readme: ---
  language: el 
  datasets:
  - common_voice 
  metrics:
  - wer
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: Greek XLSR Wav2Vec2 Large 53
  results:
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
      dataset:
        name: Common Voice el
        type: common_voice
        args: el 
      metrics:
         - name: Test WER
           type: wer
           value: 45.048955
  ---
  
  # Wav2Vec2-Large-XLSR-53-Greek
  
  Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice), 
  The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here [slack](https://huggingface.slack.com/archives/C01QZ90Q83Z/p1616741140114800)
  The text from the common-voice dataset was used to synthesize vocies of female speakers using [Googe's TTS Standard Voice model](https://cloud.google.com/text-to-speech)
  
  Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                            Resuming from checkpoints trained for another 15 epochs >> 34.00%
                              Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               
  
  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", "el", split="test[:2%]")
  
  processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
  model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1") 
  
  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["speech"][:2], 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])
  ```
  
  
  ## Evaluation
  
  The model can be evaluated as follows on the Greek 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", "el", split="test") 
  wer = load_metric("wer")
  
  processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
  model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
  model.to("cuda")
  
  chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]' 
  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):
    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 aduio 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_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**: 45.048955 % 
  
  
  ## Training
  
  The Common Voice `train`, `validation`, datasets were used for training as well as   
  
  The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
  