readme: ---
  language: 
   - cy
   - en
  datasets:
  - common_voice
  metrics:
  - wer
  tags:
  - automatic-speech-recognition
  - speech
  license: apache-2.0
  model-index:
  - name: wav2vec2-xlsr-ft-en-cy
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice cy
        type: common_voice
        args: cy
      metrics:
      - name: Test WER
        type: wer
        value: 17.70%
  ---
  
  # wav2vec2-xlsr-ft-en-cy
  
  A speech recognition acoustic model for Welsh and English, fine-tuned from [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) using English/Welsh balanced data derived from version 11 of their respective Common Voice datasets (https://commonvoice.mozilla.org/cy/datasets). Custom bilingual Common Voice train/dev and test splits were built using the scripts at https://github.com/techiaith/docker-commonvoice-custom-splits-builder#introduction
  
  Source code and scripts for training wav2vec2-xlsr-ft-en-cy can be found at [https://github.com/techiaith/docker-wav2vec2-cy](https://github.com/techiaith/docker-wav2vec2-cy/blob/main/train/fine-tune/python/run_en_cy.sh). 
  
  
  
  ## Usage
  
  The wav2vec2-xlsr-ft-en-cy model can be used directly as follows:
  
  ```python
  import torch
  import torchaudio
  import librosa
  
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-ft-en-cy")
  model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-en-cy")
  
  audio, rate = librosa.load(audio_file, sr=16000)
  
  inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True)
  
  with torch.no_grad():
    tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
  
  # greedy decoding
  predicted_ids = torch.argmax(logits, dim=-1)
  
  print("Prediction:", processor.batch_decode(predicted_ids))
  
  ```
  
  ## Evaluation
  
  
  According to a balanced English+Welsh test set derived from Common Voice version 11, the WER of techiaith/wav2vec2-xlsr-ft-en-cy is **17.7%**
  
  However, when evaluated with language specific test sets, the model exhibits a bias to perform better with Welsh.  
  
  | Common Voice Test Set Language | WER | CER | 
  | -------- | --- | --- | 
  | EN+CY | 17.07| 7.32  | 
  | EN | 27.54  | 11.6  |
  | CY | 7.13  | 2.2  |
  