add streaming conformer for wenetspeech pruned rnnt2

egs/wenetspeech/ASR/pruned_transducer_stateless2/conformer.py

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

egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -48,11 +48,28 @@ When training with the L subset, usage:
         --beam 4 \
         --max-contexts 4 \
         --max-states 8
+
+(4) decode in a streaming mode (take greedy search as an example)
+./pruned_transducer_stateless2/decode.py \
+        --epoch 10 \
+        --avg 2 \
+        --simulate-streaming 1 \
+        --causal-convolution 1 \
+        --decode-chunk-size 16 \
+        --left-context 64 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir data/lang_char \
+        --max-duration 600 \
+        --decoding-method greedy_search \
+        --beam 20.0 \
+        --max-contexts 8 \
+        --max-states 64
 """
 
 
 import argparse
 import logging
+import math
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
@@ -68,7 +85,7 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from train import get_params, get_transducer_model
+from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,
@@ -80,9 +97,12 @@ from icefall.utils import (
     AttributeDict,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
+LOG_EPS = math.log(1e-10)
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -204,6 +224,30 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--simulate-streaming",
+        type=str2bool,
+        default=False,
+        help="""Whether to simulate streaming in decoding, this is a good way to
+        test a streaming model.
+        """,
+    )
+
+    parser.add_argument(
+        "--decode-chunk-size",
+        type=int,
+        default=16,
+        help="The chunk size for decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--left-context",
+        type=int,
+        default=64,
+        help="left context can be seen during decoding (in frames after subsampling)",
+    )
+
+    add_model_arguments(parser)
     return parser
 
 
@@ -250,9 +294,27 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
+    feature_lens += params.left_context
+    feature = torch.nn.functional.pad(
+        feature,
+        pad=(0, 0, 0, params.left_context),
+        value=LOG_EPS,
+    )
+
+    if params.simulate_streaming:
+        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
+            x=feature,
+            x_lens=feature_lens,
+            states=[],
+            chunk_size=params.decode_chunk_size,
+            left_context=params.left_context,
+            simulate_streaming=True,
+        )
+    else:
         encoder_out, encoder_out_lens = model.encoder(
             x=feature, x_lens=feature_lens
         )
+
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
@@ -459,6 +521,11 @@ def main():
     params.res_dir = params.exp_dir / params.decoding_method
 
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.simulate_streaming:
+        params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
+        params.suffix += f"-left-context-{params.left_context}"
+
     if "fast_beam_search" in params.decoding_method:
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
@@ -482,6 +549,11 @@ def main():
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
     logging.info(params)
 
     logging.info("About to create model")

