---
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- hf-asr-leaderboard
datasets:
- librispeech_asr
model-index:
- name: hubert-base-libri-clean-ft100h-v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech
      type: librispeech_asr
      config: clean
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: '8.1938'
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: '16.9783'
language:
- en
---

<!-- 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. -->

# hubert-base-libri-clean-ft100h-v3

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1120
- Wer: 0.1332

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.201         | 0.14  | 250   | 3.9799          | 1.0    |
| 2.8893        | 0.28  | 500   | 3.4838          | 1.0    |
| 2.8603        | 0.42  | 750   | 3.3505          | 1.0    |
| 2.7216        | 0.56  | 1000  | 2.1194          | 0.9989 |
| 1.3372        | 0.7   | 1250  | 0.8124          | 0.6574 |
| 0.8238        | 0.84  | 1500  | 0.5712          | 0.5257 |
| 0.6449        | 0.98  | 1750  | 0.4442          | 0.4428 |
| 0.5241        | 1.12  | 2000  | 0.3442          | 0.3672 |
| 0.4458        | 1.26  | 2250  | 0.2850          | 0.3186 |
| 0.3959        | 1.4   | 2500  | 0.2507          | 0.2882 |
| 0.3641        | 1.54  | 2750  | 0.2257          | 0.2637 |
| 0.3307        | 1.68  | 3000  | 0.2044          | 0.2434 |
| 0.2996        | 1.82  | 3250  | 0.1969          | 0.2313 |
| 0.2794        | 1.96  | 3500  | 0.1823          | 0.2193 |
| 0.2596        | 2.1   | 3750  | 0.1717          | 0.2096 |
| 0.2563        | 2.24  | 4000  | 0.1653          | 0.2000 |
| 0.2532        | 2.38  | 4250  | 0.1615          | 0.1971 |
| 0.2376        | 2.52  | 4500  | 0.1559          | 0.1916 |
| 0.2341        | 2.66  | 4750  | 0.1494          | 0.1855 |
| 0.2102        | 2.8   | 5000  | 0.1464          | 0.1781 |
| 0.2222        | 2.94  | 5250  | 0.1399          | 0.1732 |
| 0.2081        | 3.08  | 5500  | 0.1450          | 0.1707 |
| 0.1963        | 3.22  | 5750  | 0.1337          | 0.1655 |
| 0.2107        | 3.36  | 6000  | 0.1344          | 0.1633 |
| 0.1866        | 3.5   | 6250  | 0.1339          | 0.1611 |
| 0.186         | 3.64  | 6500  | 0.1311          | 0.1563 |
| 0.1703        | 3.78  | 6750  | 0.1307          | 0.1537 |
| 0.1819        | 3.92  | 7000  | 0.1277          | 0.1555 |
| 0.176         | 4.06  | 7250  | 0.1280          | 0.1515 |
| 0.1837        | 4.2   | 7500  | 0.1249          | 0.1504 |
| 0.1678        | 4.34  | 7750  | 0.1236          | 0.1480 |
| 0.1624        | 4.48  | 8000  | 0.1194          | 0.1456 |
| 0.1631        | 4.62  | 8250  | 0.1215          | 0.1462 |
| 0.1736        | 4.76  | 8500  | 0.1192          | 0.1451 |
| 0.1752        | 4.9   | 8750  | 0.1206          | 0.1432 |
| 0.1578        | 5.04  | 9000  | 0.1151          | 0.1415 |
| 0.1537        | 5.18  | 9250  | 0.1185          | 0.1402 |
| 0.1771        | 5.33  | 9500  | 0.1165          | 0.1414 |
| 0.1481        | 5.47  | 9750  | 0.1152          | 0.1413 |
| 0.1509        | 5.61  | 10000 | 0.1152          | 0.1382 |
| 0.146         | 5.75  | 10250 | 0.1133          | 0.1385 |
| 0.1464        | 5.89  | 10500 | 0.1139          | 0.1371 |
| 0.1442        | 6.03  | 10750 | 0.1162          | 0.1365 |
| 0.128         | 6.17  | 11000 | 0.1147          | 0.1371 |
| 0.1381        | 6.31  | 11250 | 0.1148          | 0.1378 |
| 0.1343        | 6.45  | 11500 | 0.1113          | 0.1363 |
| 0.1325        | 6.59  | 11750 | 0.1134          | 0.1355 |
| 0.1442        | 6.73  | 12000 | 0.1142          | 0.1358 |
| 0.1286        | 6.87  | 12250 | 0.1133          | 0.1352 |
| 0.1349        | 7.01  | 12500 | 0.1129          | 0.1344 |
| 0.1338        | 7.15  | 12750 | 0.1131          | 0.1328 |
| 0.1403        | 7.29  | 13000 | 0.1124          | 0.1338 |
| 0.1314        | 7.43  | 13250 | 0.1141          | 0.1335 |
| 0.1283        | 7.57  | 13500 | 0.1124          | 0.1332 |
| 0.1347        | 7.71  | 13750 | 0.1107          | 0.1332 |
| 0.1195        | 7.85  | 14000 | 0.1119          | 0.1332 |
| 0.1326        | 7.99  | 14250 | 0.1120          | 0.1332 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1