readme: ---
  language:
  - nl
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - nl
  - robust-speech-event
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: XLS-R Wav2Vec2 Dutch by Jonatas Grosman
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8
        type: mozilla-foundation/common_voice_8_0
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 10.38
      - name: Test CER
        type: cer
        value: 3.04
      - name: Test WER (+LM)
        type: wer
        value: 6.83
      - name: Test CER (+LM)
        type: cer
        value: 2.31
    - 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: Dev WER
        type: wer
        value: 31.12
      - name: Dev CER
        type: cer
        value: 15.92
      - name: Dev WER (+LM)
        type: wer
        value: 23.95
      - name: Dev CER (+LM)
        type: cer
        value: 14.18
    - 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: 20.41
  ---
  
  # Fine-tuned XLS-R 1B model for speech recognition in Dutch
  
  Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on Dutch using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli).
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
  
  ## Usage
  
  Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
  
  ```python
  from huggingsound import SpeechRecognitionModel
  
  model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-dutch")
  audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
  
  transcriptions = model.transcribe(audio_paths)
  ```
  
  Writing your own inference script:
  
  ```python
  import torch
  import librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  LANG_ID = "nl"
  MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-dutch"
  SAMPLES = 10
  
  test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
  
  processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
  model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
      batch["speech"] = speech_array
      batch["sentence"] = batch["sentence"].upper()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
      logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  predicted_sentences = processor.batch_decode(predicted_ids)
  ```
  
  ## Evaluation Commands
  
  1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
  
  ```bash
  python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-dutch --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 jonatasgrosman/wav2vec2-xls-r-1b-dutch --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
  ```
  
  ## Citation
  If you want to cite this model you can use this:
  
  ```bibtex
  @misc{grosman2021xlsr-1b-dutch,
    title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {D}utch},
    author={Grosman, Jonatas},
    howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-dutch}},
    year={2022}
  }
  ```