metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: asr-300m-turkish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice
          config: tr
          split: test
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 0.3879072617710142

asr-300m-turkish

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

Loss: 0.4060
Wer: 0.3879

Model description

The following datasets were used for finetuning:

Common Voice 7.0 TR All validated split except test split was used for training.
MediaSpeech

Intended uses & limitations

More information needed

Training and evaluation data

The following datasets were used for finetuning:

Common Voice 7.0 TR All validated split except test split was used for training. MediaSpeech

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.9649	4.26	400	0.7179	0.7544
0.3912	8.51	800	0.4798	0.5432
0.1848	12.77	1200	0.4588	0.4792
0.1277	17.02	1600	0.4676	0.4510
0.0923	21.28	2000	0.4251	0.4218
0.07	25.53	2400	0.4164	0.4006
0.0546	29.79	2800	0.4060	0.3879

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 1.18.3
Tokenizers 0.13.3