xls-r-300m-npsc-seq2seq

Rolv-Arild/xls-r-300m-npsc-seq2seq

xls-r-300m-npsc-seq2seq is a automatic speech recognition model from Rolv-Arild. Use it when you need speech turned into text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
Model type: speech-encoder-decoder
Access: Public
Created: Mar 2, 2022
Updated: Feb 18, 2022
SHA: 6d3acfc4

Model card

From the Hugging Face model README

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2965
Wer: 0.3144

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.888	0.51	400	3.7320	0.9440
3.1636	1.02	800	2.9188	1.1916
2.773	1.53	1200	2.3347	1.0134
0.7198	2.04	1600	0.6678	0.4826
0.5255	2.55	2000	0.4605	0.4135
0.3961	3.06	2400	0.4266	0.3955
0.3424	3.57	2800	0.3786	0.3741
0.3858	4.08	3200	0.3161	0.3552
0.3218	4.59	3600	0.3029	0.3510
0.199	5.1	4000	0.2988	0.3418
0.2054	5.61	4400	0.2873	0.3434
0.1704	6.12	4800	0.3129	0.3432
0.1805	6.63	5200	0.2963	0.3413
0.2091	7.14	5600	0.2755	0.3329
0.1971	7.65	6000	0.2706	0.3309
0.1237	8.16	6400	0.2823	0.3270
0.123	8.67	6800	0.2754	0.3246
0.103	9.18	7200	0.2917	0.3272
0.1143	9.69	7600	0.2885	0.3305
0.156	10.2	8000	0.2810	0.3288
0.167	10.71	8400	0.2689	0.3232
0.0815	11.22	8800	0.2899	0.3236
0.0844	11.73	9200	0.2798	0.3225
0.0775	12.24	9600	0.2894	0.3224
0.0677	12.75	10000	0.2838	0.3204
0.1383	13.27	10400	0.2959	0.3211
0.1233	13.77	10800	0.2922	0.3213
0.0688	14.29	11200	0.2903	0.3209
0.0655	14.8	11600	0.2868	0.3182
0.0449	15.31	12000	0.2959	0.3172
0.0421	15.82	12400	0.2966	0.3180
0.0858	16.33	12800	0.2941	0.3164
0.0859	16.84	13200	0.2980	0.3165
0.0561	17.35	13600	0.2965	0.3165
0.0506	17.86	14000	0.2935	0.3148
0.0312	18.37	14400	0.2964	0.3154
0.0403	18.88	14800	0.2967	0.3160
0.0924	19.39	15200	0.2955	0.3147
0.0585	19.9	15600	0.2965	0.3144

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.0+cu113
Datasets 1.18.1
Tokenizers 0.11.0

Task: Automatic Speech Recognition
Library: transformers
Type: speech-encoder-decoder
Created: Mar 2, 2022
Updated: Feb 18, 2022