README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: wav2vec2LugandaASR
    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: 0.23959817157435953

wav2vec2LugandaASR

This model is a fine-tuned version of Gemmar/wav2vec2LugandaASR on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2014
• Wer: 0.2396

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: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.8963	0.18	100	2.8825	1.0000
1.1814	0.36	200	0.3787	0.4585
0.3331	0.54	300	0.3166	0.3918
0.2939	0.72	400	0.2811	0.3483
0.2682	0.9	500	0.2652	0.3348
0.2389	1.08	600	0.2565	0.3207
0.2137	1.27	700	0.2452	0.3066
0.2062	1.45	800	0.2356	0.3092
0.2058	1.63	900	0.2346	0.2928
0.2055	1.81	1000	0.2252	0.2901
0.1979	1.99	1100	0.2215	0.2836
0.166	2.17	1200	0.2217	0.2811
0.1623	2.35	1300	0.2200	0.2685
0.1628	2.53	1400	0.2166	0.2707
0.1593	2.71	1500	0.2131	0.2634
0.1561	2.89	1600	0.2121	0.2661
0.146	3.07	1700	0.2128	0.2552
0.1339	3.25	1800	0.2119	0.2591
0.1314	3.43	1900	0.2090	0.2492
0.1296	3.62	2000	0.2058	0.2504
0.1304	3.8	2100	0.2057	0.2500
0.1276	3.98	2200	0.2028	0.2463
0.116	4.16	2300	0.2058	0.2461
0.1122	4.34	2400	0.2074	0.2443
0.1087	4.52	2500	0.2065	0.2411
0.1087	4.7	2600	0.2042	0.2412
0.11	4.88	2700	0.2014	0.2396

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3