README.md

metadata

language:
  - tr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-tr-demo
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: COMMON_VOICE - TR
          type: common_voice
          config: tr
          split: test
          args: 'Config: tr, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.3537943008885711

wav2vec2-common_voice-tr-demo

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

• Loss: 0.3997
• Wer: 0.3538

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: 0.0003
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 500
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.92	100	3.6061	1.0
No log	1.83	200	3.0203	0.9999
No log	2.75	300	0.9479	0.7916
No log	3.67	400	0.6024	0.6285
3.1561	4.59	500	0.5112	0.5369
3.1561	5.5	600	0.4581	0.4900
3.1561	6.42	700	0.4321	0.4633
3.1561	7.34	800	0.4252	0.4400
3.1561	8.26	900	0.4204	0.4229
0.2247	9.17	1000	0.3948	0.3971
0.2247	10.09	1100	0.3997	0.3963
0.2247	11.01	1200	0.4157	0.3894
0.2247	11.93	1300	0.4142	0.3855
0.2247	12.84	1400	0.4108	0.3638
0.1022	13.76	1500	0.3929	0.3618
0.1022	14.68	1600	0.4004	0.3544

Framework versions

• Transformers 4.28.1
• Pytorch 1.12.1+cu102
• Datasets 2.12.0
• Tokenizers 0.13.3