File size: 2,628 Bytes
4d2ea61 |
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: 0.4269
- Cer: 0.1119
- Wer: 0.3072
## 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: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.54 | 0.94 | 2000 | 1.0057 | 0.2617 | 0.6135 |
| 1.1895 | 1.89 | 4000 | 0.7782 | 0.2040 | 0.5035 |
| 1.0582 | 2.83 | 6000 | 0.6767 | 0.1826 | 0.4655 |
| 0.9586 | 3.77 | 8000 | 0.6273 | 0.1690 | 0.4380 |
| 0.8831 | 4.72 | 10000 | 0.5884 | 0.1552 | 0.4071 |
| 0.8318 | 5.66 | 12000 | 0.5510 | 0.1469 | 0.3897 |
| 0.7725 | 6.6 | 14000 | 0.5327 | 0.1407 | 0.3726 |
| 0.7254 | 7.55 | 16000 | 0.5081 | 0.1416 | 0.3676 |
| 0.6802 | 8.49 | 18000 | 0.4846 | 0.1313 | 0.3502 |
| 0.6386 | 9.43 | 20000 | 0.4676 | 0.1241 | 0.3344 |
| 0.5949 | 10.37 | 22000 | 0.4510 | 0.1185 | 0.3250 |
| 0.5736 | 11.32 | 24000 | 0.4416 | 0.1161 | 0.3189 |
| 0.5451 | 12.26 | 26000 | 0.4338 | 0.1143 | 0.3144 |
| 0.5375 | 13.2 | 28000 | 0.4287 | 0.1126 | 0.3095 |
| 0.5335 | 14.15 | 30000 | 0.4273 | 0.1122 | 0.3079 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|