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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: amh_finetune
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ml-superb-subset
type: ml-superb-subset
config: amh
split: test
args: amh
metrics:
- name: Wer
type: wer
value: 97.41641337386018
---
<!-- 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. -->
# amh_finetune
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8917
- Wer: 97.4164
## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| 22.5796 | 2.2222 | 10 | 17.1583 | 100.0 |
| 9.5568 | 4.4444 | 20 | 7.4797 | 100.0 |
| 4.3875 | 6.6667 | 30 | 3.9841 | 100.0 |
| 3.8631 | 8.8889 | 40 | 3.8281 | 100.0 |
| 3.8298 | 11.1111 | 50 | 3.8117 | 100.0 |
| 3.7925 | 13.3333 | 60 | 3.7866 | 100.0 |
| 3.802 | 15.5556 | 70 | 3.7763 | 100.0 |
| 3.7845 | 17.7778 | 80 | 3.7681 | 100.0 |
| 3.7732 | 20.0 | 90 | 3.7627 | 100.0 |
| 3.7547 | 22.2222 | 100 | 3.7625 | 100.0 |
| 3.7471 | 24.4444 | 110 | 3.7588 | 100.0 |
| 3.7378 | 26.6667 | 120 | 3.7244 | 100.0 |
| 3.7278 | 28.8889 | 130 | 3.7337 | 100.0 |
| 3.71 | 31.1111 | 140 | 3.7188 | 100.0 |
| 3.6966 | 33.3333 | 150 | 3.7076 | 100.0 |
| 3.6811 | 35.5556 | 160 | 3.6916 | 100.0 |
| 3.6741 | 37.7778 | 170 | 3.6898 | 100.0 |
| 3.6337 | 40.0 | 180 | 3.6486 | 100.0 |
| 3.5766 | 42.2222 | 190 | 3.5913 | 100.0 |
| 3.5251 | 44.4444 | 200 | 3.5318 | 100.0 |
| 3.4533 | 46.6667 | 210 | 3.4549 | 100.0 |
| 3.3664 | 48.8889 | 220 | 3.3877 | 100.0 |
| 3.2963 | 51.1111 | 230 | 3.2852 | 100.0 |
| 3.1237 | 53.3333 | 240 | 3.1187 | 100.0 |
| 2.9356 | 55.5556 | 250 | 2.9620 | 100.0 |
| 2.7107 | 57.7778 | 260 | 2.7665 | 100.0 |
| 2.477 | 60.0 | 270 | 2.5155 | 99.3921 |
| 2.1786 | 62.2222 | 280 | 2.2953 | 98.4043 |
| 1.897 | 64.4444 | 290 | 2.1781 | 97.5684 |
| 1.6863 | 66.6667 | 300 | 2.1825 | 97.5684 |
| 1.4954 | 68.8889 | 310 | 2.1240 | 96.2766 |
| 1.3132 | 71.1111 | 320 | 2.1476 | 94.3769 |
| 1.1333 | 73.3333 | 330 | 2.2088 | 95.6687 |
| 0.9827 | 75.5556 | 340 | 2.2591 | 94.9088 |
| 0.9019 | 77.7778 | 350 | 2.4481 | 101.0638 |
| 0.7936 | 80.0 | 360 | 2.5467 | 103.4195 |
| 0.7015 | 82.2222 | 370 | 2.5279 | 95.5927 |
| 0.631 | 84.4444 | 380 | 2.6338 | 95.8207 |
| 0.5849 | 86.6667 | 390 | 2.6840 | 96.8085 |
| 0.5549 | 88.8889 | 400 | 2.7048 | 97.4164 |
| 0.5137 | 91.1111 | 410 | 2.7910 | 96.0486 |
| 0.4905 | 93.3333 | 420 | 2.8070 | 98.7842 |
| 0.4603 | 95.5556 | 430 | 2.8552 | 95.2888 |
| 0.457 | 97.7778 | 440 | 2.8382 | 95.8207 |
| 0.442 | 100.0 | 450 | 2.8831 | 98.2523 |
| 0.4437 | 102.2222 | 460 | 2.8800 | 97.5684 |
| 0.4346 | 104.4444 | 470 | 2.8805 | 97.7964 |
| 0.4341 | 106.6667 | 480 | 2.8864 | 97.6444 |
| 0.4319 | 108.8889 | 490 | 2.8911 | 97.3404 |
| 0.4403 | 111.1111 | 500 | 2.8917 | 97.4164 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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