Support streaming decoding

egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py

@@ -132,6 +132,7 @@ class Conformer(EncoderInterface):
                 )
             ),
         )
+        self._init_state: List[torch.Tensor] = [torch.empty(0)]
 
     def forward(
         self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0
@@ -197,6 +198,52 @@ class Conformer(EncoderInterface):
 
         return x, lengths
 
+    @torch.jit.export
+    def get_init_state(
+        self, left_context: int, device: torch.device
+    ) -> List[torch.Tensor]:
+        """Return the initial cache state of the model.
+        Args:
+          left_context: The left context size (in frames after subsampling).
+        Returns:
+          Return the initial state of the model, it is a list containing two
+          tensors, the first one is the cache for attentions which has a shape
+          of (num_encoder_layers, left_context, encoder_dim), the second one
+          is the cache of conv_modules which has a shape of
+          (num_encoder_layers, cnn_module_kernel - 1, encoder_dim).
+          NOTE: the returned tensors are on the given device.
+        """
+        if (
+            len(self._init_state) == 2
+            and self._init_state[0].size(1) == left_context
+        ):
+            # Note: It is OK to share the init state as it is
+            # not going to be modified by the model
+            return self._init_state
+
+        init_states: List[torch.Tensor] = [
+            torch.zeros(
+                (
+                    self.encoder_layers,
+                    left_context,
+                    self.d_model,
+                ),
+                device=device,
+            ),
+            torch.zeros(
+                (
+                    self.encoder_layers,
+                    self.cnn_module_kernel - 1,
+                    self.d_model,
+                ),
+                device=device,
+            ),
+        ]
+
+        self._init_state = init_states
+
+        return init_states
+
     @torch.jit.export
     def streaming_forward(
         self,
@@ -492,6 +539,98 @@ class ConformerEncoderLayer(nn.Module):
 
         return src
 
+    @torch.jit.export
+    def chunk_forward(
+        self,
+        src: Tensor,
+        pos_emb: Tensor,
+        states: List[Tensor],
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        warmup: float = 1.0,
+        left_context: int = 0,
+        right_context: int = 0,
+    ) -> Tuple[Tensor, List[Tensor]]:
+        """
+        Pass the input through the encoder layer.
+        Args:
+            src: the sequence to the encoder layer (required).
+            pos_emb: Positional embedding tensor (required).
+            states:
+              The decode states for previous frames which contains the cached data.
+              It has two elements, the first element is the attn_cache which has
+              a shape of (left_context, batch, attention_dim),
+              the second element is the conv_cache which has a shape of
+              (cnn_module_kernel-1, batch, conv_dim).
+              Note: states will be modified in this function.
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+            warmup: controls selective bypass of of layers; if < 1.0, we will
+              bypass layers more frequently.
+            left_context:
+              How many previous frames the attention can see in current chunk.
+              Note: It's not that each individual frame has `left_context` frames
+              of left context, some have more.
+            right_context:
+              How many future frames the attention can see in current chunk.
+              Note: It's not that each individual frame has `right_context` frames
+              of right context, some have more.
+        Shape:
+            src: (S, N, E).
+            pos_emb: (N, 2*(S+left_context)-1, E).
+            src_mask: (S, S).
+            src_key_padding_mask: (N, S).
+            S is the source sequence length, N is the batch size, E is the feature number
+        """
+
+        assert not self.training
+        assert len(states) == 2
+        assert states[0].shape == (left_context, src.size(1), src.size(2))
+
+        # macaron style feed forward module
+        src = src + self.dropout(self.feed_forward_macaron(src))
+
+        # We put the attention cache this level (i.e. before linear transformation)
+        # to save memory consumption, when decoding in streaming fashion, the
+        # batch size would be thousands (for 32GB machine), if we cache key & val
+        # separately, it needs extra several GB memory.
+        # TODO(WeiKang): Move cache to self_attn level (i.e. cache key & val
+        # separately) if needed.
+        key = torch.cat([states[0], src], dim=0)
+        val = key
+        if right_context > 0:
+            states[0] = key[
+                -(left_context + right_context) : -right_context, ...  # noqa
+            ]
+        else:
+            states[0] = key[-left_context:, ...]
+
+        # multi-headed self-attention module
+        src_att = self.self_attn(
+            src,
+            key,
+            val,
+            pos_emb=pos_emb,
+            attn_mask=src_mask,
+            key_padding_mask=src_key_padding_mask,
+            left_context=left_context,
+        )[0]
+
+        src = src + self.dropout(src_att)
+
+        # convolution module
+        conv, conv_cache = self.conv_module(src, states[1], right_context)
+        states[1] = conv_cache
+
+        src = src + self.dropout(conv)
+
+        # feed forward module
+        src = src + self.dropout(self.feed_forward(src))
+
+        src = self.norm_final(self.balancer(src))
+
+        return src, states
+
 
