readme: ---
  language: rw
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Large Kinyarwanda no punctuation
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice rw
        type: common_voice
        args: rw
      metrics:
         - name: Test WER
           type: wer
           value: 40.59
  ---
  
  # Wav2Vec2-Large-XLSR-53-rw
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kinyarwanda using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset, using about 20% of the training data (limited to utterances without downvotes and shorter than 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the [lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied](https://huggingface.co/lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied) model, which predicts the apostrophes that mark contractions of pronouns with vowel-initial words, this model does not predict any punctuation.
  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
  
  # WARNING! This will download and extract to use about 80GB on disk.
  test_dataset = load_dataset("common_voice", "rw", split="test[:2%]") 
  
  processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda") 
  model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")
  
  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"] = 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["sentence"][:2])
  ```
  
  Result:
  ```
  Prediction: ['yaherukaga gukora igitaramo y iki mu jyiwa na mul mumbiliki', 'ini rero ntibizashoboka ka nibo nkunrabibzi']
  Reference: ['Yaherukaga gukora igitaramo nk’iki mu Mujyi wa Namur mu Bubiligi.', 'Ibi rero, ntibizashoboka, kandi nawe arabizi.']
  ```
  
  ## Evaluation
  
  The model can be evaluated as follows on the Kinyarwanda test data of Common Voice. Note that to even load the test data, the whole 40GB Kinyarwanda dataset will be downloaded and extracted into another 40GB directory, so you will need that space available on disk (e.g. not possible in the free tier of Google Colab). This script uses the `chunked_wer` function from [pcuenq](https://huggingface.co/pcuenq/wav2vec2-large-xlsr-53-es).
  
  
  ```python
  import jiwer
  import torch
  import torchaudio
  from datasets import load_dataset, load_metric
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import re
  import unidecode
  
  test_dataset = load_dataset("common_voice", "rw", split="test")
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda") 
  model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")
  model.to("cuda")
  
  chars_to_ignore_regex = r'[!"#$%&()*+,./:;<=>?@\[\]\\_{}|~£¤¨©ª«¬®¯°·¸»¼½¾ðʺ˜˝ˮ‐–—―‚“”„‟•…″‽₋€™−√�]'
  
  def remove_special_characters(batch):
      batch["text"] = re.sub(r'[ʻʽʼ‘’´`]', r"'", batch["sentence"])  # normalize apostrophes
      batch["text"] = re.sub(chars_to_ignore_regex, "", batch["text"]).lower().strip()  # remove all other punctuation
      batch["text"] = re.sub(r"(-| ?' ?|  +)", " ", batch["text"])  # treat dash and apostrophe as word boundary
      batch["text"] = unidecode.unidecode(batch["text"])  # strip accents
      return batch
  
  ## Audio pre-processing
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  def speech_file_to_array_fn(batch):
      speech_array, sampling_rate = torchaudio.load(batch["path"])
      batch["speech"] = resampler(speech_array).squeeze().numpy()
      batch["sampling_rate"] = 16_000
      return batch
  
  def cv_prepare(batch):
      batch = remove_special_characters(batch)
      batch = speech_file_to_array_fn(batch)
      return batch
      
  test_dataset = test_dataset.map(cv_prepare)
  
  # 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("cud