From 4dc3a1b011e1e72eb737417b8e0e0bec7a7e3a6e Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期二, 21 三月 2023 15:12:21 +0800
Subject: [PATCH] resolve conflict
---
funasr/models/e2e_diar_sond.py | 26 +++++++++++++++++---------
1 files changed, 17 insertions(+), 9 deletions(-)
diff --git a/funasr/models/e2e_diar_sond.py b/funasr/models/e2e_diar_sond.py
index 258d780..de669f2 100644
--- a/funasr/models/e2e_diar_sond.py
+++ b/funasr/models/e2e_diar_sond.py
@@ -59,7 +59,8 @@
normalize_speech_speaker: bool = False,
ignore_id: int = -1,
speaker_discrimination_loss_weight: float = 1.0,
- inter_score_loss_weight: float = 0.0
+ inter_score_loss_weight: float = 0.0,
+ inputs_type: str = "raw",
):
assert check_argument_types()
@@ -86,14 +87,12 @@
)
self.criterion_bce = SequenceBinaryCrossEntropy(normalize_length=length_normalized_loss)
self.pse_embedding = self.generate_pse_embedding()
- # self.register_buffer("pse_embedding", pse_embedding)
self.power_weight = torch.from_numpy(2 ** np.arange(max_spk_num)[np.newaxis, np.newaxis, :]).float()
- # self.register_buffer("power_weight", power_weight)
self.int_token_arr = torch.from_numpy(np.array(self.token_list).astype(int)[np.newaxis, np.newaxis, :]).int()
- # self.register_buffer("int_token_arr", int_token_arr)
self.speaker_discrimination_loss_weight = speaker_discrimination_loss_weight
self.inter_score_loss_weight = inter_score_loss_weight
self.forward_steps = 0
+ self.inputs_type = inputs_type
def generate_pse_embedding(self):
embedding = np.zeros((len(self.token_list), self.max_spk_num), dtype=np.float)
@@ -125,9 +124,14 @@
binary_labels: (Batch, frames, max_spk_num)
binary_labels_lengths: (Batch,)
"""
- assert speech.shape[0] == binary_labels.shape[0], (speech.shape, binary_labels.shape)
+ assert speech.shape[0] <= binary_labels.shape[0], (speech.shape, binary_labels.shape)
batch_size = speech.shape[0]
self.forward_steps = self.forward_steps + 1
+ if self.pse_embedding.device != speech.device:
+ self.pse_embedding = self.pse_embedding.to(speech.device)
+ self.power_weight = self.power_weight.to(speech.device)
+ self.int_token_arr = self.int_token_arr.to(speech.device)
+
# 1. Network forward
pred, inter_outputs = self.prediction_forward(
speech, speech_lengths,
@@ -149,9 +153,13 @@
# the sequence length of 'pred' might be slightly less than the
# length of 'spk_labels'. Here we force them to be equal.
length_diff_tolerance = 2
- length_diff = pse_labels.shape[1] - pred.shape[1]
- if 0 < length_diff <= length_diff_tolerance:
- pse_labels = pse_labels[:, 0: pred.shape[1]]
+ length_diff = abs(pse_labels.shape[1] - pred.shape[1])
+ if length_diff <= length_diff_tolerance:
+ min_len = min(pred.shape[1], pse_labels.shape[1])
+ pse_labels = pse_labels[:, :min_len]
+ pred = pred[:, :min_len]
+ cd_score = cd_score[:, :min_len]
+ ci_score = ci_score[:, :min_len]
loss_diar = self.classification_loss(pred, pse_labels, binary_labels_lengths)
loss_spk_dis = self.speaker_discrimination_loss(profile, profile_lengths)
@@ -299,7 +307,7 @@
speech: torch.Tensor,
speech_lengths: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
- if self.encoder is not None:
+ if self.encoder is not None and self.inputs_type == "raw":
speech, speech_lengths = self.encode(speech, speech_lengths)
speech_mask = ~make_pad_mask(speech_lengths, maxlen=speech.shape[1])
speech_mask = speech_mask.to(speech.device).unsqueeze(-1).float()
--
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