support streaming on pruned_transducer_stateless2; add delay penalty; fixes for decode states

egs/librispeech/ASR/pruned_transducer_stateless/model.py

@@ -66,6 +66,8 @@ class Transducer(nn.Module):
         prune_range: int = 5,
         am_scale: float = 0.0,
         lm_scale: float = 0.0,
+        delay_penalty: float = 0.0,
+        return_sym_delay: bool = False,
     ) -> torch.Tensor:
         """
         Args:
@@ -136,10 +138,31 @@ class Transducer(nn.Module):
             lm_only_scale=lm_scale,
             am_only_scale=am_scale,
             boundary=boundary,
+            delay_penalty=delay_penalty,
             reduction="sum",
             return_grad=True,
         )
 
+        sym_delay = None
+        if return_sym_delay:
+            B, S, T0 = px_grad.shape
+            T = T0 - 1
+            if boundary is None:
+                offset = torch.tensor(
+                    (T - 1) / 2,
+                    dtype=px_grad.dtype,
+                    device=px_grad.device,
+                ).expand(B, 1, 1)
+                total_syms = S * B
+            else:
+                offset = (boundary[:, 3] - 1) / 2
+                total_syms = torch.sum(boundary[:, 2])
+            offset = torch.arange(
+                T0, device=px_grad.device
+            ).reshape(1, 1, T0) - offset.reshape(B, 1, 1)
+            sym_delay = px_grad * offset
+            sym_delay = torch.sum(sym_delay) / total_syms
+
         # ranges : [B, T, prune_range]
         ranges = k2.get_rnnt_prune_ranges(
             px_grad=px_grad,
@@ -163,7 +186,8 @@ class Transducer(nn.Module):
             ranges=ranges,
             termination_symbol=blank_id,
             boundary=boundary,
+            delay_penalty=delay_penalty,
             reduction="sum",
         )
 
-        return (simple_loss, pruned_loss)
+        return (simple_loss, pruned_loss, sym_delay)

egs/librispeech/ASR/pruned_transducer_stateless/train.py

@@ -269,6 +269,25 @@ def get_parser():
         help="How many left context can be seen in chunks when calculating attention.",
     )
 
+    parser.add_argument(
+        "--delay-penalty",
+        type=float,
+        default=0.0,
+        help="""A constant value to penalize symbol delay, this may be
+         needed when training with time masking, to avoid the time masking
+         encouraging the network to delay symbols.
+         """,
+    )
+
+    parser.add_argument(
+        "--return-sym-delay",
+        type=str2bool,
+        default=False,
+        help="""Whether to return `sym_delay` during training, this is a stat
+        to measure symbols emission delay, especially for time masking training.
+        """,
+    )
+
 
     return parser
 
@@ -536,14 +555,17 @@ def compute_loss(
     y = sp.encode(texts, out_type=int)
     y = k2.RaggedTensor(y).to(device)
 
+    sym_delay = None
     with torch.set_grad_enabled(is_training):
-        simple_loss, pruned_loss = model(
+        simple_loss, pruned_loss, sym_delay = model(
             x=feature,
             x_lens=feature_lens,
             y=y,
             prune_range=params.prune_range,
             am_scale=params.am_scale,
             lm_scale=params.lm_scale,
+            delay_penalty=params.delay_penalty,
+            return_sym_delay=params.return_sym_delay,
         )
         loss = params.simple_loss_scale * simple_loss + pruned_loss
 
@@ -561,6 +583,9 @@ def compute_loss(
     info["simple_loss"] = simple_loss.detach().cpu().item()
     info["pruned_loss"] = pruned_loss.detach().cpu().item()
 
+    if sym_delay is not None:
+        info["sym_delay"] = sym_delay.detatch().cpu().item()
+
     return loss, info
 
 

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -32,7 +32,7 @@ from scaling import (
 )
 from torch import Tensor, nn
 
-from icefall.utils import make_pad_mask
+from icefall.utils import make_pad_mask, subsequent_chunk_mask
 
 
 class Conformer(EncoderInterface):
@@ -48,6 +48,26 @@ class Conformer(EncoderInterface):
         layer_dropout (float): layer-dropout rate.
         cnn_module_kernel (int): Kernel size of convolution module
         vgg_frontend (bool): whether to use vgg frontend.
+        dynamic_chunk_training (bool): whether to use dynamic chunk training, if
+            you want to train a streaming model, this is expected to be True.
+            When setting True, it will use a masking strategy to make the attention
+            see only limited left and right context.
+        short_chunk_threshold (float): a threshold to determinize the chunk size
+            to be used in masking training, if the randomly generated chunk size
+            is greater than ``max_len * short_chunk_threshold`` (max_len is the
+            max sequence length of current batch) then it will use
+            full context in training (i.e. with chunk size equals to max_len).
+            This will be used only when dynamic_chunk_training is True.
+        short_chunk_size (int): see docs above, if the randomly generated chunk
+            size equals to or less than ``max_len * short_chunk_threshold``, the
+            chunk size will be sampled uniformly from 1 to short_chunk_size.
+            This also will be used only when dynamic_chunk_training is True.
+        num_left_chunks (int): the left context (in chunks) attention can see, the
+            chunk size is decided by short_chunk_threshold and short_chunk_size.
+            A minus value means seeing full left context.
+            This also will be used only when dynamic_chunk_training is True.
+        causal (bool): Whether to use causal convolution in conformer encoder
+            layer. This MUST be True when using dynamic_chunk_training.
     """
 
     def __init__(
@@ -61,6 +81,11 @@ class Conformer(EncoderInterface):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         cnn_module_kernel: int = 31,
+        dynamic_chunk_training: bool = False,
+        short_chunk_threshold: float = 0.75,
+        short_chunk_size: int = 25,
+        num_left_chunks: int = -1,
+        causal: bool = False,
     ) -> None:
         super(Conformer, self).__init__()
 
@@ -76,6 +101,14 @@ class Conformer(EncoderInterface):
         #   (2) embedding: num_features -> d_model
         self.encoder_embed = Conv2dSubsampling(num_features, d_model)
 
+        self.encoder_layers = num_encoder_layers
+        self.d_model = d_model
+        self.dynamic_chunk_training = dynamic_chunk_training
+        self.short_chunk_threshold = short_chunk_threshold
+        self.short_chunk_size = short_chunk_size
+        self.num_left_chunks = num_left_chunks
+        self.causal = causal
+
         self.encoder_pos = RelPositionalEncoding(d_model, dropout)
 
         encoder_layer = ConformerEncoderLayer(
@@ -85,6 +118,7 @@ class Conformer(EncoderInterface):
             dropout,
             layer_dropout,
             cnn_module_kernel,
+            causal,
         )
         self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
 
@@ -117,10 +151,31 @@ class Conformer(EncoderInterface):
             # Caution: We assume the subsampling factor is 4!
             lengths = ((x_lens - 1) // 2 - 1) // 2
         assert x.size(0) == lengths.max().item()
-        mask = make_pad_mask(lengths)
 
-        x = self.encoder(
+        src_key_padding_mask = make_pad_mask(lengths)
-            x, pos_emb, src_key_padding_mask=mask, warmup=warmup
+        mask = None
+
+        if self.dynamic_chunk_training:
+            assert (
+                self.causal
+            ), "Causal convolution is required for streaming conformer."
+            max_len = x.size(0)
+            chunk_size = torch.randint(1, max_len, (1,)).item()
+            if chunk_size > (max_len * self.short_chunk_threshold):
+                chunk_size = max_len
+            else:
+                chunk_size = chunk_size % self.short_chunk_size + 1
+
+            mask = ~subsequent_chunk_mask(
+                size=x.size(0), chunk_size=chunk_size,
+                num_left_chunks=self.num_left_chunks, device=x.device
+            )
+
+        x, _ = self.encoder(
+            x, pos_emb,
+            mask=mask,
+            src_key_padding_mask=src_key_padding_mask,
+            warmup=warmup,
         )  # (T, N, C)
 
         x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
@@ -128,6 +183,116 @@ class Conformer(EncoderInterface):
         return x, lengths
 
 
+    def streaming_forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        warmup: float = 1.0,
+        states: Optional[Tensor] = None,
+        chunk_size: int = 16,
+        left_context: int = 64,
+        simulate_streaming: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames in
+            `x` before padding.
+          warmup:
+            A floating point value that gradually increases from 0 throughout
+            training; when it is >= 1.0 we are "fully warmed up".  It is used
+            to turn modules on sequentially.
+          states:
+            The decode states for previous frames which contains the cached data.
+            It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
+            states[0,...] is the attn_cache, states[1,...] is the conv_cache.
+          chunk_size:
+            The chunk size for decoding, this will be used to simulate streaming
+            decoding using masking.
+          left_context:
+            How many old frames the attention can see in current chunk, it MUST
+            be equal to left_context in decode_states.
+          simulate_streaming:
+            If setting True, it will use a masking strategy to simulate streaming
+            fashion (i.e. every chunk data only see limited left context and
+            right context). The whole sequence is supposed to be send at a time
+            When using simulate_streaming.
+        Returns:
+          Return a tuple containing 2 tensors:
+            - logits, its shape is (batch_size, output_seq_len, output_dim)
+            - logit_lens, a tensor of shape (batch_size,) containing the number
+              of frames in `logits` before padding.
+            - decode_states, the updated DecodeStates including the information
+              of current chunk.
+        """
+
+        # x: [N, T, C]
+        # Caution: We assume the subsampling factor is 4!
+        lengths = ((x_lens - 1) // 2 - 1) // 2
+
+        if not simulate_streaming:
+            assert (
+                decode_states is not None
+            ), "Require cache when sending data in streaming mode"
+
+            assert (
+                states.shape == (2, self.encoder_layers, left_context, x.size(0), self.d_model)
+            ), f"""The shape of states MUST be equal to
+             (2, encoder_layers, left_context, batch, d_model) which is
+             {(2, self.encoder_layers, left_context, x.size(0), self.d_model)}
+             given {states.shape}."""
+
+            src_key_padding_mask = make_pad_mask(lengths + left_context)
+
+            embed = self.encoder_embed(x)
+            embed, pos_enc = self.encoder_pos(embed, left_context)
+            embed = embed.permute(1, 0, 2)  # (B, T, F) -> (T, B, F)
+
+            x, states = self.encoder(
+                embed,
+                pos_enc,
+                src_key_padding_mask=src_key_padding_mask,
+                warmup=warmup,
+                states=states,
+                left_context=left_context,
+            )  # (T, B, F)
+
+        else:
+            assert states is None
+
+            src_key_padding_mask = make_pad_mask(lengths)
+            x = self.encoder_embed(x)
+            x, pos_emb = self.encoder_pos(x)
+            x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+            assert x.size(0) == lengths.max().item()
+
+            num_left_chunks = -1
+            if left_context >= 0:
+                assert left_context % chunk_size == 0
+                num_left_chunks = left_context // chunk_size
+
+            mask = ~subsequent_chunk_mask(
+                size=x.size(0),
+                chunk_size=chunk_size,
+                num_left_chunks=num_left_chunks,
+                device=x.device
+            )
+            x, _ = self.encoder(
+                x,
+                pos_emb,
+                mask=mask,
+                src_key_padding_mask=src_key_padding_mask,
+                warmup=warmup,
+            )  # (T, N, C)
+
+        x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        return x, lengths, states
+
+
 class ConformerEncoderLayer(nn.Module):
     """
     ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks.
@@ -139,6 +304,8 @@ class ConformerEncoderLayer(nn.Module):
         dim_feedforward: the dimension of the feedforward network model (default=2048).
         dropout: the dropout value (default=0.1).
         cnn_module_kernel (int): Kernel size of convolution module.
+        causal (bool): Whether to use causal convolution in conformer encoder
+            layer. This MUST be True when using dynamic_chunk_training and streaming decoding.
 
