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: 0.493922990501481

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: 0.5335
Wer: 0.4939

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: 0.0002
train_batch_size: 32
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	1.83	100	4.1084	1.0
No log	3.67	200	3.1519	1.0
No log	5.5	300	1.9348	0.9799
No log	7.34	400	0.7185	0.7490
3.6165	9.17	500	0.6041	0.6368
3.6165	11.01	600	0.5610	0.5771
3.6165	12.84	700	0.5292	0.5398
3.6165	14.68	800	0.5242	0.5083
3.6165	16.51	900	0.5443	0.5037
0.1894	18.35	1000	0.5314	0.4944

Framework versions

Transformers 4.29.2
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.2