docker + ubiqus + pyonmttok

docker/Makefile

@@ -0,0 +1,11 @@
+build_docker:
+	docker build -t icefall/pytorch1.7.1:latest -f ./Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile ./
+
+run_docker:
+	docker run -it --rm --runtime=nvidia \
+		--gpus all \
+		-v /data1:/data1 \
+		-v /data1/merge_all_short/raw/fr_token_list/bpe_unigram5000/bpe.pyonmttok.vocab:/data/vocab \
+		-v /data1/merge_all_short/raw/fr_token_list/bpe_unigram5000/bpe.pyonmttok:/data/bpe.pyonmttok \
+		-v /nas-labs/ASR/valentin_work/icefall:/workspace/icefall \
+		--name val_icefall_3 icefall/pytorch1.7.1:latest bash

docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile

@@ -1,7 +1,10 @@
-FROM pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel
+FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-devel
 
 # install normal source
 
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         g++ \
@@ -26,13 +29,6 @@ RUN apt-get update && \
         rm -rf /var/lib/apt/lists/*
 
 
-RUN mv /opt/conda/lib/libcufft.so.10 /opt/libcufft.so.10.bak && \
-    mv /opt/conda/lib/libcurand.so.10 /opt/libcurand.so.10.bak && \
-    mv /opt/conda/lib/libcublas.so.11 /opt/libcublas.so.11.bak && \
-    mv /opt/conda/lib/libnvrtc.so.11.0 /opt/libnvrtc.so.11.1.bak && \
-    mv /opt/conda/lib/libnvToolsExt.so.1 /opt/libnvToolsExt.so.1.bak && \
-    mv /opt/conda/lib/libcudart.so.11.0 /opt/libcudart.so.11.0.bak
-
 # cmake
 
 RUN wget -P /opt https://cmake.org/files/v3.18/cmake-3.18.0.tar.gz && \
@@ -72,20 +68,26 @@ RUN git clone https://github.com/csukuangfj/kaldifeat.git /opt/kaldifeat && \
     cd -
 
 
+RUN conda install pytorch torchvision torchaudio=0.11 cudatoolkit=11.3 -c pytorch
+
+
+
 #install k2 from source
+# RUN conda install -c k2-fsa -c pytorch -c conda-forge k2 cudatoolkit=11.3 pytorch=1.10.0
 RUN git clone https://github.com/k2-fsa/k2.git /opt/k2 && \
     cd /opt/k2 && \
     python3 setup.py install && \
     cd -
+# RUN pip install k2
 
 # install  lhotse
-RUN pip install git+https://github.com/lhotse-speech/lhotse
+# RUN pip install git+https://github.com/lhotse-speech/lhotse
-#RUN pip install lhotse
+RUN pip install lhotse
 
 # install icefall
-RUN git clone https://github.com/k2-fsa/icefall && \
+# RUN git clone https://github.com/k2-fsa/icefall && \
-    cd icefall && \
+#     cd icefall && \
-    pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+#     pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-    
+
 ENV PYTHONPATH /workspace/icefall:$PYTHONPATH  
 

egs/ubiqus/ASR/transducer_emformer/asr_datamodule.py

@@ -0,0 +1,394 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest
+from lhotse.dataset import (
+    BucketingSampler,
+    CutConcatenate,
+    CutMix,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SingleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures, AudioSamples
+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 UbiqusAsrDataModule:
+    """
+    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
+
+    @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("/data1/merge_all_manifest/raw"),
+            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=300,
+            help="The number of buckets for the BucketingSampler"
+            "(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=True,
+            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. ",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        transforms = []
+        # if self.args.enable_musan:
+        #     logging.info("Enable MUSAN")
+        #     logging.info("About to get Musan cuts")
+        #     cuts_musan = load_manifest(
+        #         self.args.manifest_dir / "cuts_musan.json.gz"
+        #     )
+        #     transforms.append(
+        #         CutMix(
+        #             cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+        #         )
+        #     )
+        # else:
+        #     logging.info("Disable MUSAN")
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        input_transforms = []
+        if self.args.enable_spec_aug:
+            logging.info("Enable SpecAugment")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        train = K2SpeechRecognitionDataset(
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+            input_strategy=AudioSamples(),
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using BucketingSampler.")
+            train_sampler = BucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                bucket_method="equal_duration",
+                drop_last=True,
+            )
+        else:
+            logging.info("Using SingleCutSampler.")
+            train_sampler = SingleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+                input_strategy=AudioSamples(),
+            )
+        valid_sampler = BucketingSampler(
+            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
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        rec = load_manifest(
+            self.args.manifest_dir / "train_sp/recordings.jsonl.gz"
+        )
+        sup = load_manifest(
+            self.args.manifest_dir / "train_sp/supervisions.jsonl.gz"
+        )
+        return CutSet.from_manifests(
+            recordings=rec,
+            supervisions=sup,
+        )
+        return load_manifest(
+            self.args.manifest_dir / "train_sp/supervisions.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        rec = load_manifest(self.args.manifest_dir / "dev/recordings.jsonl.gz")
+        sup = load_manifest(
+            self.args.manifest_dir / "dev/supervisions.jsonl.gz"
+        )
+        return CutSet.from_manifests(
+            recordings=rec,
+            supervisions=sup,
+        )
+        return load_manifest(
+            self.args.manifest_dir / "dev/supervisions.jsonl.gz"
+        )

egs/ubiqus/ASR/transducer_emformer/beam_search.py

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

egs/ubiqus/ASR/transducer_emformer/decode.py

@@ -0,0 +1,549 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: 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.
+"""
+Usage:
+(1) greedy search
+./transducer_emformer/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./transducer_emformer/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) beam search
+./transducer_emformer/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./transducer_emformer/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./transducer_emformer/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./transducer_emformer/exp \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./transducer_emformer/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./transducer_emformer/exp \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+"""
+
+
+import argparse
+import logging
+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 LibriSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--avg-last-n",
+        type=int,
+        default=0,
+        help="""If positive, --epoch and --avg are ignored and it
+        will use the last n checkpoints exp_dir/checkpoint-xxx.pt
+        where xxx is the number of processed batches while
+        saving that checkpoint.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="transducer_emformer/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(
+        "--decoding-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,
+        help="""An interger 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=4,
+        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""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    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`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = model.device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+        )
+        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,
+            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,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            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}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): 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,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[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.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    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 = 100
+    else:
+        log_interval = 2
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((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[List[int], List[int]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.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",
+        "beam_search",
+        "fast_beam_search",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    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> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if params.avg_last_n > 0:
+        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        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 start >= 0:
+                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))
+
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/ubiqus/ASR/transducer_emformer/decoder.py

