Readme

README.md

@@ -1,157 +1,14 @@
-# Voice Sentiment Analysis System
-
-## Project Description
-
-This project is an automated solution for analyzing customer satisfaction from voice calls. Built using state-of-the-art machine learning models, it combines **Wav2Vec 2.0** for speech-to-text transcription with **BERT** for sentiment analysis to provide real-time feedback into customer emotions and satisfaction levels.
-
-### Key Features
-- **Automatic Speech Recognition**: Convert voice calls to text using Wav2Vec 2.0
-- **Sentiment Analysis**: Analyze emotional tone using multilingual BERT
-- **Customer Satisfaction Classification**: Categorize calls as Satisfied, Dissatisfied, or Neutral
-- **Batch Processing**: Handle multiple audio files simultaneously
-- **Web Interface**: User-friendly [Gradio](https://www.gradio.app/) interface for easy interaction
-- **CSV Export**: Detailed results export for further analysis and reporting
-
-## Models Used
-
-This project uses pre-trained models hosted on Hugging Face Hub:
-
-### Speech Recognition Model
-**Wav2Vec 2.0 - English**
-- **Model:** `facebook/wav2vec2-large-960h-lv60-self`
-- **Link:** [https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self)
-- **Description:** Large Wav2Vec 2.0 model trained on 960 hours of English LibriSpeech data
-- **Use:** Audio-to-text transcription
-
-### Sentiment Analysis Model
-**BERT - Multilingual Sentiment**
-- **Model:** `nlptown/bert-base-multilingual-uncased-sentiment`
-- **Link:** [https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment)
-- **Description:** Multilingual BERT model fine-tuned for sentiment analysis (1-5 stars)
-- **Use:** Text sentiment classification
-
-
-## Project Structure
-
-```
-voice-sentiment-project/
-├── requirements.txt           # Dependencies
-├── voice_sentiment.py         # Core analyzer class
-├── api.py                     # REST API Server
-├── app.py                     # Gradio web interface  
-├── main.py                    # CLI interface
-├── utils.py                   # Utility functions and CSS styling
-├── audios/                     # Your audio files
-│   ├── call1.wav
-│   ├── call2.mp3
-│   └── ...
-└── analysis_results.csv       # Generated results
-```
-## Language Support
-
-### Current Model: English Only
-This system is currently configured with an English-only Wav2Vec 2.0 model (`facebook/wav2vec2-large-960h-lv60-self`) for optimal English speech recognition performance.
-
-### For Other Languages
-To use this system with other languages, you need to change the Wav2Vec 2.0 model in `voice_sentiment.py`.
-
-## Quick Installation
-
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-
-### 1. Web Interface (Recommended)
-
-```bash
-python app.py
-```
-
-Opens a web browser interface at `http://localhost:7860`
-
-### 2. Command Line Interface
-
-```bash
-python main.py
-```
-
-### 3. Direct Code Usage
-
-```python
-from voice_sentiment import VoiceSentimentAnalyzer
-
-# Initialize
-analyzer = VoiceSentimentAnalyzer()
-
-# Analyze one call
-result = analyzer.analyze_call("call1.wav")
-print(result)
-
-# Analyze multiple calls
-results = analyzer.analyze_batch("audios/")
-```
-
-## Example Output
-
-```python
-{
-    'file': 'call1.wav',
-    'transcription': 'Hello I am very satisfied with your service',
-    'sentiment': 'POSITIVE', 
-    'score': 0.89,
-    'satisfaction': 'Satisfied'
-}
-```
-
-## Simple Workflow
-
-```
-Audio File → Transcription (Wav2Vec2) → Sentiment (BERT) → Classification
-```
-
-Perfect for analyzing customer call sentiment quickly and easily!
-
-## Supported Audio Formats
-
-### **Fully Supported**
-- **WAV** (.wav) - *Recommended for best quality*
-- **MP3** (.mp3) - *Most common format*
-- **M4A** (.m4a) - *Apple audio format*
-
-### **Audio Specifications**
-- **Sample Rate**: Automatically converted to 16kHz
-- **Channels**: Mono or Stereo (converted to mono)
-- **Duration**: 5 seconds to 10 minutes (optimal: 30 seconds - 2 minutes)
-- **Quality**: Clear speech, minimal background noise recommended
-
-### **Not Supported**
-- Video files (MP4, AVI, MOV, etc.)
-- Other audio formats (FLAC, OGG, etc.) - *may work but not guaranteed*
-- Extremely low quality or heavily distorted audio
-- Files with encryption or DRM protection
-
-### **Audio Quality Tips**
-- Use WAV format for highest accuracy
-- Ensure clear speech recording
-- Minimize background noise
-- Optimal recording: 16kHz, 16-bit, mono
-- Test with short samples first
-
-## CSV Output & Results
-
-### **Automatic CSV Generation**
-When using batch analysis (multiple files), the system automatically generates a detailed CSV file with all results.
-
-**File**: `analysis_results.csv`
-
-**Location**: Same folder as the project
-
-### **CSV Contents**
-```csv
-File,Transcription,Sentiment,Score,Satisfaction
-call1.wav,"Hello I am very satisfied with your service",POSITIVE,0.89,Satisfied
-call2.wav,"This is unacceptable I want a refund",NEGATIVE,0.92,Dissatisfied
-call3.wav,"Can you tell me about your pricing",NEUTRAL,0.65,Neutral
-```
+---
+title: Voice Sentiment Analysis
+emoji: 🔥
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 5.37.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: This project is an automated solution for analyzing customer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference