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/modules/attention.py | 494 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 480 insertions(+), 14 deletions(-)
diff --git a/funasr/modules/attention.py b/funasr/modules/attention.py
index e3ad56a..b007d58 100644
--- a/funasr/modules/attention.py
+++ b/funasr/modules/attention.py
@@ -11,7 +11,11 @@
import numpy
import torch
from torch import nn
+from typing import Optional, Tuple
+import torch.nn.functional as F
+from funasr.modules.nets_utils import make_pad_mask
+import funasr.modules.lora.layers as lora
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
@@ -318,7 +322,7 @@
"""
- def __init__(self, n_head, in_feat, n_feat, dropout_rate, kernel_size, sanm_shfit=0):
+ 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(MultiHeadedAttentionSANM, self).__init__()
assert n_feat % n_head == 0
@@ -328,8 +332,19 @@
# 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)
+ if lora_list is not None:
+ if "o" in lora_list:
+ self.linear_out = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
+ else:
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ lora_qkv_list = ["q" in lora_list, "k" in lora_list, "v" in lora_list]
+ if lora_qkv_list == [False, False, False]:
+ self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
+ else:
+ self.linear_q_k_v = lora.MergedLinear(in_feat, n_feat * 3, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_qkv_list)
+ else:
+ 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)
@@ -347,15 +362,17 @@
mask = torch.reshape(mask, (b, -1, 1))
if mask_shfit_chunk is not None:
mask = mask * mask_shfit_chunk
+ inputs = inputs * mask
- 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)
- return x * mask
+ if mask is not None:
+ x = x * mask
+ return x
def forward_qkv(self, x):
"""Transform query, key and value.
@@ -439,6 +456,56 @@
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 MultiHeadedAttentionSANMwithMask(MultiHeadedAttentionSANM):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
+ q_h, k_h, v_h, v = self.forward_qkv(x)
+ fsmn_memory = self.forward_fsmn(v, mask[0], 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[1], mask_att_chunk_encoder)
+ return att_outs + fsmn_memory
+
class MultiHeadedAttentionSANMDecoder(nn.Module):
"""Multi-Head Attention layer.
@@ -493,7 +560,7 @@
# print("in fsmn, cache is None, x", x.size())
x = self.pad_fn(x)
- if not self.training and t <= 1:
+ if not self.training:
cache = x
else:
# print("in fsmn, cache is not None, x", x.size())
@@ -501,7 +568,7 @@
# if t < self.kernel_size:
# x = self.pad_fn(x)
x = torch.cat((cache[:, :, 1:], x), dim=2)
- x = x[:, :, -self.kernel_size:]
+ x = x[:, :, -(self.kernel_size+t-1):]
# print("in fsmn, cache is not None, x_cat", x.size())
cache = x
x = self.fsmn_block(x)
@@ -526,18 +593,32 @@
"""
- def __init__(self, n_head, n_feat, dropout_rate, encoder_output_size=None):
+ def __init__(self, n_head, n_feat, dropout_rate, lora_list=None, lora_rank=8, lora_alpha=16, lora_dropout=0.1, encoder_output_size=None):
"""Construct an MultiHeadedAttention object."""
super(MultiHeadedAttentionCrossAtt, self).__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_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
- self.linear_out = nn.Linear(n_feat, n_feat)
+ if lora_list is not None:
+ if "q" in lora_list:
+ self.linear_q = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
+ else:
+ self.linear_q = nn.Linear(n_feat, n_feat)
+ lora_kv_list = ["k" in lora_list, "v" in lora_list]
+ if lora_kv_list == [False, False]:
+ self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
+ else:
+ self.linear_k_v = lora.MergedLinear(n_feat if encoder_output_size is None else encoder_output_size, n_feat * 2,
+ r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_kv_list)
+ if "o" in lora_list:
+ self.linear_out = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
+ else:
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ else:
+ self.linear_q = nn.Linear(n_feat, n_feat)
+ self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
+ self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout_rate)
@@ -622,4 +703,389 @@
q_h, k_h, v_h = self.forward_qkv(x, memory)
q_h = q_h * self.d_k ** (-0.5)
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
- return self.forward_attention(v_h, scores, memory_mask)
\ No newline at end of file
+ return self.forward_attention(v_h, scores, memory_mask)
+
+ def forward_chunk(self, x, memory, 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 = self.forward_qkv(x, memory)
+ if chunk_size is not None and look_back > 0:
+ if cache is not None:
+ k_h = torch.cat((cache["k"], k_h), dim=2)
+ v_h = torch.cat((cache["v"], v_h), dim=2)
+ cache["k"] = k_h[:, :, -(look_back * chunk_size[1]):, :]
+ cache["v"] = v_h[:, :, -(look_back * chunk_size[1]):, :]
+ else:
+ cache_tmp = {"k": k_h[:, :, -(look_back * chunk_size[1]):, :],
+ "v": v_h[:, :, -(look_back * chunk_size[1]):, :]}
+ cache = cache_tmp
+ q_h = q_h * self.d_k ** (-0.5)
+ scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+ return self.forward_attention(v_h, scores, None), cache
+
+
+class MultiHeadSelfAttention(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):
+ """Construct an MultiHeadedAttention object."""
