---
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