python/FunASR-XL.git

			@@ -1,39 +1,34 @@
			from typing import List
			from typing import Optional
			from typing import Sequence
			from typing import Tuple
			from typing import Union
			import logging
			import torch
			import torch.nn as nn
			import torch.nn.functional as F
			from funasr.models.scama.chunk_utilis import overlap_chunk
			import numpy as np
			from funasr.train_utils.device_funcs import to_device
			from funasr.models.transformer.utils.nets_utils import make_pad_mask
			from funasr.models.sanm.attention import MultiHeadedAttention
			from funasr.models.ct_transformer_streaming.attention import MultiHeadedAttentionSANMwithMask
			from funasr.models.transformer.embedding import SinusoidalPositionEncoder, StreamSinusoidalPositionEncoder
			from funasr.models.transformer.layer_norm import LayerNorm
			from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
			from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
			from funasr.models.transformer.positionwise_feed_forward import (
			PositionwiseFeedForward, # noqa: H301
			)
			from funasr.models.transformer.utils.repeat import repeat
			from funasr.models.transformer.utils.subsampling import Conv2dSubsampling
			from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2
			from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6
			from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8
			from funasr.models.transformer.utils.subsampling import TooShortUttError
			from funasr.models.transformer.utils.subsampling import check_short_utt
			from funasr.models.transformer.utils.mask import subsequent_mask, vad_mask
			#!/usr/bin/env python3
			# -- encoding: utf-8 --
			# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
			# MIT License (https://opensource.org/licenses/MIT)

			from funasr.models.ctc.ctc import CTC
			import torch
			from typing import List, Optional, Tuple

			from funasr.register import tables
			from funasr.models.ctc.ctc import CTC
			from funasr.models.transformer.utils.repeat import repeat
			from funasr.models.transformer.layer_norm import LayerNorm
			from funasr.models.sanm.attention import MultiHeadedAttention
			from funasr.models.transformer.utils.nets_utils import make_pad_mask
			from funasr.models.transformer.utils.subsampling import check_short_utt
			from funasr.models.transformer.utils.subsampling import TooShortUttError
			from funasr.models.transformer.embedding import SinusoidalPositionEncoder
			from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
			from funasr.models.transformer.utils.mask import subsequent_mask, vad_mask
			from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
			from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
			from funasr.models.ct_transformer_streaming.attention import MultiHeadedAttentionSANMwithMask
			from funasr.models.transformer.utils.subsampling import (
			Conv2dSubsampling,
			Conv2dSubsampling2,
			Conv2dSubsampling6,
			Conv2dSubsampling8,
			)

			class EncoderLayerSANM(nn.Module):

			class EncoderLayerSANM(torch.nn.Module):
			def __init__(
			self,
			in_size,
			@@ -51,13 +46,13 @@
			self.feed_forward = feed_forward
			self.norm1 = LayerNorm(in_size)
			self.norm2 = LayerNorm(size)
			self.dropout = nn.Dropout(dropout_rate)
			self.dropout = torch.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.concat_linear = torch.nn.Linear(size + size, size)
			self.stochastic_depth_rate = stochastic_depth_rate
			self.dropout_rate = dropout_rate

			@@ -92,7 +87,18 @@
			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)
			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:
			@@ -100,11 +106,21 @@
			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)
			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)
			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)
			@@ -156,7 +172,7 @@


			@tables.register("encoder_classes", "SANMVadEncoder")
			class SANMVadEncoder(nn.Module):
			class SANMVadEncoder(torch.nn.Module):
			"""
			Author: Speech Lab of DAMO Academy, Alibaba Group

