Merge 74200583be92b96726de086023db2a0f45a1d401 into 8eff7a4642da89bace5ea60ea9c5cae7ef5043b0

egs/librispeech/ASR/conv_emformer_transducer_stateless/asr_datamodule.py

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+../pruned_transducer_stateless2/asr_datamodule.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/beam_search.py

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+../pruned_transducer_stateless2/beam_search.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py

@@ -0,0 +1,634 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/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,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=False,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless4/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 integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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 = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    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,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            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
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0
+            start = params.epoch - params.avg
+            assert start >= 1
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    if 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/librispeech/ASR/conv_emformer_transducer_stateless/decoder.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/decoder.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/encoder_interface.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/encoder_interface.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/joiner.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/joiner.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/model.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/model.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/optim.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/optim.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/scaling.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/scaling.py

egs/librispeech/ASR/conv_emformer_transducer_stateless/test_emformer.py

@@ -0,0 +1,585 @@
+import torch
+
+
+def test_rel_positional_encoding():
+    from emformer import RelPositionalEncoding
+
+    D = 256
+    pos_enc = RelPositionalEncoding(D, dropout=0.1)
+    pos_len = 100
+    neg_len = 100
+    x = torch.randn(2, D)
+    x, pos_emb = pos_enc(x, pos_len, neg_len)
+    assert pos_emb.shape == (pos_len + neg_len - 1, D)
+
+
+def test_emformer_attention_forward():
+    from emformer import EmformerAttention
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+    attention = EmformerAttention(
+        embed_dim=D,
+        nhead=8,
+        chunk_length=chunk_length,
+        right_context_length=right_context_length,
+    )
+
+    for use_memory in [True, False]:
+        if use_memory:
+            S = num_chunks
+            M = S - 1
+        else:
+            S, M = 0, 0
+
+        Q, KV = R + U + S, M + R + U
+        utterance = torch.randn(U, B, D)
+        lengths = torch.randint(1, U + 1, (B,))
+        lengths[0] = U
+        right_context = torch.randn(R, B, D)
+        summary = torch.randn(S, B, D)
+        memory = torch.randn(M, B, D)
+        attention_mask = torch.rand(Q, KV) >= 0.5
+        PE = 2 * U + right_context_length - 1
+        pos_emb = torch.randn(PE, D)
+
+        (
+            output_right_context_utterance,
+            output_memory,
+            probs_memory,
+            probs_frames,
+        ) = attention(
+            utterance,
+            lengths,
+            right_context,
+            summary,
+            memory,
+            attention_mask,
+            pos_emb,
+        )
+        assert output_right_context_utterance.shape == (R + U, B, D)
+        assert output_memory.shape == (M, B, D)
+        assert probs_memory.shape == (B, U)
+        assert probs_frames.shape == (B, U)
+
+
+def test_emformer_attention_infer():
+    from emformer import EmformerAttention
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 1
+    U = chunk_length * num_chunks
+    R = right_context_length * num_chunks
+    L = 3
+    attention = EmformerAttention(
+        embed_dim=D,
+        nhead=8,
+        chunk_length=chunk_length,
+        right_context_length=right_context_length,
+    )
+
+    for use_memory in [True, False]:
+        if use_memory:
+            S, M = 1, 3
+        else:
+            S, M = 0, 0
+
+        utterance = torch.randn(U, B, D)
+        lengths = torch.randint(1, U + 1, (B,))
+        lengths[0] = U
+        right_context = torch.randn(R, B, D)
+        summary = torch.randn(S, B, D)
+        memory = torch.randn(M, B, D)
+        left_context_key = torch.randn(L, B, D)
