Initial commit: FunASR Speech Recognition Toolkit

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

funasr/models/e_branchformer/encoder.py

@@ -0,0 +1,503 @@
+# Copyright 2022 Kwangyoun Kim (ASAPP inc.)
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""E-Branchformer encoder definition.
+Reference:
+    Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan,
+    Prashant Sridhar, Kyu J. Han, Shinji Watanabe,
+    "E-Branchformer: Branchformer with Enhanced merging
+    for speech recognition," in SLT 2022.
+"""
+
+import logging
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from funasr.models.ctc.ctc import CTC
+from funasr.models.branchformer.cgmlp import ConvolutionalGatingMLP
+from funasr.models.branchformer.fastformer import FastSelfAttention
+from funasr.models.transformer.utils.nets_utils import get_activation, make_pad_mask
+from funasr.models.transformer.attention import (  # noqa: H301
+    LegacyRelPositionMultiHeadedAttention,
+    MultiHeadedAttention,
+    RelPositionMultiHeadedAttention,
+)
+from funasr.models.transformer.embedding import (  # noqa: H301
+    LegacyRelPositionalEncoding,
+    PositionalEncoding,
+    RelPositionalEncoding,
+    ScaledPositionalEncoding,
+)
+from funasr.models.transformer.layer_norm import LayerNorm
+from funasr.models.transformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,
+)
+from funasr.models.transformer.utils.repeat import repeat
+from funasr.models.transformer.utils.subsampling import (
+    Conv2dSubsampling,
+    Conv2dSubsampling2,
+    Conv2dSubsampling6,
+    Conv2dSubsampling8,
+    TooShortUttError,
+    check_short_utt,
+)
+from funasr.register import tables
+
+
+class EBranchformerEncoderLayer(torch.nn.Module):
+    """E-Branchformer encoder layer module.
+
+    Args:
+        size (int): model dimension
+        attn: standard self-attention or efficient attention
+        cgmlp: ConvolutionalGatingMLP
+        feed_forward: feed-forward module, optional
+        feed_forward: macaron-style feed-forward module, optional
+        dropout_rate (float): dropout probability
+        merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
+    """
+
+    def __init__(
+        self,
+        size: int,
+        attn: torch.nn.Module,
+        cgmlp: torch.nn.Module,
+        feed_forward: Optional[torch.nn.Module],
+        feed_forward_macaron: Optional[torch.nn.Module],
+        dropout_rate: float,
+        merge_conv_kernel: int = 3,
+    ):
+        """Initialize EBranchformerEncoderLayer.
+        
+            Args:
+                size: TODO.
+                attn: TODO.
+                cgmlp: TODO.
+                feed_forward: TODO.
+                feed_forward_macaron: TODO.
+                dropout_rate: TODO.
+                merge_conv_kernel: TODO.
+            """
+        super().__init__()
+
+        self.size = size
+        self.attn = attn
+        self.cgmlp = cgmlp
+
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.ff_scale = 1.0
+        if self.feed_forward is not None:
+            self.norm_ff = LayerNorm(size)
+        if self.feed_forward_macaron is not None:
+            self.ff_scale = 0.5
+            self.norm_ff_macaron = LayerNorm(size)
+
+        self.norm_mha = LayerNorm(size)  # for the MHA module
+        self.norm_mlp = LayerNorm(size)  # for the MLP module
+        self.norm_final = LayerNorm(size)  # for the final output of the block
+
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+        self.depthwise_conv_fusion = torch.nn.Conv1d(
+            size + size,
+            size + size,
+            kernel_size=merge_conv_kernel,
+            stride=1,
+            padding=(merge_conv_kernel - 1) // 2,
+            groups=size + size,
+            bias=True,
+        )
+        self.merge_proj = torch.nn.Linear(size + size, size)
+
+    def forward(self, x_input, mask, cache=None):
+        """Compute encoded features.
+
+        Args:
+            x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
+                - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
+                - w/o pos emb: Tensor (#batch, time, size).
+            mask (torch.Tensor): Mask tensor for the input (#batch, 1, time).
+            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time).
+        """
+
+        if cache is not None:
+            raise NotImplementedError("cache is not None, which is not tested")
+
+        if isinstance(x_input, tuple):
+            x, pos_emb = x_input[0], x_input[1]
+        else:
+            x, pos_emb = x_input, None
+
+        if self.feed_forward_macaron is not None:
+            residual = x
+            x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+
+        # Two branches
+        x1 = x
+        x2 = x
+
+        # Branch 1: multi-headed attention module
+        x1 = self.norm_mha(x1)
+
+        if isinstance(self.attn, FastSelfAttention):
+            x_att = self.attn(x1, mask)
+        else:
+            if pos_emb is not None:
+                x_att = self.attn(x1, x1, x1, pos_emb, mask)
+            else:
+                x_att = self.attn(x1, x1, x1, mask)
+
+        x1 = self.dropout(x_att)
+
+        # Branch 2: convolutional gating mlp
+        x2 = self.norm_mlp(x2)
+
+        if pos_emb is not None:
+            x2 = (x2, pos_emb)
+        x2 = self.cgmlp(x2, mask)
+        if isinstance(x2, tuple):
+            x2 = x2[0]
+
+        x2 = self.dropout(x2)
+
+        # Merge two branches
+        x_concat = torch.cat([x1, x2], dim=-1)
+        x_tmp = x_concat.transpose(1, 2)
+        x_tmp = self.depthwise_conv_fusion(x_tmp)
+        x_tmp = x_tmp.transpose(1, 2)
+        x = x + self.dropout(self.merge_proj(x_concat + x_tmp))
+
+        if self.feed_forward is not None:
+            # feed forward module
+            residual = x
+            x = self.norm_ff(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+
+        x = self.norm_final(x)
+
+        if pos_emb is not None:
+            return (x, pos_emb), mask
+
+        return x, mask
+
+
+@tables.register("encoder_classes", "EBranchformerEncoder")
+class EBranchformerEncoder(nn.Module):
+    """E-Branchformer encoder module."""
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        attention_layer_type: str = "rel_selfattn",
+        pos_enc_layer_type: str = "rel_pos",
+        rel_pos_type: str = "latest",
+        cgmlp_linear_units: int = 2048,
+        cgmlp_conv_kernel: int = 31,
+        use_linear_after_conv: bool = False,
+        gate_activation: str = "identity",
+        num_blocks: int = 12,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: Optional[str] = "conv2d",
+        zero_triu: bool = False,
+        padding_idx: int = -1,
+        layer_drop_rate: float = 0.0,
+        max_pos_emb_len: int = 5000,
+        use_ffn: bool = False,
+        macaron_ffn: bool = False,
+        ffn_activation_type: str = "swish",
+        linear_units: int = 2048,
+        positionwise_layer_type: str = "linear",
+        merge_conv_kernel: int = 3,
+        interctc_layer_idx=None,
+        interctc_use_conditioning: bool = False,
+    ):
+        """Initialize EBranchformerEncoder.
+        
+            Args:
+                input_size: Size/dimension parameter.
+                output_size: Size/dimension parameter.
+                attention_heads: TODO.
+                attention_layer_type: TODO.
+                pos_enc_layer_type: TODO.
+                rel_pos_type: TODO.
+                cgmlp_linear_units: TODO.
+                cgmlp_conv_kernel: TODO.
+                use_linear_after_conv: TODO.
+                gate_activation: TODO.
+                num_blocks: TODO.
+                dropout_rate: TODO.
+                positional_dropout_rate: TODO.
+                attention_dropout_rate: TODO.
+                input_layer: TODO.
+                zero_triu: TODO.
+                padding_idx: TODO.
+                layer_drop_rate: TODO.
+                max_pos_emb_len: TODO.
+                use_ffn: TODO.
+                macaron_ffn: TODO.
+                ffn_activation_type: TODO.
+                linear_units: TODO.
+                positionwise_layer_type: TODO.
+                merge_conv_kernel: TODO.
+                interctc_layer_idx: TODO.
+                interctc_use_conditioning: TODO.
+            """
+        super().__init__()
+        self._output_size = output_size
+
+        if rel_pos_type == "legacy":
+            if pos_enc_layer_type == "rel_pos":
+                pos_enc_layer_type = "legacy_rel_pos"
+            if attention_layer_type == "rel_selfattn":
+                attention_layer_type = "legacy_rel_selfattn"
+        elif rel_pos_type == "latest":
+            assert attention_layer_type != "legacy_rel_selfattn"
+            assert pos_enc_layer_type != "legacy_rel_pos"
+        else:
+            raise ValueError("unknown rel_pos_type: " + rel_pos_type)
+
+        if pos_enc_layer_type == "abs_pos":
+            pos_enc_class = PositionalEncoding
+        elif pos_enc_layer_type == "scaled_abs_pos":
+            pos_enc_class = ScaledPositionalEncoding
+        elif pos_enc_layer_type == "rel_pos":
+            assert attention_layer_type == "rel_selfattn"
+            pos_enc_class = RelPositionalEncoding
+        elif pos_enc_layer_type == "legacy_rel_pos":
+            assert attention_layer_type == "legacy_rel_selfattn"
+            pos_enc_class = LegacyRelPositionalEncoding
+            logging.warning("Using legacy_rel_pos and it will be deprecated in the future.")
+        else:
+            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
+
+        if input_layer == "linear":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Linear(input_size, output_size),
+                torch.nn.LayerNorm(output_size),
+                torch.nn.Dropout(dropout_rate),
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer == "conv2d":
+            self.embed = Conv2dSubsampling(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer == "conv2d2":
+            self.embed = Conv2dSubsampling2(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer == "conv2d6":
+            self.embed = Conv2dSubsampling6(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer == "conv2d8":
+            self.embed = Conv2dSubsampling8(
+                input_size,
+                output_size,
+                dropout_rate,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer == "embed":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif isinstance(input_layer, torch.nn.Module):
+            self.embed = torch.nn.Sequential(
+                input_layer,
+                pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+            )
+        elif input_layer is None:
+            if input_size == output_size:
+                self.embed = None
+            else:
+                self.embed = torch.nn.Linear(input_size, output_size)
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+
+        activation = get_activation(ffn_activation_type)
+        if positionwise_layer_type == "linear":
+            positionwise_layer = PositionwiseFeedForward
+            positionwise_layer_args = (
+                output_size,
+                linear_units,
+                dropout_rate,
+                activation,
+            )
+        elif positionwise_layer_type is None:
+            logging.warning("no macaron ffn")
+        else:
+            raise ValueError("Support only linear.")
+
+        if attention_layer_type == "selfattn":
+            encoder_selfattn_layer = MultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                attention_dropout_rate,