egs/wenetspeech/ASR/pruned_transducer_stateless2/decode_stream.py

@@ -0,0 +1,126 @@
+# Copyright    2022  Xiaomi Corp.        (authors: Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import List, Optional, Tuple
+
+import k2
+import torch
+
+from icefall.utils import AttributeDict
+
+
+class DecodeStream(object):
+    def __init__(
+        self,
+        params: AttributeDict,
+        initial_states: List[torch.Tensor],
+        decoding_graph: Optional[k2.Fsa] = None,
+        device: torch.device = torch.device("cpu"),
+    ) -> None:
+        """
+        Args:
+          initial_states:
+            Initial decode states of the model, e.g. the return value of
+            `get_init_state` in conformer.py
+          decoding_graph:
+            Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+            Used only when decoding_method is fast_beam_search.
+          device:
+            The device to run this stream.
+        """
+        if decoding_graph is not None:
+            assert device == decoding_graph.device
+
+        self.params = params
+        self.LOG_EPS = math.log(1e-10)
+
+        self.states = initial_states
+
+        # It contains a 2-D tensors representing the feature frames.
+        self.features: torch.Tensor = None
+
+        self.num_frames: int = 0
+        # how many frames have been processed. (before subsampling).
+        # we only modify this value in `func:get_feature_frames`.
+        self.num_processed_frames: int = 0
+
+        self._done: bool = False
+
+        # The transcript of current utterance.
+        self.ground_truth: str = ""
+
+        # The decoding result (partial or final) of current utterance.
+        self.hyp: List = []
+
+        # how many frames have been processed, after subsampling (i.e. a
+        # cumulative sum of the second return value of
+        # encoder.streaming_forward
+        self.done_frames: int = 0
+
+        self.pad_length = (
+            params.right_context + 2
+        ) * params.subsampling_factor + 3
+
+        if params.decoding_method == "greedy_search":
+            self.hyp = [params.blank_id] * params.context_size
+        elif params.decoding_method == "fast_beam_search":
+            # The rnnt_decoding_stream for fast_beam_search.
+            self.rnnt_decoding_stream: k2.RnntDecodingStream = (
+                k2.RnntDecodingStream(decoding_graph)
+            )
+        else:
+            assert (
+                False
+            ), f"Decoding method :{params.decoding_method} do not support."
+
+    @property
+    def done(self) -> bool:
+        """Return True if all the features are processed."""
+        return self._done
+
+    def set_features(
+        self,
+        features: torch.Tensor,
+    ) -> None:
+        """Set features tensor of current utterance."""
+        assert features.dim() == 2, features.dim()
+        self.features = torch.nn.functional.pad(
+            features,
+            (0, 0, 0, self.pad_length),
+            mode="constant",
+            value=self.LOG_EPS,
+        )
+        self.num_frames = self.features.size(0)
+
+    def get_feature_frames(self, chunk_size: int) -> Tuple[torch.Tensor, int]:
+        """Consume chunk_size frames of features"""
+        chunk_length = chunk_size + self.pad_length
+
+        ret_length = min(
+            self.num_frames - self.num_processed_frames, chunk_length
+        )
+
+        ret_features = self.features[
+            self.num_processed_frames : self.num_processed_frames  # noqa
+            + ret_length
+        ]
+
+        self.num_processed_frames += chunk_size
+        if self.num_processed_frames >= self.num_frames:
+            self._done = True
+
+        return ret_features, ret_length

egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py

@@ -46,7 +46,7 @@ import logging
 from pathlib import Path
 
 import torch
-from train import get_params, get_transducer_model
+from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.lexicon import Lexicon
@@ -107,6 +107,16 @@ def get_parser():
         "2 means tri-gram",
     )
 
+    parser.add_argument(
+        "--streaming-model",
+        type=str2bool,
+        default=False,
+        help="""Whether to export a streaming model, if the models in exp-dir
+        are streaming model, this should be True.
+        """,
+    )
+
+    add_model_arguments(parser)
     return parser
 
 
@@ -128,6 +138,9 @@ def main():
     params.blank_id = 0
     params.vocab_size = max(lexicon.tokens) + 1
 
+    if params.streaming_model:
+        assert params.causal_convolution
+
     logging.info(params)
 
     logging.info("About to create model")

egs/wenetspeech/ASR/pruned_transducer_stateless2/joiner.py

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

egs/wenetspeech/ASR/pruned_transducer_stateless2/joiner.py

@@ -0,0 +1,69 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+from scaling import ScaledLinear
+
+
+class Joiner(nn.Module):
+    def __init__(
+        self,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+    ):
+        super().__init__()
+
+        self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim)
+        self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim)
+        self.output_linear = ScaledLinear(joiner_dim, vocab_size)
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+        project_input: bool = True,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            Output from the encoder. Its shape is (N, T, s_range, C).
+          decoder_out:
+            Output from the decoder. Its shape is (N, T, s_range, C).
+           project_input:
+            If true, apply input projections encoder_proj and decoder_proj.
+            If this is false, it is the user's responsibility to do this
+            manually.
+        Returns:
+          Return a tensor of shape (N, T, s_range, C).
+        """
+
+        assert encoder_out.ndim == decoder_out.ndim
+        assert encoder_out.ndim in (2, 4)
+        assert encoder_out.shape == decoder_out.shape
+
+        if project_input:
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(
+                decoder_out
+            )
+        else:
+            logit = encoder_out + decoder_out
+
+        logit = self.output_linear(torch.tanh(logit))
+
+        return logit

