Comments (3)
- what's the value of (batch_size, feat_dim, chunk_len) , (batch_size, 30, ?) ?
I'm not sure if I understand the question. chunk_len is determined by kaldi when creating the archives. It represents the temporal dimension: number of MFCC frames in the utterance.
- Is MFCC of the feature in your experiment?
Yes, each input sample is a matrix - a sequence of MFCC features.
- Have you try other tools to extract features, such as librosa ...?
Not at the moment.
from pytorch_xvectors.
"loss in nan", how to solve this?
from pytorch_xvectors.
The most common reason (in this repo) was due to the stats pooling layer. If all inputs are zero or same, then var(0) seems to result in NaN loss.
Please use the -noiseEps
to avoid this.
from pytorch_xvectors.
Related Issues (15)
- Missing file for training meta learning embeddings HOT 1
- run.pl tasks all failed while running pytorch_run.sh HOT 1
- Could not find common file: exp/xvector_nnet_1a/egs//egs.1.ark HOT 13
- Running speaker embeeding training on multiple GPUs on single node HOT 1
- train_proto
- [How to?] Embeddings for each .wav file in dataset folder HOT 2
- tdnn layers HOT 1
- training data
- pre-trained model download error
- which mfcc.conf do you use? HOT 6
- How much performance can data augmentation improve? HOT 8
- Some problems when making evaluation on AMI dev and test dataset. HOT 2
- Provide example for inference in Python HOT 1
- Fail to get access to preTrained/models/
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from pytorch_xvectors.