imvladikon/wav2vec2-xls-r-1b-hebrew

Source: 🤗automatic-speech-recognitionapache-2.0963M params7.7 GBsafetensors✓ 11 checksumsupdated 1,159d ago

wav2vec2-xls-r-1b-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3533
Wer: 0.2251

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: 6
eval_batch_size: 6
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 400
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.3587	0.47	400	1.1883	0.8392
1.8377	0.95	800	0.8831	0.6852
1.7118	1.42	1200	0.8031	0.6566
1.6741	1.89	1600	0.7518	0.6104
1.6163	2.36	2000	0.6888	0.5591
1.5782	2.84	2400	0.6580	0.5165
1.5548	3.31	2800	0.6506	0.5184
1.5249	3.78	3200	0.6198	0.5028
1.5078	4.26	3600	0.5992	0.4932
1.4836	4.73	4000	0.5705	0.4651
1.4505	5.2	4400	0.5489	0.4508
1.4481	5.67	4800	0.5577	0.4562
1.4136	6.15	5200	0.5452	0.4371
1.3861	6.62	5600	0.5101	0.4087
1.3772	7.09	6000	0.4933	0.3951
1.3478	7.56	6400	0.4849	0.3922
1.3394	8.04	6800	0.4805	0.3892
1.3095	8.51	7200	0.4839	0.3834
1.306	8.98	7600	0.4611	0.3587
1.2707	9.46	8000	0.4545	0.3730
1.2626	9.93	8400	0.4516	0.3524
1.2412	10.4	8800	0.4314	0.3310
1.2456	10.87	9200	0.4401	0.3459
1.2081	11.35	9600	0.4399	0.3356
1.1998	11.82	10000	0.4195	0.3215
1.1826	12.29	10400	0.4221	0.3178
1.1573	12.77	10800	0.4098	0.3084
1.1416	13.24	11200	0.4086	0.3119
1.1174	13.71	11600	0.3854	0.2910
1.1048	14.18	12000	0.3859	0.2824
1.0748	14.66	12400	0.3854	0.2757
1.0697	15.13	12800	0.3740	0.2724
1.0477	15.6	13200	0.3693	0.2643
1.0356	16.08	13600	0.3727	0.2561
1.0083	16.55	14000	0.3652	0.2501
1.0	17.02	14400	0.3641	0.2457
0.9779	17.49	14800	0.3568	0.2409
0.9596	17.97	15200	0.3558	0.2376
0.946	18.44	15600	0.3591	0.2311
0.9389	18.91	16000	0.3540	0.2283
0.9173	19.39	16400	0.3552	0.2265
0.9122	19.86	16800	0.3535	0.2250

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0

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