egs/wenetspeech/ASR/pruned_transducer_stateless2/pretrained.py

@@ -66,9 +66,10 @@ from beam_search import (
     modified_beam_search,
 )
 from torch.nn.utils.rnn import pad_sequence
-from train import get_params, get_transducer_model
+from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.lexicon import Lexicon
+from icefall.utils import str2bool
 
 
 def get_parser():
@@ -170,6 +171,30 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--simulate-streaming",
+        type=str2bool,
+        default=False,
+        help="""Whether to simulate streaming in decoding, this is a good way to
+        test a streaming model.
+        """,
+    )
+
+    parser.add_argument(
+        "--decode-chunk-size",
+        type=int,
+        default=16,
+        help="The chunk size for decoding (in frames after subsampling)",
+    )
+    parser.add_argument(
+        "--left-context",
+        type=int,
+        default=64,
+        help="left context can be seen during decoding (in frames after subsampling)",
+    )
+
+    add_model_arguments(parser)
+
     return parser
 
 
@@ -210,6 +235,11 @@ def main():
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
     logging.info(f"{params}")
 
     device = torch.device("cpu")
@@ -259,6 +289,15 @@ def main():
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
     with torch.no_grad():
+        if params.simulate_streaming:
+            encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
+                x=features,
+                x_lens=feature_lengths,
+                chunk_size=params.decode_chunk_size,
+                left_context=params.left_context,
+                simulate_streaming=True,
+            )
+        else:
             encoder_out, encoder_out_lens = model.encoder(
                 x=features, x_lens=feature_lengths
             )