     Examples::
         >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
@@ -155,6 +322,7 @@ class ConformerEncoderLayer(nn.Module):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         cnn_module_kernel: int = 31,
+        causal: bool = False,
     ) -> None:
         super(ConformerEncoderLayer, self).__init__()
 
@@ -182,7 +350,11 @@ class ConformerEncoderLayer(nn.Module):
             ScaledLinear(dim_feedforward, d_model, initial_scale=0.25),
         )
 
-        self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
+        self.conv_module = ConvolutionModule(
+            d_model,
+            cnn_module_kernel,
+            causal=causal
+        )
 
         self.norm_final = BasicNorm(d_model)
 
@@ -200,7 +372,9 @@ class ConformerEncoderLayer(nn.Module):
         src_mask: Optional[Tensor] = None,
         src_key_padding_mask: Optional[Tensor] = None,
         warmup: float = 1.0,
-    ) -> Tensor:
+        states: Optional[Tensor] = None,
+        left_context: int = 0,
+    ) -> Tuple[Tensor, Tensor]:
         """
         Pass the input through the encoder layer.
 
@@ -211,10 +385,17 @@ class ConformerEncoderLayer(nn.Module):
             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.
+            states:
+              The decode states for previous frames which contains the cached data.
+              It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
+              states[0,...] is the attn_cache, states[1,...] is the conv_cache.
+            left_context: left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Shape:
             src: (S, N, E).
-            pos_emb: (N, 2*S-1, E)
+            pos_emb: (N, 2*S-1, E),for streaming decoding it is (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
@@ -236,19 +417,38 @@ class ConformerEncoderLayer(nn.Module):
         # macaron style feed forward module
         src = src + self.dropout(self.feed_forward_macaron(src))
 
+        key = src
+        val = src
+        if not self.training and states is not None:
+            # src: [chunk_size, N, F] e.g. [8, 41, 512]
+            key = torch.cat([states[0, ...], src], dim=0)
+            val = key
+            states[0, ...] = key[-left_context, ...]
+        else:
+            assert left_context == 0
+
         # multi-headed self-attention module
         src_att = self.self_attn(
             src,
-            src,
+            key,
-            src,
+            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
-        src = src + self.dropout(self.conv_module(src))
+        if not self.training and states is not None:
+            src = torch.cat([states[1, ...], src], dim=0)
+            states[1, ...] = src[-left_context, ...]
+
+        conv = self.conv_module(src)
+        conv = conv[-src.size(0) :, :, :]  # noqa: E203
+
+        src = src + self.dropout(conv)
 
         # feed forward module
         src = src + self.dropout(self.feed_forward(src))
@@ -258,7 +458,7 @@ class ConformerEncoderLayer(nn.Module):
         if alpha != 1.0:
             src = alpha * src + (1 - alpha) * src_orig
 
-        return src
+        return src, states
 
 
 class ConformerEncoder(nn.Module):
@@ -290,7 +490,9 @@ class ConformerEncoder(nn.Module):
         mask: Optional[Tensor] = None,
         src_key_padding_mask: Optional[Tensor] = None,
         warmup: float = 1.0,
-    ) -> Tensor:
+        states: Optional[Tensor] = None,
+        left_context: int = 0,
+    ) -> Tuple[Tensor, Tensor]:
         r"""Pass the input through the encoder layers in turn.
 
