metadata

language:
  - km
license: apache-2.0
tags:
  - automatic-speech-recognition
  - openslr
  - robust-speech-event
  - km
  - generated_from_trainer
  - hf-asr-leaderboard
model-index:
  - name: xls-r-300m-km
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OpenSLR km
          type: openslr
          args: km
        metrics:
          - name: Test WER
            type: wer
            value: 25.7
          - name: Test CER
            type: cer
            value: 7.03
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: km
        metrics:
          - name: Test WER
            type: wer
            value: 25.7
          - name: Test CER
            type: cer
            value: 7.03

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

Loss: 0.3281
Wer: 0.3462

Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py):

WER: 0.3216977389924633
CER: 0.08653361193169537

Evaluation results with language model on OpenSLR "test" (self-split 10%) (Running ./eval.py):

WER: 0.257040856802856
CER: 0.07025001801282513

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 1000
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.0795	5.47	400	4.4121	1.0
3.5658	10.95	800	3.5203	1.0
3.3689	16.43	1200	2.8984	0.9996
2.01	21.91	1600	1.0041	0.7288
1.6783	27.39	2000	0.6941	0.5989
1.527	32.87	2400	0.5599	0.5282
1.4278	38.35	2800	0.4827	0.4806
1.3458	43.83	3200	0.4429	0.4532
1.2893	49.31	3600	0.4156	0.4330
1.2441	54.79	4000	0.4020	0.4040
1.188	60.27	4400	0.3777	0.3866
1.1628	65.75	4800	0.3607	0.3858
1.1324	71.23	5200	0.3534	0.3604
1.0969	76.71	5600	0.3428	0.3624
1.0897	82.19	6000	0.3387	0.3567
1.0625	87.66	6400	0.3339	0.3499
1.0601	93.15	6800	0.3288	0.3446
1.0474	98.62	7200	0.3281	0.3462

Framework versions

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