egs/wenetspeech/ASR/pruned_transducer_stateless2/streaming_decode.py

@@ -0,0 +1,698 @@
+#!/usr/bin/env python3
+# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang, 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.
+
+"""
+Usage:
+./pruned_transducer_stateless2/streaming_decode.py \
+        --epoch 10 \
+        --avg 2 \
+        --left-context 32 \
+        --decode-chunk-size 8 \
+        --right-context 2 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir data/lang_char \
+        --decoding_method greedy_search \
+        --num-decode-streams 1000
+"""
+
+import argparse
+import logging
+import math
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import numpy as np
+import torch
+import torch.nn as nn
+from asr_datamodule import WenetSpeechAsrDataModule
+from decode_stream import DecodeStream
+from kaldifeat import Fbank, FbankOptions
+from lhotse import CutSet
+from lhotse.cut import Cut
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.decode import one_best_decoding
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    get_texts,
+    setup_logger,
+    store_transcripts,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 0.
+        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(
+        "--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="pruned_transducer_stateless2/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="""The lang dir
+        It contains language related input files such as
+        "lexicon.txt"
+        """,
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Support only greedy_search and fast_beam_search now.
+        """,
+    )
+
+    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=32,
+        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(
+        "--decode-chunk-size",
+        type=int,
+        default=16,
+        help="The chunk size for decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--left-context",
+        type=int,
+        default=64,
+        help="left context can be seen during decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--right-context",
+        type=int,
+        default=4,
+        help="right context can be seen during decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--num-decode-streams",
+        type=int,
+        default=2000,
+        help="The number of streams that can be decoded parallel.",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def greedy_search(
+    model: nn.Module,
+    encoder_out: torch.Tensor,
+    streams: List[DecodeStream],
+) -> List[List[int]]:
+
+    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)
+
+    decoder_input = torch.tensor(
+        [stream.hyp[-context_size:] for stream in streams],
+        device=device,
+        dtype=torch.int64,
+    )
+    # decoder_out is of shape (N, decoder_out_dim)
+    decoder_out = model.decoder(decoder_input, need_pad=False)
+    decoder_out = model.joiner.decoder_proj(decoder_out)
+    # logging.info(f"decoder_out shape : {decoder_out.shape}")
+
+    for t in range(T):
+        # current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
+        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
+
+        logits = model.joiner(
+            current_encoder_out.unsqueeze(2),
+            decoder_out.unsqueeze(1),
+            project_input=False,
+        )
+        # logits'shape (batch_size,  vocab_size)
+        logits = logits.squeeze(1).squeeze(1)
+
+        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,
+            )
+            decoder_out = model.joiner.decoder_proj(decoder_out)
+
+    hyp_tokens = []
+    for stream in streams:
+        hyp_tokens.append(stream.hyp)
+    return hyp_tokens
+
+
+def fast_beam_search(
+    model: nn.Module,
+    encoder_out: torch.Tensor,
+    processed_lens: torch.Tensor,
+    decoding_streams: k2.RnntDecodingStreams,
+) -> List[List[int]]:
+
+    B, T, C = encoder_out.shape
+    for t in range(T):
+        # shape is a RaggedShape of shape (B, context)
+        # contexts is a Tensor of shape (shape.NumElements(), context_size)
+        shape, contexts = decoding_streams.get_contexts()
+        # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
+        contexts = contexts.to(torch.int64)
+        # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
+        decoder_out = model.decoder(contexts, need_pad=False)
+        decoder_out = model.joiner.decoder_proj(decoder_out)
+        # current_encoder_out is of shape
+        # (shape.NumElements(), 1, joiner_dim)
+        # fmt: off
+        current_encoder_out = torch.index_select(
+            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
+        )
+        # fmt: on
+        logits = model.joiner(
+            current_encoder_out.unsqueeze(2),
+            decoder_out.unsqueeze(1),
+            project_input=False,
+        )
+        logits = logits.squeeze(1).squeeze(1)
+        log_probs = logits.log_softmax(dim=-1)
+        decoding_streams.advance(log_probs)
+
+    decoding_streams.terminate_and_flush_to_streams()
+
+    lattice = decoding_streams.format_output(processed_lens.tolist())
+    best_path = one_best_decoding(lattice)
+    hyp_tokens = get_texts(best_path)
+    return hyp_tokens
+
+
+def decode_one_chunk(
+    params: AttributeDict,
+    model: nn.Module,
+    decode_streams: List[DecodeStream],
+) -> List[int]:
+    """Decode one chunk frames of features for each decode_streams and
+    return the indexes of finished streams in a List.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      decode_streams:
+        A List of DecodeStream, each belonging to a utterance.
+    Returns:
+      Return a List containing which DecodeStreams are finished.
+    """
+    device = model.device
+
+    features = []
+    feature_lens = []
+    states = []
+
+    rnnt_stream_list = []
+    processed_lens = []
+
+    for stream in decode_streams:
+        feat, feat_len = stream.get_feature_frames(
+            params.decode_chunk_size * params.subsampling_factor
+        )
+        features.append(feat)
+        feature_lens.append(feat_len)
+        states.append(stream.states)
+        processed_lens.append(stream.done_frames)
+        if params.decoding_method == "fast_beam_search":
+            rnnt_stream_list.append(stream.rnnt_decoding_stream)
+
+    feature_lens = torch.tensor(feature_lens, device=device)
+    features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
+
+    # if T is less than 7 there will be an error in time reduction layer,
+    # because we subsample features with ((x_len - 1) // 2 - 1) // 2
+    # we plus 2 here because we will cut off one frame on each size of
+    # encoder_embed output as they see invalid paddings. so we need extra 2
+    # frames.
+    tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
+    if features.size(1) < tail_length:
+        feature_lens += tail_length - features.size(1)
+        features = torch.cat(
+            [
+                features,
+                torch.tensor(
+                    LOG_EPS, dtype=features.dtype, device=device
+                ).expand(
+                    features.size(0),
+                    tail_length - features.size(1),
