---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-fries-NL_common_voice_13b_other-train-validation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: fy-NL
      split: test
      args: fy-NL
    metrics:
    - name: Wer
      type: wer
      value: 0.19910413556026252
---

<!-- 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-mms-1b-fries-NL_common_voice_13b_other-train-validation

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1617
- Wer: 0.1991

## 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: 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: 200
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log        | 0.01  | 200   | 0.2768          | 0.2969 |
| 2.0359        | 0.03  | 400   | 0.2503          | 0.2754 |
| 2.0359        | 0.04  | 600   | 0.2378          | 0.2671 |
| 0.4005        | 0.06  | 800   | 0.2259          | 0.2572 |
| 0.4005        | 0.07  | 1000  | 0.2387          | 0.2733 |
| 0.4051        | 0.09  | 1200  | 0.2382          | 0.2645 |
| 0.4051        | 0.1   | 1400  | 0.2231          | 0.2513 |
| 0.3982        | 0.12  | 1600  | 0.2146          | 0.2470 |
| 0.3982        | 0.13  | 1800  | 0.2167          | 0.2503 |
| 0.3646        | 0.15  | 2000  | 0.2177          | 0.2499 |
| 0.3646        | 0.16  | 2200  | 0.2228          | 0.2591 |
| 0.3538        | 0.18  | 2400  | 0.2117          | 0.2445 |
| 0.3538        | 0.19  | 2600  | 0.2097          | 0.2411 |
| 0.3687        | 0.21  | 2800  | 0.2073          | 0.2425 |
| 0.3687        | 0.22  | 3000  | 0.2138          | 0.2454 |
| 0.3586        | 0.23  | 3200  | 0.2040          | 0.2375 |
| 0.3586        | 0.25  | 3400  | 0.2059          | 0.2372 |
| 0.3453        | 0.26  | 3600  | 0.2060          | 0.2397 |
| 0.3453        | 0.28  | 3800  | 0.2015          | 0.2382 |
| 0.3741        | 0.29  | 4000  | 0.2088          | 0.2457 |
| 0.3741        | 0.31  | 4200  | 0.1948          | 0.2298 |
| 0.3454        | 0.32  | 4400  | 0.2014          | 0.2342 |
| 0.3454        | 0.34  | 4600  | 0.2031          | 0.2392 |
| 0.351         | 0.35  | 4800  | 0.2018          | 0.2401 |
| 0.351         | 0.37  | 5000  | 0.1962          | 0.2321 |
| 0.3502        | 0.38  | 5200  | 0.1945          | 0.2323 |
| 0.3502        | 0.4   | 5400  | 0.1956          | 0.2323 |
| 0.3423        | 0.41  | 5600  | 0.1913          | 0.2266 |
| 0.3423        | 0.43  | 5800  | 0.1921          | 0.2277 |
| 0.3414        | 0.44  | 6000  | 0.1910          | 0.2262 |
| 0.3414        | 0.45  | 6200  | 0.1891          | 0.2223 |
| 0.3517        | 0.47  | 6400  | 0.1862          | 0.2230 |
| 0.3517        | 0.48  | 6600  | 0.1879          | 0.2206 |
| 0.3273        | 0.5   | 6800  | 0.1849          | 0.2176 |
| 0.3273        | 0.51  | 7000  | 0.1845          | 0.2163 |
| 0.321         | 0.53  | 7200  | 0.1831          | 0.2163 |
| 0.321         | 0.54  | 7400  | 0.1825          | 0.2163 |
| 0.321         | 0.56  | 7600  | 0.1797          | 0.2155 |
| 0.321         | 0.57  | 7800  | 0.1787          | 0.2144 |
| 0.3382        | 0.59  | 8000  | 0.1804          | 0.2132 |
| 0.3382        | 0.6   | 8200  | 0.1789          | 0.2158 |
| 0.3285        | 0.62  | 8400  | 0.1778          | 0.2130 |
| 0.3285        | 0.63  | 8600  | 0.1753          | 0.2094 |
| 0.3103        | 0.65  | 8800  | 0.1786          | 0.2147 |
| 0.3103        | 0.66  | 9000  | 0.1799          | 0.2157 |
| 0.3184        | 0.67  | 9200  | 0.1747          | 0.2098 |
| 0.3184        | 0.69  | 9400  | 0.1740          | 0.2068 |
| 0.3037        | 0.7   | 9600  | 0.1728          | 0.2090 |
| 0.3037        | 0.72  | 9800  | 0.1732          | 0.2084 |
| 0.3145        | 0.73  | 10000 | 0.1725          | 0.2085 |
| 0.3145        | 0.75  | 10200 | 0.1691          | 0.2052 |
| 0.3063        | 0.76  | 10400 | 0.1699          | 0.2062 |
| 0.3063        | 0.78  | 10600 | 0.1694          | 0.2072 |
| 0.3104        | 0.79  | 10800 | 0.1692          | 0.2063 |
| 0.3104        | 0.81  | 11000 | 0.1674          | 0.2044 |
| 0.2991        | 0.82  | 11200 | 0.1677          | 0.2040 |
| 0.2991        | 0.84  | 11400 | 0.1664          | 0.2025 |
| 0.3146        | 0.85  | 11600 | 0.1666          | 0.2011 |
| 0.3146        | 0.87  | 11800 | 0.1666          | 0.2020 |
| 0.3162        | 0.88  | 12000 | 0.1647          | 0.2009 |
| 0.3162        | 0.89  | 12200 | 0.1642          | 0.2014 |
| 0.3156        | 0.91  | 12400 | 0.1634          | 0.1997 |
| 0.3156        | 0.92  | 12600 | 0.1630          | 0.1994 |
| 0.3075        | 0.94  | 12800 | 0.1625          | 0.2009 |
| 0.3075        | 0.95  | 13000 | 0.1621          | 0.1994 |
| 0.3121        | 0.97  | 13200 | 0.1619          | 0.1989 |
| 0.3121        | 0.98  | 13400 | 0.1619          | 0.1989 |
| 0.2909        | 1.0   | 13600 | 0.1617          | 0.1991 |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3