 class ConformerEncoder(nn.Module):
     r"""ConformerEncoder is a stack of N encoder layers
@@ -575,6 +714,77 @@ class ConformerEncoder(nn.Module):
 
         return output
 
+    @torch.jit.export
+    def chunk_forward(
+        self,
+        src: Tensor,
+        pos_emb: Tensor,
+        states: List[Tensor],
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        warmup: float = 1.0,
+        left_context: int = 0,
+        right_context: int = 0,
+    ) -> Tuple[Tensor, List[Tensor]]:
+        r"""Pass the input through the encoder layers in turn.
+        Args:
+            src: the sequence to the encoder (required).
+            pos_emb: Positional embedding tensor (required).
+            states:
+              The decode states for previous frames which contains the cached data.
+              It has two elements, the first element is the attn_cache which has
+              a shape of (encoder_layers, left_context, batch, attention_dim),
+              the second element is the conv_cache which has a shape of
+              (encoder_layers, cnn_module_kernel-1, batch, conv_dim).
+              Note: states will be modified in this function.
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+            warmup: controls selective bypass of of layers; if < 1.0, we will
+              bypass layers more frequently.
+            left_context:
+              How many previous frames the attention can see in current chunk.
+              Note: It's not that each individual frame has `left_context` frames
+              of left context, some have more.
+            right_context:
+              How many future frames the attention can see in current chunk.
+              Note: It's not that each individual frame has `right_context` frames
+              of right context, some have more.
+        Shape:
+            src: (S, N, E).
+            pos_emb: (N, 2*(S+left_context)-1, E).
+            mask: (S, S).
+            src_key_padding_mask: (N, S).
+            S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number
+        """
+        assert not self.training
+        assert len(states) == 2
+        assert states[0].shape == (
+            self.num_layers,
+            left_context,
+            src.size(1),
+            src.size(2),
+        )
+        assert states[1].size(0) == self.num_layers
+
+        output = src
+
+        for layer_index, mod in enumerate(self.layers):
+            cache = [states[0][layer_index], states[1][layer_index]]
+            output, cache = mod.chunk_forward(
+                output,
+                pos_emb,
+                states=cache,
+                src_mask=mask,
+                src_key_padding_mask=src_key_padding_mask,
+                warmup=warmup,
+                left_context=left_context,
+                right_context=right_context,
+            )
+            states[0][layer_index] = cache[0]
+            states[1][layer_index] = cache[1]
+
+        return output, states
+
 
 class RelPositionalEncoding(torch.nn.Module):
     """Relative positional encoding module.
@@ -599,12 +809,13 @@ class RelPositionalEncoding(torch.nn.Module):
         self.pe = None
         self.extend_pe(torch.tensor(0.0).expand(1, max_len))
 
-    def extend_pe(self, x: Tensor) -> None:
+    def extend_pe(self, x: Tensor, left_context: int = 0) -> None:
         """Reset the positional encodings."""
+        x_size_1 = x.size(1) + left_context
         if self.pe is not None:
             # self.pe contains both positive and negative parts
             # the length of self.pe is 2 * input_len - 1
-            if self.pe.size(1) >= x.size(1) * 2 - 1:
+            if self.pe.size(1) >= x_size_1 * 2 - 1:
                 # Note: TorchScript doesn't implement operator== for torch.Device
                 if self.pe.dtype != x.dtype or str(self.pe.device) != str(
                     x.device
@@ -614,9 +825,9 @@ class RelPositionalEncoding(torch.nn.Module):
         # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
-        pe_positive = torch.zeros(x.size(1), self.d_model)
-        pe_negative = torch.zeros(x.size(1), self.d_model)
-        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        pe_positive = torch.zeros(x_size_1, self.d_model)
+        pe_negative = torch.zeros(x_size_1, self.d_model)
+        position = torch.arange(0, x_size_1, dtype=torch.float32).unsqueeze(1)
         div_term = torch.exp(
             torch.arange(0, self.d_model, 2, dtype=torch.float32)
             * -(math.log(10000.0) / self.d_model)
@@ -634,22 +845,28 @@ class RelPositionalEncoding(torch.nn.Module):
         pe = torch.cat([pe_positive, pe_negative], dim=1)
         self.pe = pe.to(device=x.device, dtype=x.dtype)
 
