            random.shuffle(pre_text)
+            pre_text = " ".join(pre_text)
+        elif v < 0.55:
+            splitted = text.split()
+            sampling_weights = [
+                len(w) ** 1.2 for w in splitted
+            ]  # longer words with higher weights
+            sampling_weights = [p / sum(sampling_weights) for p in sampling_weights]
+            i = random.randint(1, min(len(splitted), 20))
+            splitted = list(np.random.choice(splitted, i, p=sampling_weights))
+            num_distractors = random.randint(50, 100)
+            distractors = random.sample(rare_words_list, num_distractors)
+            splitted += distractors
+            random.shuffle(splitted)  # shuffle the list
+            pre_text = " ".join(splitted)
+        else:
+            pre_text = pre_text
+    else:
+        v = random.random()
+        if v < 0.3:
+            splitted = text.split()
+            sampling_weights = [len(w) ** 1.2 for w in splitted]
+            sampling_weights = [p / sum(sampling_weights) for p in sampling_weights]
+            i = random.randint(1, min(len(splitted), 20))
+            splitted = list(np.random.choice(splitted, i, p=sampling_weights))
+            pre_text = " ".join(splitted)
+            num_distractors = random.randint(50, 100)
+            distractors = random.sample(rare_words_list, num_distractors)
+            splitted += distractors
+            random.shuffle(splitted)  # shuffle the list
+        elif v < 0.4:
+            # full distractors
+            num_distractors = random.randint(5, 100)
+            distractors = random.sample(rare_words_list, num_distractors)
+            pre_text = " ".join(distractors)
+        elif v < 0.6:
+            pre_text = get_substring(text, min_len=15, max_len=150)
+        else:
+            pre_text = pre_text
+
+    return pre_text
+
+
+def naive_triplet_text_sampling(
+    texts: List[str],
+    pre_texts: List[str],
+    context_list: str = None,
+    rare_word_list: List[str] = None,
+    min_len_style: Optional[int] = 120,
+):
+    # The most simplest text sampling function, used only for
+    # evaluation, use a fixed sentence as the style text
+
+    return {
+        "text": train_text_normalization(texts[0]),
+        "pre_text": train_text_normalization(pre_texts[0]),
+        "style_text": "Mixed-case English transcription, with punctuation. Actually, it is fully not related. What do you think?",
+        "transform_ids": 0,
+    }
+
+
+def random_shuffle_subset(
+    data: List[str],
+    p: float = 0.2,
+    p_mask: float = 0.05,
+) -> List[str]:
+    """
+    Randomly shuffle the subset by probability `p`, which means that p% of the samples
+    in the original batch are shuffled, the others are kept in the original order.
+
+    With a probability of `p_mask`, replace the original string with an empty string.
+
+    """
+
+    num_to_shuffle = int(len(data) * p)
+    id_to_shuffle = np.random.choice(len(data), num_to_shuffle, replace=False)
+    item_to_shuffle = [data[id] for id in id_to_shuffle]
+    random.shuffle(item_to_shuffle)
+
+    for id, item in zip(id_to_shuffle, item_to_shuffle):
+        data[id] = item
+
+    # Randomly mask a proportion of the data to empty string
+    if p_mask > 0:
+        for i in range(len(data)):
+            if random.random() < p_mask:
+                data[i] = ""
+
+    return data
+
+
+if __name__ == "__main__":
+    texts = [
+        "AA, BB, cC, dD!",
+        "AA BB CC DD",
+    ]
+
+    pre_texts = [
+        "EE, Ff, Gg? EE, Ff, Gg? EE, Ff, Gg? EE, Ff, Gg?",
+        "EE FF GG EE FF GG EE FF GG EE FF GG EE FF GG",
+    ]
+    for i in range(10):
+        print(f"Run: {i}")
+        print(triplet_text_sampling(texts, pre_texts))

egs/libriheavy/ASR/zipformer_prompt_asr/decode_baseline.py

@@ -0,0 +1,791 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Xiaoyu Yang)
+#
+# 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
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) modified beam search
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+"""
+
+
+import argparse
+import logging
+import math
+import warnings
+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 LibriHeavyAsrDataModule
+from beam_search import greedy_search, greedy_search_batch, modified_beam_search
+from ls_text_normalization import word_normalization
+from text_normalization import (
+    ref_text_normalization,
+    remove_non_alphabetic,
+    upper_only_alpha,
+)
+from train_baseline import add_model_arguments, get_params, get_transducer_model
+from utils import write_error_stats
+
+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, setup_logger, store_transcripts, str2bool
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=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="pruned_transducer_stateless7/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        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
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+          - modified_beam_search_lm_shallow_fusion # for rnn lm shallow fusion
+          - modified_beam_search_LODR
+        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(
+        "--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(
+        "--post-normalization",
+        type=str2bool,
+        default=True,
+        help="Normalized the recognition results by uppercasing and removing non-alphabetic symbols. ",
+    )
+
+    parser.add_argument(
+        "--long-audio-recog",
+        type=str2bool,
+        default=False,
+    )
+
+    parser.add_argument(
+        "--use-ls-test-set",
+        type=str2bool,
+        default=False,
+        help="Use librispeech test set for evaluation.",
+    )
+
+    parser.add_argument(
+        "--compute-CER",
+        type=str2bool,
+        default=True,
+        help="Reports CER. By default, only reports WER",
+    )
+
+    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,
+) -> 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 net LM for shallow fusion. Only used when `--use-shallow-fusion`
+        set to true.
+      ngram_lm:
+        A ngram lm. Used in LODR decoding.
+      ngram_lm_scale:
+        The scale of 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"]
+    texts = batch["supervisions"]["text"]
+    batch_size = feature.size(0)
+
+    # Get the transducer encoder output
+    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)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        encoder_out, encoder_out_lens = model.encode_audio(
+            feature=feature,
+            feature_lens=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,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": 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,
+) -> 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.
+      LM:
+        A neural network LM, used during shallow fusion
+    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"]]
+        if not params.use_ls_test_set:
+            book_names = [
+                cut.text_path.split("/")[-2] for cut in batch["supervisions"]["cut"]
+            ]
+        else:
+            book_names = ["" for _ in cut_ids]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for cut_id, book_name, hyp_words, ref_text in zip(
+                cut_ids, book_names, hyps, texts
+            ):
+                ref_text = ref_text_normalization(ref_text)
+                ref_words = ref_text.split()
+                this_batch.append((cut_id, ref_words, hyp_words))
+            # if not params.use_ls_test_set:
+            #     results[name + " " + book_name].extend(this_batch)
+            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]]]],
+    biasing_words: List[str] = None,
+):
+    test_set_wers = dict()
+    test_set_cers = dict()
+    for key, results in results_dict.items():
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{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}-{params.suffix}.txt"
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                biasing_words=biasing_words,
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+        if params.compute_CER:
+            # Write CER statistics
+            recog_path = (
+                params.res_dir / f"recogs-{test_set_name}-char-{params.suffix}.txt"
+            )
+            store_transcripts(filename=recog_path, texts=results, char_level=True)
+            errs_filename = (
+                params.res_dir / f"errs-CER-{test_set_name}-{params.suffix}.txt"
+            )
+            with open(errs_filename, "w") as f:
+                cer = write_error_stats(
+                    f,
+                    f"{test_set_name}-{key}",
+                    results,
+                    enable_log=True,
+                    compute_CER=params.compute_CER,
+                )
+                test_set_cers[key] = cer
+
+            logging.info("Wrote detailed CER 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}-{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)
+
+    if params.compute_CER:
+        test_set_cers = sorted(test_set_cers.items(), key=lambda x: x[1])
+        errs_info = params.res_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
+        with open(errs_info, "w") as f:
+            print("settings\tcER", file=f)
+            for key, val in test_set_cers:
+                print("{}\t{}".format(key, val), file=f)
+
+        s = "\nFor {}, CER of different settings are:\n".format(test_set_name)
+        note = "\tbest for {}".format(test_set_name)
+        for key, val in test_set_cers:
+            s += "{} CER\t{}{}\n".format(key, val, note)
+            note = ""
+        logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriHeavyAsrDataModule.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",
+    )
+
+    if params.long_audio_recog:
+        params.res_dir = params.exp_dir / (params.decoding_method + "long_audio")
+    else:
+        params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if "ngram" in params.decoding_method:
+        params.suffix += f"-ngram-lm-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_transducer_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,
+                ),
+                strict=False,
+            )
+        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,
+                ),
+                strict=False,
+            )
+
+    model.to(device)
+    model.eval()
+
+    LM = None
+
+    decoding_graph = None
+    word_table = 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
+    libriheavy = LibriHeavyAsrDataModule(args)
+
+    test_clean_cuts = libriheavy.test_clean_cuts()
+    test_other_cuts = libriheavy.test_other_cuts()
+    ls_test_clean_cuts = libriheavy.librispeech_test_clean_cuts()
+    ls_test_other_cuts = libriheavy.librispeech_test_other_cuts()
+    long_audio_cuts = libriheavy.long_audio_cuts()
+
+    test_clean_dl = libriheavy.valid_dataloaders(
+        test_clean_cuts,
+    )
+    test_other_dl = libriheavy.valid_dataloaders(
+        test_other_cuts,
+    )
+    ls_test_clean_dl = libriheavy.test_dataloaders(ls_test_clean_cuts)
+    ls_test_other_dl = libriheavy.test_dataloaders(ls_test_other_cuts)
+    long_audio_dl = libriheavy.valid_dataloaders(
+        long_audio_cuts,
+    )
+
+    if params.use_ls_test_set:
+        test_sets = ["ls-test-clean", "ls-test-other"]
+        test_dl = [ls_test_clean_dl, ls_test_other_dl]
+    else:
+        test_sets = ["test-clean", "test-other"]
+        test_dl = [test_clean_dl, test_other_dl]
+
+    if params.long_audio_recog:
+        test_sets = ["long-audio"]
+        test_dl = [long_audio_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        if params.use_ls_test_set:
+            f = open(
+                "data/context_biasing/LibriSpeechBiasingLists/all_rare_words.txt", "r"
+            )
+            biasing_words = f.read().strip().split()
+            f.close()
+        else:
+            biasing_words = None
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+        if params.post_normalization:
+            if "-post-normalization" not in params.suffix:
+                params.suffix += "-post-normalization"
+
+            new_res = {}
+            for k in results_dict:
+                new_ans = []
+                for item in results_dict[k]:
+                    id, ref, hyp = item
+                    if params.use_ls_test_set:
+                        hyp = (
+                            " ".join(hyp).replace("-", " ").split()
+                        )  # handle the hypens
+                        hyp = upper_only_alpha(" ".join(hyp)).split()
+                        hyp = [word_normalization(w.upper()) for w in hyp]
+                        hyp = " ".join(hyp).split()
+                        hyp = [w for w in hyp if w != ""]
+                        ref = upper_only_alpha(" ".join(ref)).split()
+                    else:
+                        hyp = upper_only_alpha(" ".join(hyp)).split()
+                        ref = upper_only_alpha(" ".join(ref)).split()
+                    new_ans.append((id, ref, hyp))
+                new_res[k] = new_ans
+
+            save_results(
+                params=params,
+                test_set_name=test_set,
+                results_dict=new_res,
+                biasing_words=biasing_words,
+            )
+
+            if params.suffix.endswith("-post-normalization"):
+                params.suffix = params.suffix.replace("-post-normalization", "")
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/libriheavy/ASR/zipformer_prompt_asr/decode_bert_with_style_save_decoding_mp.py

@@ -0,0 +1,963 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Xiaoyu Yang)
+#
+# 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
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) modified beam search
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+"""
+
+
+import argparse
+import logging
+import math
+import warnings
+from collections import defaultdict
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from asr_datamodule import LibriHeavyAsrDataModule
+from beam_search import (
+    greedy_search,
+    greedy_search_batch,
+    greedy_search_batch_with_context,
+    greedy_search_with_context,
+    modified_beam_search,
+)
+from dataset import naive_triplet_text_sampling, random_shuffle_subset
+from lhotse import load_manifest_lazy
+from text_normalization import (
+    lower_all_char,
+    lower_only_alpha,
+    ref_text_normalization,
+    remove_non_alphabetic,
+    train_text_normalization,
+    upper_all_char,
+    upper_only_alpha,
+)
+from train_bert_encoder_with_style import (
+    _encode_texts_as_bytes_with_tokenizer,
+    add_model_arguments,
+    get_params,
+    get_tokenizer,
+    get_transducer_model,
+)
+from transformers import BertModel, BertTokenizer
+from utils import get_facebook_biasing_list
+
+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,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+    )
+
+    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="pruned_transducer_stateless7/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--log-dir",
+        type=str,
+        required=True,
+        help="Where to store the logs",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        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
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+          - modified_beam_search_lm_shallow_fusion # for rnn lm shallow fusion
+          - modified_beam_search_LODR
+        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(
+        "--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(
+        "--input-manifest",
+        type=str,
+        required=True,
+        help="The input manifest to be decoded",
+    )
+
+    parser.add_argument(
+        "--output-manifest",
+        type=str,
+        required=True,
+        help="Where to store the output manifest (directory)",
+    )
+
+    parser.add_argument(
+        "--use-pre-text",
+        type=str2bool,
+        default=True,
+        help="Use pre-text is available during decoding",
+    )
+
+    parser.add_argument(
+        "--use-style-prompt",
+        type=str2bool,
+        default=True,
+        help="Use style prompt when evaluation",
+    )
+
+    parser.add_argument(
+        "--use-context-embedding",
+        type=str2bool,
+        default=False,
+        help="Use context fuser when evaluation",
+    )
+
+    parser.add_argument(
+        "--post-normalization",
+        type=str2bool,
+        default=True,
+        help="Normalized the recognition results by uppercasing and removing non-alphabetic symbols. ",
+    )
+
+    parser.add_argument(
+        "--compute-CER",
+        type=str2bool,
+        default=True,
+        help="Reports CER. By default, only reports WER",
+    )
+
+    parser.add_argument(
+        "--style-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of style prompt, i.e style_text",
+    )
+
+    parser.add_argument(
+        "--pre-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of content prompt, i.e pre_text",
+    )
+
+    parser.add_argument(
+        "--use-ls-test-set",
+        type=str2bool,
+        default=False,
+        help="Use librispeech test set for evaluation.",
+    )
+
+    parser.add_argument(
+        "--use-ls-context-list",
+        type=str2bool,
+        default=False,
+        help="If use a fixed context list for LibriSpeech decoding",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def _apply_style_transform(text: List[str], transform: str) -> List[str]:
+    """Apply transform to a list of text. By default, the text are in
+    ground truth format, i.e mixed-punc.
+
+    Args:
+        text (List[str]): Input text string
+        transform (str): Transform to be applied
+
+    Returns:
+        List[str]: _description_
+    """
+    if transform == "mixed-punc":
+        return text
+    elif transform == "upper-no-punc":
+        return [upper_only_alpha(s) for s in text]
+    elif transform == "lower-no-punc":
+        return [lower_only_alpha(s) for s in text]
+    elif transform == "lower-punc":
+        return [lower_all_char(s) for s in text]
+    else:
+        raise NotImplementedError(f"Unseen transform: {transform}")
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    tokenizer,
+    batch: dict,
+    biasing_dict: dict = None,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> 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 net LM for shallow fusion. Only used when `--use-shallow-fusion`
+        set to true.
+      ngram_lm:
+        A ngram lm. Used in LODR decoding.
+      ngram_lm_scale:
+        The scale of 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"]
+    cuts = batch["supervisions"]["cut"]
+    cut_ids = [c.supervisions[0].id for c in cuts]
+    batch_size = feature.size(0)
+
+    # get pre_text
+    if "pre_text" in batch["supervisions"] and params.use_pre_text:
+        pre_texts = batch["supervisions"][
+            "text"
+        ]  # use the ground truth ref text as pre_text
+        pre_texts = [train_text_normalization(t) for t in pre_texts]
+    else:
+        pre_texts = ["" for _ in range(batch_size)]
+
+    if params.use_ls_context_list:
+        pre_texts = [biasing_dict[id] for id in cut_ids]
+
+    # get style_text
+    if params.use_style_prompt:
+        fixed_sentence = "Mixed-case English transcription, with punctuation. Actually, it's fully not related."
+        style_texts = batch["supervisions"].get(
+            "style_text", [fixed_sentence for _ in range(batch_size)]
+        )
+        style_texts = [train_text_normalization(t) for t in style_texts]
+    else:
+        style_texts = ["" for _ in range(batch_size)]  # use empty string
+
+    # Get the text embedding input
+    if params.use_pre_text or params.use_style_prompt:
+
+        # apply style transform to the pre_text and style_text
+        pre_texts = _apply_style_transform(pre_texts, params.pre_text_transform)
+        # pre_texts = random_shuffle_subset(pre_texts, p=1.0, p_mask=0.0)
+        if params.use_style_prompt:
+            style_texts = _apply_style_transform(
+                style_texts, params.style_text_transform
+            )
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            # Use tokenizer to prepare input for text encoder
+            encoded_inputs, style_lens = _encode_texts_as_bytes_with_tokenizer(
+                pre_texts=pre_texts,
+                style_texts=style_texts,
+                tokenizer=tokenizer,
+                device=device,
+            )
+
+            memory, memory_key_padding_mask = model.encode_text(
+                encoded_inputs=encoded_inputs,
+                style_lens=style_lens,
+            )  # (T,B,C)
+    else:
+        memory = None
+        memory_key_padding_mask = None
+
+    # Get the transducer encoder output
+    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)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        encoder_out, encoder_out_lens = model.encode_audio(
+            feature=feature,
+            feature_lens=feature_lens,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+        )
+
+    hyps = []
+
+    if params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        if memory is None or not params.use_context_embedding:
+            hyp_tokens = greedy_search_batch(
+                model=model,
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+            )
+        else:
+            memory = memory.permute(1, 0, 2)  # (T,N,C) -> (N,T,C)
+            context = model.context_fuser(
+                memory, padding_mask=memory_key_padding_mask
+            )  # (N,C)
+            context = model.joiner.context_proj(context)  # (N,C)
+            hyp_tokens = greedy_search_batch_with_context(
+                model=model,
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+                context=context,
+            )
+        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,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                if memory is None or not params.use_context_embedding:
+                    hyp = greedy_search(
+                        model=model,
+                        encoder_out=encoder_out_i,
+                        max_sym_per_frame=params.max_sym_per_frame,
+                    )
+                else:
+                    cur_context = context[i : i + 1, :]
+                    hyp = greedy_search_with_context(
+                        model=model,
+                        encoder_out=encoder_out_i,
+                        context=cur_context,
+                        max_sym_per_frame=params.max_sym_per_frame,
+                    )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": 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,
+    tokenizer,
+    biasing_dict: Dict = None,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> 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.
+      LM:
+        A neural network LM, used during shallow fusion
+    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 = 40
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"][
+            "text"
+        ]  # By default, this should be in mixed-punc format
+
+        # the style of ref_text should match style_text
+        texts = _apply_style_transform(texts, params.style_text_transform)
+        if params.use_style_prompt:
+            texts = _apply_style_transform(texts, params.style_text_transform)
+
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            tokenizer=tokenizer,
+            biasing_dict=biasing_dict,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                ref_text = ref_text_normalization(
+                    ref_text
+                )  # remove full-width symbols & some book marks
+                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()
+    test_set_cers = dict()
+    for key, results in results_dict.items():
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{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}-{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))
+
+        if params.compute_CER:
+            # Write CER statistics
+            recog_path = (
+                params.res_dir / f"recogs-{test_set_name}-char-{params.suffix}.txt"
+            )
+            store_transcripts(filename=recog_path, texts=results, char_level=True)
+            errs_filename = (
+                params.res_dir / f"errs-CER-{test_set_name}-{params.suffix}.txt"
+            )
+            with open(errs_filename, "w") as f:
+                cer = write_error_stats(
+                    f,
+                    f"{test_set_name}-{key}",
+                    results,
+                    enable_log=True,
+                    compute_CER=params.compute_CER,
+                )
+                test_set_cers[key] = cer
+
+            logging.info("Wrote detailed CER 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}-{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)
+
+    if params.compute_CER:
+        test_set_cers = sorted(test_set_cers.items(), key=lambda x: x[1])
+        errs_info = params.res_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
+        with open(errs_info, "w") as f:
+            print("settings\tCER", file=f)
+            for key, val in test_set_cers:
+                print("{}\t{}".format(key, val), file=f)
+
+        s = "\nFor {}, CER of different settings are:\n".format(test_set_name)
+        note = "\tbest for {}".format(test_set_name)
+        for key, val in test_set_cers:
+            s += "{} CER\t{}{}\n".format(key, val, note)
+            note = ""
+        logging.info(s)
+
+
+def add_decoding_result_to_manifest(
+    in_manifest,
+    out_manifest: str,
+    results_dict: Dict,
+):
+    # write the decoding results with prompt to the manifest as an
+    # extra ref text
+    new_ans = {}
+    for key, value in results_dict.items():
+        for items in value:
+            id, ref, hyp = items
+            new_ans[id] = " ".join(hyp)
+
+    def _add_decoding(c):
+        key = c.supervisions[0].id
+        c.supervisions[0].texts.append(new_ans[key])
+        return c
+
+    in_manifest = in_manifest.map(_add_decoding)
+    logging.info(f"Saving manifest to {out_manifest}")
+    in_manifest.to_file(out_manifest)
+
+
+def main():
+    parser = get_parser()
+    LibriHeavyAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    cuts = load_manifest_lazy(args.input_manifest)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        splitted_cuts = cuts.split(num_splits=world_size)
+        mp.spawn(
+            run, args=(world_size, args, splitted_cuts), nprocs=world_size, join=True
+        )
+    else:
+        run(rank=0, world_size=1, args=args, cuts=cuts)
+
+
+@torch.no_grad()
+def run(rank, world_size, args, cuts):
+    params = get_params()
+    params.update(vars(args))
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.use_pre_text:
+        params.suffix += f"-pre-text-{params.pre_text_transform}"
+
+    if params.use_style_prompt:
+        params.suffix += f"-style-prompt-{params.style_text_transform}"
+
+    params.suffix += f"-{rank}"
+
+    world_size = params.world_size
+
+    params.output_manifest = Path(params.output_manifest)
+    if world_size > 1:
+        cuts = cuts[rank]
+        out_name = params.output_manifest / f"with_decoding_job_{rank}.jsonl.gz"
+    else:
+        out_name = params.output_manifest / "with_decoding.jsonl.gz"
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+
+    setup_logger(f"{params.log_dir}/log-get-manifest-with-decoding-{rank}")
+    logging.info("Decoding started")
+
+    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_transducer_model(params)
+    tokenizer = get_tokenizer(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()
+
+    LM = None
+
+    decoding_graph = None
+    word_table = 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
+    libriheavy = LibriHeavyAsrDataModule(args)
+
+    dl = libriheavy.valid_dataloaders(
+        cuts, text_sampling_func=naive_triplet_text_sampling
+    )
+
+    test_sets = ["test"]
+    test_dl = [dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        biasing_dict = None
+
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            tokenizer=tokenizer,
+            biasing_dict=biasing_dict,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+
+        # save_results(
+        #     params=params,
+        #     test_set_name=test_set,
+        #     results_dict=results_dict,
+        # )
+
+        add_decoding_result_to_manifest(
+            in_manifest=cuts,
+            out_manifest=out_name,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+# torch.set_num_threads(1)
+# torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()