+            )
+        elif attention_layer_type == "legacy_rel_selfattn":
+            assert pos_enc_layer_type == "legacy_rel_pos"
+            encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                attention_dropout_rate,
+            )
+            logging.warning("Using legacy_rel_selfattn and it will be deprecated in the future.")
+        elif attention_layer_type == "rel_selfattn":
+            assert pos_enc_layer_type == "rel_pos"
+            encoder_selfattn_layer = RelPositionMultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                attention_dropout_rate,
+                zero_triu,
+            )
+        elif attention_layer_type == "fast_selfattn":
+            assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
+            encoder_selfattn_layer = FastSelfAttention
+            encoder_selfattn_layer_args = (
+                output_size,
+                attention_heads,
+                attention_dropout_rate,
+            )
+        else:
+            raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
+
+        cgmlp_layer = ConvolutionalGatingMLP
+        cgmlp_layer_args = (
+            output_size,
+            cgmlp_linear_units,
+            cgmlp_conv_kernel,
+            dropout_rate,
+            use_linear_after_conv,
+            gate_activation,
+        )
+
+        self.encoders = repeat(
+            num_blocks,
+            lambda lnum: EBranchformerEncoderLayer(
+                output_size,
+                encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                cgmlp_layer(*cgmlp_layer_args),
+                positionwise_layer(*positionwise_layer_args) if use_ffn else None,
+                positionwise_layer(*positionwise_layer_args) if use_ffn and macaron_ffn else None,
+                dropout_rate,
+                merge_conv_kernel,
+            ),
+            layer_drop_rate,
+        )
+        self.after_norm = LayerNorm(output_size)
+
+        if interctc_layer_idx is None:
+            interctc_layer_idx = []
+        self.interctc_layer_idx = interctc_layer_idx
+        if len(interctc_layer_idx) > 0:
+            assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
+        self.interctc_use_conditioning = interctc_use_conditioning
+        self.conditioning_layer = None
+
+    def output_size(self) -> int:
+        """Output size."""
+        return self._output_size
+
+    def forward(
+        self,
+        xs_pad: torch.Tensor,
+        ilens: torch.Tensor,
+        prev_states: torch.Tensor = None,
+        ctc: CTC = None,
+        max_layer: int = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+        """Calculate forward propagation.
+
+        Args:
+            xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
+            ilens (torch.Tensor): Input length (#batch).
+            prev_states (torch.Tensor): Not to be used now.
+            ctc (CTC): Intermediate CTC module.
+            max_layer (int): Layer depth below which InterCTC is applied.
+        Returns:
+            torch.Tensor: Output tensor (#batch, L, output_size).
+            torch.Tensor: Output length (#batch).
+            torch.Tensor: Not to be used now.
+        """
+
+        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+
+        if (
+            isinstance(self.embed, Conv2dSubsampling)
+            or isinstance(self.embed, Conv2dSubsampling2)
+            or isinstance(self.embed, Conv2dSubsampling6)
+            or isinstance(self.embed, Conv2dSubsampling8)
+        ):
+            short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
+            if short_status:
+                raise TooShortUttError(
+                    f"has {xs_pad.size(1)} frames and is too short for subsampling "
+                    + f"(it needs more than {limit_size} frames), return empty results",
+                    xs_pad.size(1),
+                    limit_size,
+                )
+            xs_pad, masks = self.embed(xs_pad, masks)
+        elif self.embed is not None:
+            xs_pad = self.embed(xs_pad)
+
+        intermediate_outs = []
+        if len(self.interctc_layer_idx) == 0:
+            if max_layer is not None and 0 <= max_layer < len(self.encoders):
+                for layer_idx, encoder_layer in enumerate(self.encoders):
+                    xs_pad, masks = encoder_layer(xs_pad, masks)
+                    if layer_idx >= max_layer:
+                        break
+            else:
+                xs_pad, masks = self.encoders(xs_pad, masks)
+        else:
+            for layer_idx, encoder_layer in enumerate(self.encoders):
+                xs_pad, masks = encoder_layer(xs_pad, masks)
+
+                if layer_idx + 1 in self.interctc_layer_idx:
+                    encoder_out = xs_pad
+
+                    if isinstance(encoder_out, tuple):
+                        encoder_out = encoder_out[0]
+
+                    intermediate_outs.append((layer_idx + 1, encoder_out))
+
+                    if self.interctc_use_conditioning:
+                        ctc_out = ctc.softmax(encoder_out)
+
+                        if isinstance(xs_pad, tuple):
+                            xs_pad = list(xs_pad)
+                            xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out)
+                            xs_pad = tuple(xs_pad)
+                        else:
+                            xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+        if isinstance(xs_pad, tuple):
+            xs_pad = xs_pad[0]
+
+        xs_pad = self.after_norm(xs_pad)
+        olens = masks.squeeze(1).sum(1)
+        if len(intermediate_outs) > 0:
+            return (xs_pad, intermediate_outs), olens, None
+        return xs_pad, olens, None