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

egs/ubiqus/ASR/transducer_emformer/emformer.py

@@ -0,0 +1,271 @@
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
+#
+# 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 math
+import warnings
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+from subsampling import Conv2dSubsampling, VggSubsampling
+from torchaudio.models import Emformer as _Emformer
+
+LOG_EPSILON = math.log(1e-10)
+
+
+def unstack_states(
+    states: List[List[torch.Tensor]],
+) -> List[List[List[torch.Tensor]]]:
+    """Unstack the emformer state corresponding to a batch of utterances
+    into a list of states, were the i-th entry is the state from the i-th
+    utterance in the batch.
+
+    Args:
+      states:
+        A list-of-list of tensors. ``len(states)`` equals to number of
+        layers in the emformer. ``states[i]]`` contains the states for
+        the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape
+        ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)``
+    """
+    batch_size = states[0][0].size(1)
+    num_layers = len(states)
+
+    ans = [None] * batch_size
+    for i in range(batch_size):
+        ans[i] = [[] for _ in range(num_layers)]
+
+    for li, layer in enumerate(states):
+        for s in layer:
+            s_list = s.unbind(dim=1)
+            # We will use stack(dim=1) later in stack_states()
+            for bi, b in enumerate(ans):
+                b[li].append(s_list[bi])
+    return ans
+
+
+def stack_states(
+    state_list: List[List[List[torch.Tensor]]],
+) -> List[List[torch.Tensor]]:
+    """Stack list of emformer states that correspond to separate utterances
+    into a single emformer state so that it can be used as an input for
+    emformer when those utterances are formed into a batch.
+
+    Note:
+      It is the inverse of :func:`unstack_states`.
+
+    Args:
+      state_list:
+        Each element in state_list corresponding to the internal state
+        of the emformer model for a single utterance.
+    Returns:
+      Return a new state corresponding to a batch of utterances.
+      See the input argument of :func:`unstack_states` for the meaning
+      of the returned tensor.
+    """
+    batch_size = len(state_list)
+    ans = []
+    for layer in state_list[0]:
+        # layer is a list of tensors
+        if batch_size > 1:
+            ans.append([[s] for s in layer])
+            # Note: We will stack ans[layer][s][] later to get ans[layer][s]
+        else:
+            ans.append([s.unsqueeze(1) for s in layer])
+
+    for b, states in enumerate(state_list[1:], 1):
+        for li, layer in enumerate(states):
+            for si, s in enumerate(layer):
+                ans[li][si].append(s)
+                if b == batch_size - 1:
+                    ans[li][si] = torch.stack(ans[li][si], dim=1)
+                    # We will use unbind(dim=1) later in unstack_states()
+    return ans
+
+
+class Emformer(EncoderInterface):
+    """This is just a simple wrapper around torchaudio.models.Emformer.
+    We may replace it with our own implementation some time later.
+    """
+
+    def __init__(
+        self,
+        num_features: int,
+        output_dim: int,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int,
+        num_encoder_layers: int,
+        segment_length: int,
+        left_context_length: int,
+        right_context_length: int,
+        max_memory_size: int = 0,
+        dropout: float = 0.1,
+        subsampling_factor: int = 4,
+        vgg_frontend: bool = False,
+    ) -> None:
+        """
+        Args:
+          num_features:
+            The input dimension of the model.
+          output_dim:
+            The output dimension of the model.
+          d_model:
+            Attention dimension.
+          nhead:
+            Number of heads in multi-head attention.
+          dim_feedforward:
+            The output dimension of the feedforward layers in encoder.
+          num_encoder_layers:
+            Number of encoder layers.
+          segment_length:
+            Number of frames per segment before subsampling.
+          left_context_length:
+            Number of frames in the left context before subsampling.
+          right_context_length:
+            Number of frames in the right context before subsampling.
+          max_memory_size:
+            TODO.
+          dropout:
+            Dropout in encoder.
+          subsampling_factor:
+            Number of output frames is num_in_frames // subsampling_factor.
+            Currently, subsampling_factor MUST be 4.
+          vgg_frontend:
+            True to use vgg style frontend for subsampling.
+        """
+        super().__init__()
+
+        self.subsampling_factor = subsampling_factor
+        if subsampling_factor != 4:
+            raise NotImplementedError("Support only 'subsampling_factor=4'.")
+
+        # self.encoder_embed converts the input of shape (N, T, num_features)
+        # to the shape (N, T//subsampling_factor, d_model).
+        # That is, it does two things simultaneously:
+        #   (1) subsampling: T -> T//subsampling_factor
+        #   (2) embedding: num_features -> d_model
+        print(num_features, d_model, output_dim)
+        if vgg_frontend:
+            self.encoder_embed = VggSubsampling(num_features, d_model)
+        else:
+            self.encoder_embed = Conv2dSubsampling(num_features, d_model)
+
+        self.segment_length = segment_length
+        self.right_context_length = right_context_length
+
+        assert right_context_length % subsampling_factor == 0
+        assert segment_length % subsampling_factor == 0
+        assert left_context_length % subsampling_factor == 0
+
+        left_context_length = left_context_length // subsampling_factor
+        right_context_length = right_context_length // subsampling_factor
+        segment_length = segment_length // subsampling_factor
+
+        self.model = _Emformer(
+            input_dim=d_model,
+            num_heads=nhead,
+            ffn_dim=dim_feedforward,
+            num_layers=num_encoder_layers,
+            segment_length=segment_length,
+            dropout=dropout,
+            activation="relu",
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+            weight_init_scale_strategy="depthwise",
+            tanh_on_mem=False,
+            negative_inf=-1e8,
+        )
+
+        self.encoder_output_layer = nn.Sequential(
+            nn.Dropout(p=dropout), nn.Linear(d_model, output_dim)
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            Input features of shape (N, T, C).
+          x_lens:
+            A int32 tensor of shape (N,) containing valid frames in `x` before
+            padding. We have `x.size(1) == x_lens.max()`
+        Returns:
+          Return a tuple containing two tensors:
+
+            - encoder_out, a tensor of shape (N, T', C)
+            - encoder_out_lens, a int32 tensor of shape (N,) containing the
+              valid frames in `encoder_out` before padding
+        """
+        x = nn.functional.pad(
+            x,
+            # (left, right, top, bottom)
+            # left/right are for the channel dimension, i.e., axis 2
+            # top/bottom are for the time dimension, i.e., axis 1
+            (0, 0, 0, self.right_context_length),
+            value=LOG_EPSILON,
+        )  # (N, T, C) -> (N, T+right_context_length, C)
+
+        x = self.encoder_embed(x)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            # Caution: We assume the subsampling factor is 4!
+            x_lens = ((x_lens - 1) // 2 - 1) // 2
+
+        emformer_out, emformer_out_lens = self.model(x, x_lens)
+        logits = self.encoder_output_layer(emformer_out)
+
+        return logits, emformer_out_lens
+
+    def streaming_forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        states: Optional[List[List[torch.Tensor]]] = None,
+    ):
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 2-D tensor of shap containing the number of valid frames for each
+            element in `x` before padding.
+          states:
+            Internal states of the model.
+        Returns:
+          Return a tuple containing 3 tensors:
+            - encoder_out, a 3-D tensor of shape (N, T, C)
+            - encoder_out_lens: a 1-D tensor of shape (N,)
+            - next_state, internal model states for the next chunk
+        """
+        x = self.encoder_embed(x)
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            # Caution: We assume the subsampling factor is 4!
+            x_lens = ((x_lens - 1) // 2 - 1) // 2
+        emformer_out, emformer_out_lens, states = self.model.infer(
+            x, x_lens, states
+        )
+
+        logits = self.encoder_output_layer(emformer_out)
+
+        return logits, emformer_out_lens, states