egs/libriheavy/ASR/zipformer_prompt_asr/decoder.py

@@ -0,0 +1,130 @@
+# 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
+import torch.nn.functional as F
+from scaling import Balancer
+
+
+class Decoder(nn.Module):
+    """This class modifies the stateless decoder from the following paper:
+
+        RNN-transducer with stateless prediction network
+        https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
+
+    It removes the recurrent connection from the decoder, i.e., the prediction
+    network. Different from the above paper, it adds an extra Conv1d
+    right after the embedding layer.
+
+    TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        decoder_dim: int,
+        blank_id: int,
+        context_size: int,
+    ):
+        """
+        Args:
+          vocab_size:
+            Number of tokens of the modeling unit including blank.
+          decoder_dim:
+            Dimension of the input embedding, and of the decoder output.
+          blank_id:
+            The ID of the blank symbol.
+          context_size:
+            Number of previous words to use to predict the next word.
+            1 means bigram; 2 means trigram. n means (n+1)-gram.
+        """
+        super().__init__()
+
+        self.embedding = nn.Embedding(
+            num_embeddings=vocab_size,
+            embedding_dim=decoder_dim,
+            padding_idx=blank_id,
+        )
+        # the balancers are to avoid any drift in the magnitude of the
+        # embeddings, which would interact badly with parameter averaging.
+        self.balancer = Balancer(
+            decoder_dim,
+            channel_dim=-1,
+            min_positive=0.0,
+            max_positive=1.0,
+            min_abs=0.5,
+            max_abs=1.0,
+            prob=0.05,
+        )
+
+        self.blank_id = blank_id
+
+        assert context_size >= 1, context_size
+        self.context_size = context_size
+        self.vocab_size = vocab_size
+
+        if context_size > 1:
+            self.conv = nn.Conv1d(
+                in_channels=decoder_dim,
+                out_channels=decoder_dim,
+                kernel_size=context_size,
+                padding=0,
+                groups=decoder_dim // 4,  # group size == 4
+                bias=False,
+            )
+            self.balancer2 = Balancer(
+                decoder_dim,
+                channel_dim=-1,
+                min_positive=0.0,
+                max_positive=1.0,
+                min_abs=0.5,
+                max_abs=1.0,
+                prob=0.05,
+            )
+
+    def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, U).
+          need_pad:
+            True to left pad the input. Should be True during training.
+            False to not pad the input. Should be False during inference.
+        Returns:
+          Return a tensor of shape (N, U, decoder_dim).
+        """
+        y = y.to(torch.int64)
+        # this stuff about clamp() is a temporary fix for a mismatch
+        # at utterance start, we use negative ids in beam_search.py
+        embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)
+
+        embedding_out = self.balancer(embedding_out)
+
+        if self.context_size > 1:
+            embedding_out = embedding_out.permute(0, 2, 1)
+            if need_pad is True:
+                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
+            else:
+                # During inference time, there is no need to do extra padding
+                # as we only need one output
+                assert embedding_out.size(-1) == self.context_size
+            embedding_out = self.conv(embedding_out)
+            embedding_out = embedding_out.permute(0, 2, 1)
+            embedding_out = F.relu(embedding_out)
+            embedding_out = self.balancer2(embedding_out)
+
+        return embedding_out

egs/libriheavy/ASR/zipformer_prompt_asr/encoder_interface.py

@@ -0,0 +1,43 @@
+# 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.
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+
+class EncoderInterface(nn.Module):
+    def forward(
+        self, x: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A tensor of shape (batch_size, input_seq_len, num_features)
+            containing the input features.
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames
+            in `x` before padding.
+        Returns:
+          Return a tuple containing two tensors:
+            - encoder_out, a tensor of (batch_size, out_seq_len, output_dim)
+              containing unnormalized probabilities, i.e., the output of a
+              linear layer.
+            - encoder_out_lens, a tensor of shape (batch_size,) containing
+              the number of frames in `encoder_out` before padding.
+        """
+        raise NotImplementedError("Please implement it in a subclass")

egs/libriheavy/ASR/zipformer_prompt_asr/export_PromptASR.py

@@ -0,0 +1,255 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Xiaoyu Yang)
+#
+# 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.
+
+"""
+Export `model.state_dict()`
+
+- For non-streaming model:
+
+./zipformer_prompt_asr/export_PromptASR.py \
+  --exp-dir ./zipformer_prompt_asr/exp \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --epoch 50 \
+  --avg 10
+
+- For streaming model:
+
+./zipformer_prompt_asr/export_PromptASR.py \
+  --exp-dir ./zipformer_prompt_asr/exp \
+  --causal 1 \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --epoch 50 \
+  --avg 10
+
+It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
+load it by `icefall.checkpoint.load_checkpoint()`.
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import List, Tuple
+
+import k2
+import torch
+from torch import Tensor, nn
+from train_bert_encoder import add_model_arguments, get_params, get_transducer_model
+
+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/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.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+@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_transducer_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()
+
+    assert params.jit is False, "Jit is not supported yet"
+
+    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/libriheavy/ASR/zipformer_prompt_asr/joiner.py

@@ -0,0 +1,86 @@
+# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# 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
+
+
+class Joiner(nn.Module):
+    def __init__(
+        self,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+        context_dim: int = 512,
+        context_injection: bool = False,
+    ):
+        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)
+        self.output_linear = nn.Linear(joiner_dim, vocab_size)
+        if context_injection:
+            self.context_proj = ScaledLinear(
+                context_dim, joiner_dim, initial_scale=0.25
+            )
+        else:
+            self.context_proj = None
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+        context: torch.Tensor = None,
+        project_input: bool = True,
+    ) -> 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).
+          context:
+            An embedding vector representing the previous context information
+          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 == 4
+        assert encoder_out.shape[:-1] == decoder_out.shape[:-1]
+
+        if project_input:
+            if context:
+                logit = (
+                    self.encoder_proj(encoder_out)
+                    + self.decoder_proj(decoder_out)
+                    + self.context_proj(context)
+                )
+            else:
+                logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
+        else:
+            if context is not None:
+                logit = encoder_out + decoder_out + context.unsqueeze(1).unsqueeze(1)
+            else:
+                logit = encoder_out + decoder_out
+
+        logit = self.output_linear(torch.tanh(logit))
+
+        return logit

egs/libriheavy/ASR/zipformer_prompt_asr/ls_text_normalization.py

@@ -0,0 +1,153 @@
+import re
+
+words = {
+    0: "zero",
+    1: "one",
+    2: "two",
+    3: "three",
+    4: "four",
+    5: "five",
+    6: "six",
+    7: "seven",
+    8: "eight",
+    9: "nine",
+    10: "ten",
+    11: "eleven",
+    12: "twelve",
+    13: "thirteen",
+    14: "fourteen",
+    15: "fifteen",
+    16: "sixteen",
+    17: "seventeen",
+    18: "eighteen",
+    19: "nineteen",
+    20: "twenty",
+    30: "thirty",
+    40: "forty",
+    50: "fifty",
+    60: "sixty",
+    70: "seventy",
+    80: "eighty",
+    90: "ninety",
+}
+ordinal_nums = [
+    "zeroth",
+    "first",
+    "second",
+    "third",
+    "fourth",
+    "fifth",
+    "sixth",
+    "seventh",
+    "eighth",
+    "ninth",
+    "tenth",
+    "eleventh",
+    "twelfth",
+    "thirteenth",
+    "fourteenth",
+    "fifteenth",
+    "sixteenth",
+    "seventeenth",
+    "eighteenth",
+    "nineteenth",
+    "twentieth",
+]
+
+num_ordinal_dict = {num: ordinal_nums[num] for num in range(21)}
+
+
+def year_to_words(num: int):
+    assert isinstance(num, int), num
+    # check if a num is representing a year
+    if num > 1500 and num < 2000:
+        return words[num // 100] + " " + num_to_words(num % 100)
+    elif num == 2000:
+        return "TWO THOUSAND"
+    elif num > 2000:
+        return "TWO THOUSAND AND " + num_to_words(num % 100)
+    else:
+        return num_to_words(num)
+
+
+def num_to_words(num: int):
+    # Return the English words of a integer number
+
+    # If this is a year number
+    if num > 1500 and num < 2030:
+        return year_to_words(num)
+
+    if num < 20:
+        return words[num]
+    if num < 100:
+        if num % 10 == 0:
+            return words[num // 10 * 10]
+        else:
+            return words[num // 10 * 10] + " " + words[num % 10]
+    if num < 1000:
+        return words[num // 100] + " hundred and " + num_to_words(num % 100)
+    if num < 1000000:
+        return num_to_words(num // 1000) + " thousand " + num_to_words(num % 1000)
+    return num
+
+
+def num_to_ordinal_word(num: int):
+
+    return num_ordinal_dict.get(num, num_to_words(num)).upper()
+
+
+def replace_full_width_symbol(s: str) -> str:
+    # replace full-width symbol with theri half width counterpart
+    s = s.replace("“", '"')
+    s = s.replace("”", '"')
+    s = s.replace("‘", "'")
+    s = s.replace("’", "'")
+
+    return s
+
+
+def decoding_normalization(text: str) -> str:
+    text = replace_full_width_symbol(text)
+
+    # Only keep all alpha-numeric characters, hypen and apostrophe
+    text = text.replace("-", " ")
+    text = re.sub(r"[^a-zA-Z0-9\s']+", "", text)
+    return text
+
+
+def word_normalization(word: str) -> str:
+    # 1 .Use full word for some abbreviation
+    # 2. Convert digits to english words
+    # 3. Convert ordinal number to english words
+    if word == "MRS":
+        return "MISSUS"
+    if word == "MR":
+        return "MISTER"
+    if word == "ST":
+        return "SAINT"
+    if word == "ECT":
+        return "ET CETERA"
+    if word.isnumeric():
+        word = num_to_words(int(word))
+        return str(word).upper()
+    #  e.g 9TH, 6TH
+    if word[-2:] == "TH" and word[0].isnumeric():
+        return num_to_ordinal_word(int(word[:-2])).upper()
+    if word[0] == "'":
+        return word[1:]
+
+    return word
+
+
+def simple_normalization(text: str) -> str:
+    text = replace_full_width_symbol(text)
+    text = text.replace("--", " ")
+
+    return text
+
+
+if __name__ == "__main__":
+
+    s = str(1830)
+    out = word_normalization(s)
+    print(s, out)

