---
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: 100.0
---

<!-- 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: 3.7471
- Wer: 100.0

## 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: 0.005
- 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   |
|:-------------:|:--------:|:----:|:---------------:|:-----:|
| 17.1482       | 2.2222   | 10   | 9.2733          | 100.0 |
| 4.0207        | 4.4444   | 20   | 3.9380          | 100.0 |
| 5.0108        | 6.6667   | 30   | 4.5177          | 100.0 |
| 5.4723        | 8.8889   | 40   | 4.1051          | 100.0 |
| 3.9167        | 11.1111  | 50   | 3.8586          | 100.0 |
| 3.8376        | 13.3333  | 60   | 3.8304          | 100.0 |
| 3.8317        | 15.5556  | 70   | 3.8322          | 100.0 |
| 3.8352        | 17.7778  | 80   | 3.8165          | 100.0 |
| 3.8337        | 20.0     | 90   | 3.8249          | 100.0 |
| 3.8231        | 22.2222  | 100  | 3.8129          | 100.0 |
| 3.8169        | 24.4444  | 110  | 3.8217          | 100.0 |
| 3.8195        | 26.6667  | 120  | 3.8120          | 100.0 |
| 3.8186        | 28.8889  | 130  | 3.8160          | 100.0 |
| 3.8271        | 31.1111  | 140  | 3.8150          | 100.0 |
| 3.8273        | 33.3333  | 150  | 3.8134          | 100.0 |
| 3.8186        | 35.5556  | 160  | 3.8138          | 100.0 |
| 3.816         | 37.7778  | 170  | 3.8132          | 100.0 |
| 3.817         | 40.0     | 180  | 3.8187          | 100.0 |
| 3.8196        | 42.2222  | 190  | 3.8115          | 100.0 |
| 3.8157        | 44.4444  | 200  | 3.8131          | 100.0 |
| 3.814         | 46.6667  | 210  | 3.8158          | 100.0 |
| 3.8198        | 48.8889  | 220  | 3.8073          | 100.0 |
| 3.8169        | 51.1111  | 230  | 3.8036          | 100.0 |
| 3.7913        | 53.3333  | 240  | 3.7904          | 100.0 |
| 3.791         | 55.5556  | 250  | 3.7796          | 100.0 |
| 3.765         | 57.7778  | 260  | 3.7673          | 100.0 |
| 3.768         | 60.0     | 270  | 3.7672          | 100.0 |
| 3.7594        | 62.2222  | 280  | 3.7594          | 100.0 |
| 3.7488        | 64.4444  | 290  | 3.7601          | 100.0 |
| 3.7506        | 66.6667  | 300  | 3.7583          | 100.0 |
| 3.7408        | 68.8889  | 310  | 3.7580          | 100.0 |
| 3.749         | 71.1111  | 320  | 3.7630          | 100.0 |
| 3.7575        | 73.3333  | 330  | 3.7568          | 100.0 |
| 3.7517        | 75.5556  | 340  | 3.7555          | 100.0 |
| 3.758         | 77.7778  | 350  | 3.7536          | 100.0 |
| 3.7491        | 80.0     | 360  | 3.7546          | 100.0 |
| 3.7288        | 82.2222  | 370  | 3.7563          | 100.0 |
| 3.7321        | 84.4444  | 380  | 3.7519          | 100.0 |
| 3.7326        | 86.6667  | 390  | 3.7516          | 100.0 |
| 3.7373        | 88.8889  | 400  | 3.7502          | 100.0 |
| 3.7348        | 91.1111  | 410  | 3.7494          | 100.0 |
| 3.7312        | 93.3333  | 420  | 3.7489          | 100.0 |
| 3.732         | 95.5556  | 430  | 3.7477          | 100.0 |
| 3.7431        | 97.7778  | 440  | 3.7489          | 100.0 |
| 3.737         | 100.0    | 450  | 3.7474          | 100.0 |
| 3.7364        | 102.2222 | 460  | 3.7479          | 100.0 |
| 3.7265        | 104.4444 | 470  | 3.7478          | 100.0 |
| 3.7339        | 106.6667 | 480  | 3.7471          | 100.0 |
| 3.731         | 108.8889 | 490  | 3.7470          | 100.0 |
| 3.736         | 111.1111 | 500  | 3.7471          | 100.0 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1