python/FunASR-XL.git

			@@ -10,7 +10,7 @@
			from typeguard import check_argument_types
			import numpy as np
			from funasr.modules.nets_utils import make_pad_mask
			from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM
			from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
			from funasr.modules.embedding import SinusoidalPositionEncoder
			from funasr.modules.layer_norm import LayerNorm
			from funasr.modules.multi_layer_conv import Conv1dLinear
			@@ -27,7 +27,7 @@
			from funasr.modules.subsampling import check_short_utt
			from funasr.models.ctc import CTC
			from funasr.models.encoder.abs_encoder import AbsEncoder

			from funasr.modules.mask import subsequent_mask, vad_mask

			class EncoderLayerSANM(nn.Module):
			def __init__(
			@@ -958,3 +958,231 @@
			var_dict_tf[name_tf].shape))

			return var_dict_torch_update


			class SANMVadEncoder(AbsEncoder):
			"""
			author: Speech Lab, Alibaba Group, China

			"""

			def __init__(
			self,
			input_size: int,
			output_size: int = 256,
			attention_heads: int = 4,
			linear_units: int = 2048,
			num_blocks: int = 6,
			dropout_rate: float = 0.1,
			positional_dropout_rate: float = 0.1,
			attention_dropout_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,
			interctc_layer_idx: List[int] = [],
			interctc_use_conditioning: bool = False,
			kernel_size : int = 11,
			sanm_shfit : int = 0,
			selfattention_layer_type: str = "sanm",
			):
			assert check_argument_types()
			super().__init__()
			self._output_size = output_size

			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),
			torch.nn.ReLU(),
			pos_enc_class(output_size, positional_dropout_rate),
			)
			elif input_layer == "conv2d":
			self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
			elif input_layer == "conv2d2":
			self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
			elif input_layer == "conv2d6":
			self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
			elif input_layer == "conv2d8":
			self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
			elif input_layer == "embed":
			self.embed = torch.nn.Sequential(
			torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
			SinusoidalPositionEncoder(),
			)
			elif input_layer is None:
			if input_size == output_size:
			self.embed = None
			else:
			self.embed = torch.nn.Linear(input_size, output_size)
			elif input_layer == "pe":
			self.embed = SinusoidalPositionEncoder()
			else:
			raise ValueError("unknown input_layer: " + input_layer)
			self.normalize_before = normalize_before
			if positionwise_layer_type == "linear":
			positionwise_layer = PositionwiseFeedForward
			positionwise_layer_args = (
			output_size,
			linear_units,
			dropout_rate,
			)
			elif positionwise_layer_type == "conv1d":
			positionwise_layer = MultiLayeredConv1d
			positionwise_layer_args = (
			output_size,
			linear_units,
			positionwise_conv_kernel_size,
			dropout_rate,
			)
			elif positionwise_layer_type == "conv1d-linear":
			positionwise_layer = Conv1dLinear
			positionwise_layer_args = (
			output_size,
			linear_units,
			positionwise_conv_kernel_size,
			dropout_rate,
			)
			else:
			raise NotImplementedError("Support only linear or conv1d.")

			if selfattention_layer_type == "selfattn":
			encoder_selfattn_layer = MultiHeadedAttention
			encoder_selfattn_layer_args = (
			attention_heads,
			output_size,
			attention_dropout_rate,
			)

			elif selfattention_layer_type == "sanm":
			self.encoder_selfattn_layer = MultiHeadedAttentionSANMwithMask
			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 = repeat(
			1,
			lambda lnum: EncoderLayerSANM(
			input_size,
			output_size,
			self.encoder_selfattn_layer(*encoder_selfattn_layer_args0),
			positionwise_layer(*positionwise_layer_args),
			dropout_rate,
			normalize_before,
			concat_after,
			),
			)

			self.encoders = repeat(
			num_blocks-1,
			lambda lnum: EncoderLayerSANM(
			output_size,
			output_size,
			self.encoder_selfattn_layer(*encoder_selfattn_layer_args),
			positionwise_layer(*positionwise_layer_args),
			dropout_rate,
			normalize_before,
			concat_after,
			),
			)
			if self.normalize_before:
			self.after_norm = LayerNorm(output_size)

			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
			self.dropout = nn.Dropout(dropout_rate)

			def output_size(self) -> int:
			return self._output_size

			def forward(
			self,
			xs_pad: torch.Tensor,
			ilens: torch.Tensor,
			vad_indexes: torch.Tensor,
			prev_states: torch.Tensor = None,
			ctc: CTC = None,
			) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
			"""Embed positions in tensor.

			Args:
			xs_pad: input tensor (B, L, D)
			ilens: input length (B)
			prev_states: Not to be used now.
			Returns:
			position embedded tensor and mask
			"""
			masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
			sub_masks = subsequent_mask(masks.size(-1), device=xs_pad.device).unsqueeze(0)
			no_future_masks = masks & sub_masks
			xs_pad = self.output_size()*0.5
			if self.embed is None:
			xs_pad = xs_pad
			elif (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)
			else:
			xs_pad = self.embed(xs_pad)

			# xs_pad = self.dropout(xs_pad)
			mask_tup0 = [masks, no_future_masks]
			encoder_outs = self.encoders0(xs_pad, mask_tup0)
			xs_pad, _ = encoder_outs[0], encoder_outs[1]
			intermediate_outs = []


			for layer_idx, encoder_layer in enumerate(self.encoders):
			if layer_idx + 1 == len(self.encoders):
			# This is last layer.
			coner_mask = torch.ones(masks.size(0),
			masks.size(-1),
			masks.size(-1),
			device=xs_pad.device,
			dtype=torch.bool)
			for word_index, length in enumerate(ilens):
			coner_mask[word_index, :, :] = vad_mask(masks.size(-1),
			vad_indexes[word_index],
			device=xs_pad.device)
			layer_mask = masks & coner_mask
			else:
			layer_mask = no_future_masks
			mask_tup1 = [masks, layer_mask]
			encoder_outs = encoder_layer(xs_pad, mask_tup1)
			xs_pad, layer_mask = encoder_outs[0], encoder_outs[1]

			if self.normalize_before:
			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