readme: ---
  language: 
  - id
  - jv
  - sun
  datasets:
  - mozilla-foundation/common_voice_7_0
  - openslr
  - magic_data
  - titml
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - id
  - jv
  - robust-speech-event
  - speech
  - su
  license: apache-2.0
  model-index:
  - name: Wav2Vec2 Indonesian Javanese and Sundanese by Indonesian NLP
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 6.1
        type: common_voice
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 4.056
      - name: Test CER
        type: cer
        value: 1.472
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 7
        type: mozilla-foundation/common_voice_7_0
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 4.492
      - name: Test CER
        type: cer
        value: 1.577
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 48.94
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: id
      metrics:
      - name: Test WER
        type: wer
        value: 68.95
  ---
  
  # Multilingual Speech Recognition for Indonesian Languages
  
  This is the model built for the project 
  [Multilingual Speech Recognition for Indonesian Languages](https://github.com/indonesian-nlp/multilingual-asr).
  It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
  model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice), 
  [High-quality TTS data for Javanese - SLR41](https://huggingface.co/datasets/openslr), and
  [High-quality TTS data for Sundanese - SLR44](https://huggingface.co/datasets/openslr) datasets.
  
  We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/multilingual-asr) to test the model.
  
  When using this model, make sure that your speech input is sampled at 16kHz.
  
  ## Usage
  The model can be used directly (without a language model) as follows:
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  test_dataset = load_dataset("common_voice", "id", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
  model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the aduio files as arrays
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset[:2]["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)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  print("Reference:", test_dataset[:2]["sentence"])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on the Indonesian test data of Common Voice.
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  test_dataset = load_dataset("common_voice", "id", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese")
  model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-indonesian-javanese-sundanese") 
  model.to("cuda")
  
  chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def speech_file_to_array_fn(batch):
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  
  # Preprocessing the datasets.
  # We need to read the audio files as arrays
  def evaluate(batch):
      inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
  
      with torch.no_grad():
          logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
  
      pred_i