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
base_model: facebook/wav2vec2-xls-r-1b
model-index:
  - name: wav2vec2-large-xls-r-1b-Indonesian
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          name: Common Voice id
          type: mozilla-foundation/common_voice_8_0
          args: id
        metrics:
          - type: wer
            value: 45.51
            name: Test WER
          - type: cer
            value: 16.43
            name: Test CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: id
        metrics:
          - type: wer
            value: 72.73
            name: Test WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: id
        metrics:
          - type: wer
            value: 79.29
            name: Test WER

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