readme: ---
  language: pl
  tags:
  - audio
  - automatic-speech-recognition
  - voxpopuli
  license: cc-by-nc-4.0
  ---
  
  # Wav2Vec2-Base-VoxPopuli-Finetuned
  
  [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in pl (refer to Table 1 of paper for more information).
  
  **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
  Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
  
  **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
  
  See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
  
  
  # Usage for inference
  
  In the following it is shown how the model can be used in inference on a sample of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets)
  
  ```python
  #!/usr/bin/env python3
  from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  from datasets import load_dataset
  import torchaudio
  import torch
  
  # resample audio
  
  # load model & processor
  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-pl")
  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-pl")
  
  # load dataset
  ds = load_dataset("common_voice", "pl", split="validation[:1%]")
  
  # common voice does not match target sampling rate
  common_voice_sample_rate = 48000
  target_sample_rate = 16000
  
  resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate)
  
  
  # define mapping fn to read in sound file and resample
  def map_to_array(batch):
      speech, _ = torchaudio.load(batch["path"])
      speech = resampler(speech)
      batch["speech"] = speech[0]
      return batch
  
  
  # load all audio files
  ds = ds.map(map_to_array)
  
  # run inference on the first 5 data samples
  inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True)
  
  # inference
  logits = model(**inputs).logits
  predicted_ids = torch.argmax(logits, axis=-1)
  
  print(processor.batch_decode(predicted_ids))
  ```
  
  