egs/ubiqus/ASR/transducer_emformer/emformer_raw.py

@@ -0,0 +1,289 @@
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
+#
+# 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 math
+import warnings
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+from subsampling import Conv2dSubsampling, VggSubsampling
+from torchaudio.models import Emformer as _Emformer
+from torchaudio.models.wav2vec2 import components
+
+LOG_EPSILON = math.log(1e-10)
+
+
+def unstack_states(
+    states: List[List[torch.Tensor]],
+) -> List[List[List[torch.Tensor]]]:
+    """Unstack the emformer state corresponding to a batch of utterances
+    into a list of states, were the i-th entry is the state from the i-th
+    utterance in the batch.
+
+    Args:
+      states:
+        A list-of-list of tensors. ``len(states)`` equals to number of
+        layers in the emformer. ``states[i]]`` contains the states for
+        the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape
+        ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)``
+    """
+    batch_size = states[0][0].size(1)
+    num_layers = len(states)
+
+    ans = [None] * batch_size
+    for i in range(batch_size):
+        ans[i] = [[] for _ in range(num_layers)]
+
+    for li, layer in enumerate(states):
+        for s in layer:
+            s_list = s.unbind(dim=1)
+            # We will use stack(dim=1) later in stack_states()
+            for bi, b in enumerate(ans):
+                b[li].append(s_list[bi])
+    return ans
+
+
+def stack_states(
+    state_list: List[List[List[torch.Tensor]]],
+) -> List[List[torch.Tensor]]:
+    """Stack list of emformer states that correspond to separate utterances
+    into a single emformer state so that it can be used as an input for
+    emformer when those utterances are formed into a batch.
+
+    Note:
+      It is the inverse of :func:`unstack_states`.
+
+    Args:
+      state_list:
+        Each element in state_list corresponding to the internal state
+        of the emformer model for a single utterance.
+    Returns:
+      Return a new state corresponding to a batch of utterances.
+      See the input argument of :func:`unstack_states` for the meaning
+      of the returned tensor.
+    """
+    batch_size = len(state_list)
+    ans = []
+    for layer in state_list[0]:
+        # layer is a list of tensors
+        if batch_size > 1:
+            ans.append([[s] for s in layer])
+            # Note: We will stack ans[layer][s][] later to get ans[layer][s]
+        else:
+            ans.append([s.unsqueeze(1) for s in layer])
+
+    for b, states in enumerate(state_list[1:], 1):
+        for li, layer in enumerate(states):
+            for si, s in enumerate(layer):
+                ans[li][si].append(s)
+                if b == batch_size - 1:
+                    ans[li][si] = torch.stack(ans[li][si], dim=1)
+                    # We will use unbind(dim=1) later in unstack_states()
+    return ans
+
+
+class EmformerRaw(EncoderInterface):
+    """This is just a simple wrapper around torchaudio.models.Emformer.
+    We may replace it with our own implementation some time later.
+    """
+
+    def __init__(
+        self,
+        num_features: int,
+        output_dim: int,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int,
+        num_encoder_layers: int,
+        segment_length: int,
+        left_context_length: int,
+        right_context_length: int,
+        max_memory_size: int = 0,
+        dropout: float = 0.1,
+        subsampling_factor: int = 4,
+        vgg_frontend: bool = False,
+    ) -> None:
+        """
+        Args:
+          num_features:
+            The input dimension of the model.
+          output_dim:
+            The output dimension of the model.
+          d_model:
+            Attention dimension.
+          nhead:
+            Number of heads in multi-head attention.
+          dim_feedforward:
+            The output dimension of the feedforward layers in encoder.
+          num_encoder_layers:
+            Number of encoder layers.
+          segment_length:
+            Number of frames per segment before subsampling.
+          left_context_length:
+            Number of frames in the left context before subsampling.
+          right_context_length:
+            Number of frames in the right context before subsampling.
+          max_memory_size:
+            TODO.
+          dropout:
+            Dropout in encoder.
+          subsampling_factor:
+            Number of output frames is num_in_frames // subsampling_factor.
+            Currently, subsampling_factor MUST be 4.
+          vgg_frontend:
+            True to use vgg style frontend for subsampling.
+        """
+        super().__init__()
+
+        self.subsampling_factor = subsampling_factor
+        if subsampling_factor != 4:
+            raise NotImplementedError("Support only 'subsampling_factor=4'.")
+
+        # self.encoder_embed converts the input of shape (N, T, num_features)
+        # to the shape (N, T//subsampling_factor, d_model).
+        # That is, it does two things simultaneously:
+        #   (1) subsampling: T -> T//subsampling_factor
+        #   (2) embedding: num_features -> d_model
+        extractor_conv_layer_config = (
+            [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+        )
+        extractor_mode = "layer_norm"
+        extractor_conv_bias = True
+
+        self.feature_extractor = components._get_feature_extractor(
+            extractor_mode, extractor_conv_layer_config, extractor_conv_bias
+        )
+        print(num_features, d_model, output_dim)
+        # if vgg_frontend:
+        #     self.encoder_embed = VggSubsampling(num_features, d_model)
+        # else:
+        #     self.encoder_embed = Conv2dSubsampling(num_features, d_model)
+
+        self.segment_length = segment_length
+        self.right_context_length = right_context_length
+
+        assert right_context_length % subsampling_factor == 0
+        assert segment_length % subsampling_factor == 0
+        assert left_context_length % subsampling_factor == 0
+
+        left_context_length = left_context_length // subsampling_factor
+        right_context_length = right_context_length // subsampling_factor
+        segment_length = segment_length // subsampling_factor
+
+        print(extractor_conv_layer_config[-1][0])
+        print(dim_feedforward)
+        self.model = _Emformer(
+            input_dim=extractor_conv_layer_config[-1][0],
+            num_heads=nhead,
+            ffn_dim=dim_feedforward,
+            num_layers=num_encoder_layers,
+            segment_length=segment_length,
+            dropout=dropout,
+            activation="relu",
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+            weight_init_scale_strategy="depthwise",
+            tanh_on_mem=False,
+            negative_inf=-1e8,
+        )
+
+        self.encoder_output_layer = nn.Sequential(
+            nn.Dropout(p=dropout), nn.Linear(d_model, output_dim)
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            Input features of shape (N, T, C).
+          x_lens:
+            A int32 tensor of shape (N,) containing valid frames in `x` before
+            padding. We have `x.size(1) == x_lens.max()`
+        Returns:
+          Return a tuple containing two tensors:
+
+            - encoder_out, a tensor of shape (N, T', C)
+            - encoder_out_lens, a int32 tensor of shape (N,) containing the
+              valid frames in `encoder_out` before padding
+        """
+        print(x.shape)
+        x = nn.functional.pad(
+            x,
+            # (left, right, top, bottom)
+            # left/right are for the channel dimension, i.e., axis 2
+            # top/bottom are for the time dimension, i.e., axis 1
+            (0, 0, 0, self.right_context_length),
+            value=LOG_EPSILON,
+        )  # (N, T, C) -> (N, T+right_context_length, C)
+
+        print(x.shape, x_lens)
+        x, x_lens = self.feature_extractor(x.squeeze(-1), x_lens)
+        x_lens -= 1
+        print(x.shape, x_lens)
+
+        # with warnings.catch_warnings():
+        #     warnings.simplefilter("ignore")
+        #     # Caution: We assume the subsampling factor is 4!
+        #     x_lens = ((x_lens - 1) // 2 - 1) // 2
+
+        emformer_out, emformer_out_lens = self.model(x, x_lens)
+        logits = self.encoder_output_layer(emformer_out)
+
+        return logits, emformer_out_lens
+
+    def streaming_forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        states: Optional[List[List[torch.Tensor]]] = None,
+    ):
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 2-D tensor of shap containing the number of valid frames for each
+            element in `x` before padding.
+          states:
+            Internal states of the model.
+        Returns:
+          Return a tuple containing 3 tensors:
+            - encoder_out, a 3-D tensor of shape (N, T, C)
+            - encoder_out_lens: a 1-D tensor of shape (N,)
+            - next_state, internal model states for the next chunk
+        """
+        x, x_lens = self.feature_extractor(x, x_lens)
+        x_lens -= 1
+        # Sure about that ?
+
+        # with warnings.catch_warnings():
+        #     warnings.simplefilter("ignore")
+        #     # Caution: We assume the subsampling factor is 4!
+        #     x_lens = ((x_lens - 1) // 2 - 1) // 2
+        emformer_out, emformer_out_lens, states = self.model.infer(
+            x, x_lens, states
+        )
+
+        logits = self.encoder_output_layer(emformer_out)
+
+        return logits, emformer_out_lens, states

egs/ubiqus/ASR/transducer_emformer/encoder_interface.py

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

egs/ubiqus/ASR/transducer_emformer/export.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script converts several saved checkpoints
+# to a single one using model averaging.
+"""
+Usage:
+./transducer_emformer/export.py \
+  --exp-dir ./transducer_emformer/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 20 \
+  --avg 10
+
+It will generate a file exp_dir/pretrained.pt
+
+To use the generated file with `transducer_emformer/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./transducer_emformer/decode.py \
+        --exp-dir ./transducer_emformer/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 1000 \
+        --bpe-model data/lang_bpe_500/bpe.model
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    assert args.jit is False, "Support torchscript will be added later"
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    if 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 start >= 0:
+                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))
+
+    model.eval()
+
+    model.to("cpu")
+    model.eval()
+
+    if params.jit:
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        filename = params.exp_dir / "cpu_jit.pt"
+        model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.script")
+        # 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/ubiqus/ASR/transducer_emformer/joiner.py

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

egs/ubiqus/ASR/transducer_emformer/model.py

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

egs/ubiqus/ASR/transducer_emformer/noam.py

@@ -0,0 +1,104 @@
+# Copyright    2021 University of Chinese Academy of Sciences (author: Han Zhu)
+#
+# 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
+
+
+class Noam(object):
+    """
+    Implements Noam optimizer.
+
+    Proposed in
+    "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
+
+    Modified from
+    https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py  # noqa
+
+    Args:
+      params:
+        iterable of parameters to optimize or dicts defining parameter groups
+      model_size:
+        attention dimension of the transformer model
+      factor:
+        learning rate factor
+      warm_step:
+        warmup steps
+    """
+
+    def __init__(
+        self,
+        params,
+        model_size: int = 256,
+        factor: float = 10.0,
+        warm_step: int = 25000,
+        weight_decay=0,
+    ) -> None:
+        """Construct an Noam object."""
+        self.optimizer = torch.optim.Adam(
+            params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay
+        )
+        self._step = 0
+        self.warmup = warm_step
+        self.factor = factor
+        self.model_size = model_size
+        self._rate = 0
+
+    @property
+    def param_groups(self):
+        """Return param_groups."""
+        return self.optimizer.param_groups
+
+    def step(self):
+        """Update parameters and rate."""
+        self._step += 1
+        rate = self.rate()
+        for p in self.optimizer.param_groups:
+            p["lr"] = rate
+        self._rate = rate
+        self.optimizer.step()
+
+    def rate(self, step=None):
+        """Implement `lrate` above."""
+        if step is None:
+            step = self._step
+        return (
+            self.factor
+            * self.model_size ** (-0.5)
+            * min(step ** (-0.5), step * self.warmup ** (-1.5))
+        )
+
+    def zero_grad(self):
+        """Reset gradient."""
+        self.optimizer.zero_grad()
+
+    def state_dict(self):
+        """Return state_dict."""
+        return {
+            "_step": self._step,
+            "warmup": self.warmup,
+            "factor": self.factor,
+            "model_size": self.model_size,
+            "_rate": self._rate,
+            "optimizer": self.optimizer.state_dict(),
+        }
+
+    def load_state_dict(self, state_dict):
+        """Load state_dict."""
+        for key, value in state_dict.items():
+            if key == "optimizer":
+                self.optimizer.load_state_dict(state_dict["optimizer"])
+            else:
+                setattr(self, key, value)

egs/ubiqus/ASR/transducer_emformer/streaming_decode.py

@@ -0,0 +1,748 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: 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 argparse
+import logging
+import warnings
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import k2
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from beam_search import Hypothesis, HypothesisList, get_hyps_shape
+from emformer import LOG_EPSILON, stack_states, unstack_states
+from streaming_feature_extractor import FeatureExtractionStream
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import AttributeDict, setup_logger
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--avg-last-n",
+        type=int,
+        default=0,
+        help="""If positive, --epoch and --avg are ignored and it
+        will use the last n checkpoints exp_dir/checkpoint-xxx.pt
+        where xxx is the number of processed batches while
+        saving that checkpoint.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="transducer_emformer/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(
+        "--decoding-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,
+        help="""An interger 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=4,
+        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""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--sampling-rate",
+        type=float,
+        default=16000,
+        help="Sample rate of the audio",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+class StreamingAudioSamples(object):
+    """This class takes as input a list of audio samples and returns
+    them in a streaming fashion.
+    """
+
+    def __init__(self, samples: List[torch.Tensor]) -> None:
+        """
+        Args:
+          samples:
+            A list of audio samples. Each entry is a 1-D tensor of dtype
+            torch.float32, containing the audio samples of an utterance.
+        """
+        self.samples = samples
+        self.cur_indexes = [0] * len(self.samples)
+
+    @property
+    def done(self) -> bool:
+        """Return True if all samples have been processed.
+        Return False otherwise.
+        """
+        for i, samples in zip(self.cur_indexes, self.samples):
+            if i < samples.numel():
+                return False
+        return True
+
+    def get_next(self) -> List[torch.Tensor]:
+        """Return a list of audio samples. Each entry may have different
+        lengths. It is OK if an entry contains no samples at all, which
+        means it reaches the end of the utterance.
+        """
+        ans = []
+
+        num = [1024] * len(self.samples)
+
+        for i in range(len(self.samples)):
+            start = self.cur_indexes[i]
+            end = start + num[i]
+            self.cur_indexes[i] = end
+
+            s = self.samples[i][start:end]
+            ans.append(s)
+
+        return ans
+
+
+class StreamList(object):
+    def __init__(
+        self,
+        batch_size: int,
+        context_size: int,
+        decoding_method: str,
+    ):
+        """
+        Args:
+          batch_size:
+            Size of this batch.
+          context_size:
+            Context size of the RNN-T decoder model.
+          decoding_method:
+            Decoding method. The possible values are:
+              - greedy_search
+              - modified_beam_search
+        """
+
+        self.streams = [
+            FeatureExtractionStream(
+                context_size=context_size, decoding_method=decoding_method
+            )
+            for _ in range(batch_size)
+        ]
+
+    @property
+    def done(self) -> bool:
+        """Return True if all streams have reached end of utterance.
+        That is, no more audio samples are available for all utterances.
+        """
+        return all(stream.done for stream in self.streams)
+
+    def accept_waveform(
+        self,
+        audio_samples: List[torch.Tensor],
+        sampling_rate: float,
+    ):
+        """Feed audio samples to each stream.
+        Args:
+          audio_samples:
+            A list of 1-D tensors containing the audio samples for each
+            utterance in the batch. If an entry is empty, it means
+            end-of-utterance has been reached.
+          sampling_rate:
+            Sampling rate of the given audio samples.
+        """
+        assert len(audio_samples) == len(self.streams)
+        for stream, samples in zip(self.streams, audio_samples):
+
+            if stream.done:
+                assert samples.numel() == 0
+                continue
+
+            stream.accept_waveform(
+                sampling_rate=sampling_rate,
+                waveform=samples,
+            )
+
+            if samples.numel() == 0:
+                stream.input_finished()
+
+    def build_batch(
+        self,
+        chunk_length: int,
+        segment_length: int,
+    ) -> Tuple[Optional[torch.Tensor], Optional[List[FeatureExtractionStream]]]:
+        """
+        Args:
+          chunk_length:
+            Number of frames for each chunk. It equals to
+            ``segment_length + right_context_length``.
+          segment_length
+            Number of frames for each segment.
+        Returns:
+          Return a tuple containing:
+            - features, a 3-D tensor of shape ``(num_active_streams, T, C)``
+            - active_streams, a list of active streams. We say a stream is
+              active when it has enough feature frames to be fed into the
+              encoder model.
+        """
+        feature_list = []
+        stream_list = []
+        for stream in self.streams:
+            if len(stream.feature_frames) >= chunk_length:
+                # this_chunk is a list of tensors, each of which
+                # has a shape (1, feature_dim)
+                chunk = stream.feature_frames[:chunk_length]
+                stream.feature_frames = stream.feature_frames[segment_length:]
+                features = torch.cat(chunk, dim=0)
+                feature_list.append(features)
+                stream_list.append(stream)
+            elif stream.done and len(stream.feature_frames) > 0:
+                chunk = stream.feature_frames[:chunk_length]
+                stream.feature_frames = []
+                features = torch.cat(chunk, dim=0)
+                features = torch.nn.functional.pad(
+                    features,
+                    (0, 0, 0, chunk_length - features.size(0)),
+                    mode="constant",
+                    value=LOG_EPSILON,
+                )
+                feature_list.append(features)
+                stream_list.append(stream)
+
+        if len(feature_list) == 0:
+            return None, None
+
+        features = torch.stack(feature_list, dim=0)
+        return features, stream_list
+
+
+def greedy_search(
+    model: nn.Module,
+    streams: List[FeatureExtractionStream],
+    encoder_out: torch.Tensor,
+    sp: spm.SentencePieceProcessor,
+):
+    """
+    Args:
+      model:
+        The RNN-T model.
+      streams:
+        A list of stream objects.
+      encoder_out:
+        A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of
+        the encoder model.
+      sp:
+        The BPE model.
+    """
+    assert len(streams) == encoder_out.size(0)
+    assert encoder_out.ndim == 3
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+    device = model.device
+    T = encoder_out.size(1)
+
+    if streams[0].decoder_out is None:
+        for stream in streams:
+            stream.hyp = [blank_id] * context_size
+        decoder_input = torch.tensor(
+            [stream.hyp[-context_size:] for stream in streams],
+            device=device,
+            dtype=torch.int64,
+        )
+        decoder_out = model.decoder(decoder_input, need_pad=False).squeeze(1)
+        # decoder_out is of shape (N, decoder_out_dim)
+    else:
+        decoder_out = torch.stack(
+            [stream.decoder_out for stream in streams],
+            dim=0,
+        )
+
+    for t in range(T):
+        current_encoder_out = encoder_out[:, t]
+        # current_encoder_out's shape: (batch_size, encoder_out_dim)
+
+        logits = model.joiner(current_encoder_out, decoder_out)
+        # logits'shape (batch_size,  vocab_size)
+
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                streams[i].hyp.append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = torch.tensor(
+                [stream.hyp[-context_size:] for stream in streams],
+                device=device,
+                dtype=torch.int64,
+            )
+            decoder_out = model.decoder(
+                decoder_input,
+                need_pad=False,
+            ).squeeze(1)
+
+            for k, stream in enumerate(streams):
+                result = sp.decode(stream.decoding_result())
+                logging.info(f"Partial result {k}:\n{result}")
+
+    decoder_out_list = decoder_out.unbind(dim=0)
+    for i, d in enumerate(decoder_out_list):
+        streams[i].decoder_out = d
+
+
+def modified_beam_search(
+    model: nn.Module,
+    streams: List[FeatureExtractionStream],
+    encoder_out: torch.Tensor,
+    sp: spm.SentencePieceProcessor,
+    beam: int = 4,
+):
+    """
+    Args:
+      model:
+        The RNN-T model.
+      streams:
