File size: 2,084 Bytes
fb33813 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-mr
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-large-xls-r-300m-mr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5479
- Wer: 0.5740
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 1000
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.7378 | 18.18 | 400 | 3.5047 | 1.0 |
| 3.1707 | 36.36 | 800 | 2.6166 | 0.9912 |
| 1.4942 | 54.55 | 1200 | 0.5778 | 0.6927 |
| 1.2058 | 72.73 | 1600 | 0.5168 | 0.6362 |
| 1.0558 | 90.91 | 2000 | 0.5105 | 0.6069 |
| 0.9488 | 109.09 | 2400 | 0.5151 | 0.6089 |
| 0.8588 | 127.27 | 2800 | 0.5157 | 0.5989 |
| 0.7991 | 145.45 | 3200 | 0.5179 | 0.5740 |
| 0.7545 | 163.64 | 3600 | 0.5348 | 0.5740 |
| 0.7144 | 181.82 | 4000 | 0.5518 | 0.5724 |
| 0.7041 | 200.0 | 4400 | 0.5479 | 0.5740 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0
|