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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- ./sample_speech.py
- generated_from_trainer
model-index:
- name: jako-xlsr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# jako-xlsr
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./SAMPLE_SPEECH.PY - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9486
- Cer: 0.2606
## 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_ratio: 0.03
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5667 | 1.14 | 1000 | 2.2323 | 0.5188 |
| 1.5569 | 2.28 | 2000 | 1.3106 | 0.3527 |
| 1.2238 | 3.43 | 3000 | 1.1109 | 0.3099 |
| 1.0593 | 4.57 | 4000 | 1.0390 | 0.2891 |
| 0.9658 | 5.71 | 5000 | 0.9731 | 0.2918 |
| 0.8796 | 6.85 | 6000 | 0.9479 | 0.2696 |
| 0.8022 | 8.0 | 7000 | 0.9331 | 0.2710 |
| 0.7392 | 9.14 | 8000 | 0.9252 | 0.2746 |
| 0.6694 | 10.28 | 9000 | 0.9318 | 0.2590 |
| 0.5977 | 11.42 | 10000 | 0.9349 | 0.2674 |
| 0.5484 | 12.56 | 11000 | 0.9409 | 0.2555 |
| 0.5154 | 13.71 | 12000 | 0.9510 | 0.2719 |
| 0.4767 | 14.85 | 13000 | 0.9556 | 0.2624 |
| 0.4536 | 15.99 | 14000 | 0.9850 | 0.2684 |
| 0.4195 | 17.13 | 15000 | 0.9894 | 0.2590 |
| 0.3937 | 18.28 | 16000 | 1.0197 | 0.2698 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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