+        left_context_val = torch.randn(L, B, D)
+        PE = (
+            2 * U
+            + right_context_length
+            - 1
+            + (M * chunk_length if M > 0 else L)
+        )
+        pos_emb = torch.randn(PE, D)
+
+        (
+            output_right_context_utterance,
+            output_memory,
+            next_key,
+            next_val,
+        ) = attention.infer(
+            utterance,
+            lengths,
+            right_context,
+            summary,
+            memory,
+            left_context_key,
+            left_context_val,
+            pos_emb,
+        )
+        assert output_right_context_utterance.shape == (R + U, B, D)
+        assert output_memory.shape == (S, B, D)
+        assert next_key.shape == (L + U, B, D)
+        assert next_val.shape == (L + U, B, D)
+
+
+def test_convolution_module_forward():
+    from emformer import ConvolutionModule
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+    kernel_size = 31
+    conv_module = ConvolutionModule(
+        chunk_length,
+        right_context_length,
+        D,
+        kernel_size,
+    )
+
+    utterance = torch.randn(U, B, D)
+    right_context = torch.randn(R, B, D)
+    cache = torch.randn(B, D, kernel_size - 1)
+
+    utterance, right_context, new_cache = conv_module(
+        utterance, right_context, cache
+    )
+    assert utterance.shape == (U, B, D)
+    assert right_context.shape == (R, B, D)
+    assert new_cache.shape == (B, D, kernel_size - 1)
+
+
+def test_convolution_module_infer():
+    from emformer import ConvolutionModule
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 1
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+    kernel_size = 31
+    conv_module = ConvolutionModule(
+        chunk_length,
+        right_context_length,
+        D,
+        kernel_size,
+    )
+
+    utterance = torch.randn(U, B, D)
+    right_context = torch.randn(R, B, D)
+    cache = torch.randn(B, D, kernel_size - 1)
+
+    utterance, right_context, new_cache = conv_module.infer(
+        utterance, right_context, cache
+    )
+    assert utterance.shape == (U, B, D)
+    assert right_context.shape == (R, B, D)
+    assert new_cache.shape == (B, D, kernel_size - 1)
+
+
+def test_emformer_encoder_layer_forward():
+    from emformer import EmformerEncoderLayer
+
+    B, D = 2, 256
+    chunk_length = 8
+    right_context_length = 2
+    left_context_length = 8
+    kernel_size = 31
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+
+    for use_memory in [True, False]:
+        if use_memory:
+            S = num_chunks
+            M = S - 1
+        else:
+            S, M = 0, 0
+
+        layer = EmformerEncoderLayer(
+            d_model=D,
+            nhead=8,
+            dim_feedforward=1024,
+            chunk_length=chunk_length,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=M,
+        )
+
+        Q, KV = R + U + S, M + R + U
+        utterance = torch.randn(U, B, D)
+        lengths = torch.randint(1, U + 1, (B,))
+        lengths[0] = U
+        right_context = torch.randn(R, B, D)
+        memory = torch.randn(M, B, D)
+        attention_mask = torch.rand(Q, KV) >= 0.5
+        PE = 2 * U + right_context_length - 1
+        pos_emb = torch.randn(PE, D)
+
+        output_utterance, output_right_context, output_memory = layer(
+            utterance,
+            lengths,
+            right_context,
+            memory,
+            attention_mask,
+            pos_emb,
+        )
+        assert output_utterance.shape == (U, B, D)
+        assert output_right_context.shape == (R, B, D)
+        assert output_memory.shape == (M, B, D)
+
+
+def test_emformer_encoder_layer_infer():
+    from emformer import EmformerEncoderLayer
+
+    B, D = 2, 256
+    chunk_length = 8
+    right_context_length = 2
+    left_context_length = 8
+    kernel_size = 31
+    num_chunks = 1
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+
+    for use_memory in [True, False]:
+        if use_memory:
+            max_memory_size = 3
+            M = 1
+        else:
+            max_memory_size = 0
+            M = 0
+
+        layer = EmformerEncoderLayer(
+            d_model=D,
+            nhead=8,
+            dim_feedforward=1024,
+            chunk_length=chunk_length,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
+
+        utterance = torch.randn(U, B, D)
+        lengths = torch.randint(1, U + 1, (B,))
+        lengths[0] = U
+        right_context = torch.randn(R, B, D)
+        memory = torch.randn(M, B, D)
+        state = None
+        PE = (
+            2 * U
+            + right_context_length
+            - 1
+            + (
+                max_memory_size * chunk_length
+                if max_memory_size > 0
+                else left_context_length
+            )
+        )