egs/libriheavy/ASR/zipformer_prompt_asr/model_baseline.py

@@ -0,0 +1,262 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang, 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.
+
+
+import random
+import warnings
+from typing import Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+from scaling import ScaledLinear, penalize_abs_values_gt
+from torch import Tensor
+
+from icefall.utils import add_sos, make_pad_mask
+
+
+class Transducer(nn.Module):
+    """It implements https://arxiv.org/pdf/1211.3711.pdf
+    "Sequence Transduction with Recurrent Neural Networks"
+    """
+
+    def __init__(
+        self,
+        encoder_embed: nn.Module,
+        encoder: EncoderInterface,
+        decoder: nn.Module,
+        joiner: nn.Module,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+    ):
+        """
+        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_dm) 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`.
+          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.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+        assert hasattr(decoder, "blank_id")
+
+        self.encoder_embed = encoder_embed
+        self.encoder = encoder
+        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,
+        )
+
+    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,
+    ) -> 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.
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          text:
+            A 2-D tensor of integer dtype containing prompt text, of shape (N, T).
+            It is exptected to contain the style prompt (first) and then the content
+            prompt.
+          text_lens:
+            A 1-D tensor of shape (N,). It contains the number of elements (bytes)
+            in `text` before padding, which will include the lengths of the
+            style plus the content prompt.
+          style_lens:
+            A 1-D tensor of shape (N,), containing the number of elements (bytes)
+            within each row of `text` that correspond to the style prompt (these
+            are expected to come first).
+          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 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, x_lens = self.encoder_embed(x, x_lens)
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+        encoder_out, x_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(x_lens > 0)
+
+        # Now for the decoder, i.e., the prediction network
+        row_splits = y.shape.row_splits(1)
+        y_lens = row_splits[1:] - row_splits[:-1]
+
+        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] = x_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        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",
+            )
+
+        return (simple_loss, pruned_loss)
+
+    def encode_audio(
+        self,
+        feature: Tensor,
+        feature_lens: Tensor,
+        memory: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Tensor]:
+        """Encode the input audio features
+
+        Args:
+            feature (Tensor): Input audio (N,T,C)
+            feature_lens (Tensor): Length of input audio (N,)
+        Returns:
+            Tuple[Tensor, Tensor]: Encoded acoustic features and length
+        """
+        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,
+            x_lens=x_lens,
+            src_key_padding_mask=src_key_padding_mask,
+        )
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        return encoder_out, encoder_out_lens

egs/libriheavy/ASR/zipformer_prompt_asr/model_with_BERT.py

@@ -0,0 +1,392 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang, 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.
+
+
+import random
+import warnings
+from typing import Dict, Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+from scaling import ScaledLinear, penalize_abs_values_gt
+from torch import Tensor
+
+from icefall.utils import add_sos, make_pad_mask
+
+
+class PromptedTransducer(nn.Module):
+    """It implements https://arxiv.org/pdf/1211.3711.pdf
+    "Sequence Transduction with Recurrent Neural Networks"
+    """
+
+    def __init__(
+        self,
+        encoder_embed: nn.Module,
+        encoder: EncoderInterface,
+        text_encoder: EncoderInterface,
+        decoder: nn.Module,
+        joiner: nn.Module,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+        use_BERT: bool = True,
+        text_encoder_type: str = "BERT",
+        text_encoder_adapter: bool = False,
+        freeze_text_encoder: bool = True,
+        context_fuser: nn.Module = None,
+    ):
+        """
+        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_dm) and
+            `logit_lens` of shape (N,).
+          text_encoder:
+            This is a encoder that processes text information (e.g content prompt
+            and style prompt). The input is `x` of (N,T) and `x_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`.
+          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.
+          text_encoder_type:
+            The type of the text_encoder. Supported are (BERT, DistilBERT)
+          context_fuser
+            A optional module that fuses the embeddings of text encoder. The fused embedding
+            will be added to the joiner.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+        assert hasattr(decoder, "blank_id")
+
+        self.encoder_embed = encoder_embed
+        self.encoder = encoder
+        self.text_encoder = text_encoder
+        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,
+        )
+
+        self.use_BERT = use_BERT  # if the text encoder is a pre-trained BERT
+        self.context_fuser = context_fuser
+
+        assert text_encoder_type in (
+            "BERT",
+            "DistilBERT",
+            "BERT-UNCASED",
+        ), f"Unseen text_encoder type {text_encoder_type}"
+        self.text_encoder_dim = (
+            self.text_encoder.config.hidden_size
+            if text_encoder_type in ("BERT", "BERT-UNCASED")
+            else self.text_encoder.config.dim
+        )
+        self.freeze_text_encoder = freeze_text_encoder
+
+        if text_encoder_adapter:
+            self.text_encoder_adapter = nn.Sequential(
+                nn.Linear(self.text_encoder_dim, self.text_encoder_dim, bias=False),
+                nn.Tanh(),
+            )
+        else:
+            self.text_encoder_adapter = None
+
+        self.style_prompt_embedding = nn.Parameter(
+            torch.full((self.text_encoder_dim,), 0.5)
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        encoded_inputs: Dict,
+        style_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+        use_pre_text: bool = True,
+    ) -> 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.
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          text:
+            A 2-D tensor of integer dtype containing prompt text, of shape (N, T).
+            It is exptected to contain the style prompt (first) and then the content
+            prompt.
+          text_lens:
+            A 1-D tensor of shape (N,). It contains the number of elements (bytes)
+            in `text` before padding, which will include the lengths of the
+            style plus the content prompt.
+          style_lens:
+            A 1-D tensor of shape (N,), containing the number of elements (bytes)
+            within each row of `text` that correspond to the style prompt (these
+            are expected to come first).
+          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 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
+        """
+        if self.freeze_text_encoder:
+            self.text_encoder.eval()
+        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, x_lens = self.encoder_embed(x, x_lens)
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+        # freeze the BERT text encoder
+
+        if use_pre_text:
+            memory, memory_key_padding_mask = self.encode_text(
+                encoded_inputs, style_lens=style_lens
+            )
+        else:
+            memory = None
+            memory_key_padding_mask = None
+
+        encoder_out, x_lens = self.encoder(
+            x,
+            x_lens,
+            src_key_padding_mask,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+        )
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        assert torch.all(x_lens > 0)
+
+        # Now for the decoder, i.e., the prediction network
+        row_splits = y.shape.row_splits(1)
+        y_lens = row_splits[1:] - row_splits[:-1]
+
+        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] = x_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        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).
+        if self.context_fuser is not None and memory is not None:
+            memory = memory.permute(1, 0, 2)  # (T,N,C) -> (N,T,C)
+            context = self.context_fuser(memory, padding_mask=memory_key_padding_mask)
+            context = self.joiner.context_proj(context)
+        else:
+            context = None
+
+        logits = self.joiner(am_pruned, lm_pruned, context=context, 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",
+            )
+
+        return (simple_loss, pruned_loss)
+
+    def _add_style_indicator(self, memory: Tensor, style_lens: Tensor):
+        """
+        Adds to `memory` an indicator that is 1.0 for positions that correspond to
+        the `style prompt` and 0 elsewhere.  The scale can be fixed because the
+        scale of the embedding vector can adjust to compensate.
+
+        Args:
+            memory: (memory_len, batch_size, embed_dim)
+            style_lens: (batch_size,),  a vector of lengths of the style prompt.
+        """
+
+        (memory_len, batch_size, embed_dim) = memory.shape
+
+        indicator = (
+            torch.arange(memory_len, device=memory.device).unsqueeze(-1) < style_lens
+        )
+        indicator = indicator.to(memory.dtype)
+
+        extra_term = torch.zeros_like(memory)
+        extra_term += indicator.unsqueeze(-1) * self.style_prompt_embedding.expand(
+            memory_len, batch_size, self.text_encoder_dim
+        )
+
+        return memory + extra_term
+
+    def encode_text(
+        self,
+        encoded_inputs: Dict,
+        style_lens: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """Get the embeddings of text
+
+        Args:
+            encoded_inputs: The encoded inputs generated by a tokenizer (Dict)
+
+        Returns:
+            Tuple[Tensor, Tensor]: Returns the text embeddings encoded by the
+            text_encoder and the attention mask
+        """
+        text_lens = encoded_inputs.pop("length")  # need to use pop to remove this item
+
+        # Freeze the pre-trained text encoder
+        with torch.no_grad():
+            memory = self.text_encoder(**encoded_inputs)["last_hidden_state"]  # (B,T,C)
+            memory = memory.permute(1, 0, 2)
+
+        # Text encoder adapter
+        if self.text_encoder_adapter is not None:
+            memory = self.text_encoder_adapter(memory)
+
+        memory = self._add_style_indicator(memory, style_lens)
+
+        memory_key_padding_mask = make_pad_mask(text_lens)
+
+        return memory, memory_key_padding_mask
+
+    def encode_audio(
+        self,
+        feature: Tensor,
+        feature_lens: Tensor,
+        memory: Optional[Tensor],
+        memory_key_padding_mask: Optional[Tensor],
+    ) -> Tuple[Tensor, Tensor]:
+        """Encode the input audio features
+
+        Args:
+            feature (Tensor): Input audio (N,T,C)
+            feature_lens (Tensor): Length of input audio (N,)
+            memory (Tensor): Embeddings from the text encoder
+            memory_key_padding_mask (Tensor): _description_
+
+        Returns:
+            Tuple[Tensor, Tensor]: _description_
+        """
+        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,
+            x_lens=x_lens,
+            src_key_padding_mask=src_key_padding_mask,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+        )
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        return encoder_out, encoder_out_lens
+
+
+Transducer = PromptedTransducer  # for decoding

