Initial commit: FunASR Speech Recognition Toolkit

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

funasr/models/conformer_rwkv/decoder.py

@@ -0,0 +1,553 @@
+# Copyright 2019 Shigeki Karita
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Decoder definition."""
+from typing import Any
+from typing import List
+from typing import Sequence
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+from funasr.models.transformer.attention import MultiHeadedAttention
+from funasr.models.transformer.utils.dynamic_conv import DynamicConvolution
+from funasr.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D
+from funasr.models.transformer.embedding import PositionalEncoding
+from funasr.models.transformer.layer_norm import LayerNorm
+from funasr.models.transformer.utils.lightconv import LightweightConvolution
+from funasr.models.transformer.utils.lightconv2d import LightweightConvolution2D
+from funasr.models.transformer.utils.mask import subsequent_mask
+from funasr.models.transformer.utils.nets_utils import make_pad_mask
+from funasr.models.transformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,  # noqa: H301
+)
+from funasr.models.transformer.utils.repeat import repeat
+from funasr.models.transformer.scorers.scorer_interface import BatchScorerInterface
+from omegaconf import OmegaConf
+from funasr.register import tables
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x):
+        """Forward pass for training.
+        
+            Args:
+                x: TODO.
+            """
+        return super().forward(x.float()).type(x.dtype)
+
+
+class DecoderLayer(nn.Module):
+    """Single decoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+        src_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool): Whether to use layer_norm before the first block.
+        concat_after (bool): Whether to concat attention layer's input and output.
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied. i.e. x -> x + att(x)
+
+
+    """
+
+    def __init__(
+        self,
+        size,
+        # self_attn,
+        src_attn,
+        feed_forward,
+        dropout_rate,
+        normalize_before=True,
+        concat_after=False,
+        layer_id=None,
+        args={},
+        **kwargs,
+    ):
+        """Construct an DecoderLayer object."""
+        super(DecoderLayer, self).__init__()
+        self.size = size
+        # self.self_attn = self_attn.to(torch.bfloat16)
+        self.src_attn = src_attn
+        self.feed_forward = feed_forward
+        self.norm1 = LayerNorm(size)
+        self.norm2 = LayerNorm(size)
+        self.norm3 = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.normalize_before = normalize_before
+        self.concat_after = concat_after
+        if self.concat_after:
+            self.concat_linear1 = nn.Linear(size + size, size)
+            self.concat_linear2 = nn.Linear(size + size, size)
+        self.layer_id = layer_id
+
+        if args.get("version", "v4") == "v4":
+            from funasr.models.sense_voice.rwkv_v4 import RWKVLayer
+            from funasr.models.sense_voice.rwkv_v4 import RWKV_TimeMix as RWKV_Tmix
+        elif args.get("version", "v5") == "v5":
+            from funasr.models.sense_voice.rwkv_v5 import RWKVLayer
+            from funasr.models.sense_voice.rwkv_v5 import RWKV_Tmix_x052 as RWKV_Tmix
+        else:
+            from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
+            from funasr.models.sense_voice.rwkv_v6 import RWKV_Tmix_x060 as RWKV_Tmix
+        # self.attn = RWKVLayer(args=args, layer_id=layer_id)
+        self.self_attn = RWKV_Tmix(args, layer_id=layer_id)
+
+        self.args = args
+        self.ln0 = None
+        if self.layer_id == 0 and not args.get("ln0", True):
+            self.ln0 = LayerNorm(args.n_embd)
+            if args.get("init_rwkv", True):
+                print("init_rwkv")
+                layer_id = 0
+                scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
+                nn.init.constant_(self.ln0.weight, scale)
+
+        # init
+        if args.get("init_rwkv", True):
+            print("init_rwkv")
+            scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
+            nn.init.constant_(self.norm1.weight, scale)
+            # nn.init.constant_(self.self_attn.ln2.weight, scale)
+
+        if args.get("init_rwkv", True):
+            print("init_rwkv")
+            nn.init.orthogonal_(self.self_attn.receptance.weight, gain=1)
+            nn.init.orthogonal_(self.self_attn.key.weight, gain=0.1)
+            nn.init.orthogonal_(self.self_attn.value.weight, gain=1)
+            nn.init.orthogonal_(self.self_attn.gate.weight, gain=0.1)
+            nn.init.zeros_(self.self_attn.output.weight)
+
+        if args.get("datatype", "bf16") == "bf16":
+            self.self_attn.to(torch.bfloat16)
+            # self.norm1.to(torch.bfloat16)
+
+    def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None):
+        """Compute decoded features.
+
+        Args:
+            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
+            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
+            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
+            cache (List[torch.Tensor]): List of cached tensors.
+                Each tensor shape should be (#batch, maxlen_out - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor(#batch, maxlen_out, size).
+            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+            torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+        """
+
+        if self.layer_id == 0 and self.ln0 is not None:
+            tgt = self.ln0(tgt)
+
+        if self.args.get("datatype", "bf16") == "bf16":
+            tgt = tgt.bfloat16()
+        residual = tgt
+
+        tgt = self.norm1(tgt)
+
+        if cache is None:
+
+            x = residual + self.dropout(self.self_attn(tgt, mask=tgt_mask))
+        else:
+
+            # tgt_q = tgt[:, -1:, :]
+            # residual_q = residual[:, -1:, :]
+            tgt_q_mask = None
+
+            x = residual + self.dropout(self.self_attn(tgt, mask=tgt_q_mask))
+            x = x[:, -1, :]
+        if self.args.get("datatype", "bf16") == "bf16":
+            x = x.to(torch.float32)
+        # x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
+
+        residual = x
+        x = self.norm2(x)
