readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - audio
  - automatic-speech-recognition
  license: apache-2.0
  ---
  
  # Wav2Vec2-Base-100h
  
  This is a fork of [```facebook/wav2vec2-base-100h```](https://huggingface.co/facebook/wav2vec2-base-100h)
  
  ### Changes & Notes
  1. Document reproducible evaluation (below) to new transformer and datasets version.
  2. Use batch size of 1 to reproduce results. 
  3. Validated with ```transformers v4.15.0```, ```datasets 1.18.0```
  4. You may need to manually install pypkg ```librosa```, ```jiwer```
  
   
   ## Evaluation
   
   This code snippet shows how to evaluate **facebook/wav2vec2-base-100h** on LibriSpeech's "clean" and "other" test data.
   
  ```python
  from datasets import load_dataset
  from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  import soundfile as sf
  import torch
  from jiwer import wer
  
  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
  # librispeech_eval = load_dataset("librispeech_asr", "other", split="test")
  
  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h")
  
  def map_to_array(batch):
      # speech, _ = sf.read(batch["file"])
      # batch["speech"] = speech
      batch["speech"] = batch['audio']['array']
      return batch
  
  librispeech_eval = librispeech_eval.map(map_to_array)
  
  def map_to_pred(batch):
      input_values = processor(batch["speech"], return_tensors="pt", padding="longest").input_values
      with torch.no_grad():
          logits = model(input_values.to("cuda")).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.batch_decode(predicted_ids)
      batch["transcription"] = transcription
      return batch
  
  result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
  
  print("WER:", wer(result["text"], result["transcription"]))
  ```
  
  *Result (WER)*:
  
  | "clean/test" | "other/test" |
  |--------------| ------------|
  | 6.1 | 13.5 |
  