readme: ---
  language: fon
  datasets:
  - fon_dataset 
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  - hf-asr-leaderboard
  license: apache-2.0
  model-index:
  - name: Fon XLSR Wav2Vec2 Large 53
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: fon
        type: fon_dataset
        args: fon
      metrics:
         - name: Test WER
           type: wer
           value: 14.97
  ---
  # Wav2Vec2-Large-XLSR-53-Fon
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).
  
  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 json
  import random
  import torch
  import torchaudio
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  
  #Load test_dataset from saved files in folder
  from datasets import load_dataset, load_metric
  
  #for test
  for root, dirs, files in os.walk(test/):
    test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
  
  #Remove unnecessary chars
  chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
  def remove_special_characters(batch):
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
      return batch
  
  test_dataset = test_dataset.map(remove_special_characters)
  
  processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") 
  model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") 
  
  #No need for resampling because audio dataset already at 16kHz
  #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):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"]=speech_array.squeeze().numpy()
    return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
    tlogits = 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["sentence"][:2])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on our unique Fon test data. 
  
  ```python
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  
  for root, dirs, files in os.walk(test/):
    test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
  
  chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
      batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
      return batch
  
  test_dataset = test_dataset.map(remove_special_characters)
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
  model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
  model.to("cuda")
  
  # 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"] = speech_array[0].numpy()
      batch["sampling_rate"] = sampling_rate
      batch["target_text"] = batch["sentence"]
      return batch
  
  test_dataset = test_dataset.map(speech_file_to_array_fn)
  
  #Evaluation on test dataset
  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_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch
  
  result = test_dataset.map(evaluate, batched=True, batch_size=8)
  
  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
  
  ```
  
  **Test Result**: 14.97 % 
  
  ## Training
  
  The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples).
  
  The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing)
  
  
  # Collaborators on this project
  
    - Chris C. Emezue  ([Twitter](https://twitter.com/ChrisEmezue))|(chris.emezue@gmail.com)
    - Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|(femipancrace.dossou@gmail.