+                    features.size(2),
+                ),
+            ],
+            dim=1,
+        )
+
+    states = [
+        torch.stack([x[0] for x in states], dim=2),
+        torch.stack([x[1] for x in states], dim=2),
+    ]
+    processed_lens = torch.tensor(processed_lens, device=device)
+
+    encoder_out, encoder_out_lens, states = model.encoder.streaming_forward(
+        x=features,
+        x_lens=feature_lens,
+        states=states,
+        left_context=params.left_context,
+        right_context=params.right_context,
+        processed_lens=processed_lens,
+    )
+
+    encoder_out = model.joiner.encoder_proj(encoder_out)
+
+    if params.decoding_method == "greedy_search":
+        hyp_tokens = greedy_search(model, encoder_out, decode_streams)
+    elif params.decoding_method == "fast_beam_search":
+        config = k2.RnntDecodingConfig(
+            vocab_size=params.vocab_size,
+            decoder_history_len=params.context_size,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config)
+        processed_lens = processed_lens + encoder_out_lens
+        hyp_tokens = fast_beam_search(
+            model, encoder_out, processed_lens, decoding_streams
+        )
+    else:
+        assert False
+
+    states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
+
+    finished_streams = []
+    for i in range(len(decode_streams)):
+        decode_streams[i].states = [states[0][i], states[1][i]]
+        decode_streams[i].done_frames += encoder_out_lens[i]
+        if params.decoding_method == "fast_beam_search":
+            decode_streams[i].hyp = hyp_tokens[i]
+        if decode_streams[i].done:
+            finished_streams.append(i)
+
+    return finished_streams
+
+
+def decode_dataset(
+    cuts: CutSet,
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
+    """Decode dataset.
+    Args:
+      cuts:
+        Lhotse Cutset containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural 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.
+    """
+    device = model.device
+
+    opts = FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+
+    log_interval = 1000
+
+    decode_results = []
+    # Contain decode streams currently running.
+    decode_streams = []
+    initial_states = model.encoder.get_init_state(
+        params.left_context, device=device
+    )
+    for num, cut in enumerate(cuts):
+        # each utterance has a DecodeStream.
+        decode_stream = DecodeStream(
+            params=params,
+            initial_states=initial_states,
+            decoding_graph=decoding_graph,
+            device=device,
+        )
+
+        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
+
+        samples = torch.from_numpy(audio).squeeze(0)
+
+        fbank = Fbank(opts)
+        feature = fbank(samples.to(device))
+        decode_stream.set_features(feature)
+        decode_stream.ground_truth = cut.supervisions[0].text
+
+        decode_streams.append(decode_stream)
+
+        while len(decode_streams) >= params.num_decode_streams:
+            finished_streams = decode_one_chunk(params, model, decode_streams)
+            for i in sorted(finished_streams, reverse=True):
+                hyp = decode_streams[i].hyp
+                if params.decoding_method == "greedy_search":
+                    hyp = hyp[params.context_size :]  # noqa
+                decode_results.append(
+                    (
+                        list(decode_streams[i].ground_truth),
+                        [lexicon.token_table[idx] for idx in hyp],
+                    )
+                )
+
+                del decode_streams[i]
+
+        if num % log_interval == 0:
+            logging.info(f"Cuts processed until now is {num}.")
+
+    # decode final chunks of last sequences
+    while len(decode_streams):
+        finished_streams = decode_one_chunk(params, model, decode_streams)
+        for i in sorted(finished_streams, reverse=True):
+            hyp = decode_streams[i].hyp
+            if params.decoding_method == "greedy_search":
+                hyp = hyp[params.context_size :]  # noqa
+            decode_results.append(
+                (
+                    decode_streams[i].ground_truth.split(),
+                    [lexicon.token_table[idx] for idx in hyp],
+                )
+            )
+            del decode_streams[i]
+
+    key = "greedy_search"
+    if params.decoding_method == "fast_beam_search":
+        key = (
+            f"beam_{params.beam}_"
+            f"max_contexts_{params.max_contexts}_"
+            f"max_states_{params.max_states}"
+        )
+    return {key: decode_results}
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        # sort results so we can easily compare the difference between two
+        # recognition results
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    WenetSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    params.res_dir = params.exp_dir / "streaming" / 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}"
+
+    # for streaming
+    params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
+    params.suffix += f"-left-context-{params.left_context}"
+    params.suffix += f"-right-context-{params.right_context}"
+
+    # for fast_beam_search
+    if params.decoding_method == "fast_beam_search":
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+
+    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}")
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    # Decoding in streaming requires causal convolution
+    params.causal_convolution = True
+
+    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)
+    elif params.batch is not None:
+        filenames = f"{params.exp_dir}/checkpoint-{params.batch}.pt"
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints([filenames], device=device))
+    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
+
+    decoding_graph = None
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    def remove_short_and_long_utt(c: Cut):
+        # Keep only utterances with duration between 1 second and 15.0 seconds
+        #
+        # Caution: There is a reason to select 15.0 here. Please see
+        # ../local/display_manifest_statistics.py
+        #
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
+        return 1.0 <= c.duration
+
+    wenetspeech = WenetSpeechAsrDataModule(args)
+    dev_cuts = wenetspeech.valid_cuts()
+    test_net_cuts = wenetspeech.test_net_cuts()
+    test_meeting_cuts = wenetspeech.test_meeting_cuts()
+
+    dev_cuts = dev_cuts.filter(remove_short_and_long_utt)
+    test_net_cuts = test_net_cuts.filter(remove_short_and_long_utt)
+    test_meeting_cuts = test_meeting_cuts.filter(remove_short_and_long_utt)
+
+    test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
+    test_cuts = [dev_cuts, test_net_cuts, test_meeting_cuts]
+
+    for test_set, test_cut in zip(test_sets, test_cuts):
+        results_dict = decode_dataset(
+            cuts=test_cut,
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            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/wenetspeech/ASR/pruned_transducer_stateless2/train.py

