Damien/audio-summary-with-local-LLM
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Update README paths and add shell alias section

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Damien
2026-06-17 10:12:08 +02:00
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commit 425d4db1e2
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@@ -27,7 +27,7 @@ before any work begins.
| 24 GB | `Voxtral-Mini-4B-Realtime-2602-fp16` | `Qwen3-8B-8bit` |
| 32 GB+ | `Voxtral-Mini-4B-Realtime-2602-fp16` | `Qwen3-30B-A3B-4bit` (MoE)|
The selection table lives in [`src/audio_summary/config.py`](src/audio_summary/config.py)
The selection table lives in [`src/audio_summary/models/config.py`](src/audio_summary/models/config.py)
and can be edited without touching the core logic. The highest tier whose RAM requirement
fits the detected memory is selected.
@@ -87,6 +87,23 @@ uvx --from git+https://github.com/damienarnodo/audio-summary-with-local-LLM.git
uvx --from git+https://github.com/damienarnodo/audio-summary-with-local-LLM.git audio-summary --from-local <path-to-audio-file> --language fr --output my_summary.md
```
### Shell alias (optional)
To avoid typing the full `uvx --from ...` command every time, add an alias to your
`~/.zshrc`:
```bash
# Aliases
alias audio-summary="uvx --from git+https://github.com/damienarnodo/audio-summary-with-local-LLM.git audio-summary"
```
Reload your shell (`source ~/.zshrc`) and you can then call it directly:
```bash
audio-summary --from-youtube <YouTube-Video-URL>
audio-summary --from-local <path-to-audio-file> --language fr --output my_summary.md
```
## Usage
The CLI options are:
@@ -125,20 +142,21 @@ directory, with a title and concise summary. The transcript is saved to
## Project structure
The code is split into focused modules under `src/audio_summary/`:
The code is organized into focused subpackages under `src/audio_summary/`:
| Module | Responsibility |
|--------------------|-----------------------------------------------------------|
| `config.py` | Model selection table (RAM tiers → STT/summarization). |
| `device.py` | Unified-memory detection and tier selection (`psutil`). |
| `download.py` | YouTube audio download (`yt-dlp` + `ffmpeg`). |
| `transcription.py` | Speech-to-Text (Voxtral via `mlx-audio`, Whisper fallback).|
| `summarization.py` | Summarization with Qwen3 via `mlx-lm`. |
| `cli.py` | Argument parsing and orchestration (entrypoint). |
| Module | Responsibility |
|------------------------------|-----------------------------------------------------------|
| `models/config.py` | Model selection table (RAM tiers → STT/summarization). |
| `models/device.py` | Unified-memory detection and tier selection (`psutil`). |
| `pipeline/download.py` | YouTube audio download (`yt-dlp` + `ffmpeg`). |
| `pipeline/transcription.py` | Speech-to-Text (Voxtral via `mlx-audio`, Whisper fallback).|
| `pipeline/summarization.py` | Summarization with Qwen3 via `mlx-lm`. |
| `utils/helpers.py` | Shared I/O, CLI argument, and console-output helpers. |
| `cli.py` | Argument parsing and orchestration (entrypoint). |
## Customizing the models
Edit the `TIERS` table in [`src/audio_summary/config.py`](src/audio_summary/config.py).
Edit the `TIERS` table in [`src/audio_summary/models/config.py`](src/audio_summary/models/config.py).
Each tier declares a minimum RAM threshold, an STT model (engine + Hugging Face repo),
and a summarization repo. For example, to use a different Qwen3 size or a different
Voxtral quantization, just change the relevant repo string — no other code changes needed.