update README

README.md

@@ -1,5 +1,5 @@
 ---
-title: Trial Space
+title: wav2vec2-speech-emotion-recognition
 emoji: 🦀
 colorFrom: indigo
 colorTo: green
@@ -9,4 +9,33 @@ app_file: app.py
 pinned: false
 ---
 
+Wav2Vec2 For Speech Emotion Recognition 
+
+Emotion is an important aspect for the human nature, and understanding it is critical for catering to human services better in this era of digital communication, where speech has been transformed through texts and messages and calls. Speech Emotion Recognition creates a way to classify emotions embedded in speech through careful analysis of lexical, visual, and acoustic features.
+
+Link to the main reference: https://github.com/m3hrdadfi/soxan
+
+Evaluation Scores
+
+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 Accuracy: 0.806 or 80.6%
+
+The Wav2Vec2.0 is a pretrained model for Automatic Speech Recognition, and the Wav2Vec2 for Speech Recognition used is fine-tuned using Connectionist Temporal Classification or CTC, to train neural networks for sequential problems mainly including ASR. 
+
+Google Colab Link: https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb#scrollTo=y0xJwDkA3QQR
+
+Competition board for Common Voice: https://paperswithcode.com/dataset/common-voice
+
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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+
+
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+

app.py

@@ -34,7 +34,7 @@ def inference(path):
 inputs = gr.inputs.Audio(label="Input Audio", type="filepath", source="microphone")
 outputs = gr.outputs.Label(type="confidences", label = "Output Scores")
 title = "Wav2Vec2 Speech Emotion Recognition"
-description = "This is a demo of the Wav2Vec2 Speech Emotion Recognition model. Upload an audio file and the top emotions predicted will be displayed."
+description = "This is a demo of the Wav2Vec2 Speech Emotion Recognition model. Record an audio file and the top emotions predicted will be displayed."
 examples = ['data/heart.wav', 'data/happy26.wav', 'data/jm24.wav', 'data/newton.wav', 'data/speeding.wav']
 article = "<a href = 'https://github.com/m3hrdadfi/soxan'> Wav2Vec2 Speech Classification Github Repository"