From 47f2eda4f65a4837b455f708c1d21ead2a3d7246 Mon Sep 17 00:00:00 2001 From: Damien Date: Wed, 17 Jun 2026 09:00:35 +0200 Subject: [PATCH 1/7] Update project to use MLX for Apple Silicon support Update dependencies for MLX models Add automatic model selection based on available RAM Rewrite README with MLX-specific documentation Update .gitignore for MLX project structure --- .gitignore | 5 +- README.md | 271 +++------ pyproject.toml | 30 +- src/audio_summary/__init__.py | 3 + src/audio_summary/cli.py | 102 ++++ src/audio_summary/config.py | 58 ++ src/audio_summary/device.py | 32 + src/audio_summary/download.py | 27 + src/audio_summary/summarization.py | 39 ++ src/audio_summary/transcription.py | 68 +++ src/summary.py | 167 ------ uv.lock | 935 +++++++++++++++++------------ 12 files changed, 994 insertions(+), 743 deletions(-) create mode 100644 src/audio_summary/__init__.py create mode 100644 src/audio_summary/cli.py create mode 100644 src/audio_summary/config.py create mode 100644 src/audio_summary/device.py create mode 100644 src/audio_summary/download.py create mode 100644 src/audio_summary/summarization.py create mode 100644 src/audio_summary/transcription.py delete mode 100644 src/summary.py diff --git a/.gitignore b/.gitignore index 7d10a25..2ce5872 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ -src/__pycache__ +__pycache__/ +*.pyc .ruff_cache/ -src/audio_summary_with_local_LLM.egg-info/ +*.egg-info/ # Virtual Env .venv diff --git a/README.md b/README.md index df647c2..40e1b5a 100644 --- a/README.md +++ b/README.md @@ -1,60 +1,77 @@ -# Audio Summary with Local LLM +# Audio Summary with Local LLM (Apple Silicon / MLX) -This tool is designed to provide a quick and concise summary of audio and video files. It supports summarizing content either from a local file or directly from YouTube. The tool uses Whisper for transcription and a local version of Llama3 (via Ollama) for generating summaries. +This tool provides a quick and concise summary of audio and video files. It supports +summarizing content either from a local file or directly from YouTube. Everything runs +**fully on Apple Silicon** through Apple's [MLX](https://github.com/ml-explore/mlx) +framework — no NVIDIA/CUDA, no PyTorch CUDA stack, no external server required. -> [!TIP] -> It is possible to change the model you wish to use. -> To do this, change the `OLLAMA_MODEL` variable, and download the associated model via [ollama](https://github.com/ollama/ollama) +- **Speech-to-Text**: Mistral [Voxtral](https://huggingface.co/mistralai) via + [`mlx-audio`](https://github.com/Blaizzy/mlx-audio) (with an + [`mlx-whisper`](https://github.com/ml-explore/mlx-examples) fallback for + memory-constrained Macs). Voxtral outperforms Whisper large-v3 on most transcription + benchmarks and natively handles long audio with streaming. +- **Summarization**: [Qwen3](https://huggingface.co/Qwen) via + [`mlx-lm`](https://github.com/ml-explore/mlx-lm) — the recommended family for + summarization on Apple Silicon. + +## Automatic model selection + +At startup the tool detects the machine's **unified memory** and automatically selects +the best models for the available RAM. The detected memory and chosen models are printed +before any work begins. + +| RAM | STT model | Summarization model | +|---------|--------------------------------------------|---------------------------| +| 8 GB | `whisper-large-v3-turbo` (mlx-whisper) | `Qwen3-4B-4bit` | +| 16 GB | `Voxtral-Mini-4B-Realtime-2602-4bit` | `Qwen3-8B-4bit` | +| 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) +and can be edited without touching the core logic. The highest tier whose RAM requirement +fits the detected memory is selected. + +> [!NOTE] +> Models are downloaded from the Hugging Face Hub on first use and cached locally +> (`~/.cache/huggingface`). The first run for a given model may take a while. ## Features - **YouTube Integration**: Download and summarize content directly from YouTube. - **Local File Support**: Summarize audio/video files available on your local disk. -- **Transcription**: Converts audio content to text using Whisper. -- **Summarization**: Generates a concise summary using Llama3 (Ollama). -- **Transcript Only Option**: Option to only transcribe the audio content without generating a summary. -- **Device Optimization**: Automatically uses the best available hardware (MPS for Mac, CUDA for NVIDIA