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Merge branch 'k2-fsa:master' into dev/k2ssl

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Yifan Yang 2024-10-21 15:19:05 +08:00 committed by GitHub
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3 changed files with 6 additions and 6 deletions
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2
egs/librispeech/ASR/zipformer/model.py
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@ -22,10 +22,10 @@ import k2
import torch import torch
import torch.nn as nn import torch.nn as nn
from encoder_interface import EncoderInterface from encoder_interface import EncoderInterface
from lhotse.dataset import SpecAugment
from scaling import ScaledLinear from scaling import ScaledLinear
from icefall.utils import add_sos, make_pad_mask, time_warp from icefall.utils import add_sos, make_pad_mask, time_warp
from lhotse.dataset import SpecAugment
class AsrModel(nn.Module): class AsrModel(nn.Module):

1
egs/speech_llm/ASR_LLM/whisper_llm_zh/label_smoothing.py Symbolic link
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@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/label_smoothing.py

9
icefall/utils.py
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@ -2282,13 +2282,12 @@ def time_warp(
time_warp_factor: Optional[int] = 80, time_warp_factor: Optional[int] = 80,
supervision_segments: Optional[torch.Tensor] = None, supervision_segments: Optional[torch.Tensor] = None,
): ):
"""Apply time warping on a batch of features """Apply time warping on a batch of features"""
"""
if time_warp_factor is None or time_warp_factor < 1: if time_warp_factor is None or time_warp_factor < 1:
return features return features
assert len(features.shape) == 3, ( assert (
"SpecAugment only supports batches of single-channel feature matrices." len(features.shape) == 3
) ), f"SpecAugment only supports batches of single-channel feature matrices. {features.shape}"
features = features.clone() features = features.clone()
if supervision_segments is None: if supervision_segments is None:
# No supervisions - apply spec augment to full feature matrices. # No supervisions - apply spec augment to full feature matrices.