wav2vec2-large-xlsr-53-demo1

EngNada/wav2vec2-large-xlsr-53-demo1

wav2vec2-large-xlsr-53-demo1 is a automatic speech recognition model from EngNada. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Mar 9, 2022
Updated: Mar 9, 2022
SHA: 5ebc3afe

Datasets

common_voice

Model card

From the Hugging Face model README

wav2vec2-large-xlsr-53-demo1

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.9692
Wer: 0.8462

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: 0.0003
train_batch_size: 5
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 10
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
12.978	0.06	100	3.5377	1.0
3.5026	0.13	200	3.4366	1.0
3.4084	0.19	300	3.3831	1.0
3.3551	0.26	400	3.2563	1.0
3.2668	0.32	500	3.2109	1.0
2.9398	0.38	600	2.4548	0.9987
2.2204	0.45	700	1.8870	1.0135
1.7401	0.51	800	1.6816	1.0247
1.5748	0.57	900	1.4741	0.9953
1.4539	0.64	1000	1.4573	0.9852
1.3612	0.7	1100	1.3534	0.9529
1.3328	0.77	1200	1.3380	0.9320
1.2459	0.83	1300	1.2984	0.9247
1.1976	0.89	1400	1.2515	0.9252
1.1593	0.96	1500	1.2345	0.9030
1.1094	1.02	1600	1.2135	0.9305
1.0485	1.09	1700	1.2045	0.9121
0.9893	1.15	1800	1.1876	0.8990
1.0099	1.21	1900	1.1663	0.8889
0.982	1.28	2000	1.1674	0.8901
0.9975	1.34	2100	1.1181	0.8812
0.952	1.4	2200	1.1119	0.8817
0.9311	1.47	2300	1.0786	0.8773
0.9398	1.53	2400	1.1016	0.8720
0.9148	1.6	2500	1.0878	0.8778
0.9114	1.66	2600	1.1004	0.8712
0.902	1.72	2700	1.0223	0.8744
0.8978	1.79	2800	1.0616	0.8459
0.8675	1.85	2900	1.0974	0.8643
0.8373	1.92	3000	1.0389	0.8547
0.8575	1.98	3100	1.0388	0.8480
0.8313	2.04	3200	1.0001	0.8648
0.7357	2.11	3300	1.0222	0.8705
0.743	2.17	3400	1.0859	0.8765
0.7306	2.23	3500	1.0109	0.8515
0.7525	2.3	3600	0.9942	0.8619
0.7308	2.36	3700	1.0004	0.8578
0.7266	2.43	3800	1.0003	0.8497
0.737	2.49	3900	1.0146	0.8505
0.7202	2.55	4000	1.0172	0.8653
0.6945	2.62	4100	0.9894	0.8415
0.6633	2.68	4200	0.9894	0.8496
0.6972	2.75	4300	0.9805	0.8505
0.6872	2.81	4400	0.9939	0.8509
0.7238	2.87	4500	0.9740	0.8532
0.6847	2.94	4600	0.9692	0.8462

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu111
Datasets 1.14.0
Tokenizers 0.10.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Mar 9, 2022
Updated: Mar 9, 2022