File size: 2,624 Bytes
173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d 173e9c3 27bf94d |
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 85 86 87 88 89 90 91 92 |
---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-czech-colab-cv16
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.05733702722973076
---
<!-- 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. -->
# w2v-bert-2.0-czech-colab-cv16
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1023
- Wer: 0.0573
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5297 | 0.66 | 300 | 0.1448 | 0.1299 |
| 0.0886 | 1.32 | 600 | 0.1353 | 0.1051 |
| 0.0717 | 1.98 | 900 | 0.1157 | 0.0861 |
| 0.0463 | 2.64 | 1200 | 0.0994 | 0.0759 |
| 0.0404 | 3.3 | 1500 | 0.1054 | 0.0724 |
| 0.0314 | 3.96 | 1800 | 0.0915 | 0.0694 |
| 0.0227 | 4.63 | 2100 | 0.0926 | 0.0664 |
| 0.0205 | 5.29 | 2400 | 0.0992 | 0.0652 |
| 0.0161 | 5.95 | 2700 | 0.0932 | 0.0654 |
| 0.0124 | 6.61 | 3000 | 0.0902 | 0.0629 |
| 0.0097 | 7.27 | 3300 | 0.0970 | 0.0612 |
| 0.0081 | 7.93 | 3600 | 0.0946 | 0.0602 |
| 0.0054 | 8.59 | 3900 | 0.0962 | 0.0588 |
| 0.0048 | 9.25 | 4200 | 0.1029 | 0.0579 |
| 0.0034 | 9.91 | 4500 | 0.1023 | 0.0573 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1
|