egs/libriheavy/ASR/zipformer_prompt_asr/pretrained.py

@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+# Copyright      2021-2023  Xiaomi Corp.        (authors: 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.
+
+"""
+This script loads a checkpoint (`pretrained.pt`) and uses it to decode waves.
+You can generate the checkpoint with the following command:
+
+./zipformer/export_PromptASR.py \
+  --exp-dir ./zipformer/exp \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --epoch 50 \
+  --avg 10
+
+Utterance level context biasing:
+
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --method modified_beam_search \
+  --use-pre-text True \
+  --content-prompt "bessy random words hello k2 ASR" \
+  --use-style-prompt True \
+  librispeech.flac
+
+
+Word level context biasing:
+
+./zipformer/pretrained.py \
+  --checkpoint ./zipformer/exp/pretrained.pt \
+  --tokens data/lang_bpe_500_fallback_coverage_0.99/tokens.txt \
+  --method modified_beam_search \
+  --use-pre-text True \
+  --content-prompt "The topic is about horses." \
+  --use-style-prompt True \
+  test.wav
+
+
+"""
+
+import argparse
+import logging
+import math
+import warnings
+from typing import List
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from beam_search import greedy_search_batch, modified_beam_search
+from text_normalization import _apply_style_transform, train_text_normalization
+from torch.nn.utils.rnn import pad_sequence
+from train_bert_encoder import (
+    _encode_texts_as_bytes_with_tokenizer,
+    add_model_arguments,
+    get_params,
+    get_tokenizer,
+    get_transducer_model,
+)
+
+from icefall.utils import make_pad_mask, num_tokens, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500_fallback_coverage_0.99/bpe.model",
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    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 --method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-pre-text",
+        type=str2bool,
+        default=True,
+        help="Use content prompt during decoding",
+    )
+
+    parser.add_argument(
+        "--use-style-prompt",
+        type=str2bool,
+        default=True,
+        help="Use style prompt during decoding",
+    )
+
+    parser.add_argument(
+        "--pre-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of content prompt, i.e pre_text",
+    )
+
+    parser.add_argument(
+        "--style-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of style prompt, i.e style_text",
+    )
+
+    parser.add_argument(
+        "--content-prompt", type=str, default="", help="The content prompt for decoding"
+    )
+
+    parser.add_argument(
+        "--style-prompt",
+        type=str,
+        default="Mixed-cased English text with punctuations, feel free to change it.",
+        help="The style prompt for decoding",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            sample_rate == expected_sample_rate
+        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        # We use only the first channel
+        ans.append(wave[0].contiguous())
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    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.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    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."
+
+    logging.info("Creating model")
+    model = get_transducer_model(params)
+    tokenizer = get_tokenizer(params)  # for text encoder
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    assert (
+        len(params.sound_files) == 1
+    ), "Only support decoding one audio at this moment"
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    # encode prompts
+    if params.use_pre_text:
+        pre_text = [train_text_normalization(params.content_prompt)]
+        pre_text = _apply_style_transform(pre_text, params.pre_text_transform)
+    else:
+        pre_text = [""]
+
+    if params.use_style_prompt:
+        style_text = [params.style_prompt]
+        style_text = _apply_style_transform(style_text, params.style_text_transform)
+    else:
+        style_text = [""]
+
+    if params.use_pre_text or params.use_style_prompt:
+        encoded_inputs, style_lens = _encode_texts_as_bytes_with_tokenizer(
+            pre_texts=pre_text,
+            style_texts=style_text,
+            tokenizer=tokenizer,
+            device=device,
+            no_limit=True,
+        )
+
+        memory, memory_key_padding_mask = model.encode_text(
+            encoded_inputs=encoded_inputs,
+            style_lens=style_lens,
+        )  # (T,B,C)
+    else:
+        memory = None
+        memory_key_padding_mask = None
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        encoder_out, encoder_out_lens = model.encode_audio(
+            feature=features,
+            feature_lens=feature_lengths,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+        )
+
+    hyps = []
+    msg = f"Using {params.method}"
+    logging.info(msg)
+
+    if params.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,
+        )
+        hyps.append(sp.decode(hyp_tokens)[0])
+    elif params.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,
+        )
+        hyps.append(sp.decode(hyp_tokens)[0])
+    else:
+        raise ValueError(f"Unsupported method: {params.method}")
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        s += f"{filename}:\n{hyp}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/libriheavy/ASR/zipformer_prompt_asr/subsampling.py

@@ -0,0 +1,276 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# 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 warnings
+from typing import Tuple
+
+import torch
+from scaling import (
+    Balancer,
+    BiasNorm,
+    Dropout3,
+    FloatLike,
+    Optional,
+    ScaledConv2d,
+    ScaleGrad,
+    ScheduledFloat,
+    SwooshL,
+    SwooshR,
+    Whiten,
+)
+from torch import Tensor, nn
+
+
+class ConvNeXt(nn.Module):
+    """
+    Our interpretation of the ConvNeXt module as used in https://arxiv.org/pdf/2206.14747.pdf
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        hidden_ratio: int = 3,
+        kernel_size: Tuple[int, int] = (7, 7),
+        layerdrop_rate: FloatLike = None,
+    ):
+        super().__init__()
+        padding = ((kernel_size[0] - 1) // 2, (kernel_size[1] - 1) // 2)
+        hidden_channels = channels * hidden_ratio
+        if layerdrop_rate is None:
+            layerdrop_rate = ScheduledFloat((0.0, 0.2), (20000.0, 0.015))
+        self.layerdrop_rate = layerdrop_rate
+
+        self.depthwise_conv = nn.Conv2d(
+            in_channels=channels,
+            out_channels=channels,
+            groups=channels,
+            kernel_size=kernel_size,
+            padding=padding,
+        )
+
+        self.pointwise_conv1 = nn.Conv2d(
+            in_channels=channels, out_channels=hidden_channels, kernel_size=1
+        )
+
+        self.hidden_balancer = Balancer(
+            hidden_channels,
+            channel_dim=1,
+            min_positive=0.3,
+            max_positive=1.0,
+            min_abs=0.75,
+            max_abs=5.0,
+        )
+
+        self.activation = SwooshL()
+        self.pointwise_conv2 = ScaledConv2d(
+            in_channels=hidden_channels,
+            out_channels=channels,
+            kernel_size=1,
+            initial_scale=0.01,
+        )
+
+        self.out_balancer = Balancer(
+            channels,
+            channel_dim=1,
+            min_positive=0.4,
+            max_positive=0.6,
+            min_abs=1.0,
+            max_abs=6.0,
+        )
+        self.out_whiten = Whiten(
+            num_groups=1,
+            whitening_limit=5.0,
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        if torch.jit.is_scripting() or not self.training:
+            return self.forward_internal(x)
+        layerdrop_rate = float(self.layerdrop_rate)
+
+        if layerdrop_rate != 0.0:
+            batch_size = x.shape[0]
+            mask = (
+                torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
+                > layerdrop_rate
+            )
+        else:
+            mask = None
+        # turns out this caching idea does not work with --world-size > 1
+        # return caching_eval(self.forward_internal, x, mask)
+        return self.forward_internal(x, mask)
+
+    def forward_internal(
+        self, x: Tensor, layer_skip_mask: Optional[Tensor] = None
+    ) -> Tensor:
+        """
+        x layout: (N, C, H, W), i.e. (batch_size, num_channels, num_frames, num_freqs)
+
+        The returned value has the same shape as x.
+        """
+        bypass = x
+        x = self.depthwise_conv(x)
+        x = self.pointwise_conv1(x)
+        x = self.hidden_balancer(x)
+        x = self.activation(x)
+        x = self.pointwise_conv2(x)
+
+        if layer_skip_mask is not None:
+            x = x * layer_skip_mask
+
+        x = bypass + x
+        x = self.out_balancer(x)
+        x = x.transpose(1, 3)  # (N, W, H, C); need channel dim to be last
+        x = self.out_whiten(x)
+        x = x.transpose(1, 3)  # (N, C, H, W)
+
+        return x
+
+
+class Conv2dSubsampling(nn.Module):
+    """Convolutional 2D subsampling (to 1/2 length).
+
+    Convert an input of shape (N, T, idim) to an output
+    with shape (N, T', odim), where
+    T' = (T-3)//2 - 2 == (T-7)//2
+
+    It is based on
+    https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py  # noqa
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        layer1_channels: int = 8,
+        layer2_channels: int = 32,
+        layer3_channels: int = 128,
+        dropout: FloatLike = 0.1,
+    ) -> None:
+        """
+        Args:
+          in_channels:
+            Number of channels in. The input shape is (N, T, in_channels).
+            Caution: It requires: T >=7, in_channels >=7
+          out_channels
+            Output dim. The output shape is (N, (T-3)//2, out_channels)
+          layer1_channels:
+            Number of channels in layer1
+          layer1_channels:
+            Number of channels in layer2
+          bottleneck:
+            bottleneck dimension for 1d squeeze-excite
+        """
+        assert in_channels >= 7
+        super().__init__()
+
+        # The ScaleGrad module is there to prevent the gradients
+        # w.r.t. the weight or bias of the first Conv2d module in self.conv from
+        # exceeding the range of fp16 when using automatic mixed precision (amp)
+        # training.  (The second one is necessary to stop its bias from getting
+        # a too-large gradient).
+
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                in_channels=1,
+                out_channels=layer1_channels,
+                kernel_size=3,
+                padding=(0, 1),  # (time, freq)
+            ),
+            ScaleGrad(0.2),
+            Balancer(layer1_channels, channel_dim=1, max_abs=1.0),
+            SwooshR(),
+            nn.Conv2d(
+                in_channels=layer1_channels,
+                out_channels=layer2_channels,
+                kernel_size=3,
+                stride=2,
+                padding=0,
+            ),
+            Balancer(layer2_channels, channel_dim=1, max_abs=4.0),
+            SwooshR(),
+            nn.Conv2d(
+                in_channels=layer2_channels,
+                out_channels=layer3_channels,
+                kernel_size=3,
+                stride=(1, 2),  # (time, freq)
+            ),
+            Balancer(layer3_channels, channel_dim=1, max_abs=4.0),
+            SwooshR(),
+        )
+
+        # just one convnext layer
+        self.convnext = ConvNeXt(layer3_channels, kernel_size=(7, 7))
+
+        out_width = (((in_channels - 1) // 2) - 1) // 2
+
+        self.out = nn.Linear(out_width * layer3_channels, out_channels)
+        # use a larger than normal grad_scale on this whitening module; there is
+        # only one such module, so there is not a concern about adding together
+        # many copies of this extra gradient term.
+        self.out_whiten = Whiten(
+            num_groups=1,
+            whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.02,
+        )
+
+        # max_log_eps=0.0 is to prevent both eps and the output of self.out from
+        # getting large, there is an unnecessary degree of freedom.
+        self.out_norm = BiasNorm(out_channels)
+        self.dropout = Dropout3(dropout, shared_dim=1)
+
+    def forward(self, x: torch.Tensor, x_lens: torch.Tensor) -> torch.Tensor:
+        """Subsample x.
+
+        Args:
+          x:
+            Its shape is (N, T, idim).
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames in
+
+        Returns:
+          - a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
+          - output lengths, of shape (batch_size,)
+        """
+        # On entry, x is (N, T, idim)
+        x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
+        # scaling x by 0.1 allows us to use a larger grad-scale in fp16 "amp" (automatic mixed precision)
+        # training, since the weights in the first convolution are otherwise the limiting factor for getting infinite
+        # gradients.
+        x = self.conv(x)
+        x = self.convnext(x)
+
+        # Now x is of shape (N, odim, ((T-3)//2 - 1)//2, ((idim-1)//2 - 1)//2)
+        b, c, t, f = x.size()
+
+        x = x.transpose(1, 2).reshape(b, t, c * f)
+        # now x: (N, ((T-1)//2 - 1))//2, out_width * layer3_channels))
+
+        x = self.out(x)
+        # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
+        x = self.out_whiten(x)
+        x = self.out_norm(x)
+        x = self.dropout(x)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            x_lens = (x_lens - 7) // 2
+        assert x.size(1) == x_lens.max().item()
+
+        return x, x_lens