         Args:
@@ -298,10 +500,19 @@ class ConformerEncoder(nn.Module):
             pos_emb: Positional embedding tensor (required).
             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.
+            states:
+              The decode states for previous frames which contains the cached data.
+              It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
+              states[0,...] is the attn_cache, states[1,...] is the conv_cache.
+            left_context: left context (in frames) used during streaming decoding.
+                this is used only in real streaming decoding, in other circumstances,
+                it MUST be 0.
 
         Shape:
             src: (S, N, E).
-            pos_emb: (N, 2*S-1, E)
+            pos_emb: (N, 2*S-1, E), for streaming decoding it is (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
@@ -309,16 +520,26 @@ class ConformerEncoder(nn.Module):
         """
         output = src
 
-        for i, mod in enumerate(self.layers):
+        if self.training:
-            output = mod(
+            assert left_context == 0
+            assert states is None
+        else:
+            assert left_context >= 0
+
+        for layer_index, mod in enumerate(self.layers):
+            output, cache = mod(
                 output,
                 pos_emb,
                 src_mask=mask,
                 src_key_padding_mask=src_key_padding_mask,
                 warmup=warmup,
+                states=None if states is None else states[:, layer_index, ...],
+                left_context=left_context,
             )
+            if states is not None:
+                states[:, layer_index, ...] = cache
 
-        return output
+        return output, states
 
 
 class RelPositionalEncoding(torch.nn.Module):
@@ -344,12 +565,13 @@ 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, context: int = 0) -> None:
         """Reset the positional encodings."""
+        x_size_1 = x.size(1) + 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
@@ -359,9 +581,9 @@ class RelPositionalEncoding(torch.nn.Module):
         # Suppose `i` means to the position of query vecotr 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_positive = torch.zeros(x_size_1, self.d_model)
-        pe_negative = 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)
+        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)
@@ -379,24 +601,32 @@ 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,
+            context: int = 0
+        ) -> Tuple[Tensor, Tensor]:
         """Add positional encoding.
 
         Args:
             x (torch.Tensor): Input tensor (batch, time, `*`).
+            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, context)
+        x_size_1 = x.size(1) + 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),
+            + x_size_1,
         ]
         return self.dropout(x), self.dropout(pos_emb)
 
@@ -466,6 +696,7 @@ 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:
@@ -479,6 +710,9 @@ 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:
@@ -524,14 +758,18 @@ 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)
@@ -539,14 +777,17 @@ 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 == 2 * time2 - 1, f"{n} == 2 * {time2} - 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),
         )
@@ -568,6 +809,7 @@ 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:
@@ -585,6 +827,9 @@ 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:
@@ -748,7 +993,8 @@ 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
@@ -768,9 +1014,9 @@ 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
@@ -805,6 +1051,24 @@ class RelPositionMultiheadAttention(nn.Module):
             )
 
         attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1)
+
+        # If we are using dynamic_chunk_training and setting a limited
+        # num_left_chunks, the attention may only see the padding values which
+        # will also be masked out by `key_padding_mask`, at this circumstances,
+        # the whole column of `attn_output_weights` will be `-inf`
+        # (i.e. be `nan` after softmax), so, we fill `0.0` at the masking
+        # positions to avoid invalid loss value below.
+        if attn_mask is not None and attn_mask.dtype == torch.bool and \
+                key_padding_mask is not None:
+            combined_mask = attn_mask.unsqueeze(
+                0) | key_padding_mask.unsqueeze(1).unsqueeze(2)
+            attn_output_weights = attn_output_weights.view(
+                bsz, num_heads, tgt_len, src_len)
+            attn_output_weights = attn_output_weights.masked_fill(
+                combined_mask, 0.0)
+            attn_output_weights = attn_output_weights.view(
+                bsz * num_heads, tgt_len, src_len)
+
         attn_output_weights = nn.functional.dropout(
             attn_output_weights, p=dropout_p, training=training
         )
@@ -838,16 +1102,21 @@ class ConvolutionModule(nn.Module):
         channels (int): The number of channels of conv layers.
         kernel_size (int): Kernerl size of conv layers.
         bias (bool): Whether to use bias in conv layers (default=True).
-
+        causal (bool): Whether to use causal convolution.
     """
 
     def __init__(
-        self, channels: int, kernel_size: int, bias: bool = True
+        self,
+        channels: int,
+        kernel_size: int,
+        bias: bool = True,
+        causal: bool = False
     ) -> None:
         """Construct an ConvolutionModule object."""
         super(ConvolutionModule, self).__init__()
         # kernerl_size should be a odd number for 'SAME' padding
         assert (kernel_size - 1) % 2 == 0
+        self.causal = causal
 
         self.pointwise_conv1 = ScaledConv1d(
             channels,
@@ -875,12 +1144,17 @@ class ConvolutionModule(nn.Module):
             channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0
         )
 
+        self.lorder = kernel_size - 1
+        padding = (kernel_size - 1) // 2
+        if self.causal:
+            padding = 0
+
         self.depthwise_conv = ScaledConv1d(
             channels,
             channels,
             kernel_size,
             stride=1,
-            padding=(kernel_size - 1) // 2,
+            padding=padding,
             groups=channels,
             bias=bias,
         )
@@ -921,6 +1195,10 @@ class ConvolutionModule(nn.Module):
         x = nn.functional.glu(x, dim=1)  # (batch, channels, time)
 
