From 4ace5a95b052d338947fc88809a440ccd55cf6b4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 16 十一月 2023 16:39:52 +0800
Subject: [PATCH] funasr pages
---
funasr/models/encoder/sanm_encoder.py | 395 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 385 insertions(+), 10 deletions(-)
diff --git a/funasr/models/encoder/sanm_encoder.py b/funasr/models/encoder/sanm_encoder.py
index 4c4bd7c..c15343e 100644
--- a/funasr/models/encoder/sanm_encoder.py
+++ b/funasr/models/encoder/sanm_encoder.py
@@ -6,12 +6,13 @@
import logging
import torch
import torch.nn as nn
+import torch.nn.functional as F
from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
-from typeguard import check_argument_types
import numpy as np
+from funasr.torch_utils.device_funcs import to_device
from funasr.modules.nets_utils import make_pad_mask
-from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM
-from funasr.modules.embedding import SinusoidalPositionEncoder
+from funasr.modules.attention import MultiHeadedAttention, MultiHeadedAttentionSANM, MultiHeadedAttentionSANMwithMask
+from funasr.modules.embedding import SinusoidalPositionEncoder, StreamSinusoidalPositionEncoder
from funasr.modules.layer_norm import LayerNorm
from funasr.modules.multi_layer_conv import Conv1dLinear
from funasr.modules.multi_layer_conv import MultiLayeredConv1d
@@ -25,9 +26,10 @@
from funasr.modules.subsampling import Conv2dSubsampling8
from funasr.modules.subsampling import TooShortUttError
from funasr.modules.subsampling import check_short_utt
+from funasr.modules.mask import subsequent_mask, vad_mask
+
from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
-
class EncoderLayerSANM(nn.Module):
def __init__(
@@ -112,12 +114,48 @@
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
+
class SANMEncoder(AbsEncoder):
"""
- author: Speech Lab, Alibaba Group, China
+ Author: Speech Lab of DAMO Academy, Alibaba Group
San-m: Memory equipped self-attention for end-to-end speech recognition
https://arxiv.org/abs/2006.01713
@@ -144,11 +182,14 @@
interctc_use_conditioning: bool = False,
kernel_size : int = 11,
sanm_shfit : int = 0,
+ lora_list: List[str] = None,
+ lora_rank: int = 8,
+ lora_alpha: int = 16,
+ lora_dropout: float = 0.1,
selfattention_layer_type: str = "sanm",
tf2torch_tensor_name_prefix_torch: str = "encoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
):
- assert check_argument_types()
super().__init__()
self._output_size = output_size
@@ -180,6 +221,8 @@
self.embed = torch.nn.Linear(input_size, output_size)
elif input_layer == "pe":
self.embed = SinusoidalPositionEncoder()
+ elif input_layer == "pe_online":
+ self.embed = StreamSinusoidalPositionEncoder()
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
@@ -226,6 +269,10 @@
attention_dropout_rate,
kernel_size,
sanm_shfit,
+ lora_list,
+ lora_rank,
+ lora_alpha,
+ lora_dropout,
)
encoder_selfattn_layer_args = (
@@ -235,6 +282,10 @@
attention_dropout_rate,
kernel_size,
sanm_shfit,
+ lora_list,
+ lora_rank,
+ lora_alpha,
+ lora_dropout,
)
self.encoders0 = repeat(
1,
@@ -293,7 +344,7 @@
position embedded tensor and mask
"""
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
- xs_pad *= self.output_size()**0.5
+ xs_pad = xs_pad * self.output_size()**0.5
if self.embed is None:
xs_pad = xs_pad
elif (
@@ -346,6 +397,59 @@
if len(intermediate_outs) > 0:
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None
+
+ def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
+ if len(cache) == 0:
+ return feats
+ cache["feats"] = to_device(cache["feats"], device=feats.device)
+ overlap_feats = torch.cat((cache["feats"], feats), dim=1)
+ cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
+ return overlap_feats
+
+ def forward_chunk(self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ cache: dict = None,
+ ctc: CTC = None,
+ ):
+ xs_pad *= self.output_size() ** 0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ else:
