File size: 3,598 Bytes
f6079f4 |
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 |
---
language:
- he
tags:
- automatic-speech-recognition
- robust-speech-event
- he
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-hebrew
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. -->
# wav2vec2-xls-r-300m-hebrew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the private dataset with stats:
| split |size | n_samples | duration(hrs)| |
|---|---|---|---|---|
|train|4.19gb| 20306 | 28 | |
|dev |1.05gb| 5076 | 7 | |
It achieves the following results on the evaluation set:
- Loss: 0.5438
- Wer: 0.1773
## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- 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: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 3.15 | 1000 | 0.5203 | 0.4333 |
| 1.4284 | 6.31 | 2000 | 0.4816 | 0.3951 |
| 1.4284 | 9.46 | 3000 | 0.4315 | 0.3546 |
| 1.283 | 12.62 | 4000 | 0.4278 | 0.3404 |
| 1.283 | 15.77 | 5000 | 0.4090 | 0.3054 |
| 1.1777 | 18.93 | 6000 | 0.3893 | 0.3006 |
| 1.1777 | 22.08 | 7000 | 0.3968 | 0.2857 |
| 1.0994 | 25.24 | 8000 | 0.3892 | 0.2751 |
| 1.0994 | 28.39 | 9000 | 0.4061 | 0.2690 |
| 1.0323 | 31.54 | 10000 | 0.4114 | 0.2507 |
| 1.0323 | 34.7 | 11000 | 0.4021 | 0.2508 |
| 0.9623 | 37.85 | 12000 | 0.4032 | 0.2378 |
| 0.9623 | 41.01 | 13000 | 0.4148 | 0.2374 |
| 0.9077 | 44.16 | 14000 | 0.4350 | 0.2323 |
| 0.9077 | 47.32 | 15000 | 0.4515 | 0.2246 |
| 0.8573 | 50.47 | 16000 | 0.4474 | 0.2180 |
| 0.8573 | 53.63 | 17000 | 0.4649 | 0.2171 |
| 0.8083 | 56.78 | 18000 | 0.4455 | 0.2102 |
| 0.8083 | 59.94 | 19000 | 0.4587 | 0.2092 |
| 0.769 | 63.09 | 20000 | 0.4794 | 0.2012 |
| 0.769 | 66.25 | 21000 | 0.4845 | 0.2007 |
| 0.7308 | 69.4 | 22000 | 0.4937 | 0.2008 |
| 0.7308 | 72.55 | 23000 | 0.4920 | 0.1895 |
| 0.6927 | 75.71 | 24000 | 0.5179 | 0.1911 |
| 0.6927 | 78.86 | 25000 | 0.5202 | 0.1877 |
| 0.6622 | 82.02 | 26000 | 0.5266 | 0.1840 |
| 0.6622 | 85.17 | 27000 | 0.5351 | 0.1854 |
| 0.6315 | 88.33 | 28000 | 0.5373 | 0.1811 |
| 0.6315 | 91.48 | 29000 | 0.5331 | 0.1792 |
| 0.6075 | 94.64 | 30000 | 0.5390 | 0.1779 |
| 0.6075 | 97.79 | 31000 | 0.5459 | 0.1773 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|