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import time
import torch
from torch import nn
import torch.nn.functional as F
from typing import Iterable, Optional, Sequence, Tuple, Union
from funasr.register import tables
from funasr.models.ctc.ctc import CTC
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
from funasr.models.scama.chunk_utilis import overlap_chunk
from funasr.models.transformer.embedding import StreamSinusoidalPositionEncoder
from funasr.train_utils.device_funcs import force_gatherable
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.metrics.compute_acc import compute_accuracy, th_accuracy
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from utils.ctc_alignment import ctc_forced_align
class SinusoidalPositionEncoder(torch.nn.Module):
""" """
def __init__(self, d_model=80, dropout_rate=0.1):
super().__init__()
def encode(
self, positions: torch.Tensor = None, depth: int = None, dtype: torch.dtype = torch.float32
):
batch_size = positions.size(0)
positions = positions.type(dtype)
device = positions.device
log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype, device=device)) / (
depth / 2 - 1
)
inv_timescales = torch.exp(
torch.arange(depth / 2, device=device).type(dtype) * (-log_timescale_increment)
)
inv_timescales = torch.reshape(inv_timescales, [batch_size, -1])
scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape(
inv_timescales, [1, 1, -1]
)
encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2)
return encoding.type(dtype)
def forward(self, x):
batch_size, timesteps, input_dim = x.size()
positions = torch.arange(1, timesteps + 1, device=x.device)[None, :]
position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)
return x + position_encoding
class PositionwiseFeedForward(torch.nn.Module):
"""Positionwise feed forward layer.
Args:
idim (int): Input dimenstion.
hidden_units (int): The number of hidden units.
dropout_rate (float): Dropout rate.
"""
def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
"""Construct an PositionwiseFeedForward object."""
super(PositionwiseFeedForward, self).__init__()
self.w_1 = torch.nn.Linear(idim, hidden_units)
self.w_2 = torch.nn.Linear(hidden_units, idim)
self.dropout = torch.nn.Dropout(dropout_rate)
self.activation = activation
def forward(self, x):
"""Forward function."""
return self.w_2(self.dropout(self.activation(self.w_1(x))))
class MultiHeadedAttentionSANM(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
"""
def __init__(
self,
n_head,
in_feat,
n_feat,
dropout_rate,
kernel_size,
sanm_shfit=0,
lora_list=None,
lora_rank=8,
lora_alpha=16,
lora_dropout=0.1,
):
"""Construct an MultiHeadedAttention object."""
super().__init__()
assert n_feat % n_head == 0
# We assume d_v always equals d_k
self.d_k = n_feat // n_head
self.h = n_head
# self.linear_q = nn.Linear(n_feat, n_feat)
# self.linear_k = nn.Linear(n_feat, n_feat)
# self.linear_v = nn.Linear(n_feat, n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
self.attn = None
self.dropout = nn.Dropout(p=dropout_rate)
self.fsmn_block = nn.Conv1d(
n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
)
# padding
left_padding = (kernel_size - 1) // 2
if sanm_shfit > 0:
left_padding = left_padding + sanm_shfit
right_padding = kernel_size - 1 - left_padding
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
def forward_fsmn(self, inputs, mask, mask_shfit_chunk=None):
b, t, d = inputs.size()
if mask is not None:
mask = torch.reshape(mask, (b, -1, 1))
if mask_shfit_chunk is not None:
mask = mask * mask_shfit_chunk
inputs = inputs * mask
x = inputs.transpose(1, 2)
x = self.pad_fn(x)
x = self.fsmn_block(x)
x = x.transpose(1, 2)
x += inputs
x = self.dropout(x)
if mask is not None:
x = x * mask
return x
def forward_qkv(self, x):
"""Transform query, key and value.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
Returns:
torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
"""
b, t, d = x.size()
q_k_v = self.linear_q_k_v(x)
q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose(
1, 2
) # (batch, head, time1, d_k)
k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose(
1, 2
) # (batch, head, time2, d_k)
v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose(
1, 2
) # (batch, head, time2, d_k)
return q_h, k_h, v_h, v
def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None):
"""Compute attention context vector.
Args:
value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
Returns:
torch.Tensor: Transformed value (#batch, time1, d_model)
weighted by the attention score (#batch, time1, time2).
"""
n_batch = value.size(0)
if mask is not None:
if mask_att_chunk_encoder is not None:
mask = mask * mask_att_chunk_encoder
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
min_value = -float(
"inf"
) # float(numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min)
scores = scores.masked_fill(mask, min_value)
attn = torch.softmax(scores, dim=-1).masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
else:
attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
p_attn = self.dropout(attn)
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
(#batch, time1, time2).
Returns:
torch.Tensor: Output tensor (#batch, time1, d_model).
"""
q_h, k_h, v_h, v = self.forward_qkv(x)
fsmn_memory = self.forward_fsmn(v, mask, mask_shfit_chunk)
q_h = q_h * self.d_k ** (-0.5)
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
return att_outs + fsmn_memory
def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
(#batch, time1, time2).
