metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: LugandaASRwav2Vec300M
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: lg
          split: validation
          args: lg
        metrics:
          - name: Wer
            type: wer
            value: 0.22313171042840438

LugandaASRwav2Vec300M

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1741
Wer: 0.2231

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 24
total_train_batch_size: 96
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.4394	0.14	100	2.9784	1.0
2.8739	0.27	200	2.7056	1.0000
1.2203	0.41	300	0.5656	0.7264
0.4507	0.54	400	0.3978	0.5258
0.3657	0.68	500	0.3314	0.4416
0.3131	0.81	600	0.2996	0.4049
0.2886	0.95	700	0.2823	0.3766
0.2535	1.08	800	0.2517	0.3317
0.2279	1.22	900	0.2407	0.3190
0.2209	1.36	1000	0.2296	0.3077
0.2075	1.49	1100	0.2228	0.2931
0.1983	1.63	1200	0.2139	0.2809
0.1902	1.76	1300	0.2093	0.2688
0.1931	1.9	1400	0.2019	0.2666
0.1741	2.03	1500	0.1951	0.2521
0.1481	2.17	1600	0.1934	0.2435
0.1423	2.3	1700	0.1912	0.2409
0.1413	2.44	1800	0.1841	0.2368
0.1361	2.58	1900	0.1813	0.2310
0.1337	2.71	2000	0.1775	0.2279
0.1358	2.85	2100	0.1756	0.2247
0.133	2.98	2200	0.1741	0.2231

Framework versions

Transformers 4.32.0
Pytorch 2.0.1+cu118
Datasets 2.13.0
Tokenizers 0.13.3