+        pos_emb = torch.randn(PE, D)
+        conv_cache = None
+        (
+            output_utterance,
+            output_right_context,
+            output_memory,
+            output_state,
+            conv_cache,
+        ) = layer.infer(
+            utterance,
+            lengths,
+            right_context,
+            memory,
+            pos_emb,
+            state,
+            conv_cache,
+        )
+        assert output_utterance.shape == (U, B, D)
+        assert output_right_context.shape == (R, B, D)
+        if use_memory:
+            assert output_memory.shape == (1, B, D)
+        else:
+            assert output_memory.shape == (0, B, D)
+        assert len(output_state) == 4
+        assert output_state[0].shape == (max_memory_size, B, D)
+        assert output_state[1].shape == (left_context_length, B, D)
+        assert output_state[2].shape == (left_context_length, B, D)
+        assert output_state[3].shape == (1, B)
+        assert conv_cache.shape == (B, D, kernel_size - 1)
+
+
+def test_emformer_encoder_forward():
+    from emformer import EmformerEncoder
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    left_context_length = 2
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    kernel_size = 31
+    num_encoder_layers = 2
+
+    for use_memory in [True, False]:
+        if use_memory:
+            S = num_chunks
+            M = S - 1
+        else:
+            S, M = 0, 0
+
+        encoder = EmformerEncoder(
+            chunk_length=chunk_length,
+            d_model=D,
+            dim_feedforward=1024,
+            num_encoder_layers=num_encoder_layers,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=M,
+        )
+
+        x = torch.randn(U + right_context_length, B, D)
+        lengths = torch.randint(1, U + right_context_length + 1, (B,))
+        lengths[0] = U + right_context_length
+
+        output, output_lengths = encoder(x, lengths)
+        assert output.shape == (U, B, D)
+        assert torch.equal(
+            output_lengths, torch.clamp(lengths - right_context_length, min=0)
+        )
+
+
+def test_emformer_encoder_infer():
+    from emformer import EmformerEncoder
+
+    B, D = 2, 256
+    num_encoder_layers = 2
+    chunk_length = 4
+    right_context_length = 2
+    left_context_length = 2
+    num_chunks = 3
+    kernel_size = 31
+
+    for use_memory in [True, False]:
+        if use_memory:
+            max_memory_size = 3
+        else:
+            max_memory_size = 0
+
+        encoder = EmformerEncoder(
+            chunk_length=chunk_length,
+            d_model=D,
+            dim_feedforward=1024,
+            num_encoder_layers=num_encoder_layers,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
+
+        states = None
+        conv_caches = None
+        for chunk_idx in range(num_chunks):
+            x = torch.randn(chunk_length + right_context_length, B, D)
+            lengths = torch.randint(
+                1, chunk_length + right_context_length + 1, (B,)
+            )
+            lengths[0] = chunk_length + right_context_length
+            output, output_lengths, states, conv_caches = encoder.infer(
+                x, lengths, states, conv_caches
+            )
+            assert output.shape == (chunk_length, B, D)
+            assert torch.equal(
+                output_lengths,
+                torch.clamp(lengths - right_context_length, min=0),
+            )
+            assert len(states) == num_encoder_layers
+            for state in states:
+                assert len(state) == 4
+                assert state[0].shape == (max_memory_size, B, D)
+                assert state[1].shape == (left_context_length, B, D)
+                assert state[2].shape == (left_context_length, B, D)
+                assert torch.equal(
+                    state[3],
+                    (chunk_idx + 1) * chunk_length * torch.ones_like(state[3]),
+                )
+            for conv_cache in conv_caches:
+                assert conv_cache.shape == (B, D, kernel_size - 1)
+
+
+def test_emformer_encoder_forward_infer_consistency():
+    from emformer import EmformerEncoder
+
+    chunk_length = 4
+    num_chunks = 3
+    U = chunk_length * num_chunks
+    left_context_length, right_context_length = 1, 2
+    D = 256
+    num_encoder_layers = 3
+    kernel_size = 31
+    memory_sizes = [0, 3]
+
+    for max_memory_size in memory_sizes:
+        encoder = EmformerEncoder(
+            chunk_length=chunk_length,
+            d_model=D,
+            dim_feedforward=1024,
+            num_encoder_layers=num_encoder_layers,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
+        encoder.eval()
+
+        x = torch.randn(U + right_context_length, 1, D)
+        lengths = torch.tensor([U + right_context_length])
+
+        # training mode with full utterance
