Update README.md

README.md

@@ -1,190 +1,199 @@
-# Audio Transcription and Diarization Tool
-
-## Overview
-
-This project provides a robust set of tools for transcribing audio files using the Whisper model and performing speaker diarization with PyAnnote. Users can process audio files, record audio, and save transcriptions with speaker identification.
-
-## Table of Contents
-- [Features](#features)
-- [Requirements](#requirements)
-- [Setup](#setup)
-- [Usage](#usage)
-  - [Basic Example](#basic-example)
-  - [Audio Processing Example](#audio-processing-example)
-  - [Transcribing an Existing Audio File or Recording](#transcribing-an-existing-audio-file-or-recording)
-- [Key Components](#key-components)
-  - [Transcriptor](#transcriptor)
-  - [AudioProcessor](#audioprocessor)
-  - [AudioRecording](#audiorecording)
-- [Contributing](#contributing)
-- [Acknowledgments](#acknowledgments)
-
-## Features
-
-- **Transcription**: Convert audio files in various formats to text (automatically converts to WAV).
-- **Speaker Diarization**: Identify different speakers in the audio.
-- **Speaker Retrieval**: Name speakers during transcription.
-- **Audio Recording**: Record audio directly from a microphone.
-- **Audio Preprocessing**: Includes resampling, format conversion, and audio enhancement.
-- **Multiple Model Support**: Choose from various Whisper model sizes.
-
-## Supported Whisper Models
-
-This tool supports various Whisper model sizes, allowing you to balance accuracy and computational resources:
-
-- **`tiny`**: Fastest, lowest accuracy
-- **`base`**: Fast, good accuracy
-- **`small`**: Balanced speed and accuracy
-- **`medium`**: High accuracy, slower
-- **`large`**: High accuracy, resource-intensive
-- **`large-v1`**: Improved large model
-- **`large-v2`**: Further improved large model
-- **`large-v3`**: Latest and most accurate
-- **`large-v3-turbo`**: Optimized for faster processing
-
-Specify the model size when initializing the Transcriptor:
-
-```python
-transcriptor = Transcriptor(model_size="base")
-```
-
-The default model size is "base" if not specified.
-
-## Requirements
-
-To run this project, you need Python 3.7+ and the following packages:
-
-```plaintext
-- openai-whisper
-- pyannote.audio
-- librosa
-- tqdm
-- python-dotenv
-- termcolor
-- pydub
-- SpeechRecognition
-- pyaudio
-- tabulate
-- soundfile
-- torch
-- numpy
-- transformers
-- gradio
-```
-
-Install the required packages using:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Setup
-
-1. **Clone the repository**:
-   ```bash
-   git clone https://github.com/your-username/audio-transcription-tool.git
-   cd audio-transcription-tool
-   ```
-
-2. **Install the required packages**:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. **Set up your environment variables**:
-   - Create a `.env` file in the root directory.
-   - Add your Hugging Face token:
-     ```plaintext
-     HF_TOKEN=your_hugging_face_token_here
-     ```
-
-## Usage
-
-### Basic Example
-
-Here's how to use the Transcriptor class to transcribe an audio file:
-
-```python
-from pyscript import Transcriptor
-
-# Initialize the Transcriptor
-transcriptor = Transcriptor()
-
-# Transcribe an audio file
-transcription = transcriptor.transcribe_audio("/path/to/audio")
-
-# Interactively name speakers
-transcription.get_name_speakers()
-
-# Save the transcription
-transcription.save()
-```
-
-### Audio Processing Example
-
-Use the AudioProcessor class to preprocess your audio files:
-
-```python
-from pyscript import AudioProcessor
-
-# Load an audio file
-audio = AudioProcessor("/path/to/audio.mp3")
-
-# Display audio details
-audio.display_details()
-
-# Convert to WAV format and resample to 16000 Hz
-audio.convert_to_wav()
-
-# Display updated audio details
-audio.display_changes()
-```
-
-### Transcribing an Existing Audio File or Recording
-
-To transcribe an audio file or record and transcribe audio, use the demo application provided in `demo.py`:
-
-```bash
-python demo.py
-```
-
-## Key Components
-
-### Transcriptor
-
-The `Transcriptor` class (in `pyscript/transcriptor.py`) is the core of the transcription process. It handles:
-
-- Loading the Whisper model
-- Setting up the diarization pipeline
-- Processing audio files
-- Performing transcription and diarization
-
-### AudioProcessor
-
-The `AudioProcessor` class (in `pyscript/audio_processing.py`) manages audio file preprocessing, including:
-
-- Loading audio files
-- Resampling
-- Converting to WAV format
-- Displaying audio file details and changes
-- Audio enhancement (noise reduction, voice enhancement, volume boost)
-
-### AudioRecording
-
-The `audio_recording.py` module provides functions for recording audio from a microphone, checking input devices, and saving audio files.
-
-## Contributing
-
-Contributions are welcome! Please follow these steps:
-
-1. Fork the repository
-2. Create a new branch: `git checkout -b feature-branch-name`
-3. Make your changes and commit them: `git commit -m 'Add some feature'`
-4. Push to the branch: `git push origin feature-branch-name`
-5. Submit a pull request
-
-## Acknowledgments
-
-- OpenAI for the Whisper model
-- PyAnnote for the speaker diarization pipeline
+---
+title: Audio-Transcriptor
+sdk: gradio
+emoji: ⚡
+colorFrom: green
+colorTo: red
+pinned: false
+short_description: Audio transcription with speaker diarization using Whisper.
+---
+# Audio Transcription and Diarization Tool
+
+## Overview
+
+This project provides a robust set of tools for transcribing audio files using the Whisper model and performing speaker diarization with PyAnnote. Users can process audio files, record audio, and save transcriptions with speaker identification.
+
+## Table of Contents
+- [Features](#features)
+- [Requirements](#requirements)
+- [Setup](#setup)
+- [Usage](#usage)
+  - [Basic Example](#basic-example)
+  - [Audio Processing Example](#audio-processing-example)
+  - [Transcribing an Existing Audio File or Recording](#transcribing-an-existing-audio-file-or-recording)
+- [Key Components](#key-components)
+  - [Transcriptor](#transcriptor)
+  - [AudioProcessor](#audioprocessor)
+  - [AudioRecording](#audiorecording)
+- [Contributing](#contributing)
+- [Acknowledgments](#acknowledgments)
+
+## Features
+
+- **Transcription**: Convert audio files in various formats to text (automatically converts to WAV).
+- **Speaker Diarization**: Identify different speakers in the audio.
+- **Speaker Retrieval**: Name speakers during transcription.
+- **Audio Recording**: Record audio directly from a microphone.
+- **Audio Preprocessing**: Includes resampling, format conversion, and audio enhancement.
+- **Multiple Model Support**: Choose from various Whisper model sizes.
+
+## Supported Whisper Models
+
+This tool supports various Whisper model sizes, allowing you to balance accuracy and computational resources:
+
+- **`tiny`**: Fastest, lowest accuracy
+- **`base`**: Fast, good accuracy
+- **`small`**: Balanced speed and accuracy
+- **`medium`**: High accuracy, slower
+- **`large`**: High accuracy, resource-intensive
+- **`large-v1`**: Improved large model
+- **`large-v2`**: Further improved large model
+- **`large-v3`**: Latest and most accurate
+- **`large-v3-turbo`**: Optimized for faster processing
+
+Specify the model size when initializing the Transcriptor:
+
+```python
+transcriptor = Transcriptor(model_size="base")
+```
+
+The default model size is "base" if not specified.
+
+## Requirements
+
+To run this project, you need Python 3.7+ and the following packages:
+
+```plaintext
+- openai-whisper
+- pyannote.audio
+- librosa
+- tqdm
+- python-dotenv
+- termcolor
+- pydub
+- SpeechRecognition
+- pyaudio
+- tabulate
+- soundfile
+- torch
+- numpy
+- transformers
+- gradio
+```
+
+Install the required packages using:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Setup
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/your-username/audio-transcription-tool.git
+   cd audio-transcription-tool
+   ```
+
+2. **Install the required packages**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Set up your environment variables**:
+   - Create a `.env` file in the root directory.
+   - Add your Hugging Face token:
+     ```plaintext
+     HF_TOKEN=your_hugging_face_token_here
+     ```
+
+## Usage
+
+### Basic Example
+
+Here's how to use the Transcriptor class to transcribe an audio file:
+
+```python
+from pyscript import Transcriptor
+
+# Initialize the Transcriptor
+transcriptor = Transcriptor()
+
+# Transcribe an audio file
+transcription = transcriptor.transcribe_audio("/path/to/audio")
+
+# Interactively name speakers
+transcription.get_name_speakers()
+
+# Save the transcription
+transcription.save()
+```
+
+### Audio Processing Example
+
+Use the AudioProcessor class to preprocess your audio files:
+
+```python
+from pyscript import AudioProcessor
+
+# Load an audio file
+audio = AudioProcessor("/path/to/audio.mp3")
+
+# Display audio details
+audio.display_details()
+
+# Convert to WAV format and resample to 16000 Hz
+audio.convert_to_wav()
+
+# Display updated audio details
+audio.display_changes()
+```
+
+### Transcribing an Existing Audio File or Recording
+
+To transcribe an audio file or record and transcribe audio, use the demo application provided in `demo.py`:
+
+```bash
+python demo.py
+```
+
+## Key Components
+
+### Transcriptor
+
+The `Transcriptor` class (in `pyscript/transcriptor.py`) is the core of the transcription process. It handles:
+
+- Loading the Whisper model
+- Setting up the diarization pipeline
+- Processing audio files
+- Performing transcription and diarization
+
+### AudioProcessor
+
+The `AudioProcessor` class (in `pyscript/audio_processing.py`) manages audio file preprocessing, including:
+
+- Loading audio files
+- Resampling
+- Converting to WAV format
+- Displaying audio file details and changes
+- Audio enhancement (noise reduction, voice enhancement, volume boost)
+
+### AudioRecording
+
+The `audio_recording.py` module provides functions for recording audio from a microphone, checking input devices, and saving audio files.
+
+## Contributing
+
+Contributions are welcome! Please follow these steps:
+
+1. Fork the repository
+2. Create a new branch: `git checkout -b feature-branch-name`
+3. Make your changes and commit them: `git commit -m 'Add some feature'`
+4. Push to the branch: `git push origin feature-branch-name`
+5. Submit a pull request
+
+## Acknowledgments
+
+- OpenAI for the Whisper model
+- PyAnnote for the speaker diarization pipeline
 - All contributors and users of this project