Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

funasr/models/mossformer/e2e_ss.py

@@ -0,0 +1,113 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import copy
+from funasr.models.base_model import FunASRModel
+from funasr.models.encoder.mossformer_encoder import MossFormerEncoder, MossFormer_MaskNet
+from funasr.models.decoder.mossformer_decoder import MossFormerDecoder
+
+
+class MossFormer(FunASRModel):
+    """The MossFormer model for separating input mixed speech into different speaker's speech.
+
+    Arguments
+    ---------
+    in_channels : int
+        Number of channels at the output of the encoder.
+    out_channels : int
+        Number of channels that would be inputted to the intra and inter blocks.
+    num_blocks : int
+        Number of layers of Dual Computation Block.
+    norm : str
+        Normalization type.
+    num_spks : int
+        Number of sources (speakers).
+    skip_around_intra : bool
+        Skip connection around intra.
+    use_global_pos_enc : bool
+        Global positional encodings.
+    max_length : int
+        Maximum sequence length.
+    kernel_size: int
+        Encoder and decoder kernel size
+    """
+
+    def __init__(
+        self,
+        in_channels=512,
+        out_channels=512,
+        num_blocks=24,
+        kernel_size=16,
+        norm="ln",
+        num_spks=2,
+        skip_around_intra=True,
+        use_global_pos_enc=True,
+        max_length=20000,
+    ):
+        """Initialize MossFormer.
+        
+            Args:
+                in_channels: TODO.
+                out_channels: TODO.
+                num_blocks: TODO.
+                kernel_size: Size/dimension parameter.
+                norm: TODO.
+                num_spks: TODO.
+                skip_around_intra: TODO.
+                use_global_pos_enc: TODO.
+                max_length: TODO.
+            """
+        super(MossFormer, self).__init__()
+        self.num_spks = num_spks
+        # Encoding
+        self.enc = MossFormerEncoder(
+            kernel_size=kernel_size, out_channels=in_channels, in_channels=1
+        )
+
+        ##Compute Mask
+        self.mask_net = MossFormer_MaskNet(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            num_blocks=num_blocks,
+            norm=norm,
+            num_spks=num_spks,
+            skip_around_intra=skip_around_intra,
+            use_global_pos_enc=use_global_pos_enc,
+            max_length=max_length,
+        )
+        self.dec = MossFormerDecoder(
+            in_channels=out_channels,
+            out_channels=1,
+            kernel_size=kernel_size,
+            stride=kernel_size // 2,
+            bias=False,
+        )
+
+    def forward(self, input):
+        """Forward pass for training.
+        
+            Args:
+                input: Input audio/text data.
+            """
+        x = self.enc(input)
+        mask = self.mask_net(x)
+        x = torch.stack([x] * self.num_spks)
+        sep_x = x * mask
+
+        # Decoding
+        est_source = torch.cat(
+            [self.dec(sep_x[i]).unsqueeze(-1) for i in range(self.num_spks)],
+            dim=-1,
+        )
+        T_origin = input.size(1)
+        T_est = est_source.size(1)
+        if T_origin > T_est:
+            est_source = F.pad(est_source, (0, 0, 0, T_origin - T_est))
+        else:
+            est_source = est_source[:, :T_origin, :]
+
+        out = []
+        for spk in range(self.num_spks):
+            out.append(est_source[:, :, spk])
+        return out

funasr/models/mossformer/mossformer.py

@@ -0,0 +1,422 @@
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange
+
+
+def identity(t, *args, **kwargs):
+    """Identity.
+    
+        Args:
+            t: TODO.
+            *args: Variable positional arguments.
+            **kwargs: Additional keyword arguments.
+        """
+    return t
+
+
+def append_dims(x, num_dims):
+    """Append dims.
+    
+        Args:
+            x: TODO.
+            num_dims: TODO.
