File size: 3,024 Bytes
6e9dec1 5bbb653 6e9dec1 5bbb653 6e9dec1 |
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 |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-base-pretrained_lr1e-4_at0.8_da0.3
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. -->
# w2v2-base-pretrained_lr1e-4_at0.8_da0.3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3227
- Wer: 1.0
## 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: 0.0001
- train_batch_size: 32
- 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: 100
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:---:|
| 18.4732 | 7.14 | 100 | 3.5073 | 1.0 |
| 3.2701 | 14.29 | 200 | 3.3516 | 1.0 |
| 3.234 | 21.43 | 300 | 3.3874 | 1.0 |
| 3.2196 | 28.57 | 400 | 3.3154 | 1.0 |
| 3.2146 | 35.71 | 500 | 3.3741 | 1.0 |
| 3.1916 | 42.86 | 600 | 3.3402 | 1.0 |
| 3.1749 | 50.0 | 700 | 3.3768 | 1.0 |
| 3.2124 | 57.14 | 800 | 3.2787 | 1.0 |
| 3.2133 | 64.29 | 900 | 3.3424 | 1.0 |
| 3.2101 | 71.43 | 1000 | 3.2885 | 1.0 |
| 3.2047 | 78.57 | 1100 | 3.3397 | 1.0 |
| 3.2137 | 85.71 | 1200 | 3.3420 | 1.0 |
| 3.2107 | 92.86 | 1300 | 3.3670 | 1.0 |
| 3.214 | 100.0 | 1400 | 3.3011 | 1.0 |
| 3.2089 | 107.14 | 1500 | 3.3278 | 1.0 |
| 3.2095 | 114.29 | 1600 | 3.3363 | 1.0 |
| 3.2053 | 121.43 | 1700 | 3.3086 | 1.0 |
| 3.2009 | 128.57 | 1800 | 3.3105 | 1.0 |
| 3.2037 | 135.71 | 1900 | 3.3275 | 1.0 |
| 3.2074 | 142.86 | 2000 | 3.3092 | 1.0 |
| 3.2134 | 150.0 | 2100 | 3.3099 | 1.0 |
| 3.2042 | 157.14 | 2200 | 3.3025 | 1.0 |
| 3.2006 | 164.29 | 2300 | 3.3110 | 1.0 |
| 3.2052 | 171.43 | 2400 | 3.3393 | 1.0 |
| 3.2052 | 178.57 | 2500 | 3.3191 | 1.0 |
| 3.2026 | 185.71 | 2600 | 3.3303 | 1.0 |
| 3.2113 | 192.86 | 2700 | 3.3261 | 1.0 |
| 3.2048 | 200.0 | 2800 | 3.3227 | 1.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|