GPUs, or CPU). +- **Transcription**: Converts audio to text with Voxtral (or Whisper fallback). +- **Summarization**: Generates a concise summary with Qwen3. +- **Transcript Only Option**: Only transcribe, without generating a summary. +- **Automatic model selection**: Picks the best models for your Mac's unified memory. ## Prerequisites -Before you start using this tool, you need to install the following dependencies: - -- Python 3.12 and lower than 3.13 -- [Ollama](https://ollama.com) for LLM model management -- `ffmpeg` (required for audio processing) +- An Apple Silicon Mac (M1 or newer) +- Python 3.12 (and lower than 3.13) +- `ffmpeg` (required for audio extraction/processing) - [uv](https://docs.astral.sh/uv/getting-started/installation/) for package management +Install `ffmpeg` with [Homebrew](https://brew.sh): + +```bash +brew install ffmpeg +``` + ## Installation -### Using uv - -Clone the repository and install the required Python packages using [uv](https://github.com/astral-sh/uv): +Clone the repository and install dependencies with [uv](https://github.com/astral-sh/uv): ```bash git clone https://github.com/damienarnodo/audio-summary-with-local-LLM.git cd audio-summary-with-local-LLM -# Create and activate a virtual environment with uv uv sync -source .venv/bin/activate # On Windows: .venv\Scripts\activate -``` - -### LLM Requirement - -[Download and install](https://ollama.com) Ollama to carry out LLM Management. More details about LLM models supported can be found on the Ollama [GitHub](https://github.com/ollama/ollama). - -Download and use the Llama3 model: - -```bash -ollama pull llama3 - -# Test the access: -ollama run llama3 "tell me a joke" +source .venv/bin/activate # activate the virtual environment ``` ## Usage with uvx (no installation required) -You can run this tool directly without cloning the repository using `uvx`: +You can run the tool directly without cloning the repository using `uvx`: ```bash # Summarize a YouTube video @@ -70,172 +87,84 @@ 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 --language fr --output my_summary.md ``` -> [!NOTE] -> `uvx` automatically creates a temporary virtual environment and installs all dependencies. On first run, this may take a moment. Ensure `ffmpeg` and [Ollama](https://ollama.com) are installed and running on your system. - ## Usage -The tool can be executed with the following command line options: +The CLI options are: -- `--from-youtube`: To download and summarize a video from YouTube. -- `--from-local`: To load and summarize an audio or video file from the local disk. -- `--output`: Specify the output file path (default: ./summary.md) -- `--transcript-only`: To only transcribe the audio content without generating a summary. -- `--language`: Select the language to be used for the transcription (default: en) +- `--from-youtube`: Download and summarize a video from YouTube. +- `--from-local`: Load and summarize a local audio or video file. +- `--output`: Output markdown file path (default: `./summary.md`). +- `--transcript-only`: Only transcribe, do not summarize. +- `--language`: Language code for transcription (e.g. `en`, `fr`, `es`, or `auto` for + automatic detection). Default: `en`. ### Examples -1. **Summarizing a YouTube video:** +```bash +# Summarize a YouTube video +audio-summary --from-youtube - ```bash - uv run python src/summary.py --from-youtube - ``` +# Summarize a local audio file +audio-summary --from-local -2. **Summarizing a local audio file:** +# Transcribe only (no summary) +audio-summary --from-youtube --transcript-only - ```bash - uv run python src/summary.py --from-local - ``` +# Custom language and output file +audio-summary --from-local --language fr --output my_summary.md +``` -3. **Transcribing a YouTube video without summarizing:** - - ```bash - uv run python src/summary.py --from-youtube --transcript-only - ``` - -4. **Transcribing a local audio file without summarizing:** - - ```bash - uv run python src/summary.py --from-local --transcript-only - ``` - -5. **Specifying a custom output file:** - - ```bash - uv run python src/summary.py --from-youtube --output my_summary.md - ``` - -The output summary will be saved in a markdown file in the specified output directory, while the transcript will be saved in the temporary directory. +When running from a clone you can also use `uv run audio-summary ...` or +`uv run python -m audio_summary.cli ...