+        """
+    if num_dims <= 0:
+        return x
+    return x.view(*x.shape, *((1,) * num_dims))
+
+
+def exists(val):
+    """Exists.
+    
+        Args:
+            val: TODO.
+        """
+    return val is not None
+
+
+def default(val, d):
+    """Default.
+    
+        Args:
+            val: TODO.
+            d: TODO.
+        """
+    return val if exists(val) else d
+
+
+def padding_to_multiple_of(n, mult):
+    """Padding to multiple of.
+    
+        Args:
+            n: TODO.
+            mult: TODO.
+        """
+    remainder = n % mult
+    if remainder == 0:
+        return 0
+    return mult - remainder
+
+
+class Transpose(nn.Module):
+    """Wrapper class of torch.transpose() for Sequential module."""
+
+    def __init__(self, shape: tuple):
+        """Initialize Transpose.
+        
+            Args:
+                shape: TODO.
+            """
+        super(Transpose, self).__init__()
+        self.shape = shape
+
+    def forward(self, x):
+        """Forward pass for training.
+        
+            Args:
+                x: TODO.
+            """
+        return x.transpose(*self.shape)
+
+
+class DepthwiseConv1d(nn.Module):
+    """
+    When groups == in_channels and out_channels == K * in_channels, where K is a positive integer,
+    this operation is termed in literature as depthwise convolution.
+    Args:
+        in_channels (int): Number of channels in the input
+        out_channels (int): Number of channels produced by the convolution
+        kernel_size (int or tuple): Size of the convolving kernel
+        stride (int, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0
+        bias (bool, optional): If True, adds a learnable bias to the output. Default: True
+    Inputs: inputs
+        - **inputs** (batch, in_channels, time): Tensor containing input vector
+    Returns: outputs
+        - **outputs** (batch, out_channels, time): Tensor produces by depthwise 1-D convolution.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        bias: bool = False,
+    ) -> None:
+        """Initialize DepthwiseConv1d.
+        
+            Args:
+                in_channels: TODO.
+                out_channels: TODO.
+                kernel_size: Size/dimension parameter.
+                stride: TODO.
+                padding: TODO.
+                bias: TODO.
+            """
+        super(DepthwiseConv1d, self).__init__()
+        assert (
+            out_channels % in_channels == 0
+        ), "out_channels should be constant multiple of in_channels"
+        self.conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            groups=in_channels,
+            stride=stride,
+            padding=padding,
+            bias=bias,
+        )
+
+    def forward(self, inputs):
+        """Forward pass for training.
+        
+            Args:
+                inputs: TODO.
+            """
+        return self.conv(inputs)
+
+
+class ConvModule(nn.Module):
+    """
+    Conformer convolution module starts with a pointwise convolution and a gated linear unit (GLU).
+    This is followed by a single 1-D depthwise convolution layer. Batchnorm is  deployed just after the convolution
+    to aid training deep models.
+    Args:
+        in_channels (int): Number of channels in the input
+        kernel_size (int or tuple, optional): Size of the convolving kernel Default: 31
+        dropout_p (float, optional): probability of dropout
+    Inputs: inputs
+        inputs (batch, time, dim): Tensor contains input sequences
+    Outputs: outputs
+        outputs (batch, time, dim): Tensor produces by conformer convolution module.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        kernel_size: int = 17,
+        expansion_factor: int = 2,
+        dropout_p: float = 0.1,
+    ) -> None:
+        """Initialize ConvModule.
+        
+            Args:
+                in_channels: TODO.
+                kernel_size: Size/dimension parameter.
+                expansion_factor: TODO.
+                dropout_p: TODO.
+            """
+        super(ConvModule, self).__init__()
+        assert (kernel_size - 1) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
+        assert expansion_factor == 2, "Currently, Only Supports expansion_factor 2"
+
+        self.sequential = nn.Sequential(
+            Transpose(shape=(1, 2)),
+            DepthwiseConv1d(
+                in_channels, in_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2
+            ),
+        )
+
+    def forward(self, inputs):
+        """Forward pass for training.