funasr/models/e_branchformer/model.py

@@ -0,0 +1,29 @@
+import logging
+
+from funasr.models.transformer.model import Transformer
+from funasr.register import tables
+
+
+@tables.register("model_classes", "EBranchformer")
+class EBranchformer(Transformer):
+    """E-Branchformer: Enhanced Branchformer with improved merging.
+
+    Uses element-wise merging instead of concatenation for parallel branches,
+    resulting in better parameter efficiency.
+
+    Inherits Transformer pipeline.
+    """
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+
+        """Initialize EBranchformer.
+        
+            Args:
+                *args: Variable positional arguments.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__(*args, **kwargs)

funasr/models/e_branchformer/template.yaml

@@ -0,0 +1,116 @@
+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: Branchformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: EBranchformerEncoder
+encoder_conf:
+    output_size: 256
+    attention_heads: 4
+    attention_layer_type: rel_selfattn
+    pos_enc_layer_type: rel_pos
+    rel_pos_type: latest
+    cgmlp_linear_units: 1024
+    cgmlp_conv_kernel: 31
+    use_linear_after_conv: false
+    gate_activation: identity
+    num_blocks: 12
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: conv2d
+    layer_drop_rate: 0.0
+    linear_units: 1024
+    positionwise_layer_type: linear
+    use_ffn: true
+    macaron_ffn: true
+    merge_conv_kernel: 31
+
+# decoder
+decoder: TransformerDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.
+    src_attention_dropout_rate: 0.
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    dither: 0.0
+    lfr_m: 1
+    lfr_n: 1
+
+specaug: SpecAug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 180
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.001
+   weight_decay: 0.000001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 35000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: BatchSampler
+    batch_type: example # example or length
+    batch_size: 1 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 500
+    shuffle: True
+    num_workers: 4
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+  split_with_space: true
+
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null