Returns:
torch.Tensor: Output tensor (#batch, time1, d_model).
"""
q_h, k_h, v_h, v = self.forward_qkv(x)
if chunk_size is not None and look_back > 0 or look_back == -1:
if cache is not None:
k_h_stride = k_h[:, :, : -(chunk_size[2]), :]
v_h_stride = v_h[:, :, : -(chunk_size[2]), :]
k_h = torch.cat((cache["k"], k_h), dim=2)
v_h = torch.cat((cache["v"], v_h), dim=2)
cache["k"] = torch.cat((cache["k"], k_h_stride), dim=2)
cache["v"] = torch.cat((cache["v"], v_h_stride), dim=2)
if look_back != -1:
cache["k"] = cache["k"][:, :, -(look_back * chunk_size[1]) :, :]
cache["v"] = cache["v"][:, :, -(look_back * chunk_size[1]) :, :]
else:
cache_tmp = {
"k": k_h[:, :, : -(chunk_size[2]), :],
"v": v_h[:, :, : -(chunk_size[2]), :],
}
cache = cache_tmp
fsmn_memory = self.forward_fsmn(v, None)
q_h = q_h * self.d_k ** (-0.5)
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
att_outs = self.forward_attention(v_h, scores, None)
return att_outs + fsmn_memory, cache
class LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.layer_norm(
input.float(),
self.normalized_shape,
self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None,
self.eps,
)
return output.type_as(input)
def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
if maxlen is None:
maxlen = lengths.max()
row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
matrix = torch.unsqueeze(lengths, dim=-1)
mask = row_vector < matrix
mask = mask.detach()
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
class EncoderLayerSANM(nn.Module):
def __init__(
self,
in_size,
size,
self_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
stochastic_depth_rate=0.0,
):
"""Construct an EncoderLayer object."""
super(EncoderLayerSANM, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = LayerNorm(in_size)
self.norm2 = LayerNorm(size)
self.dropout = nn.Dropout(dropout_rate)
self.in_size = in_size
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
self.dropout_rate = dropout_rate
def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 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).
"""
skip_layer = False
# with stochastic depth, residual connection `x + f(x)` becomes
# `x <- x + 1 / (1 - p) * f(x)` at training time.
stoch_layer_coeff = 1.0
if self.training and self.stochastic_depth_rate > 0:
skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
if skip_layer:
if cache is not None:
x = torch.cat([cache, x], dim=1)
return x, mask
residual = x
if self.normalize_before:
x = self.norm1(x)
if self.concat_after:
x_concat = torch.cat(
(
x,
self.self_attn(
x,
mask,
mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
),
),
dim=-1,
)
if self.in_size == self.size:
x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
else:
x = stoch_layer_coeff * self.concat_linear(x_concat)
else:
if self.in_size == self.size:
x = residual + stoch_layer_coeff * self.dropout(
self.self_attn(
x,
mask,
mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
)
)
else:
x = stoch_layer_coeff * self.dropout(
self.self_attn(
x,
mask,
mask_shfit_chunk=mask_shfit_chunk,
mask_att_chunk_encoder=mask_att_chunk_encoder,
)
)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, 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).
"""
residual = x
if self.normalize_before:
x = self.norm1(x)
if self.in_size == self.size:
attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
x = residual + attn
else:
x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + self.feed_forward(x)
if not self.normalize_before:
x = self.norm2(x)
return x, cache
@tables.register("encoder_classes", "SenseVoiceEncoderSmall")
class SenseVoiceEncoderSmall(nn.Module):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
https://arxiv.org/abs/2006.01713
"""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
tp_blocks: int = 0,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
stochastic_depth_rate: float = 0.0,
input_layer: Optional[str] = "conv2d",
pos_enc_class=SinusoidalPositionEncoder,
normalize_before: bool = True,
concat_after: bool = False,
positionwise_layer_type: str = "linear",
positionwise_conv_kernel_size: int = 1,
padding_idx: int = -1,
kernel_size: int = 11,
sanm_shfit: int = 0,
selfattention_layer_type: str = "sanm",
chunk_size: Optional[Union[int, Sequence[int]]] = None,
stride: Optional[Union[int, Sequence[int]]] = None,
pad_left: Optional[Union[int, Sequence[int]]] = None,
encoder_att_look_back_factor: Optional[Union[int, Sequence[int]]] = None,
decoder_att_look_back_factor: Optional[Union[int, Sequence[int]]] = None,
**kwargs,
):
"""Build the encoder.
Args:
chunk_size: Total chunk width(s) in frames used by the chunk-mask training
path, i.e. ``pad_left + stride + pad_right``. ``None`` (the default)
disables the chunk path completely and ``forward`` runs the unmodified
full-attention computation. A tuple with several entries turns on
dynamic chunk training: one entry is drawn uniformly at random per
training step. An entry ``<= 0`` is the *full attention sentinel* -
when that entry is drawn the step runs the plain full-attention path,
which is how full-attention batches are mixed into chunk training.
