readme: ---
  language: el
  datasets:
  - common_voice
  tags:
  - audio
  - hf-asr-leaderboard
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
  license: apache-2.0
  model-index:
  - name: XLSR Wav2Vec2 Greek by Lighteternal
    results:
    - task: 
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: CommonVoice (EL),  CSS10 (EL)
        type: CCS10 + mozilla-foundation/common_voice_7_0
        args: el 
      metrics:
         - name: Test WER
           type: wer
           value: 10.497628
         - name: Test CER
           type: cer
           value: 2.875260
  ---
  
  # Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
  ### By the Hellenic Army Academy and the Technical University of Crete
  
  
  * language: el
  * licence: apache-2.0
  * dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets + CSS10 (EL), 1.22GB: https://github.com/Kyubyong/css10
  * model: XLSR-Wav2Vec2, trained for 50 epochs
  * metrics: Word Error Rate (WER)
  
  ## Model description
  
  UPDATE: We repeated the fine-tuning process using an additional 1.22GB dataset from CSS10.
  
  Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, Facebook AI presented XLSR-Wav2Vec2. XLSR stands for cross-lingual speech representations and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.
  
  Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.
  
  This model was trained for 50 epochs on a single NVIDIA RTX 3080, for aprox. 8hrs.
  
  ## How to use for inference:
  
  For live demo, make sure that speech files are sampled at 16kHz.
  
  Instructions to test on CommonVoice extracts are provided in the ASR_Inference.ipynb. Snippet also available below: 
  
  ```python
  #!/usr/bin/env python
  # coding: utf-8
  
  # Loading dependencies and defining preprocessing functions 
  
  from transformers import Wav2Vec2ForCTC
  from transformers import Wav2Vec2Processor
  from datasets import load_dataset, load_metric
  import re
  import torchaudio
  import librosa
  import numpy as np
  from datasets import load_dataset, load_metric
  import torch
  
  chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
  
  def remove_special_characters(batch):
      batch["text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
      return batch
  
  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["text"]
      return batch
  
  def resample(batch):
      batch["speech"] = librosa.resample(np.asarray(batch["speech"]), 48_000, 16_000)
      batch["sampling_rate"] = 16_000
      return batch
  
  def prepare_dataset(batch):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch["sampling_rate"])) == 1
      ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
  
      batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values
      
      with processor.as_target_processor():
          batch["labels"] = processor(batch["target_text"]).input_ids
      return batch
  
  
  # Loading model and dataset processor
  
  model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
  
  
  # Preparing speech dataset to be suitable for inference
  
  common_voice_test = load_dataset("common_voice", "el", split="test")
  
  common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
  
  common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=["sentence"])
  
  common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)
  
  common_voice_test = common_voice_test.map(resample, num_proc=8)
  
  common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)
  
  
  # Loading test dataset
  
  common_voice_test_transcription = load_dataset("common_voice", "el", split="test")
  
  
  #Performing inference on a random sample. Change the "example" value to try inference on different CommonVoice extracts
  
  example = 123
  
  input_dict = processor(common_voice_test["i