+        
+            Args:
+                inputs: TODO.
+            """
+        return inputs + self.sequential(inputs).transpose(1, 2)
+
+
+class OffsetScale(nn.Module):
+    def __init__(self, dim, heads=1):
+        """Initialize OffsetScale.
+        
+            Args:
+                dim: TODO.
+                heads: TODO.
+            """
+        super().__init__()
+        self.gamma = nn.Parameter(torch.ones(heads, dim))
+        self.beta = nn.Parameter(torch.zeros(heads, dim))
+        nn.init.normal_(self.gamma, std=0.02)
+
+    def forward(self, x):
+        """Forward pass for training.
+        
+            Args:
+                x: TODO.
+            """
+        out = einsum("... d, h d -> ... h d", x, self.gamma) + self.beta
+        return out.unbind(dim=-2)
+
+
+class FFConvM(nn.Module):
+    def __init__(self, dim_in, dim_out, norm_klass=nn.LayerNorm, dropout=0.1):
+        """Initialize FFConvM.
+        
+            Args:
+                dim_in: TODO.
+                dim_out: TODO.
+                norm_klass: TODO.
+                dropout: TODO.
+            """
+        super().__init__()
+        self.mdl = nn.Sequential(
+            norm_klass(dim_in),
+            nn.Linear(dim_in, dim_out),
+            nn.SiLU(),
+            ConvModule(dim_out),
+            nn.Dropout(dropout),
+        )
+
+    def forward(
+        self,
+        x,
+    ):
+        """Forward pass for training.
+        
+            Args:
+                x: TODO.
+            """
+        output = self.mdl(x)
+        return output
+
+
+class FLASH_ShareA_FFConvM(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        group_size=256,
+        query_key_dim=128,
+        expansion_factor=1.0,
+        causal=False,
+        dropout=0.1,
+        rotary_pos_emb=None,
+        norm_klass=nn.LayerNorm,
+        shift_tokens=True
+    ):
+        """Initialize FLASH_ShareA_FFConvM."""
+        super().__init__()
+        hidden_dim = int(dim * expansion_factor)
+        self.group_size = group_size
+        self.causal = causal
+        self.shift_tokens = shift_tokens
+
+        # positional embeddings
+        self.rotary_pos_emb = rotary_pos_emb
+        # norm
+        self.dropout = nn.Dropout(dropout)
+        # projections
+
+        self.to_hidden = FFConvM(
+            dim_in=dim,
+            dim_out=hidden_dim,
+            norm_klass=norm_klass,
+            dropout=dropout,
+        )
+        self.to_qk = FFConvM(
+            dim_in=dim,
+            dim_out=query_key_dim,
+            norm_klass=norm_klass,
+            dropout=dropout,
+        )
+
+        self.qk_offset_scale = OffsetScale(query_key_dim, heads=4)
+
+        self.to_out = FFConvM(
+            dim_in=dim * 2,
+            dim_out=dim,
+            norm_klass=norm_klass,
+            dropout=dropout,
+        )
+
+        self.gateActivate = nn.Sigmoid()
+
+    def forward(self, x, *, mask=None):
+        """
+        b - batch
+        n - sequence length (within groups)
+        g - group dimension
+        d - feature dimension (keys)
+        e - feature dimension (values)
+        i - sequence dimension (source)
+        j - sequence dimension (target)
+        """
+
+        normed_x = x
+
+        # do token shift - a great, costless trick from an independent AI researcher in Shenzhen
+        residual = x
+
+        if self.shift_tokens:
+            x_shift, x_pass = normed_x.chunk(2, dim=-1)
+            x_shift = F.pad(x_shift, (0, 0, 1, -1), value=0.0)
+            normed_x = torch.cat((x_shift, x_pass), dim=-1)
+
+        # initial projections
+
+        v, u = self.to_hidden(normed_x).chunk(2, dim=-1)
+        qk = self.to_qk(normed_x)
+
+        # offset and scale
+        quad_q, lin_q, quad_k, lin_k = self.qk_offset_scale(qk)
+        att_v, att_u = self.cal_attention(x, quad_q, lin_q, quad_k, lin_k, v, u)
+        out = (att_u * v) * self.gateActivate(att_v * u)
+        x = x + self.to_out(out)
+        return x
+
+    def cal_attention(self, x, quad_q, lin_q, quad_k, lin_k, v, u, mask=None):
+        """Cal attention.
