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Merge branch 'main' of github.com:alibaba-damo-academy/FunASR

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merge
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游雁 2024-02-22 23:53:10 +08:00
commit 543d900522
2 changed files with 74 additions and 31 deletions
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56
funasr/models/paraformer/decoder.py
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@ -116,6 +116,22 @@ class DecoderLayerSANM(torch.nn.Module):
# 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 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
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,

49
funasr/models/seaco_paraformer/model.py
View File

@ -19,11 +19,9 @@ from distutils.version import LooseVersion
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 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
# 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 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
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 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
_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,