metadata

language:
  - eo
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_13_0
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice_13_0-eo-10_1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - EO
          type: common_voice_13_0
          config: eo
          split: validation
          args: 'Config: eo, Training split: train, Eval split: validation'
        metrics:
          - name: Wer
            type: wer
            value: 0.05342994850125446

wav2vec2-common_voice_13_0-eo-10_1

This model is a fine-tuned version of xekri/wav2vec2-common_voice_13_0-eo-10 on the MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - EO dataset. It achieves the following results on the evaluation set:

Loss: 0.0391
Cer: 0.0098
Wer: 0.0534

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
0.1142	0.22	1000	0.0483	0.0126	0.0707
0.1049	0.44	2000	0.0474	0.0123	0.0675
0.0982	0.67	3000	0.0471	0.0120	0.0664
0.092	0.89	4000	0.0459	0.0117	0.0640
0.0847	1.11	5000	0.0459	0.0115	0.0631
0.0837	1.33	6000	0.0453	0.0113	0.0624
0.0803	1.56	7000	0.0443	0.0109	0.0598
0.0826	1.78	8000	0.0441	0.0110	0.0604
0.0809	2.0	9000	0.0437	0.0110	0.0605
0.0728	2.22	10000	0.0451	0.0109	0.0597
0.0707	2.45	11000	0.0444	0.0108	0.0591
0.0698	2.67	12000	0.0442	0.0105	0.0576
0.0981	2.89	13000	0.0411	0.0104	0.0572
0.0928	3.11	14000	0.0413	0.0102	0.0561
0.0927	3.34	15000	0.0410	0.0102	0.0565
0.0886	3.56	16000	0.0402	0.0102	0.0558
0.091	3.78	17000	0.0400	0.0101	0.0553
0.0888	4.0	18000	0.0398	0.0100	0.0546
0.0885	4.23	19000	0.0395	0.0099	0.0542
0.0869	4.45	20000	0.0394	0.0099	0.0540
0.0844	4.67	21000	0.0393	0.0098	0.0539
0.0882	4.89	22000	0.0391	0.0098	0.0537

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3