File size: 4,923 Bytes
19da87d |
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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: ierg4320_en_test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: en
split: None
args: en
metrics:
- name: Wer
type: wer
value: 0.415272136474411
---
<!-- 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. -->
# ierg4320_en_test
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9548
- Wer: 0.4153
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 5.0982 | 0.6211 | 400 | 2.9438 | 0.9938 |
| 1.7877 | 1.2422 | 800 | 1.0128 | 0.6645 |
| 0.8688 | 1.8634 | 1200 | 0.8012 | 0.5662 |
| 0.6808 | 2.4845 | 1600 | 0.8370 | 0.5366 |
| 0.6252 | 3.1056 | 2000 | 0.7710 | 0.5063 |
| 0.5532 | 3.7267 | 2400 | 0.7258 | 0.5041 |
| 0.5073 | 4.3478 | 2800 | 0.7337 | 0.4864 |
| 0.4834 | 4.9689 | 3200 | 0.7023 | 0.4777 |
| 0.4419 | 5.5901 | 3600 | 0.7542 | 0.4708 |
| 0.4326 | 6.2112 | 4000 | 0.7187 | 0.4647 |
| 0.4024 | 6.8323 | 4400 | 0.7212 | 0.4671 |
| 0.3809 | 7.4534 | 4800 | 0.7139 | 0.4582 |
| 0.3752 | 8.0745 | 5200 | 0.7296 | 0.4512 |
| 0.337 | 8.6957 | 5600 | 0.7207 | 0.4578 |
| 0.3305 | 9.3168 | 6000 | 0.7233 | 0.4528 |
| 0.3329 | 9.9379 | 6400 | 0.7178 | 0.4565 |
| 0.3047 | 10.5590 | 6800 | 0.7077 | 0.4518 |
| 0.2957 | 11.1801 | 7200 | 0.7788 | 0.4512 |
| 0.2913 | 11.8012 | 7600 | 0.7483 | 0.4528 |
| 0.2685 | 12.4224 | 8000 | 0.7644 | 0.4426 |
| 0.2666 | 13.0435 | 8400 | 0.7640 | 0.4427 |
| 0.2495 | 13.6646 | 8800 | 0.7959 | 0.4401 |
| 0.2501 | 14.2857 | 9200 | 0.7978 | 0.4494 |
| 0.2369 | 14.9068 | 9600 | 0.8217 | 0.4403 |
| 0.2282 | 15.5280 | 10000 | 0.8052 | 0.4359 |
| 0.2293 | 16.1491 | 10400 | 0.8688 | 0.4357 |
| 0.2165 | 16.7702 | 10800 | 0.8566 | 0.4385 |
| 0.2067 | 17.3913 | 11200 | 0.8504 | 0.4307 |
| 0.2034 | 18.0124 | 11600 | 0.8358 | 0.4346 |
| 0.1963 | 18.6335 | 12000 | 0.8729 | 0.4307 |
| 0.1846 | 19.2547 | 12400 | 0.8562 | 0.4349 |
| 0.189 | 19.8758 | 12800 | 0.8408 | 0.4266 |
| 0.1787 | 20.4969 | 13200 | 0.8424 | 0.4288 |
| 0.1757 | 21.1180 | 13600 | 0.8947 | 0.4327 |
| 0.1691 | 21.7391 | 14000 | 0.9070 | 0.4291 |
| 0.1652 | 22.3602 | 14400 | 0.8735 | 0.4299 |
| 0.1619 | 22.9814 | 14800 | 0.9224 | 0.4315 |
| 0.1544 | 23.6025 | 15200 | 0.9199 | 0.4278 |
| 0.1551 | 24.2236 | 15600 | 0.9089 | 0.4240 |
| 0.1449 | 24.8447 | 16000 | 0.9296 | 0.4229 |
| 0.1465 | 25.4658 | 16400 | 0.9476 | 0.4198 |
| 0.1434 | 26.0870 | 16800 | 0.9167 | 0.4193 |
| 0.1431 | 26.7081 | 17200 | 0.9492 | 0.4141 |
| 0.1335 | 27.3292 | 17600 | 0.9597 | 0.4185 |
| 0.1326 | 27.9503 | 18000 | 0.9516 | 0.4156 |
| 0.1333 | 28.5714 | 18400 | 0.9490 | 0.4153 |
| 0.1293 | 29.1925 | 18800 | 0.9605 | 0.4172 |
| 0.1257 | 29.8137 | 19200 | 0.9548 | 0.4153 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1.post302
- Datasets 3.1.0
- Tokenizers 0.20.4
|