``-1`` is the canonical spelling of the sentinel.
stride: Committed frames per chunk, one entry per ``chunk_size`` entry (a
single entry is broadcast). Defaults to ``chunk_size`` (no right
context). Must satisfy ``0 < stride <= chunk_size``.
pad_left: Left context frames per chunk, broadcast like ``stride``.
Defaults to ``0``. ``pad_right`` is implied as
``chunk_size - stride - pad_left`` and must be ``>= 0``.
encoder_att_look_back_factor: How many previous chunks the encoder
self-attention may attend to. Defaults to ``(1,)``.
decoder_att_look_back_factor: Unused by SenseVoice (there is no decoder);
accepted and forwarded because ``overlap_chunk`` requires it.
Note:
The 3-tuple passed to :meth:`forward_chunk` via the cache has a *different*
meaning: ``[pad_left, stride, pad_right]``. Use :meth:`init_chunk_cache` to
derive it from the training configuration instead of writing it by hand.
"""
super().__init__()
self._output_size = output_size
self._input_size = input_size
self.embed = SinusoidalPositionEncoder()
self.normalize_before = normalize_before
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
output_size,
linear_units,
dropout_rate,
)
encoder_selfattn_layer = MultiHeadedAttentionSANM
encoder_selfattn_layer_args0 = (
attention_heads,
input_size,
output_size,
attention_dropout_rate,
kernel_size,
sanm_shfit,
)
encoder_selfattn_layer_args = (
attention_heads,
output_size,
output_size,
attention_dropout_rate,
kernel_size,
sanm_shfit,
)
self.encoders0 = nn.ModuleList(
[
EncoderLayerSANM(
input_size,
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args0),
positionwise_layer(*positionwise_layer_args),
dropout_rate,
)
for i in range(1)
]
)
self.encoders = nn.ModuleList(
[
EncoderLayerSANM(
output_size,
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
dropout_rate,
)
for i in range(num_blocks - 1)
]
)
self.tp_encoders = nn.ModuleList(
[
EncoderLayerSANM(
output_size,
output_size,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
dropout_rate,
)
for i in range(tp_blocks)
]
)
self.after_norm = LayerNorm(output_size)
self.tp_norm = LayerNorm(output_size)
# Streaming position encoder used only by ``forward_chunk``. It holds no
# parameters and no buffers, so ``state_dict()`` is byte-for-byte unchanged
# and published checkpoints still load with ``strict=True``.
self.chunk_embed = StreamSinusoidalPositionEncoder()
self.chunk_size, self.stride, self.pad_left = None, None, None
self.overlap_chunk_cls = None
if chunk_size is not None:
self.chunk_size, self.stride, self.pad_left = self._normalize_chunk_args(
chunk_size, stride, pad_left
)
self.overlap_chunk_cls = overlap_chunk(
chunk_size=self.chunk_size,
stride=self.stride,
pad_left=self.pad_left,
shfit_fsmn=(kernel_size - 1) // 2,
encoder_att_look_back_factor=self._as_int_tuple(
encoder_att_look_back_factor, (1,)
),
decoder_att_look_back_factor=self._as_int_tuple(
decoder_att_look_back_factor, (1,)
),
)
@staticmethod
def _as_int_tuple(
value: Optional[Union[int, Sequence[int]]],
default: Tuple[int, ...],
) -> Tuple[int, ...]:
"""Coerce a scalar / sequence / omitted chunk argument to a tuple of ints.
Args:
value: An int, any sequence of ints (including a Hydra ``ListConfig``),
or ``None``.
default: Returned when ``value`` is ``None``.
Returns:
The coerced tuple.
"""
if value is None:
return default
if isinstance(value, int):
return (value,)
return tuple(int(v) for v in value)
@classmethod
def _normalize_chunk_args(
cls,
chunk_size: Union[int, Sequence[int]],
stride: Optional[Union[int, Sequence[int]]],
pad_left: Optional[Union[int, Sequence[int]]],
) -> Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...]]:
"""Validate and broadcast the chunk-mask training arguments.
``overlap_chunk`` broadcasts ``pad_left`` and the look-back factors itself but
*not* ``stride``, so every tuple is expanded to ``len(chunk_size)`` here and the
per-entry geometry is checked up front rather than failing deep inside mask
generation.
Args:
chunk_size: Total chunk width(s); entries ``<= 0`` are full-attention
sentinels and skip geometry validation.
stride: Committed frames per chunk, or ``None`` to default to ``chunk_size``.
pad_left: Left context frames, or ``None`` to default to ``0``.
Returns:
``(chunk_size, stride, pad_left)`` as equal-length tuples of ints.
Raises:
ValueError: If a tuple length cannot be broadcast, or if any non-sentinel
entry violates ``0 < stride <= chunk_size`` or
``0 <= pad_left <= chunk_size - stride``.