-    def forward(self, x: torch.Tensor) -> Tuple[Tensor, Tensor]:
+    def forward(
+        self, x: torch.Tensor, left_context: int = 0
+    ) -> Tuple[Tensor, Tensor]:
         """Add positional encoding.
 
         Args:
             x (torch.Tensor): Input tensor (batch, time, `*`).
+            left_context (int): left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Returns:
             torch.Tensor: Encoded tensor (batch, time, `*`).
             torch.Tensor: Encoded tensor (batch, 2*time-1, `*`).
 
         """
-        self.extend_pe(x)
+        self.extend_pe(x, left_context)
+        x_size_1 = x.size(1) + left_context
         pos_emb = self.pe[
             :,
             self.pe.size(1) // 2
-            - x.size(1)
+            - x_size_1
             + 1 : self.pe.size(1) // 2  # noqa E203
             + x.size(1),
         ]
@@ -721,6 +938,7 @@ class RelPositionMultiheadAttention(nn.Module):
         key_padding_mask: Optional[Tensor] = None,
         need_weights: bool = True,
         attn_mask: Optional[Tensor] = None,
+        left_context: int = 0,
     ) -> Tuple[Tensor, Optional[Tensor]]:
         r"""
         Args:
@@ -734,6 +952,9 @@ class RelPositionMultiheadAttention(nn.Module):
             need_weights: output attn_output_weights.
             attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
                 the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+            left_context (int): left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Shape:
             - Inputs:
@@ -779,14 +1000,18 @@ class RelPositionMultiheadAttention(nn.Module):
             key_padding_mask=key_padding_mask,
             need_weights=need_weights,
             attn_mask=attn_mask,
+            left_context=left_context,
         )
 
-    def rel_shift(self, x: Tensor) -> Tensor:
+    def rel_shift(self, x: Tensor, left_context: int = 0) -> Tensor:
         """Compute relative positional encoding.
 
         Args:
             x: Input tensor (batch, head, time1, 2*time1-1).
                 time1 means the length of query vector.
+            left_context (int): left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Returns:
             Tensor: tensor of shape (batch, head, time1, time2)
@@ -794,14 +1019,17 @@ class RelPositionMultiheadAttention(nn.Module):
           the key, while time1 is for the query).
         """
         (batch_size, num_heads, time1, n) = x.shape
-        assert n == 2 * time1 - 1
+        time2 = time1 + left_context
+        assert (
+            n == left_context + 2 * time1 - 1
+        ), f"{n} == {left_context} + 2 * {time1} - 1"
         # Note: TorchScript requires explicit arg for stride()
         batch_stride = x.stride(0)
         head_stride = x.stride(1)
         time1_stride = x.stride(2)
         n_stride = x.stride(3)
         return x.as_strided(
-            (batch_size, num_heads, time1, time1),
+            (batch_size, num_heads, time1, time2),
             (batch_stride, head_stride, time1_stride - n_stride, n_stride),
             storage_offset=n_stride * (time1 - 1),
         )
@@ -823,6 +1051,7 @@ class RelPositionMultiheadAttention(nn.Module):
         key_padding_mask: Optional[Tensor] = None,
         need_weights: bool = True,
         attn_mask: Optional[Tensor] = None,
+        left_context: int = 0,
     ) -> Tuple[Tensor, Optional[Tensor]]:
         r"""
         Args:
@@ -840,6 +1069,9 @@ class RelPositionMultiheadAttention(nn.Module):
             need_weights: output attn_output_weights.
             attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
                 the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+            left_context (int): left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Shape:
             Inputs:
@@ -1003,7 +1235,8 @@ class RelPositionMultiheadAttention(nn.Module):
         pos_emb_bsz = pos_emb.size(0)
         assert pos_emb_bsz in (1, bsz)  # actually it is 1
         p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim)
-        p = p.transpose(1, 2)  # (batch, head, 2*time1-1, d_k)
+        # (batch, 2*time1, head, d_k) --> (batch, head, d_k, 2*time -1)
+        p = p.permute(0, 2, 3, 1)
 
         q_with_bias_u = (q + self._pos_bias_u()).transpose(
             1, 2
@@ -1023,9 +1256,9 @@ class RelPositionMultiheadAttention(nn.Module):
 
