readme: ---
  language: sw  
  datasets:
  - ALFFA,Gamayun & IWSLT
  
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Swahili XLSR-53 Wav2Vec2.0 Large
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: ALFFA sw
        args: sw
      metrics:
         - name: Test WER
           type: wer
           value: WIP
  ---
  
  # Wav2Vec2-Large-XLSR-53-Swahili 
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swahili using the following datasets:
  - [ALFFA](http://www.openslr.org/25/),
  - [Gamayun](https://gamayun.translatorswb.org/download/gamayun-5k-english-swahili/) 
  - [IWSLT](https://iwslt.org/2021/low-resource)
  
  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
  
  
  processor = Wav2Vec2Processor.from_pretrained("alokmatta/wav2vec2-large-xlsr-53-sw")
  
  model = Wav2Vec2ForCTC.from_pretrained("alokmatta/wav2vec2-large-xlsr-53-sw").to("cuda")
  
  resampler = torchaudio.transforms.Resample(48_000, 16_000)
  
  resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
  
  def load_file_to_data(file):
      batch = {}
      speech, _ = torchaudio.load(file)
      batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
      batch["sampling_rate"] = resampler.new_freq
      return batch
  
  
  def predict(data):
      features = processor(data["speech"], sampling_rate=data["sampling_rate"], padding=True, return_tensors="pt")
      input_values = features.input_values.to("cuda")
      attention_mask = features.attention_mask.to("cuda")
      with torch.no_grad():
          logits = model(input_values, attention_mask=attention_mask).logits
      pred_ids = torch.argmax(logits, dim=-1)
      return processor.batch_decode(pred_ids)
  
  predict(load_file_to_data('./demo.wav'))
  ```
  
  **Test Result**: 40 % 
  
  
  ## Training
  
  
  The script used for training can be found [here](https://colab.research.google.com/drive/1_RL6TQv_Yiu_xbWXu4ycbzdCdXCqEQYU?usp=sharing)