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xlsr_mid1_ko-ja

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
./sample_speech.py
Generated from Trainer
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - automatic-speech-recognition
  - ./sample_speech.py
  - generated_from_trainer
model-index:
  - name: en-xlsr
    results: []

en-xlsr

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

  • Loss: 0.3889
  • Cer: 0.1082

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: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1500
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Cer
1.4503 1.22 2000 1.0610 0.2687
1.0239 2.45 4000 0.6962 0.1904
0.8977 3.67 6000 0.5945 0.1687
0.804 4.9 8000 0.5328 0.1492
0.698 6.12 10000 0.5014 0.1365
0.6426 7.35 12000 0.4715 0.1322
0.61 8.57 14000 0.4530 0.1258
0.5709 9.79 16000 0.4300 0.1201
0.5235 11.02 18000 0.4168 0.1166
0.4778 12.24 20000 0.4057 0.1129
0.4571 13.47 22000 0.3945 0.1100
0.4388 14.69 24000 0.3891 0.1081

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.14.1