---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-demo
  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-xlsr-demo

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on ASCEND (a Mandarin-English codeswitching dataset).
It achieves the following results on the evaluation set:
- Loss: 1.6751
- Wer: 0.7846

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 10.5108       | 0.5701  | 500   | 15.4314         | 1.0    |
| 5.61          | 1.1403  | 1000  | 11.3143         | 1.0    |
| 5.546         | 1.7104  | 1500  | 9.6388          | 1.0    |
| 5.1105        | 2.2805  | 2000  | 5.9376          | 1.0    |
| 4.9007        | 2.8506  | 2500  | 5.7582          | 1.0    |
| 4.5876        | 3.4208  | 3000  | 6.7190          | 1.0    |
| 4.3145        | 3.9909  | 3500  | 4.6527          | 1.0    |
| 3.5332        | 4.5610  | 4000  | 3.3622          | 1.0011 |
| 2.9071        | 5.1311  | 4500  | 2.7400          | 1.0176 |
| 2.5077        | 5.7013  | 5000  | 2.8219          | 0.9460 |
| 2.4145        | 6.2714  | 5500  | 2.4336          | 0.9570 |
| 2.2432        | 6.8415  | 6000  | 2.1500          | 0.9169 |
| 2.1376        | 7.4116  | 6500  | 2.1445          | 0.8930 |
| 2.0841        | 7.9818  | 7000  | 2.1312          | 0.8864 |
| 1.8288        | 8.5519  | 7500  | 1.9040          | 0.8728 |
| 1.6863        | 9.1220  | 8000  | 1.8913          | 0.8434 |
| 1.7453        | 9.6921  | 8500  | 2.1214          | 0.8507 |
| 1.6896        | 10.2623 | 9000  | 1.8329          | 0.8548 |
| 1.6063        | 10.8324 | 9500  | 1.8248          | 0.8386 |
| 1.3838        | 11.4025 | 10000 | 1.7811          | 0.8379 |
| 1.5255        | 11.9726 | 10500 | 2.3148          | 0.8390 |
| 1.4269        | 12.5428 | 11000 | 2.1530          | 0.8184 |
| 1.3452        | 13.1129 | 11500 | 1.7208          | 0.8221 |
| 1.35          | 13.6830 | 12000 | 1.8269          | 0.8290 |
| 1.3656        | 14.2531 | 12500 | 1.6902          | 0.8313 |
| 1.2036        | 14.8233 | 13000 | 2.0816          | 0.8206 |
| 1.2144        | 15.3934 | 13500 | 1.7623          | 0.8103 |
| 1.1648        | 15.9635 | 14000 | 1.7197          | 0.8154 |
| 1.1341        | 16.5336 | 14500 | 1.7560          | 0.8110 |
| 1.0716        | 17.1038 | 15000 | 1.7750          | 0.8099 |
| 1.1187        | 17.6739 | 15500 | 1.7946          | 0.8180 |
| 1.0633        | 18.2440 | 16000 | 1.7877          | 0.7996 |
| 1.0069        | 18.8141 | 16500 | 1.8482          | 0.8243 |
| 0.9703        | 19.3843 | 17000 | 1.6073          | 0.7960 |
| 1.0122        | 19.9544 | 17500 | 1.7191          | 0.8099 |
| 0.9993        | 20.5245 | 18000 | 1.7208          | 0.7956 |
| 0.9861        | 21.0946 | 18500 | 1.6628          | 0.7949 |
| 0.9621        | 21.6648 | 19000 | 1.7685          | 0.7930 |
| 0.8936        | 22.2349 | 19500 | 1.7232          | 0.8026 |
| 0.888         | 22.8050 | 20000 | 1.7204          | 0.8015 |
| 0.9027        | 23.3751 | 20500 | 1.7844          | 0.7923 |
| 0.8808        | 23.9453 | 21000 | 1.7159          | 0.7945 |
| 0.8652        | 24.5154 | 21500 | 1.6887          | 0.7934 |
| 0.7545        | 25.0855 | 22000 | 1.6633          | 0.7937 |
| 0.7664        | 25.6556 | 22500 | 1.6745          | 0.7919 |
| 0.7518        | 26.2258 | 23000 | 1.7122          | 0.7930 |
| 0.8475        | 26.7959 | 23500 | 1.6901          | 0.7868 |
| 0.7527        | 27.3660 | 24000 | 1.6937          | 0.7835 |
| 0.7531        | 27.9361 | 24500 | 1.6835          | 0.7820 |
| 0.7686        | 28.5063 | 25000 | 1.6734          | 0.7901 |
| 0.7525        | 29.0764 | 25500 | 1.6766          | 0.7868 |
| 0.7765        | 29.6465 | 26000 | 1.6751          | 0.7846 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1