From 543d900522403eccb4e387cbc41c5dce24091d1d Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 22 二月 2024 23:53:10 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR merge
---
funasr/models/seaco_paraformer/model.py | 49 +++++++++---------------
funasr/models/paraformer/decoder.py | 56 ++++++++++++++++++++++++++++
2 files changed, 74 insertions(+), 31 deletions(-)
diff --git a/funasr/models/paraformer/decoder.py b/funasr/models/paraformer/decoder.py
index 68018a0..ad321e4 100644
--- a/funasr/models/paraformer/decoder.py
+++ b/funasr/models/paraformer/decoder.py
@@ -116,6 +116,22 @@
# x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
return x, tgt_mask, memory, memory_mask, cache
+
+ def get_attn_mat(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+ residual = tgt
+ tgt = self.norm1(tgt)
+ tgt = self.feed_forward(tgt)
+
+ x = tgt
+ if self.self_attn is not None:
+ tgt = self.norm2(tgt)
+ x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
+ x = residual + x
+
+ residual = x
+ x = self.norm3(x)
+ x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
+ return attn_mat
def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
"""Compute decoded features.
@@ -396,6 +412,46 @@
ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
)
return logp.squeeze(0), state
+
+ def forward_asf2(
+ self,
+ hs_pad: torch.Tensor,
+ hlens: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ ):
+
+ tgt = ys_in_pad
+ tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
+
+ memory = hs_pad
+ memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
+
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
+ attn_mat = self.model.decoders[1].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
+ return attn_mat
+
+ def forward_asf6(
+ self,
+ hs_pad: torch.Tensor,
+ hlens: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ ):
+
+ tgt = ys_in_pad
+ tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
+
+ memory = hs_pad
+ memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
+
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[1](tgt, tgt_mask, memory, memory_mask)
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[2](tgt, tgt_mask, memory, memory_mask)
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[3](tgt, tgt_mask, memory, memory_mask)
+ tgt, tgt_mask, memory, memory_mask, _ = self.decoders[4](tgt, tgt_mask, memory, memory_mask)
+ attn_mat = self.decoders[5].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
+ return attn_mat
def forward_chunk(
self,
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index caf2b15..cfdd26a 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -19,11 +19,9 @@
from funasr.register import tables
from funasr.utils import postprocess_utils
-from funasr.metrics.compute_acc import th_accuracy
from funasr.models.paraformer.model import Paraformer
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
-from funasr.models.paraformer.cif_predictor import mae_loss
from funasr.train_utils.device_funcs import force_gatherable
from funasr.models.bicif_paraformer.model import BiCifParaformer
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
@@ -76,7 +74,7 @@
self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim)
else:
self.lstm_proj = None
- self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
+ # self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
elif self.bias_encoder_type == 'mean':
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
else:
@@ -225,12 +223,8 @@
# ASF Core
if nfilter > 0 and nfilter < num_hot_word:
- for dec in self.seaco_decoder.decoders:
- dec.reserve_attn = True
- # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
- dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
- # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
- hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
+ hotword_scores = self.seaco_decoder.forward_asf6(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
+ hotword_scores = hotword_scores[0].sum(0).sum(0)
# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
add_filter = dec_filter
@@ -241,9 +235,6 @@
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
num_hot_word = contextual_info.shape[1]
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
- for dec in self.seaco_decoder.decoders:
- dec.attn_mat = []
- dec.reserve_attn = False
# SeACo Core
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
@@ -274,6 +265,8 @@
hotword_lengths):
if self.bias_encoder_type != 'lstm':
logging.error("Unsupported bias encoder type")
+
+ '''
hw_embed = self.decoder.embed(hotword_pad)
hw_embed, (_, _) = self.bias_encoder(hw_embed)
if self.lstm_proj is not None:
@@ -281,26 +274,20 @@
_ind = np.arange(0, hw_embed.shape[0]).tolist()
selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
return selected
+ '''
- '''
- def calc_predictor(self, encoder_out, encoder_out_lens):
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
- encoder_out.device)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out,
- None,
- encoder_out_mask,
- ignore_id=self.ignore_id)
- return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
-
-
- def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
- encoder_out.device)
- ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
- encoder_out_mask,
- token_num)
- return ds_alphas, ds_cif_peak, us_alphas, us_peaks
- '''
+ # hw_embed = self.sac_embedding(hotword_pad)
+ hw_embed = self.decoder.embed(hotword_pad)
+ hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hotword_lengths.cpu().type(torch.int64), batch_first=True, enforce_sorted=False)
+ packed_rnn_output, _ = self.bias_encoder(hw_embed)
+ rnn_output = torch.nn.utils.rnn.pad_packed_sequence(packed_rnn_output, batch_first=True)[0]
+ if self.lstm_proj is not None:
+ hw_hidden = self.lstm_proj(rnn_output)
+ else:
+ hw_hidden = rnn_output
+ _ind = np.arange(0, hw_hidden.shape[0]).tolist()
+ selected = hw_hidden[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
+ return selected
def inference(self,
data_in,
--
Gitblit v1.9.1