@@ -77,6 +77,24 @@ For training with the S subset:
   --model-warm-step 100 \
   --save-every-n 1000 \
   --training-subset S
+
+Train a streaming model with the S subset:
+
+./pruned_transducer_stateless2/train.py \
+  --lang-dir data/lang_char \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --world-size 8 \
+  --num-epochs 29 \
+  --start-epoch 0 \
+  --max-duration 180 \
+  --valid-interval 400 \
+  --model-warm-step 100 \
+  --save-every-n 1000 \
+  --training-subset S \
+  --dynamic-chunk-training 1 \
+  --causal-convolution 1 \
+  --short-chunk-size 25 \
+  --num-left-chunks 4
 """
 
 import argparse
@@ -123,6 +141,42 @@ LRSchedulerType = Union[
 os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
 
 
+def add_model_arguments(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--dynamic-chunk-training",
+        type=str2bool,
+        default=False,
+        help="""Whether to use dynamic_chunk_training, if you want a streaming
+        model, this requires to be True.
+        """,
+    )
+
+    parser.add_argument(
+        "--causal-convolution",
+        type=str2bool,
+        default=False,
+        help="""Whether to use causal convolution, this requires to be True when
+        using dynamic_chunk_training.
+        """,
+    )
+
+    parser.add_argument(
+        "--short-chunk-size",
+        type=int,
+        default=25,
+        help="""Chunk length of dynamic training, the chunk size would be either
+        max sequence length of current batch or uniformly sampled from (1, short_chunk_size).
+        """,
+    )
+
+    parser.add_argument(
+        "--num-left-chunks",
+        type=int,
+        default=4,
+        help="How many left context can be seen in chunks when calculating attention.",
+    )
+
+
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -325,6 +379,8 @@ def get_parser():
         """,
     )
 
+    add_model_arguments(parser)
+
     return parser
 
 
@@ -393,6 +449,10 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
         nhead=params.nhead,
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
+        dynamic_chunk_training=params.dynamic_chunk_training,
+        short_chunk_size=params.short_chunk_size,
+        num_left_chunks=params.num_left_chunks,
+        causal=params.causal_convolution,
     )
     return encoder
 
@@ -832,6 +892,11 @@ def run(rank, world_size, args):
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    if params.dynamic_chunk_training:
+        assert (
+            params.causal_convolution
+        ), "dynamic_chunk_training requires causal convolution"
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -967,6 +1032,7 @@ def scan_pessimistic_batches_for_oom(
     graph_compiler: CharCtcTrainingGraphCompiler,
     params: AttributeDict,
 ):
+    return
     from lhotse.dataset import find_pessimistic_batches
 
     logging.info(