93 lines
6.1 KiB
Markdown
93 lines
6.1 KiB
Markdown
## Local Transcribe with Whisper
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> **⚠ Note for Mac users (Apple Silicon):** This version uses `faster-whisper` (CTranslate2), which does **not** support Apple M-chip GPU acceleration. Transcription will run on CPU, which is slower than OpenAI's Whisper with Metal/CoreML support. The trade-off is a much simpler installation — no conda, no PyTorch, no admin rights. If you'd prefer M-chip GPU acceleration and don't mind a more involved setup, switch to the **classic** release:
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> ```
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> git checkout classic
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> ```
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Local Transcribe with Whisper is a user-friendly desktop application that allows you to transcribe audio and video files using the Whisper ASR system, powered by [faster-whisper](https://github.com/SYSTRAN/faster-whisper) (CTranslate2). This application provides a graphical user interface (GUI) built with Python and the Tkinter library, making it easy to use even for those not familiar with programming.
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## New in version 2.0!
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1. **Switched to faster-whisper** — up to 4× faster transcription with lower memory usage.
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2. **No separate FFmpeg installation needed** — audio decoding is handled by the bundled PyAV library.
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3. **No admin rights required** — a plain `pip install` covers everything.
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4. **No PyTorch dependency** — dramatically smaller install footprint.
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5. **`tiny` model added** — smallest and fastest option for quick drafts.
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## Features
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* Select the folder containing the audio or video files you want to transcribe. Tested with m4a video.
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* Choose the language of the files you are transcribing. You can either select a specific language or let the application automatically detect the language.
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* Select the Whisper model to use for the transcription. Available models include "tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v2", and "large-v3". Models with .en ending are better if you're transcribing English, especially the base and small models.
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* **Swedish-optimised models** — [KB-Whisper](https://huggingface.co/collections/KBLab/kb-whisper) from the National Library of Sweden (KBLab) is available in all sizes (tiny → large). These models reduce Word Error Rate by up to 47 % compared to OpenAI Whisper on Swedish speech. The language is set to Swedish automatically when a KB model is selected.
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* Enable the verbose mode to receive detailed information during the transcription process.
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* Monitor the progress of the transcription with the progress bar and terminal.
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* Confirmation dialog before starting the transcription to ensure you have selected the correct folder.
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* View the transcribed text in a message box once the transcription is completed.
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## Installation
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### Get the files
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Download the zip folder and extract it to your preferred working folder.
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Or by cloning the repository with:
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```
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git clone https://github.com/soderstromkr/transcribe.git
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```
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### Prerequisites
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1. Install **Python 3.10 or later** from [python.org](https://www.python.org/downloads/). During installation, **check "Add Python to PATH"**. No administrator rights are needed if you install for your user only.
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2. Run the installer. Open a terminal in the project folder and run:
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```
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python install.py
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```
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This will:
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- Install all required packages (including bundled FFmpeg — no separate install needed)
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- **Auto-detect your NVIDIA GPU** and ask if you want GPU acceleration
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- No conda, no admin rights required
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Alternatively, you can install manually with `pip install -r requirements.txt`.
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### Run on Windows
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Double-click `run_Windows.bat` (it will auto-install on first run) or run:
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```
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python app.py
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```
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### Run on Mac / Linux
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Run `./run_Mac.sh` (auto-installs on first run). See [Mac instructions](Mac_instructions.md) for details.
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> **Note:** The first run with a given model will download it (~75 MB for base, ~500 MB for medium). After that, everything works offline.
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## GPU Support
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This program **does support running on NVIDIA GPUs**, which can significantly speed up transcription times. faster-whisper uses CTranslate2, which requires NVIDIA CUDA libraries for GPU acceleration.
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### Automatic Detection
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The `install.py` script **automatically detects NVIDIA GPUs** and will ask if you want to install GPU support. If you skipped it during installation, you can add it anytime:
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```
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pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
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```
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**Note:** Make sure your NVIDIA GPU drivers are up to date. You can check by running `nvidia-smi` in your terminal. The program will automatically detect and use your GPU if available, otherwise it falls back to CPU.
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### Verifying GPU Support
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After installation, you can verify that your GPU is available by running:
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```python
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import ctranslate2
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print(ctranslate2.get_supported_compute_types("cuda"))
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```
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If this returns a list containing `"float16"`, GPU acceleration is working.
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## Usage
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1. Launch the app — the built-in console panel at the bottom shows a welcome message and all progress updates.
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2. Select the folder containing the audio or video files you want to transcribe by clicking the "Browse" button next to the "Folder" label. This will open a file dialog where you can navigate to the desired folder. Remember, you won't be choosing individual files but whole folders!
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3. Enter the desired language for the transcription in the "Language" field. You can either select a language or leave it blank to enable automatic language detection.
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4. Choose the Whisper model to use for the transcription from the dropdown list next to the "Model" label.
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5. Click the "Transcribe" button to start the transcription. The button will be disabled during the process to prevent multiple transcriptions at once.
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6. Monitor progress in the embedded console panel — it shows model loading, per-file progress, and segment timestamps in real time.
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7. Once the transcription is completed, a message box will appear displaying the result. Click "OK" to close it.
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8. You can run the application again or quit at any time by clicking the "Quit" button.
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## Jupyter Notebook
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Don't want fancy EXEs or GUIs? Use the function as is. See [example](example.ipynb) for an implementation on Jupyter Notebook.
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[](https://zenodo.org/badge/latestdoi/617404576)
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