readme: ---
  language:
  - lv
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Latvian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: lv
      metrics:
      - name: Test WER
        type: wer
        value: 9.633
      - name: Test CER
        type: cer
        value: 2.614
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: lv
      metrics:
      - name: Test WER
        type: wer
        value: 36.11
      - name: Test CER
        type: cer
        value: 14.244
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: lv
      metrics:
      - name: Test WER
        type: wer
        value: 44.12
  ---
  
  <!-- 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 - Latvian
  
  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 - LV dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1660
  - Wer: 0.1705
  
  ## 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: 1000
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.489         | 2.56  | 400  | 3.3590          | 1.0    |
  | 2.9903        | 5.13  | 800  | 2.9704          | 1.0001 |
  | 1.6712        | 7.69  | 1200 | 0.6179          | 0.6566 |
  | 1.2635        | 10.26 | 1600 | 0.3176          | 0.4531 |
  | 1.0819        | 12.82 | 2000 | 0.2517          | 0.3508 |
  | 1.0136        | 15.38 | 2400 | 0.2257          | 0.3124 |
  | 0.9625        | 17.95 | 2800 | 0.1975          | 0.2311 |
  | 0.901         | 20.51 | 3200 | 0.1986          | 0.2097 |
  | 0.8842        | 23.08 | 3600 | 0.1904          | 0.2039 |
  | 0.8542        | 25.64 | 4000 | 0.1847          | 0.1981 |
  | 0.8244        | 28.21 | 4400 | 0.1805          | 0.1847 |
  | 0.7689        | 30.77 | 4800 | 0.1736          | 0.1832 |
  | 0.7825        | 33.33 | 5200 | 0.1698          | 0.1821 |
  | 0.7817        | 35.9  | 5600 | 0.1758          | 0.1803 |
  | 0.7488        | 38.46 | 6000 | 0.1663          | 0.1760 |
  | 0.7171        | 41.03 | 6400 | 0.1636          | 0.1721 |
  | 0.7222        | 43.59 | 6800 | 0.1663          | 0.1729 |
  | 0.7156        | 46.15 | 7200 | 0.1633          | 0.1715 |
  | 0.7121        | 48.72 | 7600 | 0.1666          | 0.1718 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.2.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-lv-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config lv --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config lv --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-lv-cv8-with-lm"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "lv", 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_id)
  processor = AutoProcessor.from_pretrained(model_id)
  input_values = processor(resampled_audio, return_tensors="pt").input_values
  with torch.no_grad():
      logits = model(input_values).logits
  transcription = processor.batch_decode(logits.numpy()).text
  # => "domāju ka viņam viss labi"
  ```
  
  ### Ev