📝 Docs: Info on NVIDIA libraries for GPU execution

README.md

@@ -32,8 +32,39 @@ Before you can run this app, you'll need to have the following software installe
 
 If you want to run `faster-whisper` on your GPU, you'll also need to install the following NVIDIA libraries:
 
-- [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas)
-- [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn)
+- [cuBLAS for CUDA 12](https://developer.nvidia.com/cublas)
+- [cuDNN 8 for CUDA 12](https://developer.nvidia.com/cudnn)
+
+<details>
+<summary>More information on GPU execution</summary>
+
+The below was taken directly from the [`faster-whisper` README](https://github.com/SYSTRAN/faster-whisper?tab=readme-ov-file#gpu):
+
+**Note:** The latest versions of `ctranslate2` support CUDA 12 only. For CUDA 11, the current workaround is downgrading to the `3.24.0` version of `ctranslate2` (This can be done with `pip install --force-reinsall ctranslate2==3.24.0`).
+
+There are multiple ways to install the NVIDIA libraries mentioned above. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
+
+#### Use Docker
+
+The libraries (cuBLAS, cuDNN) are installed in these official NVIDIA CUDA Docker images: `nvidia/cuda:12.0.0-runtime-ubuntu20.04` or `nvidia/cuda:12.0.0-runtime-ubuntu22.04`.
+
+#### Install with `pip` (Linux only)
+
+On Linux these libraries can be installed with `pip`. Note that `LD_LIBRARY_PATH` must be set before launching Python.
+
+```bash
+pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
+
+export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
+```
+
+**Note**: Version 9+ of `nvidia-cudnn-cu12` appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.
+
+#### Download the libraries from Purfview's repository (Windows & Linux)
+
+Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA libraries for Windows & Linux in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs). Decompress the archive and place the libraries in a directory included in the `PATH`.
+
+</details>
 
 ### Installation
 To set up and run the project, follow these steps:
@@ -69,6 +100,9 @@ pip install -r requirements.txt
 python run.py
 ```
 
+#### 5. Configure and start WhisperWriter:
+On first run, a Settings window should appear. Once configured and saved, another window will open. Press "Start" to activate the keyboard listener. Press the activation key (`ctrl+shift+space` by default) to start recording and transcribing to the active window.
+
 ### Configuration Options
 
 WhisperWriter uses a configuration file to customize its behaviour. To set up the configuration, open the Settings window:

src/result_thread.py

@@ -53,6 +53,7 @@ def run(self):
             while self.is_running:
                 if not self.is_running:
                     break
+
                 self.is_recording = True
                 self.statusSignal.emit('recording')
                 print('Recording...') if self.config['misc']['print_to_terminal'] else ''
@@ -66,6 +67,9 @@ def run(self):
 
                 result = transcribe(self.config, audio_file, self.local_model)
 
+                if not self.is_running:
+                    break
+
                 self.statusSignal.emit('idle')
                 self.resultSignal.emit(result)
         except Exception as e: