readme: ---
  language: da
  tags:
  - speech-to-text
  license: apache-2.0
  ---
  
  # wav2vec2-base-da-ft-nst 
  This the [alvenir wav2vec2 model](https://huggingface.co/Alvenir/wav2vec2-base-da) for Danish ASR finetuned by Alvenir on the public NST dataset. The model is trained on 16kHz, so make sure your data is the same sample rate.
  
  The model was trained using fairseq and then converted to huggingface/transformers format.
  
  Alvenir is always happy to help with your own open-source ASR projects, customized domain specializations or premium models. ;-)
  
  ## Usage
  ```Python
  import soundfile as sf
  import torch
  
  from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2Tokenizer, Wav2Vec2Processor, \
      Wav2Vec2ForCTC
  
  
  def get_tokenizer(model_path: str) -> Wav2Vec2CTCTokenizer:
      return Wav2Vec2Tokenizer.from_pretrained(model_path)
  
  
  def get_processor(model_path: str) -> Wav2Vec2Processor:
      return Wav2Vec2Processor.from_pretrained(model_path)
  
  
  def load_model(model_path: str) -> Wav2Vec2ForCTC:
      return Wav2Vec2ForCTC.from_pretrained(model_path)
  
  
  model_id = "Alvenir/wav2vec2-base-da-ft-nst"
  
  model = load_model(model_id)
  model.eval()
  tokenizer = get_tokenizer(model_id)
  processor = get_processor(model_id)
  
  audio_file = "<path/to/audio.wav>"
  
  audio, _ = sf.read(audio_file)
  
  input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=16_000).input_values
  with torch.no_grad():
      logits = model(input_values).logits
  
  predicted_ids = torch.argmax(logits, dim=-1)
  transcription = processor.batch_decode(predicted_ids)
  print(transcription)
  
  ```
  ## Benchmark results
  This is some benchmark results on the public available datasets in Danish.
  
  | Dataset             | WER Greedy | WER with 3-gram Language Model |
  |---------------------|------------|--------------------|
  | NST test            | 15,8%      | 11.9%              |
  | alvenir-asr-da-eval | 19.0%      | 12.1%              |
  | common_voice_80 da test | 26,3% | 19,2%                 |
  