`. ## Output -The summarized content is saved as a markdown file (default: `summary.md`) in the current working directory. This file includes a title and a concise summary of the content. The transcript is saved in the `tmp/transcript.txt` file. +The summary is written to a markdown file (default: `summary.md`) in the current working +directory, with a title and concise summary. The transcript is saved to +`tmp/transcript.txt`. -## Hardware Acceleration +## Project structure -The tool automatically detects and uses the best available hardware: +The code is split into focused modules under `src/audio_summary/`: -- MPS (Metal Performance Shaders) for Apple Silicon Macs -- CUDA for NVIDIA GPUs -- Falls back to CPU when neither is available +| 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). | -### Handling Longer Audio Files +## Customizing the models -This tool can process audio files of any length. For files longer than 30 seconds, the script automatically: - -1. Chunks the audio into manageable segments -2. Processes each chunk separately -3. Combines the results into a single transcript - -## Sources - -- [YouTube Video Summarizer with OpenAI Whisper and GPT](https://github.com/mirabdullahyaser/Summarizing-Youtube-Videos-with-OpenAI-Whisper-and-GPT-3/tree/master) -- [Ollama GitHub Repository](https://github.com/ollama/ollama) -- [Transformers by Hugging Face](https://huggingface.co/docs/transformers/index) -- [yt-dlp Documentation](https://github.com/yt-dlp/yt-dlp) +Edit the `TIERS` table in [`src/audio_summary/config.py`](src/audio_summary/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. ## Troubleshooting ### ffmpeg not found -If you encounter this error:: +If you encounter: ```bash -yt_dlp.utils.DownloadError: ERROR: Postprocessing: ffprobe and ffmpeg not found. Please install or provide the path using --ffmpeg-location +yt_dlp.utils.DownloadError: ERROR: Postprocessing: ffprobe and ffmpeg not found. ``` -Please refer to [this post](https://www.reddit.com/r/StacherIO/wiki/ffmpeg/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) +Install ffmpeg with `brew install ffmpeg`. -### Audio Format Issues +### Out of memory -If you encounter this error: +If a model is too large for your machine, lower the relevant tier's model in +`config.py` (e.g. switch a `fp16` STT model to its `4bit` variant, or pick a smaller +Qwen3 size). The smallest tier uses the lightweight `mlx-whisper` engine. -```bash -ValueError: Soundfile is either not in the correct format or is malformed. Ensure that the soundfile has a valid audio file extension (e.g. wav, flac or mp3) and is not corrupted. -``` +## Sources -Try converting your file with ffmpeg: - -```bash -ffmpeg -i my_file.mp4 -movflags faststart my_file_fixed.mp4 -``` - -### Memory Issues on CPU - -If you're running on CPU and encounter memory issues during transcription, consider: - -1. Using a smaller Whisper model -2. Processing shorter audio segments -3. Ensuring you have sufficient RAM available - -### Slow Transcription - -Transcription can be slow on CPU. For best performance: - -1. Use a machine with GPU or Apple Silicon (MPS) -2. Keep audio files under 10 minutes when possible -3. Close other resource-intensive applications - -### Update the Whisper or LLM Model - -You can easily change the models used for transcription and summarization by modifying the variables at the top of the script: - -```python -# Default models -OLLAMA_MODEL = "llama3" -WHISPER_MODEL = "openai/whisper-large-v2" -``` - -#### Changing the Whisper Model - -To use a different Whisper model for transcription: - -1. Update the `WHISPER_MODEL` variable with one of these options: - - `"openai/whisper-tiny"` (fastest, least accurate) - - `"openai/whisper-base"` (faster, less accurate) - - `"openai/whisper-small"` (balanced) - - `"openai/whisper-medium"` (slower, more accurate) - - `"openai/whisper-large-v2"` (slowest, most accurate) - -2. Example: - - ```python - WHISPER_MODEL = "openai/whisper-medium" # A good balance between speed and accuracy - ``` - -For CPU-only systems, using a smaller model like `whisper-base` is recommended for better performance. - -#### Changing the LLM Model - -To use a different model for summarization: - -1. First, pull the desired model with Ollama: - - ```bash - ollama pull mistral # or any other supported model - ``` - -2. Then update the `OLLAMA_MODEL` variable: - - ```python - OLLAMA_MODEL = "mistral" # or any other model you've pulled - ``` - -3. Popular alternatives include: - - `"llama3"` (default) - - `"mistral"` - - `"llama2"` - - `"gemma:7b"` - - `"phi"` - -For a complete list of available models, visit the [Ollama model library](https://ollama.com/library). +- [MLX](https://github.com/ml-explore/mlx) — Apple's array framework for Apple Silicon +- [mlx-audio](https://github.com/Blaizzy/mlx-audio) — TTS/STT on MLX (Voxtral) +- [mlx-lm](https://github.com/ml-explore/mlx-lm) — LLM inference on MLX (Qwen3) +- [mlx-whisper](https://github.com/ml-explore/mlx-examples) — Whisper on MLX +- [yt-dlp](https://github.com/yt-dlp/yt-dlp) — YouTube downloader diff --git a/pyproject.toml b/pyproject.toml index 9a1840c..087060e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,32 +4,26 @@ build-backend = "setuptools.build_meta" [project] name = "audio-summary-with-local-LLM" -version = "0.1.0" -description = 'Sum up your local or remote files with a local LLM' -keywords = ["audio", "summary", "local-llm", "ollama", "whisper"] +version = "0.2.0" +description = 'Transcribe and summarize audio/video files fully on Apple Silicon via MLX' +keywords = ["audio", "summary", "local-llm", "mlx", "apple-silicon", "voxtral", "qwen3"] readme = "README.md" requires-python = ">=3.12, <3.13" authors = [ { name = "darnodo", email = "sepales.pret0h@icloud.com" }, ] dependencies = [ - "ffmpeg>=1.4", - "ollama>=0.4.7", - "openai-whisper>=20240930", - "torch>=2.6.0", - "torchaudio>=2.6.0", - "torchvision>=0.21.0", - "transformers>=4.50.2", + "mlx-lm>=0.21.0", + "mlx-audio>=0.2.0", + "mlx-whisper>=0.4.0", + "psutil>=6.0.0", "yt-dlp>=2025.3.27", ] [project.scripts] -audio-summary = "summary:main" +audio-summary = "audio_summary.cli:main" -[tool.setuptools] -py-modules = ["summary"] - -[tool.setuptools.package-dir] -"" = "src" +[tool.setuptools.packages.find] +where = ["src"] [tool.ruff] # Exclude a variety of commonly ignored directories. @@ -66,8 +60,8 @@ exclude = [ line-length = 88 indent-width = 4 -# Assume Python 3.8 -target-version = "py38" +# Match the project's required Python version. +target-version = "py312" [tool.ruff.lint] # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. diff --git a/src/audio_summary/__init__.py b/src/audio_summary/__init__.py new file mode 100644 index 0000000..5e75412 --- /dev/null +++ b/src/audio_summary/__init__.py @@ -0,0 +1,3 @@ +"""Audio Summary with local MLX models on Apple Silicon.""" + +__version__ = "0.2.0" diff --git a/src/audio_summary/cli.py b/src/audio_summary/cli.py new file mode 100644 index 0000000..f46d797 --- /dev/null +++ b/src/audio_summary/cli.py @@ -0,0 +1,102 @@ +"""Command-line entrypoint. + +Download (optional), transcribe, and summarize audio/video files fully on +Apple Silicon via MLX. Models are selected automatically from the detected +unified memory (see ``config`` and ``device``). +""" + +import argparse +from pathlib import Path + +from .device import select_models +from .download import download_from_youtube +from .summarization import summarize_text +from .transcription import transcribe_file + +DEFAULT_LANGUAGE = "en" # Use "auto" on the CLI for automatic detection. + + +def _resolve_language(arg_language: str | None) -> str | None: + """Map the CLI ``--language`` value to a code or ``None`` (auto-detect).""" + language = arg_language if arg_language else DEFAULT_LANGUAGE + if language and language.lower() == "auto": + return None + return language + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Download, transcribe, and summarize audio or video files " + "on Apple Silicon (MLX)." + ) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--from-youtube", type=str, help="YouTube URL to download.") + group.add_argument("--from-local", type=str, help="Path to the local audio file.") + parser.add_argument( + "--output", + type=str, + default="./summary.md", + help="Output markdown file path.", + ) + parser.add_argument( + "--transcript-only", + action="store_true", + help="Only transcribe the file, do not summarize.", + ) + parser.add_argument( + "--language", + type=str, + help="Language code for transcription (e.g. 'en', 'fr', 'es', " + "or 'auto' for detection).", + ) + args = parser.parse_args() + + language = _resolve_language(args.language) + + # --- Automatic model selection based on detected unified memory --- + ram_gb, tier = select_models() + print("=" * 60) + print(f"Detected unified memory : {ram_gb:.1f} GB") + print(f"Selected hardware tier : >= {tier.min_ram_gb} GB") + print(f"STT model : {tier.stt.repo} ({tier.stt.engine})") + print(f"Summarization model : {tier.summarization_repo}") + print("=" * 60) + + # --- Working directory for intermediate files --- + data_directory = Path("tmp") + if not data_directory.exists(): + data_directory.mkdir(parents=True) + print(f"Created directory: {data_directory}") + + # --- Resolve input file --- + if args.from_youtube: + print(f"Downloading YouTube video from {args.from_youtube}") + file_path = download_from_youtube(args.from_youtube, str(data_directory)) + else: + file_path = Path(args.from_local) + + # --- Transcription --- + print(f"Transcribing file: {file_path}") + transcript = transcribe_file( + str(file_path), + str(data_directory / "transcript.txt"), + tier.stt, + language, + ) + + if args.transcript_only: + print("Transcription complete. Skipping summary generation.") + return + + # --- Summarization --- + print("Generating summary...") + summary = summarize_text(transcript, tier.summarization_repo) + + with open(args.output, "w") as md_file: + md_file.write("# Summary\n\n") + md_file.write(summary) + print(f"Summary written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/src/audio_summary/config.py b/src/audio_summary/config.py new file mode 100644 index 0000000..5ae2709 --- /dev/null +++ b/src/audio_summary/config.py @@ -0,0 +1,58 @@ +"""Model configuration for Apple Silicon (MLX). + +This table maps the amount of unified memory detected at runtime to the best +Speech-to-Text (STT) and summarization models. It is intentionally kept in its +own module so the model line-up can be tuned without touching the core logic. + +Each tier declares a ``min_ram_gb`` threshold. At runtime the highest tier whose +threshold is *less than or equal to* the detected memory is selected (see +``audio_summary.device.select_models``). + +STT engines: + * ``"voxtral"`` -> Mistral Voxtral via ``mlx-audio`` (streaming, long audio). + * ``"whisper"`` -> ``mlx-whisper`` (lightweight fallback for low-RAM Macs). +""" + +from dataclasses import dataclass + + +@dataclass(frozen=True) +class STTModel: + """A Speech-to-Text model definition.""" + + engine: str # "voxtral" or "whisper" + repo: str # Hugging Face repository id + + +@dataclass(frozen=True) +class Tier: + """A hardware tier: minimum RAM and the models it unlocks.""" + + min_ram_gb: int + stt: STTModel + summarization_repo: str + + +# Ordered from the smallest to the largest hardware tier. +TIERS: list[Tier] = [ + Tier( + min_ram_gb=8, + stt=STTModel("whisper", "mlx-community/whisper-large-v3-turbo"), + summarization_repo="mlx-community/Qwen3-4B-4bit", + ), + Tier( + min_ram_gb=16, + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-4bit"), + summarization_repo="mlx-community/Qwen3-8B-4bit", + ), + Tier( + min_ram_gb=24, + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"), + summarization_repo="mlx-community/Qwen3-8B-8bit", + ), + Tier( + min_ram_gb=32, + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"), + summarization_repo="mlx-community/Qwen3-30B-A3B-4bit", + ), +] diff --git a/src/audio_summary/device.py b/src/audio_summary/device.py new file mode 100644 index 0000000..961abd4 --- /dev/null +++ b/src/audio_summary/device.py @@ -0,0 +1,32 @@ +"""Hardware detection and automatic model selection. + +Detects the amount of unified memory available on the Apple Silicon machine and +picks the most capable model tier that fits (see ``config.TIERS``). +""" + +import psutil + +from .config import TIERS, Tier + + +def detect_ram_gb() -> float: + """Return the total unified memory in gibibytes.""" + return psutil.virtual_memory().total / (1024**3) + + +def select_tier(ram_gb: float) -> Tier: + """Select the highest tier whose RAM requirement fits ``ram_gb``. + + Falls back to the smallest tier if the machine has less memory than any + declared threshold (the models simply run slower / may swap). + """ + eligible = [tier for tier in TIERS if tier.min_ram_gb <= ram_gb] + if eligible: + return max(eligible, key=lambda tier: tier.min_ram_gb) + return min(TIERS, key=lambda tier: tier.min_ram_gb) + + +def select_models() -> tuple[float, Tier]: + """Detect memory and return ``(ram_gb, selected_tier)``.""" + ram_gb = detect_ram_gb() + return ram_gb, select_tier(ram_gb) diff --git a/src/audio_summary/download.py b/src/audio_summary/download.py new file mode 100644 index 0000000..ffee862 --- /dev/null +++ b/src/audio_summary/download.py @@ -0,0 +1,27 @@ +"""YouTube audio download helper (yt-dlp + ffmpeg).""" + +from pathlib import Path + +import yt_dlp + + +def download_from_youtube(url: str, path: str) -> Path: + """Download the best audio track from ``url`` as an mp3 into ``path``. + + Returns the path to the resulting ``to_transcribe.mp3`` file. + """ + ydl_opts = { + "format": "bestaudio/best", + "outtmpl": str(Path(path) / "to_transcribe.