+        
+            Args:
+                x: TODO.
+                quad_q: TODO.
+                lin_q: TODO.
+                quad_k: TODO.
+                lin_k: TODO.
+                v: TODO.
+                u: TODO.
+                mask: TODO.
+            """
+        b, n, device, g = x.shape[0], x.shape[-2], x.device, self.group_size
+
+        if exists(mask):
+            lin_mask = rearrange(mask, "... -> ... 1")
+            lin_k = lin_k.masked_fill(~lin_mask, 0.0)
+
+        # rotate queries and keys
+
+        if exists(self.rotary_pos_emb):
+            quad_q, lin_q, quad_k, lin_k = map(
+                self.rotary_pos_emb.rotate_queries_or_keys, (quad_q, lin_q, quad_k, lin_k)
+            )
+
+        # padding for groups
+
+        padding = padding_to_multiple_of(n, g)
+
+        if padding > 0:
+            quad_q, quad_k, lin_q, lin_k, v, u = map(
+                lambda t: F.pad(t, (0, 0, 0, padding), value=0.0),
+                (quad_q, quad_k, lin_q, lin_k, v, u),
+            )
+
+            mask = default(mask, torch.ones((b, n), device=device, dtype=torch.bool))
+            mask = F.pad(mask, (0, padding), value=False)
+
+        # group along sequence
+
+        quad_q, quad_k, lin_q, lin_k, v, u = map(
+            lambda t: rearrange(t, "b (g n) d -> b g n d", n=self.group_size),
+            (quad_q, quad_k, lin_q, lin_k, v, u),
+        )
+
+        if exists(mask):
+            mask = rearrange(mask, "b (g j) -> b g 1 j", j=g)
+
+        # calculate quadratic attention output
+
+        sim = einsum("... i d, ... j d -> ... i j", quad_q, quad_k) / g
+
+        attn = F.relu(sim) ** 2
+        attn = self.dropout(attn)
+
+        if exists(mask):
+            attn = attn.masked_fill(~mask, 0.0)
+
+        if self.causal:
+            causal_mask = torch.ones((g, g), dtype=torch.bool, device=device).triu(1)
+            attn = attn.masked_fill(causal_mask, 0.0)
+
+        quad_out_v = einsum("... i j, ... j d -> ... i d", attn, v)
+        quad_out_u = einsum("... i j, ... j d -> ... i d", attn, u)
+
+        # calculate linear attention output
+
+        if self.causal:
+            lin_kv = einsum("b g n d, b g n e -> b g d e", lin_k, v) / g
+            # exclusive cumulative sum along group dimension
+            lin_kv = lin_kv.cumsum(dim=1)
+            lin_kv = F.pad(lin_kv, (0, 0, 0, 0, 1, -1), value=0.0)
+            lin_out_v = einsum("b g d e, b g n d -> b g n e", lin_kv, lin_q)
+
+            lin_ku = einsum("b g n d, b g n e -> b g d e", lin_k, u) / g
+            # exclusive cumulative sum along group dimension
+            lin_ku = lin_ku.cumsum(dim=1)
+            lin_ku = F.pad(lin_ku, (0, 0, 0, 0, 1, -1), value=0.0)
+            lin_out_u = einsum("b g d e, b g n d -> b g n e", lin_ku, lin_q)
+        else:
+            lin_kv = einsum("b g n d, b g n e -> b d e", lin_k, v) / n
+            lin_out_v = einsum("b g n d, b d e -> b g n e", lin_q, lin_kv)
+
+            lin_ku = einsum("b g n d, b g n e -> b d e", lin_k, u) / n
+            lin_out_u = einsum("b g n d, b d e -> b g n e", lin_q, lin_ku)
+
+        # fold back groups into full sequence, and excise out padding
+        return map(
+            lambda t: rearrange(t, "b g n d -> b (g n) d")[:, :n],
+            (quad_out_v + lin_out_v, quad_out_u + lin_out_u),
+        )

funasr/models/mossformer/mossformer_decoder.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+
+
+class MossFormerDecoder(nn.ConvTranspose1d):
+    """A decoder layer that consists of ConvTranspose1d.
