Update README.md

README.md

@@ -14,22 +14,93 @@ license: apache-2.0
 
 
 ## How to use
-soon ...
+
+### 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, Wav2Vec2Processor
+
+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-xlsr-greek-speech-emotion-recognition"
+config = AutoConfig.from_pretrained(model_name_or_path)
+processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)
+sampling_rate = processor.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)
+    features = processor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
+
+    input_values = features.input_values.to(device)
+    attention_mask = features.attention_mask.to(device)
+
+    with torch.no_grad():
+        logits = model(input_values, attention_mask=attention_mask).logits
+
+    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
+    return outputs
+```
+
+```python
+path = "/path/to/audio.wav"
+outputs = predict(path, sampling_rate)
+```
+
+```bash
+[
+	{'Emotion': 'anger', 'Score': '0.0%'},
+	{'Emotion': 'disgust', 'Score': '99.2%'},
+	{'Emotion': 'fear', 'Score': '0.1%'},
+	{'Emotion': 'happiness', 'Score': '0.3%'},
+	{'Emotion': 'sadness', 'Score': '0.5%'}
+]
+```
 
 
 ## Evaluation
 The following tables summarize the scores obtained by model overall and per each class.
 
 
-|  Emotions 	| precision 	| recall 	| f1-score 	| accuracy 	|
-|:---------:	|:---------:	|:------:	|:--------:	|:--------:	|
-|   anger   	|    0.92   	|  1.00  	|   0.96   	|     -    	|
-|  disgust  	|    0.85   	|  0.96  	|   0.90   	|     -    	|
-|    fear   	|    0.88   	|  0.88  	|   0.88   	|     -    	|
-| happiness 	|    0.94   	|  0.71  	|   0.81   	|     -    	|
-|  sadness  	|    0.96   	|  1.00  	|   0.98   	|     -    	|
-|     -     	|     -     	|    -   	|     -    	|   0.91   	|
+| Emotions  | precision | recall | f1-score | accuracy |
+|-----------|-----------|--------|----------|----------|
+| anger     | 0.92      | 1.00   | 0.96     |          |
+| disgust   | 0.85      | 0.96   | 0.90     |          |
+| fear      | 0.88      | 0.88   | 0.88     |          |
+| happiness | 0.94      | 0.71   | 0.81     |          |
+| sadness   | 0.96      | 1.00   | 0.98     |          |
+|           |           |        | Overal   | 0.91     |
 
 
 ## Questions?
-Post a Github issue from [HERE](https://github.com/m3hrdadfi/wav2vec/issues).
+Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues).