README.md

metadata

language:
  - id
license: apache-2.0
tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-large-xls-r-1b-Indonesian
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice id
          args: id
        metrics:
          - type: wer
            value: 45.51
            name: Test WER
          - type: cer
            value: 16.43
            name: Test CER
      - 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: 72.73
      - 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: 79.29

wav2vec2-large-xls-r-1b-Indonesian

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

• Loss: 0.9550
• Wer: 0.4551
• Cer: 0.1643

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
3.663	7.69	200	0.7898	0.6039	0.1848
0.7424	15.38	400	1.0215	0.5615	0.1924
0.4494	23.08	600	1.0901	0.5249	0.1932
0.5075	30.77	800	1.1013	0.5079	0.1935
0.4671	38.46	1000	1.1034	0.4916	0.1827
0.1928	46.15	1200	0.9550	0.4551	0.1643

Framework versions

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