+
+    Arguments
+    ---------
+    kernel_size : int
+        Length of filters.
+    in_channels : int
+        Number of  input channels.
+    out_channels : int
+        Number of output channels.
+
+
+    Example
+    ---------
+    >>> x = torch.randn(2, 100, 1000)
+    >>> decoder = Decoder(kernel_size=4, in_channels=100, out_channels=1)
+    >>> h = decoder(x)
+    >>> h.shape
+    torch.Size([2, 1003])
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initialize MossFormerDecoder.
+        
+            Args:
+                *args: Variable positional arguments.
+                **kwargs: Additional keyword arguments.
+            """
+        super(MossFormerDecoder, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        """Return the decoded output.
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            Input tensor with dimensionality [B, N, L].
+                where, B = Batchsize,
+                       N = number of filters
+                       L = time points
+        """
+
+        if x.dim() not in [2, 3]:
+            raise RuntimeError("{} accept 3/4D tensor as input".format(self.__name__))
+        x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1))
+
+        if torch.squeeze(x).dim() == 1:
+            x = torch.squeeze(x, dim=1)
+        else:
+            x = torch.squeeze(x)
+        return x

funasr/models/mossformer/mossformer_encoder.py

@@ -0,0 +1,473 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    from rotary_embedding_torch import RotaryEmbedding
+except:
+    # print(
+    #     "If you want use mossformer, lease install rotary_embedding_torch by: \n pip install -U rotary_embedding_torch"
+    # )
+    pass
+from funasr.models.transformer.layer_norm import GlobalLayerNorm, CumulativeLayerNorm, ScaleNorm
+from funasr.models.transformer.embedding import ScaledSinuEmbedding
+from funasr.models.mossformer.mossformer import FLASH_ShareA_FFConvM
+
+
+def select_norm(norm, dim, shape):
+    """Just a wrapper to select the normalization type."""
+
+    if norm == "gln":
+        return GlobalLayerNorm(dim, shape, elementwise_affine=True)
+    if norm == "cln":
+        return CumulativeLayerNorm(dim, elementwise_affine=True)
+    if norm == "ln":
+        return nn.GroupNorm(1, dim, eps=1e-8)
+    else:
+        return nn.BatchNorm1d(dim)
+
+
+class MossformerBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        group_size=256,
+        query_key_dim=128,
+        expansion_factor=4.0,
+        causal=False,
+        attn_dropout=0.1,
+        norm_type="scalenorm",
+        shift_tokens=True
+    ):
+        """Initialize MossformerBlock."""
