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feat: update README to reflect Apple Silicon GPU support and new features in version 3.0

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Mirko Milovanovic
2026-04-11 14:16:07 +02:00
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## Local Transcribe with Whisper
## Local Transcribe with Whisper
> **⚠ 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**](https://github.com/soderstromkr/whisper-local-transcribe/releases/tag/classic) release:
> ```
> git checkout classic
> ```
> **🍎 Apple Silicon GPU/NPU acceleration:** This version now supports native Apple GPU/NPU acceleration via [MLX Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper). On Apple Silicon Macs, transcription runs on the Apple GPU and Neural Engine — no CPU fallback needed.
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.
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) on Windows/Linux and [MLX Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper) on Apple Silicon. 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.
## New in version 3.0!
1. **Apple Silicon GPU/NPU support** — native MLX backend for Apple Silicon Macs, using Apple GPU + Neural Engine.
2. **SRT subtitle export** — valid SubRip files alongside the existing TXT output, ready for HandBrake or any video player.
3. **VAD filter** — removes silence, reduces hallucination, improves accuracy.
4. **Word-level timestamps** — per-word SRT timing for precise subtitle burning.
5. **Translation mode** — transcribe any language and translate to English in one step.
6. **Stop button** — immediately cancel any transcription, including model downloads.
7. **Language dropdown** — 99 languages with proper ISO codes, no more guessing formats.
8. **Model descriptions** — speed, size, quality stars, and use case shown for every model.
## New in version 2.0!
1. **Switched to faster-whisper** — up to 4× faster transcription with lower memory usage, simpler installation.
@@ -18,22 +25,26 @@ Local Transcribe with Whisper is a user-friendly desktop application that allows
## Features
* Select the folder containing the audio or video files you want to transcribe. Tested with m4a video.
* Choose the language of the files you are transcribing. You can either select a specific language or let the application automatically detect the language.
* Select the folder containing the audio or video files you want to transcribe. Tested with m4a video.
* Choose the language of the files you are transcribing from a dropdown of 99 supported languages, or let the application automatically detect the language.
* 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.
* **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.
* Enable the verbose mode to receive detailed information during the transcription process.
* Monitor the progress of the transcription with the progress bar and terminal.
* **VAD filter** — removes silence from audio before transcription, reducing hallucination and improving accuracy.
* **Word-level timestamps** — generates per-word timing in the SRT output for precise subtitle synchronization.
* **Translation mode** — transcribes audio in any language and translates the result to English.
* **SRT export** — valid SubRip subtitle files saved alongside TXT, ready for HandBrake or any video player.
* Monitor the progress of the transcription with the progress bar and terminal.
* Confirmation dialog before starting the transcription to ensure you have selected the correct folder.
* View the transcribed text in a message box once the transcription is completed.
* **Stop button** — immediately cancel transcription, including model downloads.
## Installation
### Get the files
Download the zip folder and extract it to your preferred working folder.
![](images/Picture1.png)
Download the zip folder and extract it to your preferred working folder.
![](images/Picture1.png)
Or by cloning the repository with:
```
git clone https://github.com/soderstromkr/transcribe.git
git clone https://gitea.kobim.cloud/kobim/whisper-local-transcribe.git
```
### Prerequisites
Install **Python 3.10 or later**. Some IT policies allow installing from the Microsoft Store or Mac equivalent. However, I would prefer an install 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.
@@ -62,9 +73,13 @@ python app.py
```
## GPU Support
### Apple Silicon
On Macs with Apple Silicon, the app automatically uses the **MLX backend**, which runs inference on the Apple GPU and Neural Engine. No additional setup is needed — just install and run. MLX models are downloaded from HuggingFace on first use.
### NVIDIA GPUs
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.
### Automatic Detection
#### Automatic Detection
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:
```
pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
@@ -72,7 +87,7 @@ pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
**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.
### Verifying GPU Support
#### Verifying GPU Support
After installation, you can verify that your GPU is available by running:
```python
import ctranslate2
@@ -81,14 +96,16 @@ print(ctranslate2.get_supported_compute_types("cuda"))
If this returns a list containing `"float16"`, GPU acceleration is working.
## Usage
1. Launch the app — the built-in console panel at the bottom shows a welcome message and all progress updates.
1. Launch the app — the built-in console panel at the bottom shows a welcome message and all progress updates. The backend indicator at the bottom shows which inference engine is active (MLX · Apple GPU/NPU, CUDA · GPU, or CPU · int8).
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!
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.
4. Choose the Whisper model to use for the transcription from the dropdown list next to the "Model" label.
5. Click the "Transcribe" button to start the transcription. The button will be disabled during the process to prevent multiple transcriptions at once.
6. Monitor progress in the embedded console panel — it shows model loading, per-file progress, and segment timestamps in real time.
7. Once the transcription is completed, a message box will appear displaying the result. Click "OK" to close it.
8. You can run the application again or quit at any time by clicking the "Quit" button.
3. Select the language from the dropdown — 99 languages are available, or leave it on "Auto-detect". For English-only models (.en) the language is locked to English; for KB Swedish models it's locked to Swedish.
4. Choose the Whisper model to use for the transcription from the dropdown list next to the "Model" label. A description below shows speed, size, quality stars, and recommended use case for each model.
5. Toggle advanced options if needed: **VAD filter**, **Word-level timestamps**, or **Translate to English**.
6. Click the "Transcribe" button to start the transcription. Use the "Stop" button to cancel at any time.
7. Monitor progress in the embedded console panel — it shows model loading, per-file progress, and segment timestamps in real time.
8. Once the transcription is completed, a message box will appear displaying the result. Click "OK" to close it.
9. Transcriptions are saved as both `.txt` (human-readable) and `.srt` (SubRip subtitles) in the `transcriptions/` folder within the selected directory.
10. You can run the application again or quit at any time by clicking the "Quit" button.
## Jupyter Notebook
Don't want fancy EXEs or GUIs? Use the function as is. See [example](example.ipynb) for an implementation on Jupyter Notebook.