Update README.md

README.md

@@ -2,12 +2,33 @@
 license: apache-2.0
 datasets:
 - stapesai/ssi-speech-emotion-recognition
+language:
+- en
+base_model:
+- facebook/wav2vec2-base-960h
+pipeline_tag: audio-classification
+library_name: transformers
+tags:
+- emotion
+- classification
+- audio
+- music
+- facebook
 ---
 
+# Speech-Emotion-Classification
+
+> **Speech-Emotion-Classification** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **multi-class audio classification**, specifically trained to detect **emotions** in speech. This model utilizes the `Wav2Vec2ForSequenceClassification` architecture to accurately classify speaker emotions from audio signals.
+
+> \[!note]
+> Wav2Vec2: Self-Supervised Learning for Speech Recognition
+> [https://arxiv.org/pdf/2006.11477](https://arxiv.org/pdf/2006.11477)
+
+
 ```py
 Classification Report:
 
-              precision    recall  f1-score   support
+              precision    recall  f1-score   test_support
 
        Anger       0.8314    0.9346    0.8800       306
         Calm       0.7949    0.8857    0.8378        35
@@ -26,3 +47,120 @@ weighted avg       0.8392    0.8379    0.8367      1999
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oW8Qa6MO2koMOhRQgVd6a.png)
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/w_wC5gmrWhNlPYS_ftYSC.png)
+
+---
+
+## Label Space: 8 Classes
+
+```
+Class 0: Anger  
+Class 1: Calm  
+Class 2: Disgust  
+Class 3: Fear  
+Class 4: Happy  
+Class 5: Neutral  
+Class 6: Sad  
+Class 7: Surprised
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install gradio transformers torch librosa hf_xet
+```
+
+---
+
+## Inference Code
+
+```python
+import gradio as gr
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
+import torch
+import librosa
+
+# Load model and processor
+model_name = "prithivMLmods/Speech-Emotion-Classification"
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
+processor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "Anger",
+    "1": "Calm",
+    "2": "Disgust",
+    "3": "Fear",
+    "4": "Happy",
+    "5": "Neutral",
+    "6": "Sad",
+    "7": "Surprised"
+}
+
+def classify_audio(audio_path):
+    # Load and resample audio to 16kHz
+    speech, sample_rate = librosa.load(audio_path, sr=16000)
+
+    # Process audio
+    inputs = processor(
+        speech,
+        sampling_rate=sample_rate,
+        return_tensors="pt",
+        padding=True
+    )
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(type="filepath", label="Upload Audio (WAV, MP3, etc.)"),
+    outputs=gr.Label(num_top_classes=8, label="Emotion Classification"),
+    title="Speech Emotion Classification",
+    description="Upload an audio clip to classify the speaker's emotion from voice signals."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+
+
+## Original Label
+
+```py
+  "id2label": {
+    "0": "ANG",
+    "1": "CAL",
+    "2": "DIS",
+    "3": "FEA",
+    "4": "HAP",
+    "5": "NEU",
+    "6": "SAD",
+    "7": "SUR"
+  },
+```
+
+--- 
+
+## Intended Use
+
+`Speech-Emotion-Classification` is designed for:
+
+* **Speech Emotion Analytics** – Analyze speaker emotions in call centers, interviews, or therapeutic sessions.
+* **Conversational AI Personalization** – Adjust voice assistant responses based on detected emotion.
+* **Mental Health Monitoring** – Support emotion recognition in voice-based wellness or teletherapy apps.
+* **Voice Dataset Curation** – Tag or filter speech datasets by emotion for research or model training.
+* **Media Annotation** – Automatically annotate podcasts, audiobooks, or videos with speaker emotion metadata.