+        x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask))
+        residual = x
+        x = self.norm3(x)
+        x = residual + self.dropout(self.feed_forward(x))
+
+        if cache is not None:
+            x = torch.cat([cache, x], dim=1)
+
+        return x, tgt_mask, memory, memory_mask
+
+
+class BaseTransformerDecoder(nn.Module, BatchScorerInterface):
+    """Base class of Transfomer decoder module.
+
+    Args:
+        vocab_size: output dim
+        encoder_output_size: dimension of attention
+        attention_heads: the number of heads of multi head attention
+        linear_units: the number of units of position-wise feed forward
+        num_blocks: the number of decoder blocks
+        dropout_rate: dropout rate
+        self_attention_dropout_rate: dropout rate for attention
+        input_layer: input layer type
+        use_output_layer: whether to use output layer
+        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+        normalize_before: whether to use layer_norm before the first block
+        concat_after: whether to concat attention layer's input and output
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied.
+            i.e. x -> x + att(x)
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder_output_size: int,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        input_layer: str = "embed",
+        use_output_layer: bool = True,
+        pos_enc_class=PositionalEncoding,
+        normalize_before: bool = True,
+    ):
+        """Initialize BaseTransformerDecoder.
+        
+            Args:
+                vocab_size: Size/dimension parameter.
+                encoder_output_size: Size/dimension parameter.
+                dropout_rate: TODO.
+                positional_dropout_rate: TODO.
+                input_layer: TODO.
+                use_output_layer: TODO.
+                pos_enc_class: TODO.
+                normalize_before: TODO.
+            """
+        super().__init__()
+        attention_dim = encoder_output_size
+
+        if input_layer == "embed":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Embedding(vocab_size, attention_dim),
+                pos_enc_class(attention_dim, positional_dropout_rate),
+            )
+        elif input_layer == "linear":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Linear(vocab_size, attention_dim),
+                torch.nn.LayerNorm(attention_dim),
+                torch.nn.Dropout(dropout_rate),
+                torch.nn.ReLU(),
+                pos_enc_class(attention_dim, positional_dropout_rate),
+            )
+        else:
+            raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
+
+        self.normalize_before = normalize_before
+        if self.normalize_before:
+            self.after_norm = LayerNorm(attention_dim)
+        if use_output_layer:
+            self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
+        else:
+            self.output_layer = None
+
+        # Must set by the inheritance
+        self.decoders = None
+
+    def forward(
+        self,
+        hs_pad: torch.Tensor,
+        hlens: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+
+        Args:
+            hs_pad: encoded memory, float32  (batch, maxlen_in, feat)
+            hlens: (batch)
+            ys_in_pad:
+                input token ids, int64 (batch, maxlen_out)
+                if input_layer == "embed"
+                input tensor (batch, maxlen_out, #mels) in the other cases
+            ys_in_lens: (batch)
+        Returns:
+            (tuple): tuple containing:
+
+            x: decoded token score before softmax (batch, maxlen_out, token)
+                if use_output_layer is True,
+            olens: (batch, )
+        """
+        tgt = ys_in_pad
+        # tgt_mask: (B, 1, L)
+        tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
+        # m: (1, L, L)
+        m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
+        # tgt_mask: (B, L, L)
+        tgt_mask = tgt_mask & m
+
+        memory = hs_pad
+        memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(memory.device)
+        # Padding for Longformer
+        if memory_mask.shape[-1] != memory.shape[1]:
+            padlen = memory.shape[1] - memory_mask.shape[-1]
+            memory_mask = torch.nn.functional.pad(memory_mask, (0, padlen), "constant", False)
+
+        x = self.embed(tgt)
+        x, tgt_mask, memory, memory_mask = self.decoders(x, tgt_mask, memory, memory_mask)
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.output_layer is not None:
+            x = self.output_layer(x)
+
+        olens = tgt_mask.sum(1)
+        return x, olens
+
+    def forward_one_step(
+        self,
+        tgt: torch.Tensor,
+        tgt_mask: torch.Tensor,
+        memory: torch.Tensor,
+        cache: List[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """Forward one step.
+
+        Args:
+            tgt: input token ids, int64 (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out)
+                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            cache: cached output list of (batch, max_time_out-1, size)
+        Returns:
+            y, cache: NN output value and cache per `self.decoders`.
+            y.shape` is (batch, maxlen_out, token)
+        """
+        x = self.embed(tgt)
+        if cache is None:
+            cache = [None] * len(self.decoders)
+        new_cache = []
+        for c, decoder in zip(cache, self.decoders):
+            x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, None, cache=c)
+            new_cache.append(x)
+
+        if self.normalize_before:
+            y = self.after_norm(x[:, -1])
+        else:
+            y = x[:, -1]
+        if self.output_layer is not None:
+            y = torch.log_softmax(self.output_layer(y), dim=-1)
+
+        return y, new_cache
+
+    def score(self, ys, state, x):
+        """Score."""
+        ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
+        logp, state = self.forward_one_step(ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state)
+        return logp.squeeze(0), state
+
+    def batch_score(
+        self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
+    ) -> Tuple[torch.Tensor, List[Any]]:
+        """Score new token batch.
+
+        Args:
+            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+            states (List[Any]): Scorer states for prefix tokens.
+            xs (torch.Tensor):
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+        Returns:
+            tuple[torch.Tensor, List[Any]]: Tuple of
+                batchfied scores for next token with shape of `(n_batch, n_vocab)`
+                and next state list for ys.
+
+        """
+        # merge states
+        n_batch = len(ys)
+        n_layers = len(self.decoders)
+        if states[0] is None:
+            batch_state = None
+        else:
+            # transpose state of [batch, layer] into [layer, batch]
+            batch_state = [
+                torch.stack([states[b][i] for b in range(n_batch)]) for i in range(n_layers)
+            ]
+
+        # batch decoding
+        ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0)
+        logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state)
+
+        # transpose state of [layer, batch] into [batch, layer]
+        state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
+        return logp, state_list
+
+
+@tables.register("decoder_classes", "TransformerRWKVDecoder")
+class TransformerRWKVDecoder(BaseTransformerDecoder):
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder_output_size: int,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        self_attention_dropout_rate: float = 0.0,
+        src_attention_dropout_rate: float = 0.0,
+        input_layer: str = "embed",
+        use_output_layer: bool = True,
+        pos_enc_class=PositionalEncoding,
+        normalize_before: bool = True,
+        concat_after: bool = False,
+        **kwargs,
+    ):
+        """Initialize TransformerRWKVDecoder.
+        
+            Args:
+                vocab_size: Size/dimension parameter.
+                encoder_output_size: Size/dimension parameter.
+                attention_heads: TODO.
+                linear_units: TODO.
+                num_blocks: TODO.
+                dropout_rate: TODO.
+                positional_dropout_rate: TODO.
+                self_attention_dropout_rate: TODO.
+                src_attention_dropout_rate: TODO.
+                input_layer: TODO.
+                use_output_layer: TODO.
+                pos_enc_class: TODO.
+                normalize_before: TODO.
+                concat_after: TODO.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder_output_size,
+            dropout_rate=dropout_rate,
+            positional_dropout_rate=positional_dropout_rate,
+            input_layer=input_layer,
+            use_output_layer=use_output_layer,
+            pos_enc_class=pos_enc_class,
+            normalize_before=normalize_before,
+        )
+        # from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
+
+        rwkv_cfg = kwargs.get("rwkv_cfg", {})
+        args = OmegaConf.create(rwkv_cfg)
+
+        attention_dim = encoder_output_size
+        self.decoders = repeat(
+            num_blocks,
+            lambda lnum: DecoderLayer(
+                attention_dim,
+                MultiHeadedAttention(attention_heads, attention_dim, src_attention_dropout_rate),
+                PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                dropout_rate,
+                normalize_before,
+                concat_after,
+                lnum,
+                args=args,
+            ),
+        )
+
+        # init
+        if args.get("init_rwkv", True):
+            print("init_rwkv")
+            nn.init.uniform_(self.embed[0].weight, a=-1e-4, b=1e-4)
+
+    def forward(
+        self,
+        hs_pad: torch.Tensor,
+        hlens: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+
+        Args:
+            hs_pad: encoded memory, float32  (batch, maxlen_in, feat)
+            hlens: (batch)
+            ys_in_pad:
+                input token ids, int64 (batch, maxlen_out)
+                if input_layer == "embed"
+                input tensor (batch, maxlen_out, #mels) in the other cases
+            ys_in_lens: (batch)
+        Returns:
+            (tuple): tuple containing:
+
+            x: decoded token score before softmax (batch, maxlen_out, token)
+                if use_output_layer is True,
+            olens: (batch, )
+        """
+        tgt = ys_in_pad
+        # tgt_mask: (B, 1, L)
+        tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
+        # m: (1, L, L)
+        m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
+        # tgt_mask: (B, L, L)
+        tgt_mask = tgt_mask & m
+
+        memory = hs_pad
+        memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(memory.device)
+        # Padding for Longformer
+        if memory_mask.shape[-1] != memory.shape[1]:
+            padlen = memory.shape[1] - memory_mask.shape[-1]
+            memory_mask = torch.nn.functional.pad(memory_mask, (0, padlen), "constant", False)
+
+        x = self.embed(tgt)
+        x, tgt_mask, memory, memory_mask = self.decoders(x, tgt_mask, memory, memory_mask)
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.output_layer is not None:
+            x = self.output_layer(x)
+
+        olens = tgt_mask.sum(1)
+        return x, olens
+
+    def forward_one_step(
+        self,
+        tgt: torch.Tensor,
+        tgt_mask: torch.Tensor,
+        memory: torch.Tensor,
+        cache: List[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """Forward one step.
+
+        Args:
+            tgt: input token ids, int64 (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out)
+                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            cache: cached output list of (batch, max_time_out-1, size)
+        Returns:
+            y, cache: NN output value and cache per `self.decoders`.
+            y.shape` is (batch, maxlen_out, token)
+        """
+        x = self.embed(tgt)
+        if cache is None:
+            cache = [None] * len(self.decoders)
+        new_cache = []
+        for c, decoder in zip(cache, self.decoders):
+            x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, None, cache=c)
+            new_cache.append(x)
+
+        if self.normalize_before:
+            y = self.after_norm(x[:, -1])
+        else:
+            y = x[:, -1]
+        if self.output_layer is not None:
+            y = torch.log_softmax(self.output_layer(y), dim=-1)
+
+        return y, new_cache

