metadata

language:
  - ka
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-1b-ka
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice ka
          args: ka
        metrics:
          - type: wer
            value: 7.39778066580026
            name: WER LM
          - type: cer
            value: 1.1882089427096434
            name: CER 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: ka
        metrics:
          - name: Test WER
            type: wer
            value: 22.61
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: ka
        metrics:
          - name: Test WER
            type: wer
            value: 21.58

wav2vec2-xls-r-1b-ka

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/KA/NOIZY_STUDENT_2/ - KA dataset. It achieves the following results on the evaluation set:

Loss: 0.1022
Wer: 0.1527
Cer: 0.0221

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: 7e-05
train_batch_size: 16
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.2839	6.45	400	0.2229	0.3609	0.0557
0.9775	12.9	800	0.1271	0.2202	0.0317
0.9045	19.35	1200	0.1268	0.2030	0.0294
0.8652	25.8	1600	0.1211	0.1940	0.0287
0.8505	32.26	2000	0.1192	0.1912	0.0276
0.8168	38.7	2400	0.1086	0.1763	0.0260
0.7737	45.16	2800	0.1098	0.1753	0.0256
0.744	51.61	3200	0.1054	0.1646	0.0239
0.7114	58.06	3600	0.1034	0.1573	0.0228
0.6773	64.51	4000	0.1022	0.1527	0.0221

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2
Datasets 1.18.4.dev0
Tokenizers 0.11.0