Hugging Face's logo

badrex
/
xlsr-am-adap-phon

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
xlsr-am-adap-phon / README.md
Badr Abdullah
Upload tokenizer
ceecf24 verified
preview code
|
raw
history blame contribute delete
3.06 kB
metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/wav2vec2-large-xlsr-53
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xlsr-am-adap-phon
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: am
          split: validation
          args: am
        metrics:
          - type: wer
            value: 0.9302421009437833
            name: Wer

Visualize in Weights & Biases

xlsr-am-adap-phon

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5869
  • Wer: 0.9302
  • Cer: 0.4393

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: 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
6.6235 6.8966 100 6.3103 1.0 1.0
4.227 13.7931 200 4.2662 1.0 1.0
4.1461 20.6897 300 4.1543 1.0 0.9966
4.146 27.5862 400 4.1716 1.0 0.9859
4.105 34.4828 500 4.1391 1.0 0.9740
3.5688 41.3793 600 3.6625 1.0 0.9749
1.5705 48.2759 700 2.2315 1.0029 0.5187
0.6683 55.1724 800 2.2517 0.9684 0.4595
0.577 62.0690 900 2.2995 0.9528 0.4413
0.3109 68.9655 1000 2.4239 0.9397 0.4575
0.2803 75.8621 1100 2.4491 0.9508 0.4474
0.2136 82.7586 1200 2.4916 0.9179 0.4323
0.3282 89.6552 1300 2.5652 0.9302 0.4401
0.2118 96.5517 1400 2.5869 0.9302 0.4393

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1