         # compute matrix b and matrix d
         matrix_bd = torch.matmul(
-            q_with_bias_v, p.transpose(-2, -1)
+            q_with_bias_v, p
         )  # (batch, head, time1, 2*time1-1)
-        matrix_bd = self.rel_shift(matrix_bd)
+        matrix_bd = self.rel_shift(matrix_bd, left_context)
 
         attn_output_weights = (
             matrix_ac + matrix_bd
@@ -1201,13 +1434,19 @@ class ConvolutionModule(nn.Module):
             initial_scale=0.25,
         )
 
-    def forward(self, x: Tensor, cache: Optional[Tensor] = None) -> Tensor:
+    def forward(
+        self, x: Tensor, cache: Optional[Tensor] = None, right_context: int = 0
+    ) -> Tuple[Tensor, Tensor]:
         """Compute convolution module.
 
         Args:
             x: Input tensor (#time, batch, channels).
             cache: The cache of depthwise_conv, only used in real streaming
                 decoding.
+            right_context:
+              How many future frames the attention can see in current chunk.
+              Note: It's not that each individual frame has `right_context` frames
+              of right context, some have more.
 
         Returns:
             Tensor: Output tensor (#time, batch, channels).
@@ -1237,7 +1476,15 @@ class ConvolutionModule(nn.Module):
                 ), "Cache should be None in training time"
                 assert cache.size(0) == self.lorder
                 x = torch.cat([cache.permute(1, 2, 0), x], dim=2)
-                cache = x.permute(2, 0, 1)[-self.lorder :, ...]  # noqa
+                if right_context > 0:
+                    cache = x.permute(2, 0, 1)[
+                        -(self.lorder + right_context) : (  # noqa
+                            -right_context
+                        ),
+                        ...,
+                    ]
+                else:
+                    cache = x.permute(2, 0, 1)[-self.lorder :, ...]  # noqa
 
         x = self.depthwise_conv(x)
 

egs/librispeech/ASR/pruned_transducer_stateless5/decode_stream.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless/decode_stream.py

egs/librispeech/ASR/pruned_transducer_stateless5/export.py

@@ -137,6 +137,15 @@ 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
@@ -162,6 +171,9 @@ def main():
     params.blank_id = sp.piece_to_id("<blk>")
     params.vocab_size = sp.get_piece_size()
 
+    if params.streaming_model:
+        assert params.causal_convolution
+
     logging.info(params)
 
     logging.info("About to create model")

egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py

@@ -0,0 +1,750 @@
+#!/usr/bin/env python3
+# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, 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:
+./pruned_transducer_stateless5/streaming_decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --left-context 32 \
+        --decode-chunk-size 8 \
+        --right-context 0 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --decoding_method greedy_search \
+        --num-decode-streams 200
+"""
+
+import argparse
+import logging
+import math
+from pathlib import Path
+from typing import Dict, 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 decode_stream import DecodeStream
+from kaldifeat import Fbank, FbankOptions
+from lhotse import CutSet
+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,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.decode import one_best_decoding
+from icefall.utils import (
+    AttributeDict,
+    get_texts,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--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(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/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="""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=0,
+        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,
+    sp: spm.SentencePieceProcessor,
+    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.
+      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.
+    """
+    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 = 50
+
+    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
+
+        # The trained model is using normalized samples
+        assert audio.max() <= 1, "Should be normalized to [-1, 1])"
+
+        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=params, model=model, decode_streams=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(),
+                        sp.decode(hyp).split(),
+                    )
+                )
+                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=params, model=model, decode_streams=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(),
+                    sp.decode(hyp).split(),
+                )
+            )
+            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"
+        )
+        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()
+    LibriSpeechAsrDataModule.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}"
+
+    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()
+
+    # 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 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 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))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+    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}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_sets = ["test-clean", "test-other"]
+    test_cuts = [test_clean_cuts, test_other_cuts]
+
+    for test_set, test_cut in zip(test_sets, test_cuts):
+        results_dict = decode_dataset(
+            cuts=test_cut,
+            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()