"""
chunk_size = cls._as_int_tuple(chunk_size, ())
if not chunk_size:
raise ValueError("chunk_size must contain at least one entry")
def _broadcast(value: Tuple[int, ...], name: str) -> Tuple[int, ...]:
if len(value) == 1:
return value * len(chunk_size)
if len(value) != len(chunk_size):
raise ValueError(
f"{name} has {len(value)} entries but chunk_size has "
f"{len(chunk_size)}; pass one entry per chunk_size entry or a "
"single entry to broadcast"
)
return value
stride = _broadcast(cls._as_int_tuple(stride, chunk_size), "stride")
pad_left = _broadcast(cls._as_int_tuple(pad_left, (0,)), "pad_left")
for i, total in enumerate(chunk_size):
if total <= 0:
continue # full-attention sentinel: geometry is not used
if not 0 < stride[i] <= total:
raise ValueError(
f"stride[{i}]={stride[i]} must satisfy 0 < stride <= "
f"chunk_size[{i}]={total}"
)
if not 0 <= pad_left[i] <= total - stride[i]:
raise ValueError(
f"pad_left[{i}]={pad_left[i]} must satisfy 0 <= pad_left <= "
f"chunk_size[{i}] - stride[{i}] = {total - stride[i]}"
)
return chunk_size, stride, pad_left
def _check_chunk_ind(self, ind: Optional[int], name: str) -> None:
"""Range-check an index into the chunk configuration tuples.
``overlap_chunk.random_choice`` and ``overlap_chunk.get_chunk_size`` do no
bounds checking, so an out-of-range index surfaces as a bare ``IndexError``
deep inside mask generation.
Args:
ind: Index to check. ``None`` is accepted and means "not supplied".
name: Argument name to quote in the error message.
Raises:
ValueError: If the chunk path is disabled or ``ind`` is out of range.
"""
if ind is None:
return
if self.overlap_chunk_cls is None:
raise ValueError(
f"{name}={ind} was given but this encoder was built without chunk_size"
)
if not 0 <= ind < len(self.chunk_size):
raise ValueError(
f"{name}={ind} is out of range for {len(self.chunk_size)} configured "
"chunk size(s)"
)
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
decoding_ind: Optional[int] = None,
**kwargs,
):
"""Embed positions in tensor.
Args:
xs_pad: Input features (batch, time, input_size). Scaled in place, as
before.
ilens: Input lengths (batch,).
decoding_ind: Chunk configuration index to use when not training. Ignored
unless the chunk path is enabled; ``None`` uses index 0. During
training the index is drawn at random from the configured tuple
(dynamic chunk training).
**kwargs: Ignored; accepted so callers can pass extra keywords.
Returns:
``(xs_pad, olens)`` with the same shape and length semantics as the
full-attention path. When the chunk path is active the chunk-expanded
sequence is folded back to the original time axis before returning, so the
CTC head and loss need no changes.
Note:
When the chunk path is active the returned time axis is truncated to
``ilens.max()``; the full-attention path keeps ``xs_pad.size(1)``. These
agree for the usual case of a batch padded exactly to its longest item.
"""
masks = sequence_mask(ilens, device=ilens.device)[:, None, :]
xs_pad *= self.output_size() ** 0.5
xs_pad = self.embed(xs_pad)
self._check_chunk_ind(decoding_ind, "decoding_ind")
chunk_outs, olens_chunk = None, None
mask_shfit_chunk, mask_att_chunk_encoder = None, None
if self.overlap_chunk_cls is not None:
ind = self.overlap_chunk_cls.random_choice(self.training, decoding_ind)
# A non-positive entry is the full-attention sentinel: leave every chunk
# tensor as None so this step runs the plain full-attention computation.
if self.chunk_size[ind] > 0:
chunk_ilens = masks.squeeze(1).sum(1).to(torch.int64)
chunk_outs = self.overlap_chunk_cls.gen_chunk_mask(chunk_ilens, ind)
xs_pad, olens_chunk = self.overlap_chunk_cls.split_chunk(
xs_pad, chunk_ilens, chunk_outs=chunk_outs
)
masks = sequence_mask(
olens_chunk, maxlen=xs_pad.size(1), device=xs_pad.device
)[:, None, :]
mask_shfit_chunk = self.overlap_chunk_cls.get_mask_shfit_chunk(
chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
)
mask_att_chunk_encoder = self.overlap_chunk_cls.get_mask_att_chunk_encoder(
chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
)
# forward encoder1
for layer_idx, encoder_layer in enumerate(self.encoders0):
encoder_outs = encoder_layer(
xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
for layer_idx, encoder_layer in enumerate(self.encoders):
encoder_outs = encoder_layer(
xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
xs_pad = self.after_norm(xs_pad)
# forward encoder2
olens = masks.squeeze(1).sum(1).int()
for layer_idx, encoder_layer in enumerate(self.tp_encoders):
encoder_outs = encoder_layer(
xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
)
xs_pad, masks = encoder_outs[0], encoder_outs[1]
xs_pad = self.tp_norm(xs_pad)
if chunk_outs is not None:
# Fold the chunk-expanded sequence back onto the original time axis so the
# CTC loss is computed at full length, unlike funasr's SANMEncoderChunkOpt
# which returns the expanded sequence. remove_chunk masks its input in
# place, so hand it a clone and keep tp_norm's output untouched for
# autograd.
xs_pad, olens = self.overlap_chunk_cls.remove_chunk(
xs_pad.clone(), olens_chunk, chunk_outs
)
olens = olens.int()
return xs_pad, olens
def init_chunk_cache(
self,
pad_left: Optional[int] = None,
stride: Optional[int] = None,
pad_right: Optional[int] = None,
encoder_chunk_look_back: int = 0,
batch_size: int = 1,
device: Optional[Union[str, torch.device]] = None,
dtype: torch.dtype = torch.float32,
ind: int = 0,
) -> dict:
"""Build a fresh streaming cache for :meth:`forward_chunk`.
Args:
pad_left: Left context frames carried over from the previous chunk.
stride: Committed frames per :meth:`forward_chunk` call. This is how many
new frames the caller feeds each step.
pad_right: Lookahead frames withheld from the output until the next call.
encoder_chunk_look_back: Number of previous chunks the encoder
self-attention may attend to through the per-layer key/value cache.
``0`` (the default) keeps attention inside the current window, ``-1``
keeps every past chunk.
batch_size: Batch size of the stream. Streaming decode is normally 1.
device: Device for the cached tensors. Defaults to CPU.
dtype: Dtype for the cached tensors.
ind: Index into the encoder's training chunk configuration, used only when
``stride`` is omitted.
Returns:
The cache dict. It is mutated in place by :meth:`forward_chunk`, so hold on
to it and pass the same object every call. Keys:
- ``start_idx`` (int): absolute frame offset consumed so far, used to keep
the sinusoidal position encoding continuous across calls.
- ``chunk_size`` (list[int]): ``[pad_left, stride, pad_right]``.
- ``encoder_chunk_look_back`` (int): as above.
- ``opt`` (list | None): per-layer attention key/value caches, one entry per
encoder layer in ``encoders0 + encoders + tp_encoders``. Each entry is
``None`` or ``{"k": (batch, head, time, d_k), "v": (batch, head, time,
d_k)}``. Stays ``None`` when ``encoder_chunk_look_back == 0``.
- ``feats`` (Tensor): ``(batch, kept, input_size)`` overlap of already
scaled and position-encoded frames prepended to the next chunk. Starts
with ``pad_left`` zero frames and holds ``pad_left + pad_right`` frames
afterwards. That initial length deviates from upstream funasr; see the
note below.
- ``tail_chunk`` (bool): set to ``True`` by the caller before the final
call to flush the withheld lookahead frames.
Raises:
ValueError: If the geometry is invalid, or if ``stride`` is omitted while
the encoder was built without ``chunk_size``.
Note:
When ``stride`` is omitted the geometry is derived from the training
configuration at ``ind``: ``pad_left = self.pad_left[ind]``,
``stride = self.stride[ind]``,
``pad_right = self.chunk_size[ind] - stride - pad_left``.
Note:
``encoder_chunk_look_back != 0`` requires ``pad_right >= 1`` because
:meth:`MultiHeadedAttentionSANM.forward_chunk` trims the lookahead with
``k_h[:, :, :-pad_right, :]``, which would empty the cache at
``pad_right == 0``. Combining it with ``pad_left > 0`` also re-appends the
left-context frames to that cache (upstream funasr behaviour), so prefer
``pad_left = 0`` whenever look-back is enabled.
Note:
``feats`` is seeded with ``pad_left`` zero frames, where upstream funasr
(``funasr/models/scama/model.py``, ``init_cache``) seeds
``pad_left + pad_right``. On the very first call nothing has been withheld
yet, so upstream's extra ``pad_right`` frames stand for a lookahead that
does not exist. They are not merely wasted compute: they enter
self-attention and the FSMN convolution, so the first call emits ``stride``
frames instead of ``stride - pad_right`` and the stream carries
``pad_right`` phantom output frames for the whole utterance, breaking the
one-to-one input/output frame alignment CTC decoding depends on.
``tests/test_chunk_streaming_equivalence.py`` pins this. The raw first
layer-0 windows cannot be compared directly, since upstream's is exactly
``pad_right`` frames longer; those surplus leading frames are exactly
``0.0``, and after realigning (dropping them) the two windows are
bit-identical for every geometry tested. The resulting output corruption is
confined to the first emitted chunk -- it peaks at ``1.514661`` for
``(pad_left, stride, pad_right) = (0, 10, 5)``, and every frame from index
``stride`` onward is bit-identical.