egs/libriheavy/ASR/zipformer_prompt_asr/test_model.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+To run this file, do:
+
+    cd icefall/egs/librispeech/ASR
+    python ./pruned_transducer_stateless4/test_model.py
+"""
+
+from scaling import ScheduledFloat
+from train_subformer import get_params, get_text_encoder, get_transducer_model
+from zipformer import Zipformer2
+
+
+def test_model_1():
+    params = get_params()
+    params.vocab_size = 500
+    params.blank_id = 0
+    params.context_size = 2
+    params.num_encoder_layers = 24
+    params.dim_feedforward = 1536  # 384 * 4
+    params.encoder_dim = 384
+    model = get_transducer_model(params)
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+
+# See Table 1 from https://arxiv.org/pdf/2005.08100.pdf
+def test_model_M():
+    params = get_params()
+    params.vocab_size = 500
+    params.blank_id = 0
+    params.context_size = 2
+    params.num_encoder_layers = "2,4,3,2,4"
+    params.feedforward_dims = "1024,1024,2048,2048,1024"
+    params.nhead = "8,8,8,8,8"
+    params.encoder_dims = "384,384,384,384,384"
+    params.attention_dims = "192,192,192,192,192"
+    params.encoder_unmasked_dims = "256,256,256,256,256"
+    params.zipformer_downsampling_factors = "1,2,4,8,2"
+    params.cnn_module_kernels = "31,31,15,15"
+
+    params.text_encoder_dim = (192, 192, 256, 384)
+    params.decoder_dim = 512
+    params.joiner_dim = 512
+    model = Zipformer2(
+        output_downsampling_factor=8,
+        downsampling_factor=(1, 2, 4, 8),
+        num_encoder_layers=(2, 4, 4, 4),
+        encoder_dim=(192, 192, 256, 384),
+        encoder_unmasked_dim=(192, 192, 256, 256),
+        query_head_dim=(32, 32, 32, 32),
+        pos_head_dim=(4, 4, 4, 4),
+        value_head_dim=(12, 12, 12, 12),
+        pos_dim=48,
+        num_heads=(4, 4, 4, 8),
+        feedforward_dim=(
+            384,
+            512,
+            768,
+            1024,
+        ),  # could increase this if there is nough data
+        cnn_module_kernel=(31, 31, 15, 15),
+        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
+        warmup_batches=4000.0,
+        causal=False,
+    )
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+    model = Zipformer2(
+        output_downsampling_factor=8,
+        downsampling_factor=(1, 2, 4, 8),
+        num_encoder_layers=(2, 4, 6, 6),
+        encoder_dim=(256, 256, 384, 512),
+        encoder_unmasked_dim=(196, 196, 256, 256),
+        query_head_dim=(32, 32, 32, 32),
+        pos_head_dim=(4, 4, 4, 4),
+        value_head_dim=(12, 12, 12, 12),
+        pos_dim=48,
+        num_heads=(4, 4, 4, 8),
+        feedforward_dim=(
+            384,
+            512,
+            768,
+            1024,
+        ),  # could increase this if there is nough data
+        cnn_module_kernel=(31, 31, 15, 15),
+        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
+        warmup_batches=4000.0,
+        causal=False,
+    )
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+
+def main():
+    #  test_model_1()
+    test_model_M()
+
+
+if __name__ == "__main__":
+    main()

egs/libriheavy/ASR/zipformer_prompt_asr/text_normalization.py

@@ -0,0 +1,101 @@
+#      Copyright      2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# 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 re
+from typing import List
+
+
+def train_text_normalization(s: str) -> str:
+    # replace full-width with half-width
+    s = s.replace("“", '"')
+    s = s.replace("”", '"')
+    s = s.replace("‘", "'")
+    s = s.replace("’", "'")
+    if s[:2] == '" ':  # remove the starting double quote
+        s = s[2:]
+
+    return s
+
+
+def ref_text_normalization(ref_text: str) -> str:
+    # Rule 1: Remove the [FN#[]]
+    p = r"[FN#[0-9]*]"
+    pattern = re.compile(p)
+
+    res = pattern.findall(ref_text)
+    ref_text = re.sub(p, "", ref_text)
+
+    ref_text = train_text_normalization(ref_text)
+
+    return ref_text
+
+
+def remove_non_alphabetic(text: str, strict: bool = True) -> str:
+    # Recommend to set strict to False
+    if not strict:
+        # Note, this also keeps space, single quote(') and hypen (-)
+        text = text.replace("-", " ")
+        text = text.replace("—", " ")
+        return re.sub(r"[^a-zA-Z0-9\s']+", "", text)
+    else:
+        # only keeps space
+        return re.sub(r"[^a-zA-Z\s]+", "", text)
+
+
+def upper_only_alpha(text: str) -> str:
+    return remove_non_alphabetic(text.upper(), strict=False)
+
+
+def lower_only_alpha(text: str) -> str:
+    return remove_non_alphabetic(text.lower(), strict=False)
+
+
+def lower_all_char(text: str) -> str:
+    return text.lower()
+
+
+def upper_all_char(text: str) -> str:
+    return text.upper()
+
+
+def _apply_style_transform(text: List[str], transform: str) -> List[str]:
+    """Apply transform to a list of text. By default, the text are in
+    ground truth format, i.e mixed-punc.
+
+    Args:
+        text (List[str]): Input text string
+        transform (str): Transform to be applied
+
+    Returns:
+        List[str]: _description_
+    """
+    if transform == "mixed-punc":
+        return text
+    elif transform == "upper-no-punc":
+        return [upper_only_alpha(s) for s in text]
+    elif transform == "lower-no-punc":
+        return [lower_only_alpha(s) for s in text]
+    elif transform == "lower-punc":
+        return [lower_all_char(s) for s in text]
+    else:
+        raise NotImplementedError(f"Unseen transform: {transform}")
+
+
+if __name__ == "__main__":
+    ref_text = "Mixed-case English transcription, with punctuation. Actually, it is fully not related."
+    print(ref_text)
+    res = upper_only_alpha(ref_text)
+    print(res)

egs/libriheavy/ASR/zipformer_prompt_asr/transcribe_bert.py

@@ -0,0 +1,515 @@
+# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# 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:
+
+python ./zipformer_prompt_asr/transcribe_bert.py \
+    --epoch 50 \
+    --avg 10 \
+    --exp-dir ./zipformer_prompt_asr/exp \
+    --manifest-dir data/long_audios/long_audio.jsonl.gz \
+    --pre-text-transform mixed-punc \
+    --style-text-transform mixed-punc \
+    --num-history 5 \
+    --use-pre-text True \
+    --use-gt-pre-text False
+
+
+"""
+
+import argparse
+import logging
+import math
+import warnings
+from pathlib import Path
+from typing import List
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from decode_bert import _apply_style_transform
+from lhotse import Fbank, load_manifest
+from text_normalization import (
+    lower_all_char,
+    lower_only_alpha,
+    ref_text_normalization,
+    remove_non_alphabetic,
+    train_text_normalization,
+    upper_all_char,
+    upper_only_alpha,
+)
+from tqdm import tqdm
+from train_bert_encoder import (
+    _encode_texts_as_bytes_with_tokenizer,
+    add_model_arguments,
+    get_params,
+    get_tokenizer,
+    get_transducer_model,
+)
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+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(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless7/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="""Path to bpe.model.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+    )
+
+    parser.add_argument(
+        "--manifest-dir",
+        type=str,
+        default="data/long_audios/long_audio.jsonl.gz",
+        help="""This is the manfiest for long audio transcription.
+        The cust are intended to be sorted, i.e first sort by recording ID and
+        then sort by start timestamp""",
+    )
+
+    parser.add_argument(
+        "--use-pre-text",
+        type=str2bool,
+        default=False,
+        help="Whether use pre-text when decoding the current chunk",
+    )
+
+    parser.add_argument(
+        "--use-style-prompt",
+        type=str2bool,
+        default=True,
+        help="Use style prompt when evaluation",
+    )
+
+    parser.add_argument(
+        "--pre-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of content prompt, i.e pre_text",
+    )
+
+    parser.add_argument(
+        "--style-text-transform",
+        type=str,
+        choices=["mixed-punc", "upper-no-punc", "lower-no-punc", "lower-punc"],
+        default="mixed-punc",
+        help="The style of style prompt, i.e style_text",
+    )
+
+    parser.add_argument(
+        "--num-history",
+        type=int,
+        default=2,
+        help="How many previous chunks to look if using pre-text for decoding",
+    )
+
+    parser.add_argument(
+        "--use-gt-pre-text",
+        type=str2bool,
+        default=False,
+        help="Whether use gt pre text when using content prompt",
+    )
+
+    parser.add_argument(
+        "--post-normalization",
+        type=str2bool,
+        default=True,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    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.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    params.res_dir = params.exp_dir / "long_audio_transcribe"
+    params.res_dir.mkdir(exist_ok=True)
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if "beam_search" in params.method:
+        params.suffix += f"-{params.method}-beam-size-{params.beam_size}"
+
+    if params.use_pre_text:
+        if params.use_gt_pre_text:
+            params.suffix += f"-use-gt-pre-text-{params.pre_text_transform}-history-{params.num_history}"
+        else:
+            params.suffix += (
+                f"-pre-text-{params.pre_text_transform}-history-{params.num_history}"
+            )
+
+    book_name = params.manifest_dir.split("/")[-1].replace(".jsonl.gz", "")
+    setup_logger(
+        f"{params.res_dir}/log-decode-{book_name}-{params.suffix}", log_level="info"
+    )
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = get_transducer_model(params)
+    tokenizer = get_tokenizer(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    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()
+    model.device = device
+
+    # load manifest
+    manifest = load_manifest(params.manifest_dir)
+
+    results = []
+    count = 0
+
+    last_recording = ""
+    last_end = -1
+    history = []
+    num_pre_texts = []
+
+    for cut in manifest:
+        if cut.has_features:
+            feat = cut.load_features()
+            feat_lens = cut.num_frames
+        else:
+            feat = cut.compute_features(extractor=Fbank())
+            feat_lens = feat.shape[0]
+
+        cur_recording = cut.recording.id
+
+        if cur_recording != last_recording:
+            last_recording = cur_recording
+            history = []  # clean up the history
+            last_end = -1
+            logging.info("Moving on to the next recording")
+        else:
+            if cut.start < last_end - 0.2:  # overlap with the previous cuts
+                logging.warning("An overlap exists between current cut and last cut")
+                logging.warning("Skipping this cut!")
+                continue
+            if cut.start > last_end + 10:
+                logging.warning(
+                    f"Large time gap between the current and previous utterance: {cut.start - last_end}."
+                )
+
+        # prepare input
+        x = torch.tensor(feat, device=device).unsqueeze(0)
+        x_lens = torch.tensor(
+            [
+                feat_lens,
+            ],
+            device=device,
+        )
+
+        if params.use_pre_text:
+            if params.num_history > 0:
+                pre_texts = history[-params.num_history :]
+            else:
+                pre_texts = []
+            num_pre_texts.append(len(pre_texts))
+            pre_texts = [train_text_normalization(" ".join(pre_texts))]
+            fixed_sentence = "Mixed-case English transcription, with punctuation. Actually, it is fully not related."
+            style_texts = [fixed_sentence]
+
+            pre_texts = _apply_style_transform(pre_texts, params.pre_text_transform)
+            if params.use_style_prompt:
+                style_texts = _apply_style_transform(
+                    style_texts, params.style_text_transform
+                )
+
+            # encode prompts
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+
+                encoded_inputs, style_lens = _encode_texts_as_bytes_with_tokenizer(
+                    pre_texts=pre_texts,
+                    style_texts=style_texts,
+                    tokenizer=tokenizer,
+                    device=device,
+                    no_limit=True,
+                )
+                if params.num_history > 5:
+                    logging.info(
+                        f"Shape of encoded texts: {encoded_inputs['input_ids'].shape} "
+                    )
+
+                memory, memory_key_padding_mask = model.encode_text(
+                    encoded_inputs=encoded_inputs,
+                    style_lens=style_lens,
+                )  # (T,B,C)
+        else:
+            memory = None
+            memory_key_padding_mask = None
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            encoder_out, encoder_out_lens = model.encode_audio(
+                feature=x,
+                feature_lens=x_lens,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+            )
+
+        if params.method == "greedy_search":
+            hyp_tokens = greedy_search_batch(
+                model=model,
+                encoder_out=encoder_out,
+                encoder_out_lens=encoder_out_lens,
+            )
+        elif params.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,
+            )
+
+        hyp = sp.decode(hyp_tokens)[0]  # in string format
+        ref_text = ref_text_normalization(
+            cut.supervisions[0].texts[0]
+        )  # required to match the training
+
+        # extend the history
+        if params.use_gt_pre_text:
+            history.append(ref_text)
+        else:
+            history.append(hyp)
+        last_end = cut.end  # update the last end timestamp
+
+        # append the current decoding result
+        hyp = hyp.split()
+        ref = ref_text.split()
+        results.append((cut.id, ref, hyp))
+
+        count += 1
+        if count % 100 == 0:
+            logging.info(f"Cuts processed until now: {count}/{len(manifest)}")
+            logging.info(
+                f"Averaged context numbers of last 100 samples is: {sum(num_pre_texts[-100:])/100}"
+            )
+
+    logging.info(f"A total of {count} cuts")
+    logging.info(
+        f"Averaged context numbers of whole set is: {sum(num_pre_texts)/len(num_pre_texts)}"
+    )
+
+    results = sorted(results)
+    recog_path = (
+        params.res_dir / f"recogs-long-audio-{params.method}-{params.suffix}.txt"
+    )
+    store_transcripts(filename=recog_path, texts=results)
+    logging.info(f"The transcripts are stored in {recog_path}")
+
+    errs_filename = (
+        params.res_dir / f"errs-long-audio-{params.method}-{params.suffix}.txt"
+    )
+    with open(errs_filename, "w") as f:
+        wer = write_error_stats(
+            f,
+            f"long-audio-{params.method}",
+            results,
+            enable_log=True,
+            compute_CER=False,
+        )
+
+    logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    if params.post_normalization:
+        params.suffix += "-post-normalization"
+
+        new_res = []
+        for item in results:
+            id, ref, hyp = item
+            hyp = upper_only_alpha(" ".join(hyp)).split()
+            ref = upper_only_alpha(" ".join(ref)).split()
+            new_res.append((id, ref, hyp))
+
+    new_res = sorted(new_res)
+    recog_path = (
+        params.res_dir
+        / f"recogs-long-audio-{params.method}-{params.suffix}-post-normalization.txt"
+    )
+    store_transcripts(filename=recog_path, texts=new_res)
+    logging.info(f"The transcripts are stored in {recog_path}")
+
+    errs_filename = (
+        params.res_dir
+        / f"errs-long-audio-{params.method}-{params.suffix}-post-normalization.txt"
+    )
+    with open(errs_filename, "w") as f:
+        wer = write_error_stats(
+            f,
+            f"long-audio-{params.method}",
+            new_res,
+            enable_log=True,
+            compute_CER=False,
+        )
+
+    logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+
+if __name__ == "__main__":
+    main()

