readme: ---
  tags:
  - audio
  - automatic-speech-recognition
  - audio-classification
  ---
  
  # Eating Sound Classification using Wav2Vec 2.0
  
  
  ## How to use
  
  ### Requirements
  
  ```bash
  # requirement packages
  !pip install git+https://github.com/huggingface/datasets.git
  !pip install git+https://github.com/huggingface/transformers.git
  !pip install torchaudio
  !pip install librosa
  ```
  
  ### Prediction
  
  ```python
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torchaudio
  from transformers import AutoConfig, Wav2Vec2FeatureExtractor
  
  import librosa
  import IPython.display as ipd
  import numpy as np
  import pandas as pd
  ```
  
  ```python
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model_name_or_path = "m3hrdadfi/wav2vec2-base-100k-eating-sound-collection"
  config = AutoConfig.from_pretrained(model_name_or_path)
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
  sampling_rate = feature_extractor.sampling_rate
  model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
  ```
  
  ```python
  def speech_file_to_array_fn(path, sampling_rate):
      speech_array, _sampling_rate = torchaudio.load(path)
      resampler = torchaudio.transforms.Resample(_sampling_rate)
      speech = resampler(speech_array).squeeze().numpy()
      return speech
  
  
  def predict(path, sampling_rate):
      speech = speech_file_to_array_fn(path, sampling_rate)
      inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
      inputs = {key: inputs[key].to(device) for key in inputs}
  
      with torch.no_grad():
          logits = model(**inputs).logits
  
      scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
      outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
      return outputs
  ```
  
  ```python
  path = "clips_rd/gummies/gummies_6_04.wav"
  outputs = predict(path, sampling_rate)
  ```
  
  ```bash
  [
  {'Label': 'aloe', 'Score': '0.0%'},
  {'Label': 'burger', 'Score': '0.0%'},
  {'Label': 'cabbage', 'Score': '0.0%'},
  {'Label': 'candied_fruits', 'Score': '0.0%'},
  {'Label': 'carrots', 'Score': '0.0%'},
  {'Label': 'chips', 'Score': '0.0%'},
  {'Label': 'chocolate', 'Score': '0.0%'},
  {'Label': 'drinks', 'Score': '0.0%'},
  {'Label': 'fries', 'Score': '0.0%'},
  {'Label': 'grapes', 'Score': '0.0%'},
  {'Label': 'gummies', 'Score': '99.8%'},
  {'Label': 'ice-cream', 'Score': '0.0%'},
  {'Label': 'jelly', 'Score': '0.1%'},
  {'Label': 'noodles', 'Score': '0.0%'},
  {'Label': 'pickles', 'Score': '0.0%'},
  {'Label': 'pizza', 'Score': '0.0%'},
  {'Label': 'ribs', 'Score': '0.0%'},
  {'Label': 'salmon', 'Score': '0.0%'},
  {'Label': 'soup', 'Score': '0.0%'},
  {'Label': 'wings', 'Score': '0.0%'}
  ]
  ```
  
  
  ## Evaluation
  The following tables summarize the scores obtained by model overall and per each class.
  
  
  |      label     | precision | recall | f1-score | support |
  |:--------------:|:---------:|:------:|:--------:|:-------:|
  |      aloe      |   0.989   |  0.807 |   0.889  |   109   |
  |     burger     |   1.000   |  0.471 |   0.640  |   119   |
  |     cabbage    |   0.907   |  0.970 |   0.937  |   100   |
  | candied_fruits |   0.952   |  0.988 |   0.970  |   161   |
  |     carrots    |   0.970   |  0.992 |   0.981  |   132   |
  |      chips     |   0.993   |  0.951 |   0.972  |   144   |
  |    chocolate   |   0.828   |  0.914 |   0.869  |    58   |
  |     drinks     |   0.982   |  0.948 |   0.965  |    58   |
  |      fries     |   0.935   |  0.783 |   0.852  |   129   |
  |     grapes     |   0.965   |  0.940 |   0.952  |   116   |
  |     gummies    |   0.880   |  0.971 |   0.923  |   136   |
  |    ice-cream   |   0.953   |  0.972 |   0.962  |   145   |
  |      jelly     |   0.906   |  0.875 |   0.890  |    88   |
  |     noodles    |   0.817   |  0.817 |   0.817  |    82   |
  |     pickles    |   0.933   |  0.960 |   0.946  |   174   |
  |      pizza     |   0.704   |  0.934 |   0.803  |   122   |
  |      ribs      |   0.796   |  0.755 |   0.775  |    98   |
  |     salmon     |   0.647   |  0.970 |   0.776  |   100   |
  |      soup      |   0.941   |  0.857 |   0.897  |    56   |
  |      wings     |   0.842   |  0.792 |   0.816  |   101   |
  |    accuracy    |   0.890   |  0.890 |   0.890  |    0    |
  |    macro avg   |   0.897   |  0.883 |   0.882  |   2228  |
  |  weighted avg  |   0.903   |  0.890 |   0.888  |   2228  |
  
  
  ## Questions?
  Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues).