From df5f263e5fe3d7961b1aeb3589012400a9905a8f Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 16:17:41 +0800
Subject: [PATCH] update
---
funasr/models/e2e_diar_sond.py | 18 ++++++------------
1 files changed, 6 insertions(+), 12 deletions(-)
diff --git a/funasr/models/e2e_diar_sond.py b/funasr/models/e2e_diar_sond.py
index de669f2..dc7135f 100644
--- a/funasr/models/e2e_diar_sond.py
+++ b/funasr/models/e2e_diar_sond.py
@@ -14,15 +14,9 @@
from torch.nn import functional as F
from typeguard import check_argument_types
-from funasr.modules.nets_utils import to_device
from funasr.modules.nets_utils import make_pad_mask
-from funasr.models.decoder.abs_decoder import AbsDecoder
-from funasr.models.encoder.abs_encoder import AbsEncoder
-from funasr.models.frontend.abs_frontend import AbsFrontend
-from funasr.models.specaug.abs_specaug import AbsSpecAug
-from funasr.layers.abs_normalize import AbsNormalize
+from funasr.models.base_model import FunASRModel
from funasr.torch_utils.device_funcs import force_gatherable
-from funasr.train.abs_espnet_model import AbsESPnetModel
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss, SequenceBinaryCrossEntropy
from funasr.utils.misc import int2vec
@@ -35,7 +29,7 @@
yield
-class DiarSondModel(AbsESPnetModel):
+class DiarSondModel(FunASRModel):
"""Speaker overlap-aware neural diarization model
reference: https://arxiv.org/abs/2211.10243
"""
@@ -43,9 +37,9 @@
def __init__(
self,
vocab_size: int,
- frontend: Optional[AbsFrontend],
- specaug: Optional[AbsSpecAug],
- normalize: Optional[AbsNormalize],
+ frontend: Optional[torch.nn.Module],
+ specaug: Optional[torch.nn.Module],
+ normalize: Optional[torch.nn.Module],
encoder: torch.nn.Module,
speaker_encoder: Optional[torch.nn.Module],
ci_scorer: torch.nn.Module,
@@ -348,7 +342,7 @@
cd_simi = torch.reshape(cd_simi, [bb, self.max_spk_num, tt, 1])
cd_simi = cd_simi.squeeze(dim=3).permute([0, 2, 1])
- if isinstance(self.ci_scorer, AbsEncoder):
+ if isinstance(self.ci_scorer, torch.nn.Module):
ci_simi = self.ci_scorer(ge_in, ge_len)[0]
ci_simi = torch.reshape(ci_simi, [bb, self.max_spk_num, tt]).permute([0, 2, 1])
else:
--
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