egs/libriheavy/ASR/zipformer_prompt_asr/utils.py

@@ -0,0 +1,439 @@
+import argparse
+import ast
+import glob
+import logging
+import os
+from collections import defaultdict
+from typing import Dict, Iterable, List, TextIO, Tuple, Union
+
+import kaldialign
+from lhotse import load_manifest, load_manifest_lazy
+from lhotse.cut import Cut, CutSet
+from text_normalization import remove_non_alphabetic
+from tqdm import tqdm
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--manifest-dir",
+        type=str,
+        default="data/fbank",
+        help="Where are the manifest stored",
+    )
+
+    parser.add_argument(
+        "--subset", type=str, default="medium", help="Which subset to work with"
+    )
+
+    parser.add_argument(
+        "--top-k",
+        type=int,
+        default=10000,
+        help="How many words to keep",
+    )
+
+    return parser
+
+
+def get_facebook_biasing_list(
+    test_set: str,
+    num_distractors: int = 100,
+) -> Dict:
+    # Get the biasing list from the meta paper: https://arxiv.org/pdf/2104.02194.pdf
+    assert num_distractors in (0, 100, 500, 1000, 2000), num_distractors
+    if num_distractors == 0:
+        if test_set == "test-clean":
+            biasing_file = "data/context_biasing/fbai-speech/is21_deep_bias/ref/test-clean.biasing_100.tsv"
+        elif test_set == "test-other":
+            biasing_file = "data/context_biasing/fbai-speech/is21_deep_bias/ref/test-other.biasing_100.tsv"
+        else:
+            raise ValueError(f"Unseen test set {test_set}")
+    else:
+        if test_set == "test-clean":
+            biasing_file = f"data/context_biasing/fbai-speech/is21_deep_bias/ref/test-clean.biasing_{num_distractors}.tsv"
+        elif test_set == "test-other":
+            biasing_file = f"data/context_biasing/fbai-speech/is21_deep_bias/ref/test-other.biasing_{num_distractors}.tsv"
+        else:
+            raise ValueError(f"Unseen test set {test_set}")
+
+    f = open(biasing_file, "r")
+    data = f.readlines()
+    f.close()
+
+    output = dict()
+    for line in data:
+        id, _, l1, l2 = line.split("\t")
+        if num_distractors > 0:  # use distractors
+            biasing_list = ast.literal_eval(l2)
+        else:
+            biasing_list = ast.literal_eval(l1)
+        biasing_list = [w.strip().upper() for w in biasing_list]
+        output[id] = " ".join(biasing_list)
+
+    return output
+
+
+def brian_biasing_list(level: str):
+    # The biasing list from Brian's paper: https://arxiv.org/pdf/2109.00627.pdf
+    root_dir = f"data/context_biasing/LibriSpeechBiasingLists/{level}Level"
+    all_files = glob.glob(root_dir + "/*")
+    biasing_dict = {}
+    for f in all_files:
+        k = f.split("/")[-1]
+        fin = open(f, "r")
+        data = fin.read().strip().split()
+        biasing_dict[k] = " ".join(data)
+        fin.close()
+
+    return biasing_dict
+
+
+def get_rare_words(
+    subset: str = "medium",
+    top_k: int = 10000,
+    # min_count: int = 10000,
+):
+    """Get a list of rare words appearing less than `min_count` times
+
+    Args:
+        subset: The dataset
+        top_k (int): How many frequent words
+    """
+    txt_path = f"data/tmp/transcript_words_{subset}.txt"
+    rare_word_file = f"data/context_biasing/{subset}_rare_words_topk_{top_k}.txt"
+
+    if os.path.exists(rare_word_file):
+        print("File exists, do not proceed!")
+        return
+
+    print("---Identifying rare words in the manifest---")
+    count_file = f"data/tmp/transcript_words_{subset}_count.txt"
+    if not os.path.exists(count_file):
+        with open(txt_path, "r") as file:
+            words = file.read().upper().split()
+            word_count = {}
+            for word in words:
+                word = remove_non_alphabetic(word, strict=False)
+                word = word.split()
+                for w in word:
+                    if w not in word_count:
+                        word_count[w] = 1
+                    else:
+                        word_count[w] += 1
+
+        word_count = list(word_count.items())  # convert to a list of tuple
+        word_count = sorted(word_count, key=lambda w: int(w[1]), reverse=True)
+        with open(count_file, "w") as fout:
+            for w, count in word_count:
+                fout.write(f"{w}\t{count}\n")
+
+    else:
+        word_count = {}
+        with open(count_file, "r") as fin:
+            word_count = fin.read().strip().split("\n")
+            word_count = [pair.split("\t") for pair in word_count]
+            word_count = sorted(word_count, key=lambda w: int(w[1]), reverse=True)
+
+    print(f"A total of {len(word_count)} words appeared!")
+    rare_words = []
+    for word, count in word_count[top_k:]:
+        rare_words.append(word + "\n")
+    print(f"A total of {len(rare_words)} are identified as rare words.")
+
+    with open(rare_word_file, "w") as f:
+        f.writelines(rare_words)
+
+
+def add_context_list_to_manifest(
+    manifest_dir: str,
+    subset: str = "medium",
+    top_k: int = 10000,
+):
+    """Generate a context list of rare words for each utterance in the manifest
+
+    Args:
+        manifest_dir: Where to store the manifest with context list
+        subset (str): Subset
+        top_k (int): How many frequent words
+
+    """
+    orig_manifest_dir = f"{manifest_dir}/libriheavy_cuts_{subset}.jsonl.gz"
+    target_manifest_dir = orig_manifest_dir.replace(
+        ".jsonl.gz", f"_with_context_list_topk_{top_k}.jsonl.gz"
+    )
+    if os.path.exists(target_manifest_dir):
+        print(f"Target file exits at {target_manifest_dir}!")
+        return
+
+    rare_words_file = f"data/context_biasing/{subset}_rare_words_topk_{top_k}.txt"
+    print(f"---Reading rare words from {rare_words_file}---")
+    with open(rare_words_file, "r") as f:
+        rare_words = f.read()
+    rare_words = rare_words.split("\n")
+    rare_words = set(rare_words)
+    print(f"A total of {len(rare_words)} rare words!")
+
+    cuts = load_manifest_lazy(orig_manifest_dir)
+    print(f"Loaded manifest from {orig_manifest_dir}")
+
+    def _add_context(c: Cut):
+        splits = (
+            remove_non_alphabetic(c.supervisions[0].texts[0], strict=False)
+            .upper()
+            .split()
+        )
+        found = []
+        for w in splits:
+            if w in rare_words:
+                found.append(w)
+        c.supervisions[0].context_list = " ".join(found)
+        return c
+
+    cuts = cuts.map(_add_context)
+    print(f"---Saving manifest with context list to {target_manifest_dir}---")
+    cuts.to_file(target_manifest_dir)
+    print("Finished")
+
+
+def check(
+    manifest_dir: str,
+    subset: str = "medium",
+    top_k: int = 10000,
+):
+    # Show how many samples in the training set have a context list
+    # and the average length of context list
+    print("--- Calculating the stats over the manifest ---")
+
+    manifest_dir = f"{manifest_dir}/libriheavy_cuts_{subset}_with_context_list_topk_{top_k}.jsonl.gz"
+    cuts = load_manifest_lazy(manifest_dir)
+    total_cuts = len(cuts)
+    has_context_list = [c.supervisions[0].context_list != "" for c in cuts]
+    context_list_len = [len(c.supervisions[0].context_list.split()) for c in cuts]
+    print(f"{sum(has_context_list)}/{total_cuts} cuts have context list! ")
+    print(
+        f"Average length of non-empty context list is {sum(context_list_len)/sum(has_context_list)}"
+    )
+
+
+def write_error_stats(
+    f: TextIO,
+    test_set_name: str,
+    results: List[Tuple[str, str]],
+    enable_log: bool = True,
+    compute_CER: bool = False,
+    biasing_words: List[str] = None,
+) -> float:
+    """Write statistics based on predicted results and reference transcripts. It also calculates the
+    biasing word error rate as described in https://arxiv.org/pdf/2104.02194.pdf
+
+    It will write the following to the given file:
+
+        - WER
+        - number of insertions, deletions, substitutions, corrects and total
+          reference words. For example::
+
+              Errors: 23 insertions, 57 deletions, 212 substitutions, over 2606
+              reference words (2337 correct)
+
+        - The difference between the reference transcript and predicted result.
+          An instance is given below::
+
+            THE ASSOCIATION OF (EDISON->ADDISON) ILLUMINATING COMPANIES
+
+          The above example shows that the reference word is `EDISON`,
+          but it is predicted to `ADDISON` (a substitution error).
+
+          Another example is::
+
+            FOR THE FIRST DAY (SIR->*) I THINK
+
+          The reference word `SIR` is missing in the predicted
+          results (a deletion error).
+      results:
+        An iterable of tuples. The first element is the cut_id, the second is
+        the reference transcript and the third element is the predicted result.
+      enable_log:
+        If True, also print detailed WER to the console.
+        Otherwise, it is written only to the given file.
+      biasing_words:
+        All the words in the biasing list
+    Returns:
+      Return None.
+    """