         # 1D Depthwise Conv
+        if self.causal and self.lorder > 0:
+            # Make depthwise_conv causal by
+            # manualy padding self.lorder zeros to the left
+            x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
         x = self.depthwise_conv(x)
 
         x = self.deriv_balancer2(x)

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -53,6 +53,18 @@ Usage:
         --beam 4 \
         --max-contexts 4 \
         --max-states 8
+
+(5) decode in streaming mode (take greedy search as an example)
+./pruned_transducer_stateless2/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --simulate-streaming 1 \
+    --causal-convolution 1 \
+    --right-chunk-size 16 \
+    --left-context 64 \
+    --exp-dir ./pruned_transducer_stateless2/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
 """
 
 
@@ -85,6 +97,7 @@ from icefall.utils import (
     AttributeDict,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
@@ -190,6 +203,7 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
+
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
@@ -198,6 +212,70 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    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.
+        Note: not needed for decoding, adding it here to construct transducer model,
+              as we reuse the code in train.py.
+        """,
+    )
+
+    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).
+        Note: not needed for decoding, adding it here to construct transducer model,
+              as we reuse the code in train.py.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-left-chunks",
+        type=int,
+        default=4,
+        help="""How many left context can be seen in chunks when calculating attention.
+        Note: not needed for decoding, adding it here to construct transducer model,
+              as we reuse the code in train.py.
+        """,
+    )
+
+    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(
+        "--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(
+        "--right-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)",
+    )
+
     return parser
 
 
@@ -246,9 +324,19 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    encoder_out, encoder_out_lens = model.encoder(
+    if params.simulate_streaming:
-        x=feature, x_lens=feature_lens
+        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
-    )
+            x=feature,
+            x_lens=feature_lens,
+            chunk_size=params.right_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":
@@ -461,6 +549,10 @@ def main():
     else:
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
+    if params.simulate_streaming:
+        params.suffix += f"-streaming-chunk-size-{params.right_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}"
@@ -490,6 +582,11 @@ def main():
     params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
 
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
     logging.info(params)
 
     logging.info("About to create model")

egs/librispeech/ASR/pruned_transducer_stateless2/model.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 
+import logging
 import k2
 import torch
 import torch.nn as nn
@@ -77,6 +78,8 @@ class Transducer(nn.Module):
         am_scale: float = 0.0,
         lm_scale: float = 0.0,
         warmup: float = 1.0,
+        delay_penalty: float = 0.0,
+        return_sym_delay: bool = False,
     ) -> torch.Tensor:
         """
         Args:
@@ -154,10 +157,31 @@ class Transducer(nn.Module):
                 lm_only_scale=lm_scale,
                 am_only_scale=am_scale,
                 boundary=boundary,
+                delay_penalty=delay_penalty,
                 reduction="sum",
                 return_grad=True,
             )
 
+        sym_delay = None
+        if return_sym_delay:
+            B, S, T0 = px_grad.shape
+            T = T0 - 1
+            if boundary is None:
+                offset = torch.tensor(
+                    (T - 1) / 2,
+                    dtype=px_grad.dtype,
+                    device=px_grad.device,
+                ).expand(B, 1, 1)
+                total_syms = S * B
+            else:
+                offset = (boundary[:, 3] - 1) / 2
+                total_syms = torch.sum(boundary[:, 2])
+            offset = torch.arange(
+                T0, device=px_grad.device
+            ).reshape(1, 1, T0) - offset.reshape(B, 1, 1)
+            sym_delay = px_grad * offset
+            sym_delay = torch.sum(sym_delay) / total_syms
+
         # ranges : [B, T, prune_range]
         ranges = k2.get_rnnt_prune_ranges(
             px_grad=px_grad,
@@ -186,8 +210,9 @@ class Transducer(nn.Module):
                 symbols=y_padded,
                 ranges=ranges,
                 termination_symbol=blank_id,
+                delay_penalty=delay_penalty,
                 boundary=boundary,
                 reduction="sum",
             )
 
-        return (simple_loss, pruned_loss)
+        return (simple_loss, pruned_loss, sym_delay)

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -40,6 +40,19 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --full-libri 1 \
   --max-duration 550
 
+# train a streaming model
+./pruned_transducer_stateless2/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir pruned_transducer_stateless/exp \
+  --full-libri 1 \
+  --dynamic-chunk-training 1 \
+  --causal-convolution 1 \
+  --short-chunk-size 25 \
+  --num-left-chunks 4 \
+  --max-duration 300
+
 """
 
 
@@ -263,6 +276,59 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    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.",
+    )
+
+    parser.add_argument(
+        "--delay-penalty",
+        type=float,
+        default=0.0,
+        help="""A constant value to penalize symbol delay, this may be
+         needed when training with time masking, to avoid the time masking
+         encouraging the network to delay symbols.
+         """,
+    )
+
+    parser.add_argument(
+        "--return-sym-delay",
+        type=str2bool,
+        default=False,
+        help="""Whether to return `sym_delay` during training, this is a stat
+        to measure symbols emission delay, especially for time masking training.
+        """,
+    )
+
     return parser
 