funasr/models/conformer_rwkv/model.py

@@ -0,0 +1,25 @@
+import logging
+
+import torch
+
+from funasr.models.transformer.model import Transformer
+from funasr.register import tables
+
+
+@tables.register("model_classes", "Conformer")
+class Conformer(Transformer):
+    """CTC-attention hybrid Encoder-Decoder model"""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+
+        """Initialize Conformer.
+        
+            Args:
+                *args: Variable positional arguments.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__(*args, **kwargs)

funasr/models/conformer_rwkv/template.yaml

@@ -0,0 +1,123 @@
+# 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: Conformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: ConformerEncoder
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder architecture type
+    normalize_before: true
+    pos_enc_layer_type: rel_pos
+    selfattention_layer_type: rel_selfattn
+    activation_type: swish
+    macaron_style: true
+    use_cnn_module: true
+    cnn_module_kernel: 15
+
+# decoder
+decoder: TransformerRWKVDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
+    input_layer: embed
+    rwkv_cfg:
+      n_embd: 256
+      dropout: 0
+      head_size_a: 64
+      ctx_len: 512
+      dim_att: 256 #${model_conf.rwkv_cfg.n_embd}
+      dim_ffn: null
+      head_size_divisor: 4
+      n_layer: 6
+      pre_ffn: 0
+      ln0: false
+      ln1: false
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    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: 150
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 25000 # 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: 1024
+    shuffle: True
+    num_workers: 4
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null