File size: 2,628 Bytes
8ded835 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- ./sample_speech.py
- generated_from_trainer
metrics:
- wer
model-index:
- name: ko-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. -->
# ko-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: 0.4228
- Cer: 0.1091
- Wer: 0.3025
## 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: 1000
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.5566 | 0.94 | 2000 | 1.0226 | 0.2632 | 0.6184 |
| 1.179 | 1.89 | 4000 | 0.7682 | 0.2001 | 0.4990 |
| 1.0432 | 2.83 | 6000 | 0.6633 | 0.1749 | 0.4516 |
| 0.9413 | 3.77 | 8000 | 0.6159 | 0.1624 | 0.4259 |
| 0.8765 | 4.72 | 10000 | 0.5792 | 0.1538 | 0.4061 |
| 0.8248 | 5.66 | 12000 | 0.5456 | 0.1446 | 0.3877 |
| 0.7714 | 6.6 | 14000 | 0.5316 | 0.1397 | 0.3710 |
| 0.7388 | 7.55 | 16000 | 0.5172 | 0.1356 | 0.3657 |
| 0.6912 | 8.49 | 18000 | 0.4892 | 0.1291 | 0.3508 |
| 0.6549 | 9.43 | 20000 | 0.4694 | 0.1241 | 0.3397 |
| 0.614 | 10.37 | 22000 | 0.4615 | 0.1205 | 0.3309 |
| 0.5901 | 11.32 | 24000 | 0.4489 | 0.1177 | 0.3215 |
| 0.555 | 12.26 | 26000 | 0.4419 | 0.1148 | 0.3163 |
| 0.5377 | 13.2 | 28000 | 0.4320 | 0.1122 | 0.3103 |
| 0.5253 | 14.15 | 30000 | 0.4251 | 0.1102 | 0.3052 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|