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wav2vec2-base-turkish

Automatic Speech Recognition Transformers PyTorch
Turkish wav2vec2 common_voice generated_from_trainer hf-asr-leaderboard robust-speech-event Eval Results Inference Endpoints
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metadata
language:
  - tr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
  - tr
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: Wav2Vec2 Base Turkish by Cahya
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 6.1
          type: mozilla-foundation/common_voice_7_0
          args: tr
        metrics:
          - name: Test WER
            type: wer
            value: 9.437
          - name: Test CER
            type: cer
            value: 3.325
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: tr
        metrics:
          - name: Test WER
            type: wer
            value: 8.147
          - name: Test CER
            type: cer
            value: 2.802
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: tr
        metrics:
          - name: Test WER
            type: wer
            value: 28.011
          - name: Test CER
            type: cer
            value: 10.66
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: tr
        metrics:
          - name: Test WER
            type: wer
            value: 33.62

This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

Dataset WER CER
1 Common Voice 6.1 9.437 3.325
2 Common Voice 7.0 8.147 2.802
3 Common Voice 8.0 8.335 2.336
4 Speech Recognition Community 28.011 10.66

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

The following datasets were used for finetuning:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7.5e-06
  • train_batch_size: 6
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 24
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 5.0

Training results

Training Loss Epoch Step Validation Loss Wer
1.1224 3.45 500 0.1641 0.1396

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.2
  • Tokenizers 0.10.3