%(ext)s"), + "postprocessors": [ + { + "key": "FFmpegExtractAudio", + "preferredcodec": "mp3", + "preferredquality": "192", + } + ], + } + with yt_dlp.YoutubeDL(ydl_opts) as ydl: + ydl.download([url]) + + return Path(path) / "to_transcribe.mp3" diff --git a/src/audio_summary/summarization.py b/src/audio_summary/summarization.py new file mode 100644 index 0000000..4620c72 --- /dev/null +++ b/src/audio_summary/summarization.py @@ -0,0 +1,39 @@ +"""Text summarization with Qwen3 on Apple Silicon via mlx-lm.""" + +from mlx_lm import generate, load + +SYSTEM_PROMPT = "I would like for you to assume the role of a Technical Expert." + +USER_PROMPT_TEMPLATE = """Generate a concise summary of the text below. +Text : {text} +Add a title to the summary. +Make sure your summary has useful and true information about the main points of the topic. +Begin with a short introduction explaining the topic. If you can, use bullet points to list important details, +and finish your summary with a concluding sentence.""" + + +def summarize_text(text: str, model_repo: str, max_tokens: int = 2048) -> str: + """Summarize ``text`` using the Qwen3 model at ``model_repo``.""" + print(f"Loading summarization model: {model_repo}") + model, tokenizer = load(model_repo) + + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": USER_PROMPT_TEMPLATE.format(text=text)}, + ] + + # Qwen3 supports a "thinking" mode; disable it for clean, direct summaries. + prompt = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + enable_thinking=False, + ) + + summary = generate( + model, + tokenizer, + prompt=prompt, + max_tokens=max_tokens, + verbose=False, + ) + return summary.strip() diff --git a/src/audio_summary/transcription.py b/src/audio_summary/transcription.py new file mode 100644 index 0000000..b4660a4 --- /dev/null +++ b/src/audio_summary/transcription.py @@ -0,0 +1,68 @@ +"""Speech-to-Text on Apple Silicon. + +Two engines are supported, selected through ``config.STTModel.engine``: + +* ``voxtral`` -> Mistral Voxtral via ``mlx-audio``. Outperforms Whisper + large-v3 on most benchmarks and natively handles long audio with streaming. +* ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs. +""" + +from .config import STTModel + + +def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str: + """Transcribe using Mistral Voxtral through mlx-audio.""" + from mlx_audio.stt.utils import load + + model = load(model_repo) + # Voxtral handles long audio natively; ``generate`` returns an object with a + # ``.text`` attribute. ``language`` is optional (auto-detected when None). + kwargs: dict = {} + if language: + kwargs["language"] = language + result = model.generate(file_path, **kwargs) + return result.text.strip() + + +def _transcribe_whisper(file_path: str, model_repo: str, language: str | None) -> str: + """Transcribe using the mlx-whisper fallback engine.""" + import mlx_whisper + + result = mlx_whisper.transcribe( + file_path, + path_or_hf_repo=model_repo, + language=language, # None triggers auto-detection + ) + return result["text"].strip() + + +def transcribe_file( + file_path: str, + output_file: str, + stt_model: STTModel, + language: str | None = None, +) -> str: + """Transcribe ``file_path`` and persist the transcript to ``output_file``. + + ``language`` may be a language code (e.g. ``"en"``, ``"fr"``) or ``None`` for + automatic detection. Returns the full transcript text. + """ + print(f"Transcribing with {stt_model.engine} model: {stt_model.repo}") + if language: + print(f"Transcription language: {language}") + else: + print("Using automatic language detection") + print("Starting transcription (this may take a while for longer files)...") + + if stt_model.engine == "voxtral": + text = _transcribe_voxtral(file_path, stt_model.repo, language) + elif stt_model.engine == "whisper": + text = _transcribe_whisper(file_path, stt_model.repo, language) + else: + raise ValueError(f"Unknown STT engine: {stt_model.engine!r}") + + with open(output_file, "w") as tmp_file: + tmp_file.write(text) + print(f"Transcription saved to file: {output_file}") + + return text diff --git a/src/summary.py b/src/summary.py deleted file mode 100644 index 6ef7372..0000000 --- a/src/summary.py +++ /dev/null @@ -1,167 +0,0 @@ -import ollama -import argparse -from pathlib import Path -from transformers import pipeline -import yt_dlp -import torch - -OLLAMA_MODEL = "llama3" -WHISPER_MODEL = "openai/whisper-large-v2" -WHISPER_LANGUAGE = "en" # Set to desired language or None for auto-detection - -# Function to download a video from YouTube using yt-dlp -def download_from_youtube(url: str, path: str): - ydl_opts = { - 'format': 'bestaudio/best', - 'outtmpl': str(Path(path) / 'to_transcribe.%(ext)s'), - 'postprocessors': [{ - 'key': 'FFmpegExtractAudio', - 'preferredcodec': 'mp3', - 'preferredquality': '192', - }], - } - with yt_dlp.YoutubeDL(ydl_opts) as ydl: - ydl.download([url]) - -# Function to get the best available device -def get_device(): - if torch.backends.mps.is_available(): - return "mps" - elif torch.cuda.is_available(): - return "cuda" - else: - return "cpu" - -# Function to transcribe an audio file using the transformers pipeline -def transcribe_file(file_path: str, output_file: str, language: str = None) -> str: - # Get the best available device - device = get_device() - print(f"Using device: {device} for transcription") - - # Load the pipeline model for automatic speech recognition - transcriber = pipeline( - "automatic-speech-recognition", - model=WHISPER_MODEL, - device=device, - chunk_length_s=30, # Process in 30-second chunks - return_timestamps=True # Enable timestamp generation for longer audio - ) - - # Transcribe the audio file - # For CPU, we might want to use a smaller model or chunk the audio if memory is an issue - if device == "cpu": - print("Warning: Using CPU for transcription. This may be slow.") - - # Set up generation keyword arguments including language - generate_kwargs = {} - if language and language.lower() != "auto": - generate_kwargs["language"] = language - print(f"Transcribing in language: {language}") - else: - print("Using automatic language detection") - - # Transcribe the audio file - print("Starting transcription (this may take a while for longer files)...") - transcribe = transcriber(file_path, generate_kwargs=generate_kwargs) - - # Extract the full text from the chunked transcription - if isinstance(transcribe, dict) and "text" in transcribe: - # Simple case - just one chunk - full_text = transcribe["text"] - elif isinstance(transcribe, dict) and "chunks" in transcribe: - # Multiple chunks with timestamps - full_text = " ".join([chunk["text"] for chunk in transcribe["chunks"]]) - else: - # Fallback for other return formats - full_text = transcribe["text"] if "text" in transcribe else str(transcribe) - - # Save the transcribed text to the specified temporary file - with open(output_file, 'w') as tmp_file: - tmp_file.write(full_text) - print(f"Transcription saved to file: {output_file}") - - # Return the transcribed text - return full_text - -# Function to summarize a text using the Ollama model -def summarize_text(text: str, output_path: str) -> str: - # Define the system prompt for the Ollama model - system_prompt = "I would like for you to assume the role of a Technical Expert" - # Define the user prompt for the Ollama model - user_prompt = f"""Generate a concise summary of the text below. - Text : {text} - Add a title to the summary. - Make sure your summary has useful and true information about the main points of the topic. - Begin with a short introduction explaining the topic. If you can, use bullet points to list important details, - and finish your summary with a concluding sentence.""" - - # Use the Ollama model to generate a summary - response = ollama.chat( - model=OLLAMA_MODEL, - messages=[ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": user_prompt, - }, - ], - ) - # Print the generated summary - return response["message"]["content"] - -def main(): - # Parse command line arguments - parser = argparse.ArgumentParser(description="Download, transcribe, and summarize audio or video files.") - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument("--from-youtube", type=str, help="YouTube