readme: ---
  language:
  - nl
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - mozilla-foundation/common_voice_7_0
  - nl
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R-300M - Dutch
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8 NL
        type: mozilla-foundation/common_voice_8_0
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 46.94
      - name: Test CER
        type: cer
        value: 21.65
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: ???
      - name: Test CER
        type: cer
        value: ???
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 42.56
  ---
  
  # xlsr300m_cv_8.0_nl
  
  
  #### Evaluation Commands
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset mozilla-foundation/common_voice_8_0 --config nl --split test
  ```
  
  2. To evaluate on `speech-recognition-community-v2/dev_data`
  
  ```bash
  python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```
  
  ### Inference
  
  ```python
  import torch
  from datasets import load_dataset
  from transformers import AutoModelForCTC, AutoProcessor
  import torchaudio.functional as F
  
  model_id = "Iskaj/xlsr300m_cv_8.0_nl"
  
  sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "nl", 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)
  
  inputs = processor(resampled_audio, sampling_rate=16_000, return_tensors="pt")
  with torch.no_grad():
    logits = model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
  
  transcription[0].lower()
  #'het kontine schip lag aangemeert in de aven'
  ```
  