Audio Transcription App

A user-friendly audio transcription web application built using Gradio and OpenAI's Whisper model. This app allows users to upload audio files, transcribe them to text, and even save the output for later use.

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Features

• Model Selection: Choose from a variety of Whisper models based on your performance and accuracy needs.
• Hint Support: Optionally provide context for better transcription accuracy.
• Audio Upload: Supports audio files in various formats.
• Automatic Formatting: Converts audio to the WAV format (16kHz, mono) for compatibility with the Whisper model.
• Accurate Transcription: Uses OpenAI's Whisper model for speech-to-text transcription.
• Subtitle Generation: Create SRT subtitle files directly from the transcription.
• Translate Feature: "Translate" button for translating transcriptions. Available for specific models (medium, large, small, and base) and hidden for unsupported models (tiny and turbo).
• Downloadable Results: Save your transcription and subtitles with a single click.
• Intuitive Web UI: Built using Gradio for a smooth and interactive user interface.
• Direct Programmatic Usage: Use audio_print.py as a standalone utility to transcribe audio files without the GUI.

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Requirements

To use the app, ensure the following are installed on your system:

• Python 3.10+
• ffmpeg (cross-platform multimedia framework, required for audio processing)
• Python packages:
  • openai-whisper
  • gradio
  • ffmpeg-python (optional, if used)
  • torch (PyTorch) — Make sure to install the version compatible with your CUDA version. You can check the official PyTorch compatibility table to find the correct version. Example:

     pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121  

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Installation

1. Clone the Repository

Clone this repository to your local system:

git clone https://github.com/loglux/FlexAudioPrint.git
cd FlexAudioPrint

2. Install Python Dependencies

Install the required Python packages using the requirements.txt file:

pip install -r requirements.txt

3. Ensure ffmpeg is Installed

• For Windows, download ffmpeg from FFmpeg.org and add it to your system's PATH.
• For Linux/MacOS, install via your package manager:

  sudo apt install ffmpeg   # For Debian/Ubuntu
  brew install ffmpeg       # For Mac using Homebrew

4. Installing PyTorch with CUDA Support

To enable GPU acceleration for Whisper, you need to install a PyTorch version that supports your CUDA version. Use the following command, replacing cu118 with your specific CUDA version:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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Whisper Model

By default, FlexAudioPrint uses the large model of OpenAI's Whisper for transcription, as it provides the highest accuracy for most use cases. However, the model can be replaced with other available Whisper models depending on your resource constraints or accuracy needs. The choice of model can significantly impact both performance and accuracy.

Available Whisper Models

Model Name	Parameters	English-only Model	Multilingual Model	Required VRAM	Relative Speed
tiny	39M	tiny.en	tiny	~1 GB	~10x
base	74M	base.en	base	~1 GB	~7x
small	244M	small.en	small	~2 GB	~4x
medium	769M	medium.en	medium	~5 GB	~2x
large (default)	1550M	N/A	large	~10 GB	~1x
turbo	809M	N/A	turbo	~6 GB	~8x

Notes on Model Selection

• Models like tiny, base, and small can run efficiently on a CPU, making them suitable for systems without a GPU.
• While the turbo model is faster and more resource-efficient, it has a noticeable drawback: it tends to "swallow" small or less distinct words during transcription. This issue is specific to turbo and does not occur with other models like base, small, or medium. Even these smaller models often provide more reliable results, making them preferable over turbo when accuracy matters. Personally, I prefer the large model over turbo due to its superior accuracy, despite its higher resource requirements.

Note: Larger models require more computational resources (RAM, GPU, etc.). Make sure your system meets the requirements for the selected model.

Default Model on Page Refresh

When the Gradio interface is refreshed, the default model (turbo) is reloaded automatically, ensuring consistency across sessions. Users can select other models from the dropdown menu during their session, but these changes will not persist after a refresh.

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Usage

Use with GUI (Gradio)

1. Run the Gradio app:

   python app.py

2. Open the URL provided by Gradio (e.g., http://127.0.0.1:7860/) in your browser and interact with the web interface.

Translate Button Visibility

The "Translate" button is dynamically shown or hidden depending on the selected model:

• Visible for: base, small, medium, large
• Hidden for: tiny, turbo

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Use audio_print.py Without the GUI

To run the transcription process directly from the command line or using Python scripts, you can use audio_print.py.

Example Command Line Usage

If you have an audio file and want to transcribe its contents without the GUI:

python audio_print.py

Modify the paths in audio_print.py (input_audio and output_text_file) with the file names of your choice for the audio file you'd like to transcribe and the output file to save the transcription.

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How It Works

1. Audio File Upload

Users can upload an audio file in the Gradio UI. Supported formats include .wav, .mp3, .aac, and others.

2. Audio Conversion

The app converts the uploaded audio to a .wav file with a 16kHz sampling rate and mono channel for compatibility with Whisper.

3. Transcription and Subtitle Generation

The AudioTranscriber class uses Whisper's speech-to-text model to transcribe the audio into text and generates subtitles in SRT format

4. Output

The transcribed text is displayed in the Gradio interface, and the user can save it as a file to download.

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Example Usage with Code

If you want to test the transcription programmatically, you can use audio_print.py:

from audio_print import AudioTranscriber

# Path to your audio file
input_audio = "example_audio.mp3"

# Path for saving the text transcription
output_text_file = "transcription.txt"

# Create an instance of the AudioTranscriber class
audio_transcriber = AudioTranscriber(model_name="base")

# Process the audio file and save the transcription
result = audio_transcriber.process_audio(input_audio, output_text_path=output_text_file)

print("Recognized Text:", result)

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License

This project is licensed under the MIT License. You are free to use, modify, and distribute this project according to the terms of the license.

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Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for any improvements or bug fixes.

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Acknowledgments

• Gradio: For building an effortless web UI for machine learning models.
• OpenAI Whisper: For the incredible whisper-based speech-to-text transcription model.
• FFmpeg: For reliable audio processing and conversion.