+        A list of stream objects.
+      encoder_out:
+        A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of
+        the encoder model.
+      sp:
+        The BPE model.
+      beam:
+        Number of active paths during the beam search.
+    """
+    assert encoder_out.ndim == 3, encoder_out.shape
+    assert len(streams) == encoder_out.size(0)
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+    device = model.device
+    batch_size = len(streams)
+    T = encoder_out.size(1)
+
+    for stream in streams:
+        if len(stream.hyps) == 0:
+            stream.hyps.add(
+                Hypothesis(
+                    ys=[blank_id] * context_size,
+                    log_prob=torch.zeros(1, dtype=torch.float32, device=device),
+                )
+            )
+    B = [stream.hyps for stream in streams]
+    for t in range(T):
+        current_encoder_out = encoder_out[:, t]
+        # current_encoder_out's shape: (batch_size, encoder_out_dim)
+
+        hyps_shape = get_hyps_shape(B).to(device)
+
+        A = [list(b) for b in B]
+        B = [HypothesisList() for _ in range(batch_size)]
+
+        ys_log_probs = torch.stack(
+            [hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0
+        )  # (num_hyps, 1)
+
+        decoder_input = torch.tensor(
+            [hyp.ys[-context_size:] for hyps in A for hyp in hyps],
+            device=device,
+            dtype=torch.int64,
+        )  # (num_hyps, context_size)
+
+        decoder_out = model.decoder(decoder_input, need_pad=False).squeeze(1)
+        # decoder_out is of shape (num_hyps, decoder_output_dim)
+
+        # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
+        # as index, so we use `to(torch.int64)` below.
+        current_encoder_out = torch.index_select(
+            current_encoder_out,
+            dim=0,
+            index=hyps_shape.row_ids(1).to(torch.int64),
+        )  # (num_hyps, encoder_out_dim)
+
+        logits = model.joiner(current_encoder_out, decoder_out)
+        # logits is of shape (num_hyps, vocab_size)
+
+        log_probs = logits.log_softmax(dim=-1)  # (num_hyps, vocab_size)
+
+        log_probs.add_(ys_log_probs)
+
+        vocab_size = log_probs.size(-1)
+
+        log_probs = log_probs.reshape(-1)
+
+        row_splits = hyps_shape.row_splits(1) * vocab_size
+        log_probs_shape = k2.ragged.create_ragged_shape2(
+            row_splits=row_splits, cached_tot_size=log_probs.numel()
+        )
+        ragged_log_probs = k2.RaggedTensor(
+            shape=log_probs_shape, value=log_probs
+        )
+
+        for i in range(batch_size):
+            topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
+                topk_token_indexes = (topk_indexes % vocab_size).tolist()
+
+            for k in range(len(topk_hyp_indexes)):
+                hyp_idx = topk_hyp_indexes[k]
+                hyp = A[i][hyp_idx]
+
+                new_ys = hyp.ys[:]
+                new_token = topk_token_indexes[k]
+                if new_token != blank_id:
+                    new_ys.append(new_token)
+
+                new_log_prob = topk_log_probs[k]
+                new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
+                B[i].add(new_hyp)
+
+            streams[i].hyps = B[i]
+            result = sp.decode(streams[i].decoding_result())
+            logging.info(f"Partial result {i}:\n{result}")
+
+
+def process_features(
+    model: nn.Module,
+    features: torch.Tensor,
+    streams: List[FeatureExtractionStream],
+    params: AttributeDict,
+    sp: spm.SentencePieceProcessor,
+) -> None:
+    """Process features for each stream in parallel.
+
+    Args:
+      model:
+        The RNN-T model.
+      features:
+        A 3-D tensor of shape (N, T, C).
+      streams:
+        A list of streams of size (N,).
+      params:
+        It is the return value of :func:`get_params`.
+      sp:
+        The BPE model.
+    """
+    assert features.ndim == 3
+    assert features.size(0) == len(streams)
+    batch_size = features.size(0)
+
+    device = model.device
+    features = features.to(device)
+    feature_lens = torch.full(
+        (batch_size,),
+        fill_value=features.size(1),
+        device=device,
+    )
+
+    # Caution: It has a limitation as it assumes that
+    # if one of the stream has an empty state, then all other
+    # streams also have empty states.
+    if streams[0].states is None:
+        states = None
+    else:
+        state_list = [stream.states for stream in streams]
+        states = stack_states(state_list)
+
+    (encoder_out, encoder_out_lens, states,) = model.encoder.streaming_forward(
+        features,
+        feature_lens,
+        states,
+    )
+    state_list = unstack_states(states)
+    for i, s in enumerate(state_list):
+        streams[i].states = s
+
+    if params.decoding_method == "greedy_search":
+        greedy_search(
+            model=model,
+            streams=streams,
+            encoder_out=encoder_out,
+            sp=sp,
+        )
+    elif params.decoding_method == "modified_beam_search":
+        modified_beam_search(
+            model=model,
+            streams=streams,
+            encoder_out=encoder_out,
+            sp=sp,
+            beam=params.beam_size,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported decoding method: {params.decoding_method}"
+        )
+
+
+def decode_batch(
+    batched_samples: List[torch.Tensor],
+    model: nn.Module,
+    params: AttributeDict,
+    sp: spm.SentencePieceProcessor,
+) -> List[str]:
+    """
+    Args:
+      batched_samples:
+        A list of 1-D tensors containing the audio samples of each utterance.
+      model:
+        The RNN-T model.
+      params:
+        It is the return value of :func:`get_params`.
+      sp:
+        The BPE model.
+    """
+    # number of frames before subsampling
+    segment_length = model.encoder.segment_length
+
+    right_context_length = model.encoder.right_context_length
+
+    # We add 3 here since the subsampling method is using
+    # ((len - 1) // 2 - 1) // 2)
+    chunk_length = (segment_length + 3) + right_context_length
+
+    batch_size = len(batched_samples)
+    streaming_audio_samples = StreamingAudioSamples(batched_samples)
+
+    stream_list = StreamList(
+        batch_size=batch_size,
+        context_size=params.context_size,
+        decoding_method=params.decoding_method,
+    )
+
+    while not streaming_audio_samples.done:
+        samples = streaming_audio_samples.get_next()
+        stream_list.accept_waveform(
+            audio_samples=samples,
+            sampling_rate=params.sampling_rate,
+        )
+        features, active_streams = stream_list.build_batch(
+            chunk_length=chunk_length,
+            segment_length=segment_length,
+        )
+        if features is not None:
+            process_features(
+                model=model,
+                features=features,
+                streams=active_streams,
+                params=params,
+                sp=sp,
+            )
+    results = []
+    for stream in stream_list.streams:
+        text = sp.decode(stream.decoding_result())
+        results.append(text)
+    return results
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    # Note: params.decoding_method is currently not used.
+    params.res_dir = params.exp_dir / "streaming" / params.decoding_method
+
+    setup_logger(f"{params.res_dir}/log-streaming-decode")
+    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> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    params.device = device
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if params.avg_last_n > 0:
+        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        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 start >= 0:
+                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))
+
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+
+    batch_size = 3
+
+    ground_truth = []
+    batched_samples = []
+    for num, cut in enumerate(test_clean_cuts):
+        audio: np.ndarray = cut.load_audio()
+        # audio.shape: (1, num_samples)
+        assert len(audio.shape) == 2
+        assert audio.shape[0] == 1, "Should be single channel"
+        assert audio.dtype == np.float32, audio.dtype
+
+        # The trained model is using normalized samples
+        assert audio.max() <= 1, "Should be normalized to [-1, 1])"
+
+        samples = torch.from_numpy(audio).squeeze(0)
+
+        batched_samples.append(samples)
+        ground_truth.append(cut.supervisions[0].text)
+
+        if len(batched_samples) >= batch_size:
+            decoded_results = decode_batch(
+                batched_samples=batched_samples,
+                model=model,
+                params=params,
+                sp=sp,
+            )
+            s = "\n"
+            for i, (hyp, ref) in enumerate(zip(decoded_results, ground_truth)):
+                s += f"hyp {i}:\n{hyp}\n"
+                s += f"ref {i}:\n{ref}\n\n"
+            logging.info(s)
+            batched_samples = []
+            ground_truth = []
+            # break after processing the first batch for test purposes
+            break
+
+
+if __name__ == "__main__":
+    torch.manual_seed(20220410)
+    main()

egs/ubiqus/ASR/transducer_emformer/streaming_feature_extractor.py

@@ -0,0 +1,132 @@
+# 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.
+
+from typing import List, Optional
+
+import torch
+from beam_search import HypothesisList
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+
+
+def _create_streaming_feature_extractor() -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+class FeatureExtractionStream(object):
+    def __init__(self, context_size: int, decoding_method: str) -> None:
+        """
+        Args:
+          context_size:
+            Context size of the RNN-T decoder model.
+          decoding_method:
+            Decoding method. The possible values are:
+              - greedy_search
+              - modified_beam_search
+        """
+        self.feature_extractor = _create_streaming_feature_extractor()
+        # It contains a list of 1-D tensors representing the feature frames.
+        self.feature_frames: List[torch.Tensor] = []
+        self.num_fetched_frames = 0
+        # After calling `self.input_finished()`, we set this flag to True
+        self._done = False
+
+        # For the emformer model, it contains the states of each
+        # encoder layer.
+        self.states: Optional[List[List[torch.Tensor]]] = None
+
+        # It use different attributes for different decoding methods.
+        self.context_size = context_size
+        self.decoding_method = decoding_method
+        if decoding_method == "greedy_search":
+            self.hyp: Optional[List[int]] = None
+            self.decoder_out: Optional[torch.Tensor] = None
+        elif decoding_method == "modified_beam_search":
+            self.hyps = HypothesisList()
+        else:
+            raise ValueError(f"Unsupported decoding method: {decoding_method}")
+
+    def accept_waveform(
+        self,
+        sampling_rate: float,
+        waveform: torch.Tensor,
+    ) -> None:
+        """Feed audio samples to the feature extractor and compute features
+        if there are enough samples available.
+
+        Caution:
+          The range of the audio samples should match the one used in the
+          training. That is, if you use the range [-1, 1] in the training, then
+          the input audio samples should also be normalized to [-1, 1].
+
+        Args
+          sampling_rate:
+            The sampling rate of the input audio samples. It is used for sanity
+            check to ensure that the input sampling rate equals to the one
+            used in the extractor. If they are not equal, then no resampling
+            will be performed; instead an error will be thrown.
+          waveform:
+            A 1-D torch tensor of dtype torch.float32 containing audio samples.
+            It should be on CPU.
+        """
+        self.feature_extractor.accept_waveform(
+            sampling_rate=sampling_rate,
+            waveform=waveform,
+        )
+        self._fetch_frames()
+
+    def input_finished(self) -> None:
+        """Signal that no more audio samples available and the feature
+        extractor should flush the buffered samples to compute frames.
+        """
+        self.feature_extractor.input_finished()
+        self._fetch_frames()
+        self._done = True
+
+    @property
+    def done(self) -> bool:
+        """Return True if `self.input_finished()` has been invoked"""
+        return self._done
+
+    def _fetch_frames(self) -> None:
+        """Fetch frames from the feature extractor"""
+        while self.num_fetched_frames < self.feature_extractor.num_frames_ready:
+            frame = self.feature_extractor.get_frame(self.num_fetched_frames)
+            self.feature_frames.append(frame)
+            self.num_fetched_frames += 1
+
+    def decoding_result(self) -> List[int]:
+        """Obtain current decoding result."""
+        if self.decoding_method == "greedy_search":
+            return self.hyp[self.context_size :]
+        else:
+            assert self.decoding_method == "modified_beam_search"
+            best_hyp = self.hyps.get_most_probable(length_norm=True)
+            return best_hyp.ys[self.context_size :]