+        forward_output, forward_output_lengths = encoder(x, lengths)
+
+        # streaming inference mode with individual chunks
+        states = None
+        conv_caches = None
+        for chunk_idx in range(num_chunks):
+            start_idx = chunk_idx * chunk_length
+            end_idx = start_idx + chunk_length
+            chunk = x[start_idx : end_idx + right_context_length]  # noqa
+            chunk_length = torch.tensor([chunk_length])
+            (
+                infer_output_chunk,
+                infer_output_lengths,
+                states,
+                conv_caches,
+            ) = encoder.infer(chunk, chunk_length, states, conv_caches)
+            forward_output_chunk = forward_output[start_idx:end_idx]
+            assert torch.allclose(
+                infer_output_chunk,
+                forward_output_chunk,
+                atol=1e-4,
+                rtol=0.0,
+            ), (
+                infer_output_chunk - forward_output_chunk
+            )
+
+
+def test_emformer_forward():
+    from emformer import Emformer
+
+    num_features = 80
+    chunk_length = 16
+    right_context_length = 8
+    left_context_length = 8
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    B, D = 2, 256
+    kernel_size = 31
+
+    for use_memory in [True, False]:
+        if use_memory:
+            max_memory_size = 3
+        else:
+            max_memory_size = 0
+        model = Emformer(
+            num_features=num_features,
+            chunk_length=chunk_length,
+            subsampling_factor=4,
+            d_model=D,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
+        x = torch.randn(B, U + right_context_length + 3, num_features)
+        x_lens = torch.randint(1, U + right_context_length + 3 + 1, (B,))
+        x_lens[0] = U + right_context_length + 3
+        output, output_lengths = model(x, x_lens)
+        assert output.shape == (B, U // 4, D)
+        assert torch.equal(
+            output_lengths,
+            torch.clamp(
+                ((x_lens - 1) // 2 - 1) // 2 - right_context_length // 4, min=0
+            ),
+        )
+
+
+def test_emformer_infer():
+    from emformer import Emformer
+
+    num_features = 80
+    chunk_length = 8
+    U = chunk_length
+    left_context_length, right_context_length = 32, 4
+    B, D = 2, 256
+    num_chunks = 3
+    num_encoder_layers = 2
+    kernel_size = 31
+
+    for use_memory in [True, False]:
+        if use_memory:
+            max_memory_size = 32
+        else:
+            max_memory_size = 0
+        model = Emformer(
+            num_features=num_features,
+            chunk_length=chunk_length,
+            subsampling_factor=4,
+            d_model=D,
+            num_encoder_layers=num_encoder_layers,
+            cnn_module_kernel=kernel_size,
+            left_context_length=left_context_length,
+            right_context_length=right_context_length,
+            max_memory_size=max_memory_size,
+        )
+        states = None
+        conv_caches = None
+        for chunk_idx in range(num_chunks):
+            x = torch.randn(B, U + right_context_length + 3, num_features)
+            x_lens = torch.randint(1, U + right_context_length + 3 + 1, (B,))
+            x_lens[0] = U + right_context_length + 3
+            output, output_lengths, states, conv_caches = model.infer(
+                x, x_lens, states, conv_caches
+            )
+            assert output.shape == (B, U // 4, D)
+            assert torch.equal(
+                output_lengths,
+                torch.clamp(
+                    ((x_lens - 1) // 2 - 1) // 2 - right_context_length // 4,
+                    min=0,
+                ),
+            )
+            assert len(states) == num_encoder_layers
+            for state in states:
+                assert len(state) == 4
+                assert state[0].shape == (max_memory_size, B, D)
+                assert state[1].shape == (left_context_length // 4, B, D)
+                assert state[2].shape == (left_context_length // 4, B, D)
+                assert torch.equal(
+                    state[3],
+                    U // 4 * (chunk_idx + 1) * torch.ones_like(state[3]),
+                )
+            for conv_cache in conv_caches:
+                assert conv_cache.shape == (B, D, kernel_size - 1)
+
+
+if __name__ == "__main__":
+    test_rel_positional_encoding()
+    test_emformer_attention_forward()
+    test_emformer_attention_infer()
+    test_convolution_module_forward()
+    test_convolution_module_infer()
+    test_emformer_encoder_layer_forward()
+    test_emformer_encoder_layer_infer()
+    test_emformer_encoder_forward()
+    test_emformer_encoder_infer()
+    test_emformer_encoder_forward_infer_consistency()
+    test_emformer_forward()
+    test_emformer_infer()