xlsr_mid1_zh-ko / README.md
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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: zh-xlsr
    results: []

zh-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: 1.8449
  • Cer: 0.4954

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: 2
  • total_train_batch_size: 32
  • 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: 150
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Cer
6.0153 0.5 330 5.3438 0.9522
5.3776 1.0 660 5.1534 0.9409
5.2604 1.5 990 5.0832 0.9108
5.2393 2.01 1320 5.0655 0.9073
5.1721 2.51 1650 5.0464 0.9000
5.1619 3.01 1980 5.0244 0.9045
5.1308 3.51 2310 5.0216 0.9020
5.0971 4.01 2640 4.9341 0.9040
5.0137 4.51 2970 4.8795 0.9144
4.9341 5.02 3300 4.7250 0.9039
4.6832 5.52 3630 4.2140 0.8367
4.1627 6.02 3960 3.4010 0.7318
3.5448 6.52 4290 2.8830 0.6480
3.2576 7.02 4620 2.6253 0.6266
2.8561 7.52 4950 2.4300 0.5866
2.7894 8.02 5280 2.2998 0.5750
2.6018 8.53 5610 2.1878 0.5549
2.546 9.03 5940 2.1450 0.5351
2.3787 9.53 6270 2.1027 0.5340
2.335 10.03 6600 2.0304 0.5166
2.2138 10.53 6930 2.0100 0.5165
2.2381 11.03 7260 1.9651 0.5031
2.1108 11.53 7590 1.9666 0.5035
2.0916 12.04 7920 1.9136 0.4998
2.0229 12.54 8250 1.8988 0.5028
2.0056 13.04 8580 1.8769 0.4996
1.9245 13.54 8910 1.8716 0.4955
1.9378 14.04 9240 1.8561 0.4946
1.9003 14.54 9570 1.8485 0.4936

Framework versions

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