 
@@ -349,6 +415,10 @@ 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
 
@@ -541,7 +611,7 @@ def compute_loss(
     y = k2.RaggedTensor(y).to(device)
 
     with torch.set_grad_enabled(is_training):
-        simple_loss, pruned_loss = model(
+        simple_loss, pruned_loss, sym_delay = model(
             x=feature,
             x_lens=feature_lens,
             y=y,
@@ -549,6 +619,8 @@ def compute_loss(
             am_scale=params.am_scale,
             lm_scale=params.lm_scale,
             warmup=warmup,
+            delay_penalty=params.delay_penalty,
+            return_sym_delay=params.return_sym_delay,
         )
         # after the main warmup step, we keep pruned_loss_scale small
         # for the same amount of time (model_warm_step), to avoid
@@ -577,6 +649,9 @@ def compute_loss(
     info["loss"] = loss.detach().cpu().item()
     info["simple_loss"] = simple_loss.detach().cpu().item()
     info["pruned_loss"] = pruned_loss.detach().cpu().item()
+    
+    if params.return_sym_delay:
+        info["sym_delay"] = sym_delay.detach().cpu().item()
 
     return loss, info
 
@@ -806,6 +881,15 @@ def run(rank, world_size, args):
     params.blank_id = sp.piece_to_id("<blk>")
     params.vocab_size = sp.get_piece_size()
 
+    if params.dynamic_chunk_training:
+        assert (
+            params.causal_convolution
+        ), "dynamic_chunk_training requires causal convolution"
+    else:
+        assert (
+            params.delay_penalty == 0.0
+        ), "delay_penalty is intended for dynamic_chunk_training"
+
     logging.info(params)
 
     logging.info("About to create model")

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -18,7 +18,7 @@
 import copy
 import math
 import warnings
-from typing import List, Optional, Tuple
+from typing import Optional, Tuple
 
 import torch
 from torch import Tensor, nn
@@ -27,70 +27,6 @@ from transformer import Transformer
 from icefall.utils import make_pad_mask, subsequent_chunk_mask
 
 
-class DecodeStates(object):
-    def __init__(self,
-                 layers: int,
-                 left_context: int,
-                 dim: int,
-                 init: bool = True,
-                 dtype: torch.dtype = torch.float32,
-                 device: torch.device = torch.device('cpu')):
-        self.layers = layers
-        self.left_context = left_context
-        self.dim = dim
-        self.dtype = dtype
-        self.device = device
-        if init:
-            # shape (layer, T, dim)
-            self.attn_cache = torch.zeros((layers, left_context, dim),
-                                          dtype=dtype,
-                                          device=device)
-            self.conv_cache = torch.zeros((layers, left_context, dim),
-                                          dtype=dtype,
-                                          device=device)
-            self.offset = torch.tensor([0], dtype=dtype, device=device)
-
-    @staticmethod
-    def stack(states: List['DecodeStates']) -> 'DecodeStates':
-        assert len(states) >= 1
-        obj = DecodeStates(layers=states[0].layers,
-                           left_context=states[0].left_context,
-                           dim=states[0].dim,
-                           init=False,
-                           dtype=states[0].dtype,
-                           device=states[0].device)
-        attn_cache = []
-        conv_cache = []
-        offset = []
-        for i in range(len(states)):
-            attn_cache.append(states[i].attn_cache)
-            conv_cache.append(states[i].conv_cache)
-            offset.append(states[i].offset)
-        obj.attn_cache = torch.stack(attn_cache, dim=2)
-        obj.conv_cache = torch.stack(conv_cache, dim=2)
-        obj.offset = torch.stack(offset, dim=0)
-        return obj
-
-    @staticmethod
-    def unstack(states: 'DecodeStates') -> List['DecodeStates']:
-        results = []
-        attn_cache = torch.unbind(states.attn_cache, dim=2)
-        conv_cache = torch.unbind(states.conv_cache, dim=2)
-        offset = torch.unbind(states.offset, dim=0)
-        for i in range(states.attn_cache.size(2)):
-            obj = DecodeStates(layers=states.layers,
-                               left_context=states.left_context,
-                               dim=states.dim,
-                               init=False,
-                               dtype=states.dtype,
-                               device=states.device)
-            obj.attn_cache = attn_cache[i]
-            obj.conv_cache = conv_cache[i]
-            obj.offset = offset[i]
-            results.append(obj)
-        return results
-
-
 class Conformer(Transformer):
     """
     Args:
@@ -119,7 +55,7 @@ class Conformer(Transformer):
             size equals to or less than ``max_len * short_chunk_threshold``, the
             chunk size will be sampled uniformly from 1 to short_chunk_size.
             This also will be used only when dynamic_chunk_training is True.
-        num_left_chunks (int): the left context attention can see in chunks, the
+        num_left_chunks (int): the left context (in chunks) attention can see, the
             chunk size is decided by short_chunk_threshold and short_chunk_size.
             A minus value means seeing full left context.
             This also will be used only when dynamic_chunk_training is True.
@@ -159,6 +95,8 @@ class Conformer(Transformer):
             vgg_frontend=vgg_frontend,
         )
 
