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---

license: apache-2.0
language: fi
metrics:
- wer
- cer
tags:
- generated_from_trainer
- automatic-speech-recognition
- fi
- finnish
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xlsr-300m-finnish-lm
  results:
  - task: 
      name: Automatic Speech Recognition 
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 7
      type: mozilla-foundation/common_voice_7_0
      args: fi
    metrics:
       - name: Test WER
         type: wer
         value: 8.24
       - name: Test CER
         type: cer
         value: 1.98

---


<!-- 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-xlsr-300m-finnish-lm

This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data.
It achieves the following results on the Common Voice 7 test set together with language model (Finnish KenLM):
- Wer: 8.24
- Cer: 1.98

## Model description

TODO

## Intended uses & limitations

TODO

## Training and evaluation data

This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:

| Dataset                                                                                                                       | Hours    | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:|
| [Common Voice 7.0 Finnish train+evaluation+other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h   | 3.52 %           |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4)                             | 0.24 h   | 0.09 %           |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli)                                                            | 21.97 h  | 7.97 %           |
| [CSS10 Finnish](https://github.com/kyubyong/css10)                                                                            | 10.32 h  | 3.74 %           |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903)                                                 | 228.00 h | 82.73 %          |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502)                                                            | 5.37 h   | 1.95 %           |


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005

- train_batch_size: 32

- eval_batch_size: 32

- seed: 42

- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08

- lr_scheduler_type: linear

- lr_scheduler_warmup_steps: 500
- num_epochs: 10

- mixed_precision_training: Native AMP



### Training results



| Training Loss | Epoch | Step  | Validation Loss | Wer    |

|:-------------:|:-----:|:-----:|:---------------:|:------:|

| 0.973         | 0.17  | 500   | 0.5750          | 0.6844 |

| 0.713         | 0.34  | 1000  | 0.3356          | 0.4518 |

| 0.6563        | 0.5   | 1500  | 0.3007          | 0.4039 |

| 0.642         | 0.67  | 2000  | 0.2619          | 0.3674 |

| 0.6203        | 0.84  | 2500  | 0.2488          | 0.3558 |

| 0.6016        | 1.01  | 3000  | 0.2795          | 0.3835 |

| 0.5423        | 1.17  | 3500  | 0.2652          | 0.3310 |

| 0.5639        | 1.34  | 4000  | 0.2479          | 0.3462 |

| 0.586         | 1.51  | 4500  | 0.2409          | 0.3295 |

| 0.5169        | 1.68  | 5000  | 0.2728          | 0.3352 |

| 0.5176        | 1.84  | 5500  | 0.2254          | 0.3149 |

| 0.4983        | 2.01  | 6000  | 0.2169          | 0.3009 |

| 0.4982        | 2.18  | 6500  | 0.2215          | 0.3079 |

| 0.4898        | 2.35  | 7000  | 0.2174          | 0.3023 |

| 0.4922        | 2.51  | 7500  | 0.2217          | 0.3081 |

| 0.5025        | 2.68  | 8000  | 0.2002          | 0.2710 |

| 0.4745        | 2.85  | 8500  | 0.1935          | 0.2783 |

| 0.4377        | 3.02  | 9000  | 0.1859          | 0.2742 |

| 0.4511        | 3.18  | 9500  | 0.2038          | 0.2786 |

| 0.4411        | 3.35  | 10000 | 0.1863          | 0.2651 |

| 0.4501        | 3.52  | 10500 | 0.1948          | 0.2605 |

| 0.4557        | 3.69  | 11000 | 0.1872          | 0.2695 |

| 0.4493        | 3.85  | 11500 | 0.1888          | 0.2632 |

| 0.4047        | 4.02  | 12000 | 0.1818          | 0.2559 |

| 0.4319        | 4.19  | 12500 | 0.1896          | 0.2648 |

| 0.4162        | 4.36  | 13000 | 0.1953          | 0.2595 |

| 0.4046        | 4.52  | 13500 | 0.1864          | 0.2606 |

| 0.4195        | 4.69  | 14000 | 0.1843          | 0.2467 |

| 0.4146        | 4.86  | 14500 | 0.1686          | 0.2450 |

| 0.378         | 5.03  | 15000 | 0.1731          | 0.2401 |

| 0.3792        | 5.19  | 15500 | 0.1676          | 0.2325 |

| 0.3855        | 5.36  | 16000 | 0.1740          | 0.2326 |

| 0.4029        | 5.53  | 16500 | 0.1674          | 0.2345 |

| 0.386         | 5.7   | 17000 | 0.1735          | 0.2280 |

| 0.3811        | 5.86  | 17500 | 0.1692          | 0.2258 |

| 0.3607        | 6.03  | 18000 | 0.1797          | 0.2279 |

| 0.3604        | 6.2   | 18500 | 0.1651          | 0.2206 |

| 0.3362        | 6.37  | 19000 | 0.1627          | 0.2199 |

| 0.3611        | 6.53  | 19500 | 0.1652          | 0.2172 |

| 0.3671        | 6.7   | 20000 | 0.1564          | 0.2140 |

| 0.3769        | 6.87  | 20500 | 0.1525          | 0.2101 |

| 0.3539        | 7.04  | 21000 | 0.1639          | 0.2096 |

| 0.3225        | 7.21  | 21500 | 0.1611          | 0.2087 |

| 0.3323        | 7.37  | 22000 | 0.1633          | 0.2008 |

| 0.3327        | 7.54  | 22500 | 0.1692          | 0.1975 |

| 0.3456        | 7.71  | 23000 | 0.1555          | 0.1991 |

| 0.3058        | 7.88  | 23500 | 0.1590          | 0.1959 |

| 0.3034        | 8.04  | 24000 | 0.1531          | 0.1973 |

| 0.2925        | 8.21  | 24500 | 0.1583          | 0.1978 |

| 0.2967        | 8.38  | 25000 | 0.1546          | 0.1906 |

| 0.2974        | 8.55  | 25500 | 0.1540          | 0.1869 |

| 0.3131        | 8.71  | 26000 | 0.1534          | 0.1850 |

| 0.3306        | 8.88  | 26500 | 0.1482          | 0.1844 |

| 0.2842        | 9.05  | 27000 | 0.1490          | 0.1854 |

| 0.2879        | 9.22  | 27500 | 0.1463          | 0.1799 |

| 0.27          | 9.38  | 28000 | 0.1454          | 0.1798 |

| 0.2874        | 9.55  | 28500 | 0.1504          | 0.1787 |

| 0.2757        | 9.72  | 29000 | 0.1512          | 0.1784 |

| 0.3017        | 9.89  | 29500 | 0.1484          | 0.1800 |





### Framework versions



- Transformers 4.17.0.dev0

- Pytorch 1.10.2+cu102

- Datasets 1.18.3

- Tokenizers 0.11.0