README.md · arampacha/wav2vec2-xls-r-1b-uk at 651210f77157db50ea67163719f6de94b60509f1

metadata

language:
  - uk
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-1b-hy
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice uk
          args: uk
        metrics:
          - type: wer
            value: 10.406342913776015
            name: WER LM
          - type: cer
            value: 2.0387492208601703
            name: CER LM
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 40.57
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 28.95

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/UK/COMPOSED_DATASET/ - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.1092
Wer: 0.1752
Cer: 0.0323

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: 5e-05
train_batch_size: 16
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 12000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.7005	1.61	500	0.4082	0.5584	0.1164
1.1555	3.22	1000	0.2020	0.2953	0.0557
1.0927	4.82	1500	0.1708	0.2584	0.0480
1.0707	6.43	2000	0.1563	0.2405	0.0450
1.0728	8.04	2500	0.1620	0.2442	0.0463
1.0268	9.65	3000	0.1588	0.2378	0.0458
1.0328	11.25	3500	0.1466	0.2352	0.0442
1.0249	12.86	4000	0.1552	0.2341	0.0449
1.016	14.47	4500	0.1602	0.2435	0.0473
1.0164	16.08	5000	0.1491	0.2337	0.0444
0.9935	17.68	5500	0.1539	0.2373	0.0458
0.9626	19.29	6000	0.1458	0.2305	0.0434
0.9505	20.9	6500	0.1368	0.2157	0.0407
0.9389	22.51	7000	0.1437	0.2231	0.0426
0.9129	24.12	7500	0.1313	0.2076	0.0394
0.9118	25.72	8000	0.1292	0.2040	0.0384
0.8848	27.33	8500	0.1299	0.2028	0.0384
0.8667	28.94	9000	0.1228	0.1945	0.0367
0.8641	30.55	9500	0.1223	0.1939	0.0364
0.8516	32.15	10000	0.1184	0.1876	0.0349
0.8379	33.76	10500	0.1137	0.1821	0.0338
0.8235	35.37	11000	0.1127	0.1779	0.0331
0.8112	36.98	11500	0.1103	0.1766	0.0327
0.8069	38.59	12000	0.1092	0.1752	0.0323

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2
Datasets 1.18.4.dev0
Tokenizers 0.11.0