+        super().__init__()
+        assert norm_type in (
+            "scalenorm",
+            "layernorm",
+        ), "norm_type must be one of scalenorm or layernorm"
+
+        if norm_type == "scalenorm":
+            norm_klass = ScaleNorm
+        elif norm_type == "layernorm":
+            norm_klass = nn.LayerNorm
+
+        self.group_size = group_size
+
+        rotary_pos_emb = RotaryEmbedding(dim=min(32, query_key_dim))
+        # max rotary embedding dimensions of 32, partial Rotary embeddings, from Wang et al - GPT-J
+        self.layers = nn.ModuleList(
+            [
+                FLASH_ShareA_FFConvM(
+                    dim=dim,
+                    group_size=group_size,
+                    query_key_dim=query_key_dim,
+                    expansion_factor=expansion_factor,
+                    causal=causal,
+                    dropout=attn_dropout,
+                    rotary_pos_emb=rotary_pos_emb,
+                    norm_klass=norm_klass,
+                    shift_tokens=shift_tokens,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+    def forward(self, x, *, mask=None):
+        """Forward pass for training.
+        
+            Args:
+                x: TODO.
+            """
+        ii = 0
+        for flash in self.layers:
+            x = flash(x, mask=mask)
+            ii = ii + 1
+        return x
+
+
+class MossFormer_MaskNet(nn.Module):
+    """The MossFormer module for computing output masks.
+
+    Arguments
+    ---------
+    in_channels : int
+        Number of channels at the output of the encoder.
+    out_channels : int
+        Number of channels that would be inputted to the intra and inter blocks.
+    num_blocks : int
+        Number of layers of Dual Computation Block.
+    norm : str
+        Normalization type.
+    num_spks : int
+        Number of sources (speakers).
+    skip_around_intra : bool
+        Skip connection around intra.
+    use_global_pos_enc : bool
+        Global positional encodings.
+    max_length : int
+        Maximum sequence length.
+
+    Example
+    ---------
+    >>> mossformer_block = MossFormerM(1, 64, 8)
+    >>> mossformer_masknet = MossFormer_MaskNet(64, 64, intra_block, num_spks=2)
+    >>> x = torch.randn(10, 64, 2000)
+    >>> x = mossformer_masknet(x)
+    >>> x.shape
+    torch.Size([2, 10, 64, 2000])
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        num_blocks=24,
+        norm="ln",
+        num_spks=2,
+        skip_around_intra=True,
+        use_global_pos_enc=True,
+        max_length=20000,
+    ):
+        """Initialize MossFormer_MaskNet.
+        
+            Args:
+                in_channels: TODO.
+                out_channels: TODO.
+                num_blocks: TODO.
+                norm: TODO.
+                num_spks: TODO.
+                skip_around_intra: TODO.
+                use_global_pos_enc: TODO.
+                max_length: TODO.
+            """
+        super(MossFormer_MaskNet, self).__init__()
+        self.num_spks = num_spks
+        self.num_blocks = num_blocks
+        self.norm = select_norm(norm, in_channels, 3)
+        self.conv1d_encoder = nn.Conv1d(in_channels, out_channels, 1, bias=False)
+        self.use_global_pos_enc = use_global_pos_enc
+
+        if self.use_global_pos_enc:
+            self.pos_enc = ScaledSinuEmbedding(out_channels)
+
+        self.mdl = Computation_Block(
+            num_blocks,
+            out_channels,
+            norm,
+            skip_around_intra=skip_around_intra,
+        )
+
+        self.conv1d_out = nn.Conv1d(out_channels, out_channels * num_spks, kernel_size=1)
+        self.conv1_decoder = nn.Conv1d(out_channels, in_channels, 1, bias=False)
+        self.prelu = nn.PReLU()
+        self.activation = nn.ReLU()
+        # gated output layer
+        self.output = nn.Sequential(nn.Conv1d(out_channels, out_channels, 1), nn.Tanh())
+        self.output_gate = nn.Sequential(nn.Conv1d(out_channels, out_channels, 1), nn.Sigmoid())
+
+    def forward(self, x):
+        """Returns the output tensor.
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            Input tensor of dimension [B, N, S].