+    subs: Dict[Tuple[str, str], int] = defaultdict(int)
+    ins: Dict[str, int] = defaultdict(int)
+    dels: Dict[str, int] = defaultdict(int)
+
+    # `words` stores counts per word, as follows:
+    #   corr, ref_sub, hyp_sub, ins, dels
+    words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
+    num_corr = 0
+    ERR = "*"
+
+    if compute_CER:
+        for i, res in enumerate(results):
+            cut_id, ref, hyp = res
+            ref = list("".join(ref))
+            hyp = list("".join(hyp))
+            results[i] = (cut_id, ref, hyp)
+
+    for cut_id, ref, hyp in results:
+        ali = kaldialign.align(ref, hyp, ERR)
+        for ref_word, hyp_word in ali:
+            if ref_word == ERR:  # INSERTION
+                ins[hyp_word] += 1
+                words[hyp_word][3] += 1
+            elif hyp_word == ERR:  # DELETION
+                dels[ref_word] += 1
+                words[ref_word][4] += 1
+            elif hyp_word != ref_word:  # SUBSTITUTION
+                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)
+
+    if enable_log:
+        logging.info(
+            f"[{test_set_name}] %WER {tot_errs / ref_len:.2%} "
+            f"[{tot_errs} / {ref_len}, {ins_errs} ins, "
+            f"{del_errs} del, {sub_errs} sub ]"
+        )
+
+    print(f"%WER = {tot_err_rate}", file=f)
+    print(
+        f"Errors: {ins_errs} insertions, {del_errs} deletions, "
+        f"{sub_errs} substitutions, over {ref_len} reference "
+        f"words ({num_corr} correct)",
+        file=f,
+    )
+    print(
+        "Search below for sections starting with PER-UTT DETAILS:, "
+        "SUBSTITUTIONS:, DELETIONS:, INSERTIONS:, PER-WORD STATS:",
+        file=f,
+    )
+
+    print("", file=f)
+    print("PER-UTT DETAILS: corr or (ref->hyp)  ", file=f)
+    for cut_id, ref, hyp in results:
+        ali = kaldialign.align(ref, hyp, ERR)
+        combine_successive_errors = True
+        if combine_successive_errors:
+            ali = [[[x], [y]] for x, y in ali]
+            for i in range(len(ali) - 1):
+                if ali[i][0] != ali[i][1] and ali[i + 1][0] != ali[i + 1][1]:
+                    ali[i + 1][0] = ali[i][0] + ali[i + 1][0]
+                    ali[i + 1][1] = ali[i][1] + ali[i + 1][1]
+                    ali[i] = [[], []]
+            ali = [
+                [
+                    list(filter(lambda a: a != ERR, x)),
+                    list(filter(lambda a: a != ERR, y)),
+                ]
+                for x, y in ali
+            ]
+            ali = list(filter(lambda x: x != [[], []], ali))
+            ali = [
+                [
+                    ERR if x == [] else " ".join(x),
+                    ERR if y == [] else " ".join(y),
+                ]
+                for x, y in ali
+            ]
+
+        print(
+            f"{cut_id}:\t"
+            + " ".join(
+                (
+                    ref_word if ref_word == hyp_word else f"({ref_word}->{hyp_word})"
+                    for ref_word, hyp_word in ali
+                )
+            ),
+            file=f,
+        )
+
+    print("", file=f)
+    print("SUBSTITUTIONS: count ref -> hyp", file=f)
+
+    for count, (ref, hyp) in sorted([(v, k) for k, v in subs.items()], reverse=True):
+        print(f"{count}   {ref} -> {hyp}", file=f)
+
+    print("", file=f)
+    print("DELETIONS: count ref", file=f)
+    for count, ref in sorted([(v, k) for k, v in dels.items()], reverse=True):
+        print(f"{count}   {ref}", file=f)
+
+    print("", file=f)
+    print("INSERTIONS: count hyp", file=f)
+    for count, hyp in sorted([(v, k) for k, v in ins.items()], reverse=True):
+        print(f"{count}   {hyp}", file=f)
+
+    unbiased_word_counts = 0
+    unbiased_word_errs = 0
+    biased_word_counts = 0
+    biased_word_errs = 0
+
+    print("", file=f)
+    print("PER-WORD STATS: word  corr tot_errs count_in_ref count_in_hyp", file=f)
+
+    for _, word, counts in sorted(
+        [(sum(v[1:]), k, v) for k, v in words.items()], reverse=True
+    ):
+        (corr, ref_sub, hyp_sub, ins, dels) = counts
+        tot_errs = ref_sub + hyp_sub + ins + dels
+        # number of appearances of "word" in reference text
+        ref_count = (
+            corr + ref_sub + dels
+        )  # correct + in ref but got substituted + deleted
+        # number of appearances of "word" in hyp text
+        hyp_count = corr + hyp_sub + ins
+
+        if biasing_words is not None:
+            if word in biasing_words:
+                biased_word_counts += ref_count
+                biased_word_errs += ins + dels + ref_sub
+            else:
+                unbiased_word_counts += ref_count
+                unbiased_word_errs += ins + dels + hyp_sub
+
+        print(f"{word}   {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
+
+    if biasing_words is not None:
+        B_WER = "%.2f" % (100 * biased_word_errs / biased_word_counts)
+        U_WER = "%.2f" % (100 * unbiased_word_errs / unbiased_word_counts)
+        logging.info(f"Biased WER: {B_WER} [{biased_word_errs}/{biased_word_counts}] ")
+        logging.info(
+            f"Un-biased WER: {U_WER} [{unbiased_word_errs}/{unbiased_word_counts}]"
+        )
+
+    return float(tot_err_rate)
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    manifest_dir = args.manifest_dir
+    subset = args.subset
+    top_k = args.top_k
+    get_rare_words(subset=subset, top_k=top_k)
+    add_context_list_to_manifest(
+        manifest_dir=manifest_dir,
+        subset=subset,
+        top_k=top_k,
+    )
+    check(
+        manifest_dir=manifest_dir,
+        subset=subset,
+        top_k=top_k,
+    )

icefall/utils.py

@@ -483,7 +483,7 @@ def load_alignments(filename: str) -> Tuple[int, Dict[str, List[int]]]:
 
 
 def store_transcripts(
-    filename: Pathlike, texts: Iterable[Tuple[str, str, str]]
+    filename: Pathlike, texts: Iterable[Tuple[str, str, str]], char_level: bool = False
 ) -> None:
     """Save predicted results and reference transcripts to a file.
 
@@ -500,6 +500,9 @@ def store_transcripts(
     """
     with open(filename, "w") as f:
         for cut_id, ref, hyp in texts:
+            if char_level:
+                ref = list("".join(ref))
+                hyp = list("".join(hyp))
             print(f"{cut_id}:\tref={ref}", file=f)
             print(f"{cut_id}:\thyp={hyp}", file=f)
 
@@ -557,6 +560,7 @@ def write_error_stats(
     test_set_name: str,
     results: List[Tuple[str, str]],
     enable_log: bool = True,
+    compute_CER: bool = False,
     sclite_mode: bool = False,
 ) -> float:
     """Write statistics based on predicted results and reference transcripts.
@@ -585,7 +589,7 @@ def write_error_stats(
           The reference word `SIR` is missing in the predicted
           results (a deletion error).
       results:
-        An iterable of tuples. The first element is the cur_id, the second is
+        An iterable of tuples. The first element is the cut_id, the second is
         the reference transcript and the third element is the predicted result.
       enable_log:
         If True, also print detailed WER to the console.
@@ -602,6 +606,14 @@ def write_error_stats(
     words: Dict[str, List[int]] = defaultdict(lambda: [0, 0, 0, 0, 0])
     num_corr = 0
     ERR = "*"
+
+    if compute_CER:
+        for i, res in enumerate(results):
+            cut_id, ref, hyp = res
+            ref = list("".join(ref))
+            hyp = list("".join(hyp))
+            results[i] = (cut_id, ref, hyp)
+
     for cut_id, ref, hyp in results:
         ali = kaldialign.align(ref, hyp, ERR, sclite_mode=sclite_mode)
         for ref_word, hyp_word in ali:
@@ -1426,7 +1438,10 @@ def measure_gradient_norms(model: nn.Module, norm: str = "l1") -> Dict[str, floa
 
 
 def get_parameter_groups_with_lrs(
-    model: nn.Module, lr: float, include_names: bool = False
+    model: nn.Module,
+    lr: float,
+    include_names: bool = False,
+    freeze_modules: List[str] = [],
 ) -> List[dict]:
     """
     This is for use with the ScaledAdam optimizers (more recent versions that accept lists of
@@ -1450,6 +1465,8 @@ def get_parameter_groups_with_lrs(
          ...   ]
 
     """
+    named_modules = list(model.named_modules())
+
     # flat_lr_scale just contains the lr_scale explicitly specified
     # for each prefix of the name, e.g. 'encoder.layers.3', these need
     # to be multiplied for all prefix of the name of any given parameter.
@@ -1469,6 +1486,15 @@ def get_parameter_groups_with_lrs(
         split_name = name.split(".")
         # caution: as a special case, if the name is '', split_name will be [ '' ].
         prefix = split_name[0]
+        if prefix == "module":  # DDP
+            module_name = split_name[1]
+            if module_name in freeze_modules:
+                logging.info(f"Remove {name} from parameters")
+                continue
+        else:
+            if prefix in freeze_modules:
+                logging.info(f"Remove {name} from parameters")
+                continue
         cur_lr = lr * flat_lr_scale[prefix]
         if prefix != "":
             cur_lr *= flat_lr_scale[""]