readme: ---
  language: en
  datasets:
  - superb
  tags:
  - speech
  - audio
  - wav2vec2
  - audio-classification
  widget:
  - example_title: Speech Commands "down"
    src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
  - example_title: Speech Commands "go"
    src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_go.wav
  license: apache-2.0
  ---
  
  # Wav2Vec2-Base for Keyword Spotting
  
  ## Model description
  
  This is a ported version of 
  [S3PRL's Wav2Vec2 for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
  
  The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), which is pretrained on 16kHz 
  sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 
  
  For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
  
  ## Task and dataset description
  
  Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of 
  words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
  inference time are all crucial. SUPERB uses the widely used 
  [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task.
  The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
  false positive. 
  
  For the original model's training and evaluation instructions refer to the 
  [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ks-keyword-spotting).
  
  
  ## Usage examples
  
  You can use the model via the Audio Classification pipeline:
  ```python
  from datasets import load_dataset
  from transformers import pipeline
  
  dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
  
  classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ks")
  labels = classifier(dataset[0]["file"], top_k=5)
  ```
  
  Or use the model directly:
  ```python
  import torch
  from datasets import load_dataset
  from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
  from torchaudio.sox_effects import apply_effects_file
  
  effects = [["channels", "1"], ["rate", "16000"], ["gain", "-3.0"]]
  def map_to_array(example):
      speech, _ = apply_effects_file(example["file"], effects)
      example["speech"] = speech.squeeze(0).numpy()
      return example
  
  # load a demo dataset and read audio files
  dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
  dataset = dataset.map(map_to_array)
  
  model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
  
  # compute attention masks and normalize the waveform if needed
  inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
  
  logits = model(**inputs).logits
  predicted_ids = torch.argmax(logits, dim=-1)
  labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
  ```
  
  ## Eval results
  
  The evaluation metric is accuracy.
  
  |        | **s3prl** | **transformers** |
  |--------|-----------|------------------|
  |**test**| `0.9623`  | `0.9643`         |
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{yang2021superb,
    title={SUPERB: Speech processing Universal PERformance Benchmark},
    author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others},
    journal={arXiv preprint arXiv:2105.01051},
    year={2021}
  }
  ```