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Added example

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  1. README.md +41 -16
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@@ -51,25 +51,50 @@ Further details are available in the corresponding [**paper**](https://huggingfa
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  ### Usage
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  ```python
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- import torch
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- import torch.nn as nn
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- from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # CONFIG and MODEL SETUP
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- model_name = 'amiriparian/ExHuBERT'
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- feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
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- model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True,revision="b158d45ed8578432468f3ab8d46cbe5974380812")
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-
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- # Freezing half of the encoder for further transfer learning
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- model.freeze_og_encoder()
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-
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- sampling_rate=16000
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- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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- model = model.to(device)
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-
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-
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  ```
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  ### Citation Info
 
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  ### Usage
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  ```python
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+ import torch
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+ import torch.nn as nn
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+ from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
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+
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+
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+ # CONFIG and MODEL SETUP
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+ model_name = 'amiriparian/ExHuBERT'
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+ feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
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+ model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True,
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+ revision="b158d45ed8578432468f3ab8d46cbe5974380812")
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+
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+ # Freezing half of the encoder for further transfer learning
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+ model.freeze_og_encoder()
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+
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+ sampling_rate = 16000
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ model = model.to(device)
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+
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+
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+
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+ # Example application from a local audiofile
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+ import numpy as np
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+ import librosa
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+ import torch.nn.functional as F
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+ # Sample taken from the Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
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+ waveform, sr_wav = librosa.load("YAF_date_angry.wav")
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+ # Max Padding to 3 Seconds at 16k sampling rate for the best results
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+ waveform = feature_extractor(waveform, sampling_rate=sampling_rate,padding = 'max_length',max_length = 48000)
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+ waveform = waveform['input_values'][0]
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+ waveform = waveform.reshape(1, -1)
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+ waveform = torch.from_numpy(waveform).to(device)
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+ with torch.no_grad():
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+ output = model(waveform)
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+ output = F.softmax(output.logits, dim = 1)
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+ output = output.detach().cpu().numpy().round(2)
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+ print(output)
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+
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+ # [[0. 0. 0. 1. 0. 0.]]
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+ # Low | High Arousal
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+ # Neg. Neut. Pos. | Neg. Neut. Pos Valence
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+ # Disgust, Neutral, Kind| Anger, Surprise, Joy Example emotions
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  ```
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  ### Citation Info