Update README.md

README.md

@@ -1,81 +1,89 @@
 ---
-language: el
+language: en
-datasets:
+datasets: Toronto emotional speech set (TESS)(https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess)
-- aesdd
 tags:
 - audio
-- audio-classification
+- automatic-speech-recognition
 - speech
+- speech-emotion-recognition
 license: apache-2.0
 ---
-
+# Emotion Recognition in Speech 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
-!git clone https://github.com/m3hrdadfi/soxan
+```
-cd soxan
+### Prediction
-~~~
+```python
-
-
-# prediction
-~~~
 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 = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition"
+model_name_or_path = "harshit345/xlsr-wav2vec-speech-emotion-recognition"
 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 = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
     return outputs
-~~~
+```
-
+```python
-# prediction
+path = "/path/to/disgust.wav"
-~~~
-# path for a sample
-path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'   
 outputs = predict(path, sampling_rate)
-~~~
+```
+```bash
+[
+{'Emotion': 'anger', 'Score': '12.2%'},
+{'Emotion': 'disgust', 'Score': '78.8%'},
+{'Emotion': 'fear', 'Score': '7.2%'},
+{'Emotion': 'happiness', 'Score': '1.3%'},
+{'Emotion': 'sadness', 'Score': '1.5%'}
+]
+```
 
-~~~
+
-[{'Emotion': 'anger', 'Score': '98.3%'},
+## Evaluation
- {'Emotion': 'disgust', 'Score': '0.0%'},
+The following tables summarize the scores obtained by model overall and per each class.
- {'Emotion': 'fear', 'Score': '0.4%'},
+
- {'Emotion': 'happiness', 'Score': '0.7%'},
+
- {'Emotion': 'sadness', 'Score': '0.5%'}]
+| Emotions  | precision | recall | f1-score | accuracy |
- ~~~
+|-----------|-----------|--------|----------|----------|
+| anger     | 0.82      | 1.00   | 0.81     |          |
+| disgust   | 0.85      | 0.96   | 0.85     |          |
+| fear      | 0.78      | 0.88   | 0.80     |          |
+| happiness | 0.84      | 0.71   | 0.78     |          |
+| sadness   | 0.86      | 1.00   | 0.79     |          |
+|           |           |        | Overall  | 0.806    |
+
+
+## 
+
+Colab Notebook
+https://colab.research.google.com/drive/1aPPb_ZVS5dlFVZySly8Q80a44La1XjJu?usp=sharing