"""
if stride is None:
if self.overlap_chunk_cls is None:
raise ValueError(
"stride must be given because this encoder was built without "
"chunk_size, so there is no training configuration to derive from"
)
self._check_chunk_ind(ind, "ind")
if self.chunk_size[ind] <= 0:
raise ValueError(
f"chunk_size[{ind}]={self.chunk_size[ind]} is the full-attention "
"sentinel and has no streaming geometry; pick another ind or pass "
"stride explicitly"
)
pad_left = self.pad_left[ind]
stride = self.stride[ind]
pad_right = self.chunk_size[ind] - stride - pad_left
else:
pad_left = 0 if pad_left is None else int(pad_left)
stride = int(stride)
pad_right = 0 if pad_right is None else int(pad_right)
if stride < 1:
raise ValueError(f"stride must be >= 1, got {stride}")
if pad_left < 0 or pad_right < 0:
raise ValueError(
f"pad_left and pad_right must be >= 0, got {pad_left} and {pad_right}"
)
if encoder_chunk_look_back != 0 and pad_right < 1:
raise ValueError(
"encoder_chunk_look_back != 0 requires pad_right >= 1; the attention "
"cache is built by dropping the last pad_right frames and would be "
"empty otherwise"
)
return {
"start_idx": 0,
"chunk_size": [pad_left, stride, pad_right],
"encoder_chunk_look_back": encoder_chunk_look_back,
"opt": None,
"feats": torch.zeros(
(batch_size, pad_left, self._input_size), dtype=dtype, device=device
),
"tail_chunk": False,
}
def _add_overlap_chunk(self, feats: torch.Tensor, cache: dict) -> torch.Tensor:
"""Prepend the cached overlap to the incoming frames and refresh the cache.
The SANM memory block has no streaming cache of its own; its context is
supplied by overlapping the chunks instead, which is what this does.
Args:
feats: Scaled and position-encoded new frames (batch, time, input_size).
cache: Cache dict from :meth:`init_chunk_cache`.
Returns:
The concatenated window (batch, kept + time, input_size).
"""
cached = cache.get("feats")
if cached is None:
return feats
overlap_feats = torch.cat(
(cached.to(device=feats.device, dtype=feats.dtype), feats), dim=1
)
keep = cache["chunk_size"][0] + cache["chunk_size"][2]
cache["feats"] = overlap_feats[:, max(0, overlap_feats.size(1) - keep) :, :]
return overlap_feats
def forward_chunk(
self,
xs_pad: torch.Tensor,
cache: Optional[dict] = None,
**kwargs,
) -> Tuple[torch.Tensor, dict]:
"""Encode one streaming chunk.
Args:
xs_pad: New frames for this step, ``(batch, stride, input_size)``. The last
real chunk may be shorter. Not modified in place, unlike
:meth:`forward`. Ignored when ``cache["tail_chunk"]`` is set.
cache: Cache dict from :meth:`init_chunk_cache`, mutated in place. When
``None`` a default cache is built from the training configuration.
**kwargs: ``trim`` (bool, default ``True``) returns only the frames that
are final at this step. Pass ``trim=False`` for the raw
``pad_left + time + pad_right`` window, matching funasr's
``SANMEncoderChunkOpt.forward_chunk``.
Returns:
``(xs_pad, cache)``. With ``trim=True`` the output frames line up one to
one with the input frames, delayed by ``pad_right``: the first call returns
``time - pad_right`` frames, later calls return ``time`` frames, and the
``tail_chunk`` call flushes the final ``pad_right`` frames. Concatenating
every call's output reproduces the whole utterance in order.
Note:
Run this under ``torch.no_grad()`` with the module in ``eval()`` mode; the
per-layer caches assume a single monotonic pass over one stream.
Note:
The caller owns the SenseVoice prompt frames. Prepend the four
``[language, event, emo, textnorm]`` frames to the first chunk so they take
absolute positions 1-4, exactly as ``SenseVoiceSmall.encode`` does.
"""
if cache is None:
cache = self.init_chunk_cache(
batch_size=xs_pad.size(0), device=xs_pad.device, dtype=xs_pad.dtype
)
pad_left, _, pad_right = cache["chunk_size"]
is_tail = cache.get("tail_chunk", False)
if is_tail:
# The cached overlap is already scaled and position-encoded; re-running it
# is what flushes the frames whose lookahead never arrived.
xs_pad = cache["feats"].to(device=xs_pad.device, dtype=xs_pad.dtype)
else:
xs_pad = xs_pad * self.output_size() ** 0.5
xs_pad = self.chunk_embed(xs_pad, cache)
xs_pad = self._add_overlap_chunk(xs_pad, cache)
if cache["opt"] is None:
cache_layer_num = (
len(self.encoders0) + len(self.encoders) + len(self.tp_encoders)
)
new_cache = [None] * cache_layer_num
else:
new_cache = cache["opt"]
chunk_size, look_back = cache["chunk_size"], cache["encoder_chunk_look_back"]
offset = 0
for layer_idx, encoder_layer in enumerate(self.encoders0):
idx = offset + layer_idx
xs_pad, new_cache[idx] = encoder_layer.forward_chunk(
xs_pad, new_cache[idx], chunk_size, look_back
)
offset += len(self.encoders0)
for layer_idx, encoder_layer in enumerate(self.encoders):
idx = offset + layer_idx
xs_pad, new_cache[idx] = encoder_layer.forward_chunk(
xs_pad, new_cache[idx], chunk_size, look_back
)
xs_pad = self.after_norm(xs_pad)
offset += len(self.encoders)
for layer_idx, encoder_layer in enumerate(self.tp_encoders):
idx = offset + layer_idx
xs_pad, new_cache[idx] = encoder_layer.forward_chunk(
xs_pad, new_cache[idx], chunk_size, look_back
)
xs_pad = self.tp_norm(xs_pad)
if look_back > 0 or look_back == -1:
cache["opt"] = new_cache
if kwargs.get("trim", True):
end = xs_pad.size(1) if is_tail else xs_pad.size(1) - pad_right
xs_pad = xs_pad[:, pad_left : max(0, end), :]
return xs_pad, cache
@tables.register("model_classes", "SenseVoiceSmall")
class SenseVoiceSmall(nn.Module):
"""CTC-attention hybrid Encoder-Decoder model"""
def __init__(
self,
specaug: str = None,
specaug_conf: dict = None,
normalize: str = None,
normalize_conf: dict = None,
encoder: str = None,
encoder_conf: dict = None,
ctc_conf: dict = None,
input_size: int = 80,
vocab_size: int = -1,
ignore_id: int = -1,
blank_id: int = 0,
sos: int = 1,
eos: int = 2,
length_normalized_loss: bool = False,
**kwargs,
):
super().__init__()
if specaug is not None:
specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
if ctc_conf is None:
ctc_conf = {}
ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
self.blank_id = blank_id
self.sos = sos if sos is not None else vocab_size - 1
self.eos = eos if eos is not None else vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
self.error_calculator = None
self.ctc = ctc
self.length_normalized_loss = length_normalized_loss
self.encoder_output_size = encoder_output_size
self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
self.textnorm_dict = {"withitn": 14, "woitn": 15}
self.textnorm_int_dict = {25016: 14, 25017: 15}
self.embed = torch.nn.Embedding(7 + len(self.lid_dict) + len(self.textnorm_dict), input_size)
self.emo_dict = {"unk": 25009, "happy": 25001, "sad": 25002, "angry": 25003, "neutral": 25004}
self.criterion_att = LabelSmoothingLoss(
size=self.vocab_size,
padding_idx=self.ignore_id,
smoothing=kwargs.get("lsm_weight", 0.0),
normalize_length=self.length_normalized_loss,
)
@staticmethod
def from_pretrained(model:str=None, **kwargs):
from funasr import AutoModel
model, kwargs = AutoModel.build_model(model=model, trust_remote_code=True, **kwargs)
return model, kwargs
def forward(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
**kwargs,
):
"""Encoder + Decoder + Calc loss
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
"""
# import pdb;
# pdb.set_trace()
if len(text_lengths.size()) > 1:
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
batch_size = speech.shape[0]
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, text)
loss_ctc, cer_ctc = None, None
loss_rich, acc_rich = None, None
stats = dict()
loss_ctc, cer_ctc = self._calc_ctc_loss(
encoder_out[:, 4:, :], encoder_out_lens - 4, text[:, 4:], text_lengths - 4
)
loss_rich, acc_rich = self._calc_rich_ce_loss(
encoder_out[:, :4, :], text[:, :4]
)
loss = loss_ctc + loss_rich
# Collect total loss stats
stats["loss_ctc"] = torch.clone(loss_ctc.detach()) if loss_ctc is not None else None
stats["loss_rich"] = torch.clone(loss_rich.detach()) if loss_rich is not None else None
stats["loss"] = torch.clone(loss.detach()) if loss is not None else None
stats["acc_rich"] = acc_rich
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
batch_size = int((text_lengths + 1).sum())
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def encode(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
text: torch.Tensor,
**kwargs,
):
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
ind: int
"""
# Data augmentation
if self.specaug is not None and self.training:
speech, speech_lengths = self.specaug(speech, speech_lengths)
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
if self.normalize is not None:
speech, speech_lengths = self.normalize(speech, speech_lengths)
lids = torch.LongTensor([[self.lid_int_dict[int(lid)] if torch.rand(1) > 0.2 and int(lid) in self.lid_int_dict else 0 ] for lid in text[:, 0]]).to(speech.device)
language_query = self.embed(lids)
styles = torch.LongTensor([[self.textnorm_int_dict[int(style)]] for style in text[:, 3]]).to(speech.device)
