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update _hw_repre

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shixian.shi 2024-02-22 14:39:52 +08:00
parent 26a53fb181
commit 66d3b5c212
1 changed files with 16 additions and 22 deletions
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38
funasr/models/seaco_paraformer/model.py
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@ -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:
@ -274,6 +272,8 @@ 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 +281,20 @@ 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,