File size: 3,093 Bytes
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"""*********************************************************************************************"""
# FileName [ tutorial_use_pretrained_model_without_preprocessing.py ]
# Synopsis [ an example code of using the wrapper class for downstream feature extraction or finetune ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
"""*********************************************************************************************"""
"""
[Introduction]
This is a tutorial for using pre-trained models without doing preprocessing.
Only for pre-trained models that has `on-the-fly` in their dir name (They are trained with on-the-fly feature extractors).
"""
###############
# IMPORTATION #
###############
import torch
from transformer.nn_transformer import TRANSFORMER
################
# EXAMPLE CODE #
################
"""
`options`: a python dictionary containing the following keys:
ckpt_file: str, a path specifying the pre-trained ckpt file
load_pretrain: str, ['True', 'False'], whether to load pre-trained weights
no_grad: str, ['True', 'False'], whether to have gradient flow over this class
dropout: float/str, use float to modify dropout value during downstream finetune, or use the str `default` for pre-train default values
spec_aug: str, ['True', 'False'], whether to apply SpecAugment on inputs (used for ASR training)
spec_aug_prev: str, ['True', 'False'], apply spec augment on input acoustic features if True, else apply on output representations (used for ASR training)
weighted_sum: str, ['True', 'False'], whether to use a learnable weighted sum to integrate hidden representations from all layers, if False then use the last
select_layer: int, select from all hidden representations, set to -1 to select the last (will only be used when weighted_sum is False)
permute_input: str, ['True', 'False'], this attribute is for the forward method. If Ture then input ouput is in the shape of (T, B, D), if False then in (B, T, D)
"""
options = {
'ckpt_file' : './result/result_transformer/on-the-fly-melBase960-b12-T-libri/states-1000000.ckpt',
'load_pretrain' : 'True',
'no_grad' : 'True',
'dropout' : 'default',
'spec_aug' : 'False',
'spec_aug_prev' : 'True',
'weighted_sum' : 'False',
'select_layer' : -1,
'permute_input' : 'False',
}
# setup the transformer model
model = TRANSFORMER(options=options, inp_dim=0) # set inp_dim to 0 for auto setup
# load raw wav
example_wav = '../LibriSpeech/test-clean/61/70970/61-70970-0000.flac'
input_wav = TRANSFORMER.load_data(example_wav, **model.config['online']) # size: (seq_len, dim) = (97200, 1)
# forward
input_wav = input_wav.unsqueeze(0) # add batch dim, size: (batch, seq_len, dim) = (1, 97200, 1)
output_repr = model(input_wav) # preprocessing of "wav -> acoustic feature" is done during forward
# show size
print('input_wav size:', input_wav.size())
print('output_repr size:', output_repr.size()) # size: (batch, seq_len, dim) = (1, 608, 768) |