+        self.encoder_layers = num_encoder_layers
+        self.d_model = d_model
         self.dynamic_chunk_training = dynamic_chunk_training
         self.short_chunk_threshold = short_chunk_threshold
         self.short_chunk_size = short_chunk_size
@@ -231,7 +169,7 @@ class Conformer(Transformer):
                 num_left_chunks=self.num_left_chunks, device=x.device
             )
 
-        x = self.encoder(
+        x, _ = self.encoder(
             x, pos_emb, mask=mask, src_key_padding_mask=src_key_padding_mask
         )  # (T, N, C)
 
@@ -248,11 +186,11 @@ class Conformer(Transformer):
         self,
         x: torch.Tensor,
         x_lens: torch.Tensor,
-        decode_states: Optional[DecodeStates] = None,
+        states: Optional[torch.Tensor] = None,
         chunk_size: int = 16,
         left_context: int = 64,
         simulate_streaming: bool = False,
-    ) -> Tuple[torch.Tensor, torch.Tensor, DecodeStates]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Args:
           x:
@@ -260,9 +198,10 @@ class Conformer(Transformer):
           x_lens:
             A tensor of shape (batch_size,) containing the number of frames in
             `x` before padding.
-          decode_states:
+          states:
-            The decode states for previous frames which contains the cached data
+            The decode states for previous frames which contains the cached data.
-            and the offset of current chunk in the whole sequence.
+            It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
+            states[0,...] is the attn_cache, states[1,...] is the conv_cache.
           chunk_size:
             The chunk size for decoding, this will be used to simulate streaming
             decoding using masking.
@@ -289,13 +228,15 @@ class Conformer(Transformer):
 
         if not simulate_streaming:
             assert (
-                decode_states is not None
+                states is not None
             ), "Require cache when sending data in streaming mode"
+
             assert (
-                left_context == decode_states.left_context
+                states.shape == (2, self.encoder_layers, left_context, x.size(0), self.d_model)
-            ), f"""The given left_context must equal to the left_context in
+            ), f"""The shape of states MUST be equal to
-            `decode_states`, need {decode_states.left_context} given
+             (2, encoder_layers, left_context, batch, d_model) which is
-            {left_context}."""
+             {(2, self.encoder_layers, left_context, x.size(0), self.d_model)}
+             given {states.shape}."""
 
             src_key_padding_mask = make_pad_mask(lengths + left_context)
 
@@ -303,18 +244,16 @@ class Conformer(Transformer):
             embed, pos_enc = self.encoder_pos(embed, left_context)
             embed = embed.permute(1, 0, 2)  # (B, T, F) -> (T, B, F)
 
-            x = self.encoder(
+            x, states = self.encoder(
                 embed,
                 pos_enc,
                 src_key_padding_mask=src_key_padding_mask,
-                attn_cache=decode_states.attn_cache,
+                states=states,
-                conv_cache=decode_states.conv_cache,
+                left_context=left_context,
-                left_context=decode_states.left_context,
             )  # (T, B, F)
 
-            decode_states.offset += embed.size(0)
         else:
-            assert decode_states is None
+            assert states is None
 
             src_key_padding_mask = make_pad_mask(lengths)
             x = self.encoder_embed(x)
@@ -322,8 +261,11 @@ class Conformer(Transformer):
             x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
             assert x.size(0) == lengths.max().item()
-            assert left_context % chunk_size == 0
+
-            num_left_chunks = left_context // chunk_size
+            num_left_chunks = -1
+            if left_context >= 0:
+                assert left_context % chunk_size == 0
+                num_left_chunks = left_context // chunk_size
 
             mask = ~subsequent_chunk_mask(
                 size=x.size(0),
@@ -331,7 +273,7 @@ class Conformer(Transformer):
                 num_left_chunks=num_left_chunks,
                 device=x.device
             )
-            x = self.encoder(
+            x, _ = self.encoder(
                 x,
                 pos_emb,
                 mask=mask,
@@ -344,7 +286,7 @@ class Conformer(Transformer):
         logits = self.encoder_output_layer(x)
         logits = logits.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
 
-        return logits, lengths, decode_states
+        return logits, lengths, states
 
 
 class ConformerEncoderLayer(nn.Module):
@@ -425,10 +367,9 @@ class ConformerEncoderLayer(nn.Module):
         pos_emb: Tensor,
         src_mask: Optional[Tensor] = None,
         src_key_padding_mask: Optional[Tensor] = None,
-        attn_cache: Optional[Tensor] = None,
+        states: Optional[Tensor] = None,
-        conv_cache: Optional[Tensor] = None,
         left_context: int = 0,
-    ) -> Tensor:
+    ) -> Tuple[Tensor, Tensor]:
         """
         Pass the input through the encoder layer.
 
@@ -437,9 +378,10 @@ class ConformerEncoderLayer(nn.Module):
             pos_emb: Positional embedding tensor (required).
             src_mask: the mask for the src sequence (optional).
             src_key_padding_mask: the mask for the src keys per batch (optional).
-            attn_cache: attention cache for previous frames.
+            states: The decode states for previous frames which contains the cached data.
-            conv_cache: convolution cache for previous frames.
+                It has a shape of (2, left_context, batch, attention_dim),
-            left_context: left context in frames used during streaming decoding.
+                states[0,...] is the attn_cache, states[1,...] is the conv_cache.
+            left_context: left context (in frames) used during streaming decoding.
                 this is used only in real streaming decoding, in other circumstances,
                 it MUST be 0.
         Shape:
@@ -467,11 +409,11 @@ class ConformerEncoderLayer(nn.Module):
 
         key = src
         val = src
-        if not self.training and attn_cache is not None:
+        if not self.training and states is not None:
             # src: [chunk_size, N, F] e.g. [8, 41, 512]
-            key = torch.cat([attn_cache, src], dim=0)
+            key = torch.cat([states[0, ...], src], dim=0)
             val = key
-            attn_cache = key
+            states[0, ...] = key[-left_context:, ...]
         else:
             assert left_context == 0
 
@@ -493,9 +435,9 @@ class ConformerEncoderLayer(nn.Module):
         if self.normalize_before:
             src = self.norm_conv(src)
 
-        if not self.training and conv_cache is not None:
+        if not self.training and states is not None:
-            src = torch.cat([conv_cache, src], dim=0)
+            src = torch.cat([states[1, ...], src], dim=0)
-            conv_cache = src
+            states[1, ...] = src[-left_context:, ...]
 
         src = self.conv_module(src)
         src = src[-residual.size(0) :, :, :]  # noqa: E203
@@ -515,7 +457,7 @@ class ConformerEncoderLayer(nn.Module):
         if self.normalize_before:
             src = self.norm_final(src)
 
-        return src, attn_cache, conv_cache
+        return src, states
 
 
 class ConformerEncoder(nn.Module):
@@ -546,10 +488,9 @@ class ConformerEncoder(nn.Module):
         pos_emb: Tensor,
         mask: Optional[Tensor] = None,
         src_key_padding_mask: Optional[Tensor] = None,
-        attn_cache: Optional[Tensor] = None,
+        states: Optional[Tensor] = None,
-        conv_cache: Optional[Tensor] = None,
         left_context: int = 0,
-    ) -> Tensor:
+    ) -> Tuple[Tensor, Tensor]:
         r"""Pass the input through the encoder layers in turn.
 
         Args:
@@ -557,9 +498,10 @@ class ConformerEncoder(nn.Module):
             pos_emb: Positional embedding tensor (required).
             mask: the mask for the src sequence (optional).
             src_key_padding_mask: the mask for the src keys per batch (optional).
-            attn_cache: attention cache for previous frames.
+            states: The decode states for previous frames which contains the cached data.
-            conv_cache: convolution cache for previous frames.
+                It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
-            left_context: left context in frames used during streaming decoding.
+                states[0,...] is the attn_cache, states[1,...] is the conv_cache.
+            left_context: left context (in frames) used during streaming decoding.
                 this is used only in real streaming decoding, in other circumstances,
                 it MUST be 0.
         Shape:
@@ -576,26 +518,23 @@ class ConformerEncoder(nn.Module):
 
         if self.training:
             assert left_context == 0
-            assert attn_cache is None
+            assert states is None
-            assert conv_cache is None
         else:
             assert left_context >= 0
 
         for layer_index, mod in enumerate(self.layers):
-            output, a_cache, c_cache = mod(
+            output, cache = mod(
                 output,
                 pos_emb,
                 src_mask=mask,
                 src_key_padding_mask=src_key_padding_mask,
-                attn_cache=None if attn_cache is None else attn_cache[layer_index],
+                states=None if states is None else states[:,layer_index, ...],
-                conv_cache=None if conv_cache is None else conv_cache[layer_index],
                 left_context=left_context,
             )
-            if attn_cache is not None and conv_cache is not None:
+            if states is not None:
-                attn_cache[layer_index, ...] = a_cache[-left_context:, ...]
+                states[:, layer_index, ...] = cache
-                conv_cache[layer_index, ...] = c_cache[-left_context:, ...]
 
-        return output
+        return output, states
 
 
 class RelPositionalEncoding(torch.nn.Module):
@@ -667,7 +606,7 @@ class RelPositionalEncoding(torch.nn.Module):
 
         Args:
             x (torch.Tensor): Input tensor (batch, time, `*`).
-            context (int): left context in frames used during streaming decoding.
+            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:
@@ -762,7 +701,7 @@ 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.
+            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.
 
@@ -819,7 +758,7 @@ class RelPositionMultiheadAttention(nn.Module):
         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.
+            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.
 
@@ -879,7 +818,7 @@ 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.
+            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.
 

icefall/utils.py

@@ -535,8 +535,11 @@ class MetricsTracker(collections.defaultdict):
         ans = []
         for k, v in self.items():
             if k != "frames":
-                norm_value = float(v) / num_frames
+                if k != "sym_delay":
-                ans.append((k, norm_value))
+                    norm_value = float(v) / num_frames
+                    ans.append((k, norm_value))
+                else:
+                    ans.append((k, float(v)))
         return ans
 
     def reduce(self, device):