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yeyupiaoling / AudioClassification-Pytorch

The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.
Apache License 2.0
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How to run with features MFCC #12

Closed JxuHenry closed 1 year ago

JxuHenry commented 1 year ago

I can't runing with MFCC features

My config: dataset_conf: batch_size: 32 num_class: 10 num_workers: 8 min_duration: 0.5 chunk_duration: 3 do_vad: False sample_rate: 16000 use_dB_normalization: True target_dB: -20 train_list: 'dataset/train_list.txt' test_list: 'dataset/test_list.txt' label_list_path: 'dataset/label_list.txt'

preprocess_conf: feature_method: 'MFCC'

feature_conf: sample_rate: 16000 n_mfcc: 40 melkwargs: n_fft: 1024 hop_length: 320 win_length: 1024 f_min: 50.0 f_max: 14000.0 n_mels: 64

optimizer_conf: learning_rate: 0.001 weight_decay: 1e-6

model_conf: embd_dim: 192 channels: 256

train_conf: max_epoch: 30 log_interval: 10

use_model: 'ecapa_tdnn'

RuntimeError: Given groups=1, weight of size [256, 64, 5], expected input[32, 40, 151] to have 64 channels, but got 40 channels instead

yeyupiaoling commented 1 year ago

https://github.com/yeyupiaoling/AudioClassification-Pytorch/blob/77b7708a72f6e68a653c53960cdb7980d04d99e9/macls/data_utils/featurizer.py#L58

to

return self._feature_conf.n_mfcc
JxuHenry commented 1 year ago

https://github.com/yeyupiaoling/AudioClassification-Pytorch/blob/77b7708a72f6e68a653c53960cdb7980d04d99e9/macls/data_utils/featurizer.py#L58

to

return self._feature_conf.n_mfcc

Thank you very mach ！