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speech_asr 2023-03-15 16:11:44 +08:00
parent fbec0f003d
commit f33ebfd1c7
2 changed files with 16 additions and 9 deletions
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14
funasr/models/e2e_diar_eend_ola.py
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@ -76,7 +76,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
def forward_post_net(self, logits, ilens):
maxlen = torch.max(ilens).to(torch.int).item()
logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1)
logits = nn.utils.rnn.pack_padded_sequence(logits, ilens, batch_first=True, enforce_sorted=False)
logits = nn.utils.rnn.pack_padded_sequence(logits, ilens.cpu().to(torch.int64), batch_first=True, enforce_sorted=False)
outputs, (_, _) = self.postnet(logits)
outputs = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True, padding_value=-1, total_length=maxlen)[0]
outputs = [output[:ilens[i].to(torch.int).item()] for i, output in enumerate(outputs)]
@ -231,7 +231,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
pred[i] = pred[i - 1]
else:
pred[i] = 0
pred = [self.reporter.inv_mapping_func(i, self.mapping_dict) for i in pred]
pred = [self.inv_mapping_func(i) for i in pred]
decisions = [bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred]
decisions = torch.from_numpy(
np.stack([np.array([int(i) for i in dec]) for dec in decisions], axis=0)).to(logit.device).to(
@ -239,5 +239,15 @@ class DiarEENDOLAModel(AbsESPnetModel):
decisions = decisions[:, :n_speaker]
return decisions
def inv_mapping_func(self, label):
if not isinstance(label, int):
label = int(label)
if label in self.mapping_dict['label2dec'].keys():
num = self.mapping_dict['label2dec'][label]
else:
num = -1
return num
def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]:
pass

11
funasr/modules/eend_ola/encoder_decoder_attractor.py
View File

@ -2,8 +2,7 @@ import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from modelscope.utils.logger import get_logger
logger = get_logger()
class EncoderDecoderAttractor(nn.Module):
@ -17,14 +16,12 @@ class EncoderDecoderAttractor(nn.Module):
self.n_units = n_units
def forward_core(self, xs, zeros):
logger.info("xs: ".format(xs))
ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.float32).to(xs[0].device)
logger.info("ilens: ".format(ilens))
ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.int64)
xs = [self.enc0_dropout(x) for x in xs]
xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1)
xs = nn.utils.rnn.pack_padded_sequence(xs, ilens, batch_first=True, enforce_sorted=False)
_, (hx, cx) = self.encoder(xs)
zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.float32).to(zeros[0].device)
zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.int64)
max_zlen = torch.max(zlens).to(torch.int).item()
zeros = [self.enc0_dropout(z) for z in zeros]
zeros = nn.utils.rnn.pad_sequence(zeros, batch_first=True, padding_value=-1)
@ -50,4 +47,4 @@ class EncoderDecoderAttractor(nn.Module):
zeros = [torch.zeros(max_n_speakers, self.n_units).to(torch.float32).to(xs[0].device) for _ in xs]
attractors = self.forward_core(xs, zeros)
probs = [torch.sigmoid(torch.flatten(self.counter(att))) for att in attractors]
return attractors, probs
return attractors, probs