+ super(MultiHeadSelfAttention, self).__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_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)
+
+ 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(
+ numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
+ )
+ scores = scores.masked_fill(mask, min_value)
+ self.attn = torch.softmax(scores, dim=-1).masked_fill(
+ mask, 0.0
+ ) # (batch, head, time1, time2)
+ else:
+ self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
+
+ p_attn = self.dropout(self.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_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)
+ 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
+
+class RelPositionMultiHeadedAttentionChunk(torch.nn.Module):
+ """RelPositionMultiHeadedAttention definition.
+ Args:
+ num_heads: Number of attention heads.
+ embed_size: Embedding size.
+ dropout_rate: Dropout rate.
+ """
+
+ def __init__(
+ self,
+ num_heads: int,
+ embed_size: int,
+ dropout_rate: float = 0.0,
+ simplified_attention_score: bool = False,
+ ) -> None:
+ """Construct an MultiHeadedAttention object."""
+ super().__init__()
+
+ self.d_k = embed_size // num_heads
+ self.num_heads = num_heads
+
+ assert self.d_k * num_heads == embed_size, (
+ "embed_size (%d) must be divisible by num_heads (%d)",
+ (embed_size, num_heads),
+ )
+
+ self.linear_q = torch.nn.Linear(embed_size, embed_size)
+ self.linear_k = torch.nn.Linear(embed_size, embed_size)
+ self.linear_v = torch.nn.Linear(embed_size, embed_size)
+
+ self.linear_out = torch.nn.Linear(embed_size, embed_size)
+
+ if simplified_attention_score:
+ self.linear_pos = torch.nn.Linear(embed_size, num_heads)
+
+ self.compute_att_score = self.compute_simplified_attention_score
+ else:
+ self.linear_pos = torch.nn.Linear(embed_size, embed_size, bias=False)
+
+ self.pos_bias_u = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
+ self.pos_bias_v = torch.nn.Parameter(torch.Tensor(num_heads, self.d_k))
+ torch.nn.init.xavier_uniform_(self.pos_bias_u)
+ torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+ self.compute_att_score = self.compute_attention_score
+
+ self.dropout = torch.nn.Dropout(p=dropout_rate)
+ self.attn = None
+
+ def rel_shift(self, x: torch.Tensor, left_context: int = 0) -> torch.Tensor:
+ """Compute relative positional encoding.
+ Args:
+ x: Input sequence. (B, H, T_1, 2 * T_1 - 1)
+ left_context: Number of frames in left context.
+ Returns:
+ x: Output sequence. (B, H, T_1, T_2)
+ """
+ batch_size, n_heads, time1, n = x.shape
+ time2 = time1 + left_context
+
+ batch_stride, n_heads_stride, time1_stride, n_stride = x.stride()
+
+ return x.as_strided(
+ (batch_size, n_heads, time1, time2),
+ (batch_stride, n_heads_stride, time1_stride - n_stride, n_stride),
+ storage_offset=(n_stride * (time1 - 1)),
+ )
+
+ def compute_simplified_attention_score(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ pos_enc: torch.Tensor,
+ left_context: int = 0,
+ ) -> torch.Tensor:
+ """Simplified attention score computation.
+ Reference: https://github.com/k2-fsa/icefall/pull/458
+ Args:
+ query: Transformed query tensor. (B, H, T_1, d_k)
+ key: Transformed key tensor. (B, H, T_2, d_k)
+ pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
+ left_context: Number of frames in left context.
+ Returns:
+ : Attention score. (B, H, T_1, T_2)
+ """
+ pos_enc = self.linear_pos(pos_enc)
+
+ matrix_ac = torch.matmul(query, key.transpose(2, 3))
+
+ matrix_bd = self.rel_shift(
+ pos_enc.transpose(1, 2).unsqueeze(2).repeat(1, 1, query.size(2), 1),
+ left_context=left_context,
+ )
+
+ return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
+
+ def compute_attention_score(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ pos_enc: torch.Tensor,
+ left_context: int = 0,
+ ) -> torch.Tensor:
+ """Attention score computation.
+ Args:
+ query: Transformed query tensor. (B, H, T_1, d_k)
+ key: Transformed key tensor. (B, H, T_2, d_k)
+ pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
+ left_context: Number of frames in left context.
