readme: ---
  language: en
  tags:
  - audio
  - automatic-speech-recognition
  metrics:
  - wer
  license: mit
  ---
  We took `facebook/wav2vec2-large-960h` and fine tuned it using 1400 audio clips (around 10-15 seconds each) from various cryptocurrency related podcasts. To label the data, we downloaded cryptocurrency podcasts from youtube with their subtitle data and split the clips up by sentence. We then compared the youtube transcription with `facebook/wav2vec2-large-960h` to correct many mistakes in the youtube transcriptions. We can probably achieve better results with more data clean up.
  
  On our data we achieved a WER of 13.1%. `facebook/wav2vec2-large-960h` only reached a WER of 27% on our data.
  
  ## Usage
  ```python
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  from datasets import load_dataset
  import soundfile as sf
  import torch
  
  
  # load model and tokenizer
  processor = Wav2Vec2Processor.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency")
  model = Wav2Vec2ForCTC.from_pretrained("distractedm1nd/wav2vec-en-finetuned-on-cryptocurrency"
  
  filename = "INSERT_FILENAME"
  audio, sampling_rate = sf.read(filename)
  
  input_values = processor(audio, return_tensors="pt", padding="longest", sampling_rate=sampling_rate).input_values  # Batch size 1
  
  
  # retrieve logits
  logits = model(input_values).logits
  
  # take argmax and decode
  predicted_ids = torch.argmax(logits, dim=-1)
  tokenizer.batch_decode(predicted_ids
  ```
  