From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/models/specaug/profileaug.py | 70 ++++++++++++++++++++++------------
1 files changed, 45 insertions(+), 25 deletions(-)
diff --git a/funasr/models/specaug/profileaug.py b/funasr/models/specaug/profileaug.py
index 3c7d147..669d323 100644
--- a/funasr/models/specaug/profileaug.py
+++ b/funasr/models/specaug/profileaug.py
@@ -2,25 +2,26 @@
import numpy as np
import torch
from torch.nn import functional as F
-from funasr.models.specaug.abs_profileaug import AbsProfileAug
+import torch.nn as nn
-class ProfileAug(AbsProfileAug):
+class ProfileAug(nn.Module):
"""
Implement the augmentation for profiles including:
- Split aug: split one profile into two profiles, i.e., main and inaccurate, labels assigned to main
- Merge aug: merge two profiles into one, labels are also merged into one, the other set to zero
- Disturb aug: disturb some profile with others to simulate the inaccurate clustering centroids.
"""
+
def __init__(
- self,
- apply_split_aug: bool = True,
- split_aug_prob: float = 0.05,
- apply_merge_aug: bool = True,
- merge_aug_prob: float = 0.2,
- apply_disturb_aug: bool = True,
- disturb_aug_prob: float = 0.4,
- disturb_alpha: float = 0.2,
+ self,
+ apply_split_aug: bool = True,
+ split_aug_prob: float = 0.05,
+ apply_merge_aug: bool = True,
+ merge_aug_prob: float = 0.2,
+ apply_disturb_aug: bool = True,
+ disturb_aug_prob: float = 0.4,
+ disturb_alpha: float = 0.2,
) -> None:
super().__init__()
self.apply_split_aug = apply_split_aug
@@ -47,8 +48,9 @@
to_cover_idx = pad_spk_idx[torch.randint(len(pad_spk_idx), ())]
disturb_vec = torch.randn((dim,)).to(profile)
disturb_vec = F.normalize(disturb_vec, dim=-1)
- profile[idx, to_cover_idx] = F.normalize(profile[idx, split_spk_idx] +
- self.disturb_alpha * disturb_vec)
+ profile[idx, to_cover_idx] = F.normalize(
+ profile[idx, split_spk_idx] + self.disturb_alpha * disturb_vec
+ )
mask[idx, split_spk_idx] = 0
mask[idx, to_cover_idx] = 0
return profile, binary_labels, mask
@@ -63,15 +65,19 @@
valid_spk_idx = torch.nonzero(profile_norm[idx] * mask[idx])
if len(valid_spk_idx) == 0:
continue
- to_merge = torch.randint(len(valid_spk_idx), (2, ))
+ to_merge = torch.randint(len(valid_spk_idx), (2,))
spk_idx_1, spk_idx_2 = valid_spk_idx[to_merge[0]], valid_spk_idx[to_merge[1]]
# merge profile
profile[idx, spk_idx_1] = profile[idx, spk_idx_1] + profile[idx, spk_idx_2]
profile[idx, spk_idx_1] = F.normalize(profile[idx, spk_idx_1], dim=-1)
profile[idx, spk_idx_2] = 0
# merge binary labels
- binary_labels[idx, :, spk_idx_1] = binary_labels[idx, :, spk_idx_1] + binary_labels[idx, :, spk_idx_2]
- binary_labels[idx, :, spk_idx_1] = (binary_labels[idx, :, spk_idx_1] > 0).to(binary_labels)
+ binary_labels[idx, :, spk_idx_1] = (
+ binary_labels[idx, :, spk_idx_1] + binary_labels[idx, :, spk_idx_2]
+ )
+ binary_labels[idx, :, spk_idx_1] = (binary_labels[idx, :, spk_idx_1] > 0).to(
+ binary_labels
+ )
binary_labels[idx, :, spk_idx_2] = 0
mask[idx, spk_idx_1] = 0
@@ -93,30 +99,44 @@
to_disturb_idx = pos_spk_idx[torch.randint(len(pos_spk_idx), ())]
disturb_idx = valid_spk_idx[torch.randint(len(valid_spk_idx), ())]
alpha = self.disturb_alpha * torch.rand(()).item()
- profile[idx, to_disturb_idx] = ((1 - alpha) * profile[idx, to_disturb_idx]
- + alpha * profile[idx, disturb_idx])
+ profile[idx, to_disturb_idx] = (1 - alpha) * profile[
+ idx, to_disturb_idx
+ ] + alpha * profile[idx, disturb_idx]
profile[idx, to_disturb_idx] = F.normalize(profile[idx, to_disturb_idx], dim=-1)
mask[idx, to_disturb_idx] = 0
return profile, binary_labels, mask
def forward(
- self,
- speech: torch.Tensor, speech_lengths: torch.Tensor = None,
- profile: torch.Tensor = None, profile_lengths: torch.Tensor = None,
- binary_labels: torch.Tensor = None, labels_length: torch.Tensor = None
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor = None,
+ profile: torch.Tensor = None,
+ profile_lengths: torch.Tensor = None,
+ binary_labels: torch.Tensor = None,
+ labels_length: torch.Tensor = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
# copy inputs to avoid inplace-operation
- speech, profile, binary_labels = torch.clone(speech), torch.clone(profile), torch.clone(binary_labels)
+ speech, profile, binary_labels = (
+ torch.clone(speech),
+ torch.clone(profile),
+ torch.clone(binary_labels),
+ )
profile = F.normalize(profile, dim=-1)
profile_mask = torch.ones(profile.shape[:2]).to(profile)
if self.apply_disturb_aug:
- profile, binary_labels, profile_mask = self.disturb_aug(profile, binary_labels, profile_mask)
+ profile, binary_labels, profile_mask = self.disturb_aug(
+ profile, binary_labels, profile_mask
+ )
if self.apply_split_aug:
- profile, binary_labels, profile_mask = self.split_aug(profile, binary_labels, profile_mask)
+ profile, binary_labels, profile_mask = self.split_aug(
+ profile, binary_labels, profile_mask
+ )
if self.apply_merge_aug:
- profile, binary_labels, profile_mask = self.merge_aug(profile, binary_labels, profile_mask)
+ profile, binary_labels, profile_mask = self.merge_aug(
+ profile, binary_labels, profile_mask
+ )
return speech, profile, binary_labels
--
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