+ Returns:
+ : Attention score. (B, H, T_1, T_2)
+ """
+ p = self.linear_pos(pos_enc).view(pos_enc.size(0), -1, self.num_heads, self.d_k)
+
+ query = query.transpose(1, 2)
+ q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
+ q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
+
+ matrix_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
+
+ matrix_bd = torch.matmul(q_with_bias_v, p.permute(0, 2, 3, 1))
+ matrix_bd = self.rel_shift(matrix_bd, left_context=left_context)
+
+ return (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
+
+ def forward_qkv(
+ self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Transform query, key and value.
+ Args:
+ query: Query tensor. (B, T_1, size)
+ key: Key tensor. (B, T_2, size)
+ v: Value tensor. (B, T_2, size)
+ Returns:
+ q: Transformed query tensor. (B, H, T_1, d_k)
+ k: Transformed key tensor. (B, H, T_2, d_k)
+ v: Transformed value tensor. (B, H, T_2, d_k)
+ """
+ n_batch = query.size(0)
+
+ q = (
+ self.linear_q(query)
+ .view(n_batch, -1, self.num_heads, self.d_k)
+ .transpose(1, 2)
+ )
+ k = (
+ self.linear_k(key)
+ .view(n_batch, -1, self.num_heads, self.d_k)
+ .transpose(1, 2)
+ )
+ v = (
+ self.linear_v(value)
+ .view(n_batch, -1, self.num_heads, self.d_k)
+ .transpose(1, 2)
+ )
+
+ return q, k, v
+
+ def forward_attention(
+ self,
+ value: torch.Tensor,
+ scores: torch.Tensor,
+ mask: torch.Tensor,
+ chunk_mask: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """Compute attention context vector.
+ Args:
+ value: Transformed value. (B, H, T_2, d_k)
+ scores: Attention score. (B, H, T_1, T_2)
+ mask: Source mask. (B, T_2)
+ chunk_mask: Chunk mask. (T_1, T_1)
+ Returns:
+ attn_output: Transformed value weighted by attention score. (B, T_1, H * d_k)
+ """
+ batch_size = scores.size(0)
+ mask = mask.unsqueeze(1).unsqueeze(2)
+ if chunk_mask is not None:
+ mask = chunk_mask.unsqueeze(0).unsqueeze(1) | mask
+ scores = scores.masked_fill(mask, float("-inf"))
+ self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
+
+ attn_output = self.dropout(self.attn)
+ attn_output = torch.matmul(attn_output, value)
+
+ attn_output = self.linear_out(
+ attn_output.transpose(1, 2)
+ .contiguous()
+ .view(batch_size, -1, self.num_heads * self.d_k)
+ )
+
+ return attn_output
+
+ def forward(
+ self,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ pos_enc: torch.Tensor,
+ mask: torch.Tensor,
+ chunk_mask: Optional[torch.Tensor] = None,
+ left_context: int = 0,
+ ) -> torch.Tensor:
+ """Compute scaled dot product attention with rel. positional encoding.
+ Args:
+ query: Query tensor. (B, T_1, size)
+ key: Key tensor. (B, T_2, size)
+ value: Value tensor. (B, T_2, size)
+ pos_enc: Positional embedding tensor. (B, 2 * T_1 - 1, size)
+ mask: Source mask. (B, T_2)
+ chunk_mask: Chunk mask. (T_1, T_1)
+ left_context: Number of frames in left context.
+ Returns:
+ : Output tensor. (B, T_1, H * d_k)
+ """
+ q, k, v = self.forward_qkv(query, key, value)
+ scores = self.compute_att_score(q, k, pos_enc, left_context=left_context)
+ return self.forward_attention(v, scores, mask, chunk_mask=chunk_mask)
+
+
+class CosineDistanceAttention(nn.Module):
+ """ Compute Cosine Distance between spk decoder output and speaker profile
+ Args:
+ profile_path: speaker profile file path (.npy file)
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, spk_decoder_out, profile, profile_lens=None):
+ """
+ Args:
+ spk_decoder_out(torch.Tensor):(B, L, D)
+ spk_profiles(torch.Tensor):(B, N, D)
+ """
+ x = spk_decoder_out.unsqueeze(2) # (B, L, 1, D)
+ if profile_lens is not None:
+
+ mask = (make_pad_mask(profile_lens)[:, None, :]).to(profile.device)
+ min_value = float(
+ numpy.finfo(torch.tensor(0, dtype=x.dtype).numpy().dtype).min
+ )
+ weights_not_softmax=F.cosine_similarity(x, profile.unsqueeze(1), dim=-1).masked_fill(mask, min_value)
+ weights = self.softmax(weights_not_softmax).masked_fill(mask, 0.0) # (B, L, N)
+ else:
+ x = x[:, -1:, :, :]
+ weights_not_softmax=F.cosine_similarity(x, profile.unsqueeze(1).to(x.device), dim=-1)
+ weights = self.softmax(weights_not_softmax) # (B, 1, N)
+ spk_embedding = torch.matmul(weights, profile.to(weights.device)) # (B, L, D)
+
+ return spk_embedding, weights
--
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