+
+        Returns
+        -------
+        out : torch.Tensor
+            Output tensor of dimension [spks, B, N, S]
+            where, spks = Number of speakers
+               B = Batchsize,
+               N = number of filters
+               S = the number of time frames
+        """
+
+        # before each line we indicate the shape after executing the line
+
+        # [B, N, L]
+        x = self.norm(x)
+
+        # [B, N, L]
+        x = self.conv1d_encoder(x)
+        if self.use_global_pos_enc:
+            # x = self.pos_enc(x.transpose(1, -1)).transpose(1, -1) + x * (
+            #    x.size(1) ** 0.5)
+            base = x
+            x = x.transpose(1, -1)
+            emb = self.pos_enc(x)
+            emb = emb.transpose(0, -1)
+            # print('base: {}, emb: {}'.format(base.shape, emb.shape))
+            x = base + emb
+
+        # [B, N, S]
+        # for i in range(self.num_modules):
+        #    x = self.dual_mdl[i](x)
+        x = self.mdl(x)
+        x = self.prelu(x)
+
+        # [B, N*spks, S]
+        x = self.conv1d_out(x)
+        B, _, S = x.shape
+
+        # [B*spks, N, S]
+        x = x.view(B * self.num_spks, -1, S)
+
+        # [B*spks, N, S]
+        x = self.output(x) * self.output_gate(x)
+
+        # [B*spks, N, S]
+        x = self.conv1_decoder(x)
+
+        # [B, spks, N, S]
+        _, N, L = x.shape
+        x = x.view(B, self.num_spks, N, L)
+        x = self.activation(x)
+
+        # [spks, B, N, S]
+        x = x.transpose(0, 1)
+
+        return x
+
+
+class MossFormerEncoder(nn.Module):
+    """Convolutional Encoder Layer.
+
+    Arguments
+    ---------
+    kernel_size : int
+        Length of filters.
+    in_channels : int
+        Number of  input channels.
+    out_channels : int
+        Number of output channels.
+
+    Example
+    -------
+    >>> x = torch.randn(2, 1000)
+    >>> encoder = Encoder(kernel_size=4, out_channels=64)
+    >>> h = encoder(x)
+    >>> h.shape
+    torch.Size([2, 64, 499])
+    """
+
+    def __init__(self, kernel_size=2, out_channels=64, in_channels=1):
+        """Initialize MossFormerEncoder.
+        
+            Args:
+                kernel_size: Size/dimension parameter.
+                out_channels: TODO.
+                in_channels: TODO.
+            """
+        super(MossFormerEncoder, self).__init__()
+        self.conv1d = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=kernel_size // 2,
+            groups=1,
+            bias=False,
+        )
+        self.in_channels = in_channels
+
+    def forward(self, x):
+        """Return the encoded output.
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            Input tensor with dimensionality [B, L].
+        Return
+        ------
+        x : torch.Tensor
+            Encoded tensor with dimensionality [B, N, T_out].
+
+        where B = Batchsize
+              L = Number of timepoints
+              N = Number of filters
+              T_out = Number of timepoints at the output of the encoder
+        """
+        # B x L -> B x 1 x L
+        if self.in_channels == 1:
+            x = torch.unsqueeze(x, dim=1)
+        # B x 1 x L -> B x N x T_out
+        x = self.conv1d(x)
+        x = F.relu(x)
+
+        return x
+
+
+class MossFormerM(nn.Module):
+    """This class implements the transformer encoder.
+
+    Arguments
+    ---------
+    num_blocks : int
+        Number of mossformer blocks to include.
+    d_model : int
+        The dimension of the input embedding.
+    attn_dropout : float
+        Dropout for the self-attention (Optional).
+    group_size: int
+        the chunk size
+    query_key_dim: int
+        the attention vector dimension
+    expansion_factor: int
+        the expansion factor for the linear projection in conv module
+    causal: bool
+        true for causal / false for non causal
+
+    Example
+    -------
+    >>> import torch
+    >>> x = torch.rand((8, 60, 512))
+    >>> net = TransformerEncoder_MossFormerM(num_blocks=8, d_model=512)
+    >>> output, _ = net(x)
+    >>> output.shape
+    torch.Size([8, 60, 512])
+    """
+
+    def __init__(
+        self,
+        num_blocks,
+        d_model=None,
+        causal=False,
+        group_size=256,
+        query_key_dim=128,
+        expansion_factor=4.0,
+        attn_dropout=0.1,
+    ):
+        """Initialize MossFormerM.
