metadata

language:
  - id
license: apache-2.0
tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-large-xls-r-300m-Indonesian
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_7_0
          name: Common Voice id
          args: id
        metrics:
          - type: wer
            value: 25.06
            name: Test WER With LM
          - type: cer
            value: 6.5
            name: Test CER With 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: id
        metrics:
          - name: Test WER
            type: wer
            value: 99.61
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: id
        metrics:
          - name: Test WER
            type: wer
            value: 106.39

wav2vec2-large-xls-r-300m-Indonesian

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

Loss: 0.4087
Wer: 0.2461
Cer: 0.0666

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 64
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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: 400
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
5.0788	4.26	200	2.9389	1.0	1.0
2.8288	8.51	400	2.2535	1.0	0.8004
0.907	12.77	600	0.4558	0.4243	0.1095
0.4071	17.02	800	0.4013	0.3468	0.0913
0.3	21.28	1000	0.4167	0.3075	0.0816
0.2544	25.53	1200	0.4132	0.2835	0.0762
0.2145	29.79	1400	0.3878	0.2693	0.0729
0.1923	34.04	1600	0.4023	0.2623	0.0702
0.1681	38.3	1800	0.3984	0.2581	0.0686
0.1598	42.55	2000	0.3982	0.2493	0.0663
0.1464	46.81	2200	0.4087	0.2461	0.0666

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0