---
language: 
- rm-sursilv
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-rm-sursilv-d11
  results:
  - task: 
      type: automatic-speech-recognition
      name: Speech Recognition
    dataset:
      type: mozilla-foundation/common_voice_8_0
      name: Common Voice 8
      args: rm-sursilv
    metrics:
      - type: wer  
        value: []  
        name: Test WER 
      - name: Test CER
        type: cer
        value: []
---

<!-- 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. -->

# 

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-SURSILV dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2511
- Wer: 0.2415 

## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 125.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.3958        | 17.44  | 1500  | 0.6808          | 0.6521 |
| 0.9663        | 34.88  | 3000  | 0.3023          | 0.3718 |
| 0.7963        | 52.33  | 4500  | 0.2588          | 0.3046 |
| 0.6893        | 69.77  | 6000  | 0.2436          | 0.2718 |
| 0.6148        | 87.21  | 7500  | 0.2521          | 0.2572 |
| 0.5556        | 104.65 | 9000  | 0.2490          | 0.2442 |
| 0.5258        | 122.09 | 10500 | 0.2515          | 0.2442 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0