readme: ---
  language:
  - mt
  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
  metrics:
  - wer
  model-index:
  - name: XLS-R-300M - Maltese
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: mozilla-foundation/common_voice_8_0
        name: Common Voice 8
        args: mt
      metrics:
      - type: wer
        value: 15.967
        name: Test WER
      - name: Test CER
        type: cer
        value: 3.657
  ---
  
  <!-- 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 - Maltese
  
  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 - MT dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1895
  - Wer: 0.1984
  
  ## 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: 60.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.4219        | 3.6   | 400  | 3.3127          | 1.0    |
  | 3.0399        | 7.21  | 800  | 3.0330          | 1.0    |
  | 1.5756        | 10.81 | 1200 | 0.6108          | 0.5724 |
  | 1.0995        | 14.41 | 1600 | 0.3091          | 0.3154 |
  | 0.9639        | 18.02 | 2000 | 0.2596          | 0.2841 |
  | 0.9032        | 21.62 | 2400 | 0.2270          | 0.2514 |
  | 0.8145        | 25.23 | 2800 | 0.2172          | 0.2483 |
  | 0.7845        | 28.83 | 3200 | 0.2084          | 0.2333 |
  | 0.7694        | 32.43 | 3600 | 0.1974          | 0.2234 |
  | 0.7333        | 36.04 | 4000 | 0.2020          | 0.2185 |
  | 0.693         | 39.64 | 4400 | 0.1947          | 0.2148 |
  | 0.6802        | 43.24 | 4800 | 0.1960          | 0.2102 |
  | 0.667         | 46.85 | 5200 | 0.1904          | 0.2072 |
  | 0.6486        | 50.45 | 5600 | 0.1881          | 0.2009 |
  | 0.6339        | 54.05 | 6000 | 0.1877          | 0.1989 |
  | 0.6254        | 57.66 | 6400 | 0.1893          | 0.2003 |
  
  
  ### 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-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --split test
  ```
  
  
  ### 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-mt-cv8-with-lm"
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", 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
  # => "għadu jilagħbu ċirku tant bilfondi"
  ```
  
  ### Eval results on Common Voice 8 "test" (WER):
  
  | Without LM | With LM (run `./eval.py`) |
  |---|---|
  | 19.853 | 15.967 |