readme: ---
  language: en
  datasets:
  - superb
  tags:
  - speech
  - audio
  - wav2vec2
  - audio-classification
  license: apache-2.0
  widget:
  - example_title: VoxCeleb Speaker id10003
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
  - example_title: VoxCeleb Speaker id10004
    src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
  ---
  
  # Wav2Vec2-Large for Speaker Identification
  
  ## Model description
  
  This is a ported version of 
  [S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
  
  The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), 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
  
  Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
  classification, where speakers are in the same predefined set for both training and testing. The widely
  used [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) dataset is adopted
  
  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#sid-speaker-identification).
  
  
  ## 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", "si", split="test")
  
  classifier = pipeline("audio-classification", model="superb/wav2vec2-large-superb-sid")
  labels = classifier(dataset[0]["file"], top_k=5)
  ```
  
  Or use the model directly:
  ```python
  import torch
  import librosa
  from datasets import load_dataset
  from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
  
  def map_to_array(example):
      speech, _ = librosa.load(example["file"], sr=16000, mono=True)
      example["speech"] = speech
      return example
  
  # load a demo dataset and read audio files
  dataset = load_dataset("anton-l/superb_demo", "si", split="test")
  dataset = dataset.map(map_to_array)
  
  model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-large-superb-sid")
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-large-superb-sid")
  
  # compute attention masks and normalize the waveform if needed
  inputs = feature_extractor(dataset[:2]["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**| `0.8614`  | `0.8613`         |
  
  ### 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}
  }
  ```