+        
+            Args:
+                num_blocks: TODO.
+                d_model: D Model instance.
+                causal: TODO.
+                group_size: Size/dimension parameter.
+                query_key_dim: Size/dimension parameter.
+                expansion_factor: TODO.
+                attn_dropout: TODO.
+            """
+        super().__init__()
+
+        self.mossformerM = MossformerBlock(
+            dim=d_model,
+            depth=num_blocks,
+            group_size=group_size,
+            query_key_dim=query_key_dim,
+            expansion_factor=expansion_factor,
+            causal=causal,
+            attn_dropout=attn_dropout,
+        )
+        self.norm = nn.LayerNorm(d_model, eps=1e-6)
+
+    def forward(
+        self,
+        src,
+    ):
+        """
+        Arguments
+        ----------
+        src : torch.Tensor
+            Tensor shape [B, L, N],
+            where, B = Batchsize,
+                   L = time points
+                   N = number of filters
+            The sequence to the encoder layer (required).
+        src_mask : tensor
+            The mask for the src sequence (optional).
+        src_key_padding_mask : tensor
+            The mask for the src keys per batch (optional).
+        """
+        output = self.mossformerM(src)
+        output = self.norm(output)
+
+        return output
+
+
+class Computation_Block(nn.Module):
+    """Computation block for dual-path processing.
+
+    Arguments
+    ---------
+     out_channels : int
+        Dimensionality of inter/intra model.
+     norm : str
+        Normalization type.
+     skip_around_intra : bool
+        Skip connection around the intra layer.
+
+    Example
+    ---------
+        >>> comp_block = Computation_Block(64)
+        >>> x = torch.randn(10, 64, 100)
+        >>> x = comp_block(x)
+        >>> x.shape
+        torch.Size([10, 64, 100])
+    """
+
+    def __init__(
+        self,
+        num_blocks,
+        out_channels,
+        norm="ln",
+        skip_around_intra=True,
+    ):
+        """Initialize Computation_Block.
+        
+            Args:
+                num_blocks: TODO.
+                out_channels: TODO.
+                norm: TODO.
+                skip_around_intra: TODO.
+            """
+        super(Computation_Block, self).__init__()
+
+        ##MossFormer2M: MossFormer with recurrence
+        # self.intra_mdl = MossFormer2M(num_blocks=num_blocks, d_model=out_channels)
+        ##MossFormerM: the orignal MossFormer
+        self.intra_mdl = MossFormerM(num_blocks=num_blocks, d_model=out_channels)
+        self.skip_around_intra = skip_around_intra
+
+        # Norm
+        self.norm = norm
+        if norm is not None:
+            self.intra_norm = select_norm(norm, out_channels, 3)
+
+    def forward(self, x):
+        """Returns the output tensor.
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            Input tensor of dimension [B, N, S].
+
+
+        Return
+        ---------
+        out: torch.Tensor
+            Output tensor of dimension [B, N, S].
+            where, B = Batchsize,
+               N = number of filters
+               S = sequence time index
+        """
+        B, N, S = x.shape
+        # intra RNN
+        # [B, S, N]
+        intra = x.permute(0, 2, 1).contiguous()  # .view(B, S, N)
+
+        intra = self.intra_mdl(intra)
+
+        # [B, N, S]
+        intra = intra.permute(0, 2, 1).contiguous()
+        if self.norm is not None:
+            intra = self.intra_norm(intra)
+
+        # [B, N, S]
+        if self.skip_around_intra:
+            intra = intra + x
+
+        out = intra
+        return out