			@@ -181,8 +197,8 @@
			padding_idx: int = -1,
			interctc_layer_idx: List[int] = [],
			interctc_use_conditioning: bool = False,
			kernel_size : int = 11,
			sanm_shfit : int = 0,
			kernel_size: int = 11,
			sanm_shfit: int = 0,
			selfattention_layer_type: str = "sanm",
			):
			super().__init__()
			@@ -287,7 +303,7 @@
			)

			self.encoders = repeat(
			num_blocks-1,
			num_blocks - 1,
			lambda lnum: EncoderLayerSANM(
			output_size,
			output_size,
			@@ -306,7 +322,7 @@
			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)
			self.dropout = torch.nn.Dropout(dropout_rate)

			def output_size(self) -> int:
			return self._output_size
			@@ -331,16 +347,20 @@
			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
			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)):
			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",
			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,
			)
			@@ -354,25 +374,26 @@
			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 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)
			@@ -381,3 +402,105 @@
			if len(intermediate_outs) > 0:
			return (xs_pad, intermediate_outs), olens, None
			return xs_pad, olens, None


			class EncoderLayerSANMExport(torch.nn.Module):
			def __init__(
			self,
			model,
			):
			"""Construct an EncoderLayer object."""
			super().__init__()
			self.self_attn = model.self_attn
			self.feed_forward = model.feed_forward
			self.norm1 = model.norm1
			self.norm2 = model.norm2
			self.in_size = model.in_size
			self.size = model.size

			def forward(self, x, mask):

			residual = x
			x = self.norm1(x)
			x = self.self_attn(x, mask)
			if self.in_size == self.size:
			x = x + residual
			residual = x
			x = self.norm2(x)
			x = self.feed_forward(x)
			x = x + residual

			return x, mask


			@tables.register("encoder_classes", "SANMVadEncoderExport")
			class SANMVadEncoderExport(torch.nn.Module):
			def __init__(
			self,
			model,
			max_seq_len=512,
			feats_dim=560,
			model_name="encoder",
			onnx: bool = True,
			):
			super().__init__()
			self.embed = model.embed
			self.model = model
			self._output_size = model._output_size

			from funasr.utils.torch_function import sequence_mask

			self.make_pad_mask = sequence_mask(max_seq_len, flip=False)

			from funasr.models.sanm.attention import MultiHeadedAttentionSANMExport

			if hasattr(model, "encoders0"):
			for i, d in enumerate(self.model.encoders0):
			if isinstance(d.self_attn, MultiHeadedAttentionSANMwithMask):
			d.self_attn = MultiHeadedAttentionSANMExport(d.self_attn)
			self.model.encoders0[i] = EncoderLayerSANMExport(d)

			for i, d in enumerate(self.model.encoders):
			if isinstance(d.self_attn, MultiHeadedAttentionSANMwithMask):
			d.self_attn = MultiHeadedAttentionSANMExport(d.self_attn)
			self.model.encoders[i] = EncoderLayerSANMExport(d)

			def prepare_mask(self, mask, sub_masks):
			mask_3d_btd = mask[:, :, None]
			mask_4d_bhlt = (1 - sub_masks) * -10000.0

			return mask_3d_btd, mask_4d_bhlt

			def forward(
			self,
			speech: torch.Tensor,
			speech_lengths: torch.Tensor,
			vad_masks: torch.Tensor,
			sub_masks: torch.Tensor,
			):
			speech = speech * self._output_size**0.5
			mask = self.make_pad_mask(speech_lengths)
			vad_masks = self.prepare_mask(mask, vad_masks)
			mask = self.prepare_mask(mask, sub_masks)

			if self.embed is None:
			xs_pad = speech
			else:
			xs_pad = self.embed(speech)

			encoder_outs = self.model.encoders0(xs_pad, mask)
			xs_pad, masks = encoder_outs[0], encoder_outs[1]

			# encoder_outs = self.model.encoders(xs_pad, mask)
			for layer_idx, encoder_layer in enumerate(self.model.encoders):
			if layer_idx == len(self.model.encoders) - 1:
			mask = vad_masks
			encoder_outs = encoder_layer(xs_pad, mask)
			xs_pad, masks = encoder_outs[0], encoder_outs[1]

			xs_pad = self.model.after_norm(xs_pad)

			return xs_pad, speech_lengths

			def get_output_size(self):
			return self.model.encoders[0].size