readme: ---
  language: en
  datasets:
  - timit_asr
  tags:
  - audio
  - automatic-speech-recognition
  - speech
  license: apache-2.0
  ---
  
  # Wav2Vec2-Base-TIMIT
  
  Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)
  on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
  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 soundfile as sf
  import torch
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  
  model_name = "elgeish/wav2vec2-base-timit-asr"
  processor = Wav2Vec2Processor.from_pretrained(model_name)
  model = Wav2Vec2ForCTC.from_pretrained(model_name)
  model.eval()
  
  dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10))
  char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""})
  
  def prepare_example(example):
      example["speech"], _ = sf.read(example["file"])
      example["text"] = example["text"].translate(char_translations)
      example["text"] = " ".join(example["text"].split())  # clean up whitespaces
      example["text"] = example["text"].lower()
      return example
  
  dataset = dataset.map(prepare_example, remove_columns=["file"])
  inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
  
  with torch.no_grad():
      predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1)
  predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id  # see fine-tuning script
  predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids)
  
  for reference, predicted in zip(dataset["text"], predicted_transcripts):
      print("reference:", reference)
      print("predicted:", predicted)
      print("--")
  ```
  
  Here's the output:
  
  ```
  reference: she had your dark suit in greasy wash water all year
  predicted: she had your dark suit in greasy wash water all year
  --
  reference: where were you while we were away
  predicted: where were you while we were away
  --
  reference: cory and trish played tag with beach balls for hours
  predicted: tcory and trish played tag with beach balls for hours
  --
  reference: tradition requires parental approval for under age marriage
  predicted: tradition requires parrental proval for under age marrage
  --
  reference: objects made of pewter are beautiful
  predicted: objects made of puder are bautiful
  --
  reference: don't ask me to carry an oily rag like that
  predicted: don't o ask me to carry an oily rag like that
  --
  reference: cory and trish played tag with beach balls for hours
  predicted: cory and trish played tag with beach balls for ours
  --
  reference: don't ask me to carry an oily rag like that
  predicted: don't ask me to carry an oily rag like that
  --
  reference: don't do charlie's dirty dishes
  predicted: don't  do chawly's tirty dishes
  --
  reference: only those story tellers will remain who can imitate the style of the virtuous
  predicted: only those story tillaers will remain who can imvitate the style the virtuous
  ```
  
  ## Fine-Tuning Script
  
  You can find the script used to produce this model
  [here](https://github.com/elgeish/transformers/blob/cfc0bd01f2ac2ea3a5acc578ef2e204bf4304de7/examples/research_projects/wav2vec2/finetune_base_timit_asr.sh).
  