egs/ubiqus/ASR/transducer_emformer/subsampling.py

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

egs/ubiqus/ASR/transducer_emformer/test_emformer.py

@@ -0,0 +1,107 @@
+#!/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 ./transducer_emformer/test_emformer.py
+"""
+
+import warnings
+
+import torch
+from emformer import Emformer, stack_states, unstack_states
+
+
+def test_emformer():
+    N = 3
+    T = 300
+    C = 80
+
+    output_dim = 500
+
+    encoder = Emformer(
+        num_features=C,
+        output_dim=output_dim,
+        d_model=512,
+        nhead=8,
+        dim_feedforward=2048,
+        num_encoder_layers=20,
+        segment_length=16,
+        left_context_length=120,
+        right_context_length=4,
+        vgg_frontend=False,
+    )
+
+    x = torch.rand(N, T, C)
+    x_lens = torch.randint(100, T, (N,))
+    x_lens[0] = T
+
+    y, y_lens = encoder(x, x_lens)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        assert (y_lens == ((x_lens - 1) // 2 - 1) // 2).all()
+    assert x.size(0) == x.size(0)
+    assert y.size(1) == max(y_lens)
+    assert y.size(2) == output_dim
+
+    num_param = sum([p.numel() for p in encoder.parameters()])
+    print(f"Number of encoder parameters: {num_param}")
+
+
+def test_emformer_streaming_forward():
+    N = 3
+    C = 80
+
+    output_dim = 500
+
+    encoder = Emformer(
+        num_features=C,
+        output_dim=output_dim,
+        d_model=512,
+        nhead=8,
+        dim_feedforward=2048,
+        num_encoder_layers=20,
+        segment_length=16,
+        left_context_length=120,
+        right_context_length=4,
+        vgg_frontend=False,
+    )
+
+    x = torch.rand(N, 23, C)
+    x_lens = torch.full((N,), 23)
+    y, y_lens, states = encoder.streaming_forward(x=x, x_lens=x_lens)
+
+    state_list = unstack_states(states)
+    states2 = stack_states(state_list)
+
+    for ss, ss2 in zip(states, states2):
+        for s, s2 in zip(ss, ss2):
+            assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}"
+
+
+@torch.no_grad()
+def main():
+    #  test_emformer()
+    test_emformer_streaming_forward()
+
+
+if __name__ == "__main__":
+    torch.manual_seed(20220329)
+    main()

egs/ubiqus/ASR/transducer_emformer/test_streaming_feature_extractor.py

@@ -0,0 +1,53 @@
+#!/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 ./transducer_emformer/test_streaming_feature_extractor.py
+"""
+
+import torch
+from streaming_feature_extractor import FeatureExtractionStream
+
+
+def test_streaming_feature_extractor():
+    stream = FeatureExtractionStream(context_size=2, blank_id=0)
+    samples = torch.rand(16000)
+    start = 0
+    while True:
+        n = torch.randint(50, 500, (1,)).item()
+        end = start + n
+        this_chunk = samples[start:end]
+        start = end
+
+        if len(this_chunk) == 0:
+            break
+        stream.accept_waveform(sampling_rate=16000, waveform=this_chunk)
+        print(len(stream.feature_frames))
+    stream.input_finished()
+    print(len(stream.feature_frames))
+
+
+def main():
+    test_streaming_feature_extractor()
+
+
+if __name__ == "__main__":
+    main()

egs/ubiqus/ASR/transducer_emformer/tokenizer.py

@@ -0,0 +1,57 @@
+# sp = spm.SentencePieceProcessor()
+# sp.load(params.bpe_model)
+
+# # <blk> is defined in local/train_bpe_model.py
+# params.blank_id = sp.piece_to_id("<blk>")
+# params.vocab_size = sp.get_piece_size()
+# sp.encode(texts, out_type=int)
+from typing import List
+
+
+import pyonmttok
+
+
+class PyonmttokProcessor:
+    def __init__(self):
+        self.tok = None
+
+    def load(self, path: str) -> None:
+        args = {
+            "mode": "aggressive",
+            "joiner_annotate": True,
+            "preserve_placeholders": True,
+            "case_markup": True,
+            "soft_case_regions": True,
+            "preserve_segmented_tokens": True,
+        }
+        self.tok = pyonmttok.Tokenizer(
+            **args,
+            bpe_model_path="/data/bpe.pyonmttok",
+            vocabulary_path="/data/vocab"
+        )
+        self.vocab = []
+        self.reverse_vocab = dict()
+        with open("/data/vocab", "r") as f:
+            for i, l in enumerate(f):
+                word = l.rstrip("\n")
+                self.vocab.append(word)
+                self.reverse_vocab[word] = i
+
+    def piece_to_id(self, token: str) -> int:
+        return self.reverse_vocab.get(token, self.reverse_vocab["<unk>"])
+
+    def encode(self, texts: List[str], out_type: type = int) -> List[int]:
+        batch_tokens = [self.tok.tokenize(text)[0] for text in texts]
+        # print(texts)
+        # print(batch_tokens)
+        if out_type == str:
+            return batch_tokens
+        elif out_type == int:
+            return [
+                [self.piece_to_id(token) for token in tokens]
+                for tokens in batch_tokens
+            ]
+        raise ValueError
+
+    def get_piece_size(self) -> int:
+        return len(self.vocab)