+ xs_pad = self.embed(xs_pad, cache)
+ if cache["tail_chunk"]:
+ xs_pad = to_device(cache["feats"], device=xs_pad.device)
+ else:
+ xs_pad = self._add_overlap_chunk(xs_pad, cache)
+ encoder_outs = self.encoders0(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ intermediate_outs = []
+ if len(self.interctc_layer_idx) == 0:
+ encoder_outs = self.encoders(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ else:
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ encoder_outs = encoder_layer(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ if layer_idx + 1 in self.interctc_layer_idx:
+ encoder_out = xs_pad
+
+ # intermediate outputs are also normalized
+ if self.normalize_before:
+ encoder_out = self.after_norm(encoder_out)
+
+ intermediate_outs.append((layer_idx + 1, encoder_out))
+
+ if self.interctc_use_conditioning:
+ ctc_out = ctc.softmax(encoder_out)
+ xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+ if self.normalize_before:
+ xs_pad = self.after_norm(xs_pad)
+
+ if len(intermediate_outs) > 0:
+ return (xs_pad, intermediate_outs), None, None
+ return xs_pad, ilens, None
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
@@ -507,7 +611,7 @@
class SANMEncoderChunkOpt(AbsEncoder):
"""
- author: Speech Lab, Alibaba Group, China
+ 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
@@ -543,7 +647,6 @@
tf2torch_tensor_name_prefix_torch: str = "encoder",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
):
- assert check_argument_types()
super().__init__()
self._output_size = output_size
@@ -575,6 +678,8 @@
self.embed = torch.nn.Linear(input_size, output_size)
elif input_layer == "pe":
self.embed = SinusoidalPositionEncoder()
+ elif input_layer == "pe_online":
+ self.embed = StreamSinusoidalPositionEncoder()
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
@@ -760,6 +865,49 @@
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None
+ def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
+ if len(cache) == 0:
+ return feats
+ cache["feats"] = to_device(cache["feats"], device=feats.device)
+ overlap_feats = torch.cat((cache["feats"], feats), dim=1)
+ cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
+ return overlap_feats
+
+ def forward_chunk(self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ cache: dict = None,
+ ):
+ xs_pad *= self.output_size() ** 0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ else:
+ xs_pad = self.embed(xs_pad, cache)
+ if cache["tail_chunk"]:
+ xs_pad = to_device(cache["feats"], device=xs_pad.device)
+ else:
+ xs_pad = self._add_overlap_chunk(xs_pad, cache)
+ if cache["opt"] is None:
+ cache_layer_num = len(self.encoders0) + len(self.encoders)
+ new_cache = [None] * cache_layer_num
+ else:
+ new_cache = cache["opt"]
+
+ for layer_idx, encoder_layer in enumerate(self.encoders0):
+ encoder_outs = encoder_layer.forward_chunk(xs_pad, new_cache[layer_idx], cache["chunk_size"], cache["encoder_chunk_look_back"])
+ xs_pad, new_cache[0] = encoder_outs[0], encoder_outs[1]
+
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ encoder_outs = encoder_layer.forward_chunk(xs_pad, new_cache[layer_idx+len(self.encoders0)], cache["chunk_size"], cache["encoder_chunk_look_back"])
+ xs_pad, new_cache[layer_idx+len(self.encoders0)] = encoder_outs[0], encoder_outs[1]
+
+ if self.normalize_before:
+ xs_pad = self.after_norm(xs_pad)
+ if cache["encoder_chunk_look_back"] > 0 or cache["encoder_chunk_look_back"] == -1:
+ cache["opt"] = new_cache
+
+ return xs_pad, ilens, None
+
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
@@ -916,3 +1064,230 @@
var_dict_tf[name_tf].shape))
return var_dict_torch_update
+
+
+class SANMVadEncoder(AbsEncoder):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+
+ """
+
+ 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",
+ ):
+ 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
--
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