style_query = self.embed(styles)
speech = torch.cat((style_query, speech), dim=1)
speech_lengths += 1
event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(speech.size(0), 1, 1)
input_query = torch.cat((language_query, event_emo_query), dim=1)
speech = torch.cat((input_query, speech), dim=1)
speech_lengths += 3
encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
return encoder_out, encoder_out_lens
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
ys_pad: torch.Tensor,
ys_pad_lens: torch.Tensor,
):
# Calc CTC loss
loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
# Calc CER using CTC
cer_ctc = None
if not self.training and self.error_calculator is not None:
ys_hat = self.ctc.argmax(encoder_out).data
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
return loss_ctc, cer_ctc
def _calc_rich_ce_loss(
self,
encoder_out: torch.Tensor,
ys_pad: torch.Tensor,
):
decoder_out = self.ctc.ctc_lo(encoder_out)
# 2. Compute attention loss
loss_rich = self.criterion_att(decoder_out, ys_pad.contiguous())
acc_rich = th_accuracy(
decoder_out.view(-1, self.vocab_size),
ys_pad.contiguous(),
ignore_label=self.ignore_id,
)
return loss_rich, acc_rich
def inference(
self,
data_in,
data_lengths=None,
key: list = ["wav_file_tmp_name"],
tokenizer=None,
frontend=None,
**kwargs,
):
meta_data = {}
if (
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
): # fbank
speech, speech_lengths = data_in, data_lengths
if len(speech.shape) < 3:
speech = speech[None, :, :]
if speech_lengths is None:
speech_lengths = speech.shape[1]
else:
# extract fbank feats
time1 = time.perf_counter()
audio_sample_list = load_audio_text_image_video(
data_in,
fs=frontend.fs,
audio_fs=kwargs.get("fs", 16000),
data_type=kwargs.get("data_type", "sound"),
tokenizer=tokenizer,
)
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = (
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
)
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
language = kwargs.get("language", "auto")
language_query = self.embed(
torch.LongTensor(
[[self.lid_dict[language] if language in self.lid_dict else 0]]
).to(speech.device)
).repeat(speech.size(0), 1, 1)
use_itn = kwargs.get("use_itn", False)
output_timestamp = kwargs.get("output_timestamp", False)
textnorm = kwargs.get("text_norm", None)
if textnorm is None:
textnorm = "withitn" if use_itn else "woitn"
textnorm_query = self.embed(
torch.LongTensor([[self.textnorm_dict[textnorm]]]).to(speech.device)
).repeat(speech.size(0), 1, 1)
speech = torch.cat((textnorm_query, speech), dim=1)
speech_lengths += 1
event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
speech.size(0), 1, 1
)
input_query = torch.cat((language_query, event_emo_query), dim=1)
speech = torch.cat((input_query, speech), dim=1)
speech_lengths += 3
# Encoder
encoder_out, encoder_out_lens = self.encoder(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
# c. Passed the encoder result and the beam search
ctc_logits = self.ctc.log_softmax(encoder_out)
if kwargs.get("ban_emo_unk", False):
ctc_logits[:, :, self.emo_dict["unk"]] = -float("inf")
results = []
b, n, d = encoder_out.size()
if isinstance(key[0], (list, tuple)):
key = key[0]
if len(key) < b:
key = key * b
for i in range(b):
x = ctc_logits[i, : encoder_out_lens[i].item(), :]
yseq = x.argmax(dim=-1)
yseq = torch.unique_consecutive(yseq, dim=-1)
ibest_writer = None
if kwargs.get("output_dir") is not None:
if not hasattr(self, "writer"):
self.writer = DatadirWriter(kwargs.get("output_dir"))
ibest_writer = self.writer[f"1best_recog"]
mask = yseq != self.blank_id
token_int = yseq[mask].tolist()
# Change integer-ids to tokens
text = tokenizer.decode(token_int)
if ibest_writer is not None:
ibest_writer["text"][key[i]] = text
if output_timestamp:
from itertools import groupby
timestamp = []
tokens = tokenizer.text2tokens(text)[4:]
logits_speech = self.ctc.softmax(encoder_out)[i, 4:encoder_out_lens[i].item(), :]
pred = logits_speech.argmax(-1).cpu()
logits_speech[pred==self.blank_id, self.blank_id] = 0
align = ctc_forced_align(
logits_speech.unsqueeze(0).float(),
torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device),
(encoder_out_lens-4).long()[i],
torch.tensor(len(token_int)-4).unsqueeze(0).long().to(logits_speech.device),
ignore_id=self.ignore_id,
)
pred = groupby(align[0, :encoder_out_lens[0]])
_start = 0
token_id = 0
ts_max = encoder_out_lens[i] - 4
for pred_token, pred_frame in pred:
_end = _start + len(list(pred_frame))
if pred_token != 0 and token_id < len(tokens):
ts_left = max((_start*60-30)/1000, 0)
ts_right = min((_end*60-30)/1000, (ts_max*60-30)/1000)
timestamp.append([tokens[token_id], ts_left, ts_right])
token_id += 1
_start = _end
result_i = {"key": key[i], "text": text, "timestamp": timestamp}
results.append(result_i)
else:
result_i = {"key": key[i], "text": text}
results.append(result_i)
return results, meta_data
def export(self, **kwargs):
from export_meta import export_rebuild_model
if "max_seq_len" not in kwargs:
kwargs["max_seq_len"] = 512
models = export_rebuild_model(model=self, **kwargs)
return models
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