first test

.gitignore

@@ -0,0 +1,11 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.pyw
+*.pyz
+*.pywz
+*.pyzw
+*.pyzwz
+config.ini
+.venv

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README.md

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+# Whisper Transcription Web App
+
+A user-friendly web application for transcribing audio and video files using OpenAI's Whisper model, powered by Gradio and faster-whisper.
+
+## Features
+
+- 🎙️ Transcribe audio and video files
+- 🚀 GPU acceleration support
+- 🌐 Multiple language support
+- 📱 Responsive and modern UI
+- 🔄 Multiple model options (tiny to large-v3)
+- ⚙️ Configurable settings via config.ini
+
+## Requirements
+
+- Python 3.8+
+- CUDA-capable GPU (recommended)
+- FFmpeg (for audio/video processing)
+
+## Installation
+
+1. Clone this repository:
+```bash
+git clone <repository-url>
+cd whisperapp
+```
+
+2. Create a virtual environment and activate it:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install uv (recommended package installer):
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+4. Install the required packages using uv:
+```bash
+uv pip install -r requirements.txt
+```
+
+## Configuration
+
+The application can be configured through the `config.ini` file. Here are the available settings:
+
+### Whisper Settings
+- `default_model`: Default Whisper model to use
+- `device`: Device to use (cuda/cpu)
+- `compute_type`: Computation type (float16/float32)
+- `beam_size`: Beam size for transcription
+- `vad_filter`: Enable/disable voice activity detection
+
+### App Settings
+- `max_duration`: Maximum audio duration in seconds
+- `server_name`: Server hostname
+- `server_port`: Server port
+- `share`: Enable/disable public sharing
+
+### Models and Languages
+- `available_models`: Comma-separated list of available models
+- `available_languages`: Comma-separated list of supported languages
+
+## Usage
+
+1. Start the application:
+```bash
+python app.py
+```
+
+2. Open your web browser and navigate to `http://localhost:7860`
+
+3. Upload an audio or video file and select your preferred model and language settings
+
+4. Click "Transcribe" and wait for the results
+
+## Model Options
+
+- tiny: Fastest, lowest accuracy
+- base: Good balance of speed and accuracy
+- small: Better accuracy, moderate speed
+- medium: High accuracy, slower
+- large-v1/v2/v3: Highest accuracy, slowest
+
+## Tips
+
+- For better accuracy, use larger models (medium, large)
+- Processing time increases with model size
+- GPU is recommended for faster processing
+- Maximum audio duration is configurable in config.ini
+- Use uv for faster package installation and dependency resolution
+
+## License
+
+MIT License 

app.py

@@ -0,0 +1,121 @@
+import os
+import gradio as gr
+from faster_whisper import WhisperModel
+import torch
+import configparser
+from typing import List
+
+
+def load_config() -> configparser.ConfigParser:
+    """Load configuration from config.ini file."""
+    config = configparser.ConfigParser()
+    config_path = os.path.join(os.path.dirname(__file__), "config.ini")
+    config.read(config_path)
+    return config
+
+
+# Load configuration
+config = load_config()
+
+# Whisper configuration
+DEFAULT_MODEL = config["whisper"]["default_model"]
+DEVICE = config["whisper"]["device"] if torch.cuda.is_available() else "cpu"
+COMPUTE_TYPE = config["whisper"]["compute_type"] if DEVICE == "cuda" else "float32"
+BEAM_SIZE = config["whisper"].getint("beam_size")
+VAD_FILTER = config["whisper"].getboolean("vad_filter")
+
+# App configuration
+MAX_DURATION = config["app"].getint("max_duration")
+SERVER_NAME = config["app"]["server_name"]
+SERVER_PORT = config["app"].getint("server_port")
+SHARE = config["app"].getboolean("share")
+
+# Available models and languages
+WHISPER_MODELS = config["models"]["available_models"].split(",")
+AVAILABLE_LANGUAGES = config["languages"]["available_languages"].split(",")
+
+
+def load_model(model_name: str) -> WhisperModel:
+    """Load the Whisper model with the specified configuration."""
+    return WhisperModel(model_name, device=DEVICE, compute_type=COMPUTE_TYPE)
+
+
+def transcribe_audio(
+    audio_file: str, model_name: str, language: str = None
+) -> tuple[str, str]:
+    """Transcribe audio using the selected Whisper model."""
+    try:
+        # Load the model
+        model = load_model(model_name)
+
+        # Transcribe the audio
+        segments, info = model.transcribe(
+            audio_file,
+            language=language if language != "Auto-detect" else None,
+            beam_size=BEAM_SIZE,
+            vad_filter=VAD_FILTER,
+        )
+
+        # Combine all segments into one text
+        full_text = " ".join([segment.text for segment in segments])
+
+        return full_text, info.language
+    except Exception as e:
+        return f"Error during transcription: {str(e)}", None
+
+
+def create_interface():
+    """Create and return the Gradio interface."""
+    with gr.Blocks(theme=gr.themes.Soft()) as app:
+        gr.Markdown("# 🎙️ Audio/Video Transcription with Whisper")
+        gr.Markdown("Upload an audio or video file to transcribe it using Whisper AI.")
+
+        with gr.Row():
+            with gr.Column():
+                # Input components
+                audio_input = gr.Audio(
+                    label="Upload Audio/Video", type="filepath", format="mp3"
+                )
+                model_dropdown = gr.Dropdown(
+                    choices=WHISPER_MODELS,
+                    value=DEFAULT_MODEL,
+                    label="Select Whisper Model",
+                )
+                language_dropdown = gr.Dropdown(
+                    choices=["Auto-detect"] + AVAILABLE_LANGUAGES,
+                    value="Auto-detect",
+                    label="Language (optional)",
+                )
+                transcribe_btn = gr.Button("Transcribe", variant="primary")
+
+            with gr.Column():
+                # Output components
+                output_text = gr.Textbox(label="Transcription", lines=10, max_lines=20)
+                detected_language = gr.Textbox(
+                    label="Detected Language", interactive=False
+                )
+
+        # Set up the event handler
+        transcribe_btn.click(
+            fn=transcribe_audio,
+            inputs=[audio_input, model_dropdown, language_dropdown],
+            outputs=[output_text, detected_language],
+        )
+
+        # Add some helpful information
+        gr.Markdown(
+            f"""
+        ### Tips:
+        - For better accuracy, use larger models (medium, large)
+        - Processing time increases with model size
+        - GPU is recommended for faster processing
+        - Maximum audio duration is {MAX_DURATION // 60} minutes
+        """
+        )
+
+    return app
+
+
+if __name__ == "__main__":
+    app = create_interface()
+    app.launch(share=SHARE, server_name=SERVER_NAME, server_port=SERVER_PORT)

config.ini.example

@@ -0,0 +1,18 @@
+[whisper]
+default_model = base
+device = cuda
+compute_type = float16
+beam_size = 5
+vad_filter = true
+
+[app]
+max_duration = 3600
+server_name = 0.0.0.0
+server_port = 7860
+share = true
+
+[models]
+available_models = tiny,base,small,medium,large-v1,large-v2,large-v3
+
+[languages]
+available_languages = en,es,fr,de,it,pt,nl,ja,ko,zh 

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=4.0.0
+faster-whisper>=0.9.0
+python-dotenv>=1.0.0
+torch>=2.0.0
+torchaudio>=2.0.0