File size: 2,347 Bytes
785d84c 082ea11 0602e0c 278b6c6 082ea11 0602e0c 082ea11 785d84c 0602e0c 082ea11 0602e0c 082ea11 0602e0c 082ea11 0602e0c |
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 |
---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: w2v-bert-2.0-sr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 0.05344857999647204
---
<!-- 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-sr
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_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1469
- Wer: 0.0534
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1994 | 1.89 | 300 | 0.1350 | 0.1078 |
| 0.2331 | 3.77 | 600 | 0.2306 | 0.1341 |
| 0.1879 | 5.66 | 900 | 0.1354 | 0.0766 |
| 0.1579 | 7.54 | 1200 | 0.1646 | 0.0958 |
| 0.1293 | 9.43 | 1500 | 0.1207 | 0.0713 |
| 0.1182 | 11.31 | 1800 | 0.1376 | 0.0737 |
| 0.1061 | 13.2 | 2100 | 0.1244 | 0.0580 |
| 0.1011 | 15.08 | 2400 | 0.1390 | 0.0602 |
| 0.0933 | 16.97 | 2700 | 0.1313 | 0.0524 |
| 0.0948 | 18.85 | 3000 | 0.1469 | 0.0534 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|