readme: ---
  language:
  - sk
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Slovak
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: sk
      metrics:
      - name: Test WER
        type: wer
        value: 18.609
      - name: Test CER
        type: cer
        value: 5.488
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: sk
      metrics:
      - name: Test WER
        type: wer
        value: 40.548
      - name: Test CER
        type: cer
        value: 17.733
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: sk
      metrics:
      - name: Test WER
        type: wer
        value: 44.1
  ---
  
  <!-- 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. -->
  
  # XLS-R-300M - Slovak
  
  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 - SK dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3067
  - Wer: 0.2678
  
  ## 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: 7.5e-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: 1500
  - num_epochs: 60.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 5.175         | 2.41  | 400  | 4.6909          | 1.0    |
  | 3.3785        | 4.82  | 800  | 3.3080          | 1.0    |
  | 2.6964        | 7.23  | 1200 | 2.0651          | 1.1055 |
  | 1.3008        | 9.64  | 1600 | 0.5845          | 0.6207 |
  | 1.1185        | 12.05 | 2000 | 0.4195          | 0.4193 |
  | 1.0252        | 14.46 | 2400 | 0.3824          | 0.3570 |
  | 0.935         | 16.87 | 2800 | 0.3693          | 0.3462 |
  | 0.8818        | 19.28 | 3200 | 0.3587          | 0.3318 |
  | 0.8534        | 21.69 | 3600 | 0.3420          | 0.3180 |
  | 0.8137        | 24.1  | 4000 | 0.3426          | 0.3130 |
  | 0.7968        | 26.51 | 4400 | 0.3349          | 0.3102 |
  | 0.7558        | 28.92 | 4800 | 0.3216          | 0.3019 |
  | 0.7313        | 31.33 | 5200 | 0.3451          | 0.3060 |
  | 0.7358        | 33.73 | 5600 | 0.3272          | 0.2967 |
  | 0.718         | 36.14 | 6000 | 0.3315          | 0.2882 |
  | 0.6991        | 38.55 | 6400 | 0.3299          | 0.2830 |
  | 0.6529        | 40.96 | 6800 | 0.3140          | 0.2836 |
  | 0.6225        | 43.37 | 7200 | 0.3128          | 0.2751 |
  | 0.633         | 45.78 | 7600 | 0.3211          | 0.2774 |
  | 0.5876        | 48.19 | 8000 | 0.3162          | 0.2764 |
  | 0.588         | 50.6  | 8400 | 0.3082          | 0.2722 |
  | 0.5915        | 53.01 | 8800 | 0.3120          | 0.2681 |
  | 0.5798        | 55.42 | 9200 | 0.3133          | 0.2709 |
  | 0.5736        | 57.83 | 9600 | 0.3086          | 0.2676 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.0
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sk --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sk --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```
  
  ### Inference With LM
  
  ```python
  import torch
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torchaudio.functional as F
  model_id = "anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test", streaming=True, use_auth_token=True))
  sample = next(sample_iter)
  resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
  model = AutoModelForCTC.from_pretrained(model