metadata

license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-russian
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlsr-53-ru-snr10-commonvoice_train3000_val200
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: ru
          split: test[0:200]
          args: ru
        metrics:
          - name: Wer
            type: wer
            value: 0.5075114304376225

wav2vec2-large-xlsr-53-ru-snr10-commonvoice_train3000_val200

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-russian on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

Loss: 1.3736
Wer: 0.5075
Cer: 0.2395

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.9298	0.96	180	1.0829	0.6153	0.2984
0.6185	1.91	360	1.1071	0.5924	0.2899
0.5395	2.87	540	1.0219	0.5558	0.2604
0.464	3.83	720	1.0042	0.5363	0.2564
0.4346	4.79	900	0.9817	0.5323	0.2403
0.4025	5.74	1080	1.0918	0.5558	0.2549
0.358	6.7	1260	1.0987	0.5336	0.2437
0.3466	7.66	1440	1.0802	0.5349	0.2437
0.3215	8.62	1620	1.1377	0.5467	0.2588
0.3247	9.57	1800	1.0324	0.5153	0.2350
0.287	10.53	1980	1.1466	0.5565	0.2603
0.2716	11.49	2160	1.2634	0.5532	0.2536
0.2555	12.45	2340	1.1859	0.5160	0.2318
0.2454	13.4	2520	1.1147	0.5186	0.2278
0.2299	14.36	2700	1.1287	0.5167	0.2282
0.2269	15.32	2880	1.2123	0.5042	0.2275
0.2132	16.28	3060	1.1219	0.5082	0.2297
0.1965	17.23	3240	1.2263	0.5167	0.2345
0.1943	18.19	3420	1.2679	0.5284	0.2353
0.1867	19.15	3600	1.2097	0.5186	0.2422
0.1851	20.11	3780	1.3118	0.5147	0.2330
0.1709	21.06	3960	1.1834	0.5193	0.2374
0.1757	22.02	4140	1.3010	0.5036	0.2272
0.1661	22.98	4320	1.2384	0.5075	0.2313
0.1607	23.94	4500	1.3642	0.5219	0.2421
0.1611	24.89	4680	1.3055	0.5108	0.2363
0.1567	25.85	4860	1.3666	0.5140	0.2383
0.1469	26.81	5040	1.3888	0.5101	0.2367
0.1432	27.77	5220	1.3478	0.5206	0.2333
0.1479	28.72	5400	1.3297	0.4918	0.2291
0.144	29.68	5580	1.3736	0.5075	0.2395

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0