URL to download.") - group.add_argument("--from-local", type=str, help="Path to the local audio file.") - parser.add_argument("--output", type=str, default="./summary.md", help="Output markdown file path.") - parser.add_argument("--transcript-only", action='store_true', help="Only transcribe the file, do not summarize.") - parser.add_argument("--language", type=str, help="Language code for transcription (e.g., 'en', 'fr', 'es', or 'auto' for detection)") - - args = parser.parse_args() - - # Determine language setting - language = args.language if args.language else WHISPER_LANGUAGE - if language and language.lower() == "auto": - language = None # None triggers automatic language detection - - # Set up data directory - data_directory = Path("tmp") - # Check if the directory exists, if not, create it - if not data_directory.exists(): - data_directory.mkdir(parents=True) - print(f"Created directory: {data_directory}") - - if args.from_youtube: - # Download from YouTube - print(f"Downloading YouTube video from {args.from_youtube}") - download_from_youtube(args.from_youtube, str(data_directory)) - file_path = data_directory / "to_transcribe.mp3" - elif args.from_local: - # Use local file - file_path = Path(args.from_local) - - print(f"Transcribing file: {file_path}") - # Transcribe the audio file - transcript = transcribe_file(str(file_path), data_directory / "transcript.txt", language) - - if args.transcript_only: - print("Transcription complete. 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383e85c05e12c5502e791702ec437c0d99751e2e Mon Sep 17 00:00:00 2001 From: Damien Date: Wed, 17 Jun 2026 09:55:07 +0200 Subject: [PATCH 3/7] Refactor project structure for modularity Move model and pipeline components into dedicated modules Extract utilities from CLI into shared helpers Update imports throughout codebase Refactor project structure for modularity Move model configuration and hardware selection into `models/` package Extract pipeline steps into `pipeline/` package Add utility helpers in `utils/` package Update CLI imports and references --- src/audio_summary/__init__.py | 1 + src/audio_summary/cli.py | 51 +++++++------------ src/audio_summary/models/__init__.py | 13 +++++ src/audio_summary/{ => models}/config.py | 0 src/audio_summary/{ => models}/device.py | 0 src/audio_summary/pipeline/__init__.py | 11 ++++ src/audio_summary/{ => pipeline}/download.py | 0 .../{ => pipeline}/summarization.py | 0 .../{ => pipeline}/transcription.py | 2 +- src/audio_summary/utils/__init__.py | 15 ++++++ src/audio_summary/utils/helpers.py | 46 +++++++++++++++++ 11 files changed, 105 insertions(+), 34 deletions(-) create mode 100644 src/audio_summary/__init__.py create mode 100644 src/audio_summary/models/__init__.py rename src/audio_summary/{ => models}/config.py (100%) rename src/audio_summary/{ => models}/device.py (100%) create mode 100644 src/audio_summary/pipeline/__init__.py rename src/audio_summary/{ => pipeline}/download.py (100%) rename src/audio_summary/{ => pipeline}/summarization.py (100%) rename src/audio_summary/{ => pipeline}/transcription.py (98%) create mode 100644 src/audio_summary/utils/__init__.py create mode 100644 src/audio_summary/utils/helpers.py diff --git a/src/audio_summary/__init__.py b/src/audio_summary/__init__.py new file mode 100644 index 0000000..84f02e9 --- /dev/null +++ b/src/audio_summary/__init__.py @@ -0,0 +1 @@ +"""Transcribe and summarize audio/video files on Apple Silicon via MLX.""" diff --git a/src/audio_summary/cli.py b/src/audio_summary/cli.py index f46d797..b972002 100644 --- a/src/audio_summary/cli.py +++ b/src/audio_summary/cli.py @@ -2,29 +2,23 @@ Download (optional), transcribe, and summarize audio/video files fully on Apple Silicon via MLX. Models are selected automatically from the detected -unified memory (see ``config`` and ``device``). +unified memory (see ``models.config`` and ``models.device``). """ import argparse from pathlib import Path -from .device import select_models -from .download import download_from_youtube -from .summarization import summarize_text -from .transcription import transcribe_file - -DEFAULT_LANGUAGE = "en" # Use "auto" on the CLI for automatic detection. +from .models import select_models +from .pipeline import download_from_youtube, summarize_text, transcribe_file +from .utils import ( + ensure_directory, + print_model_banner, + resolve_language, + write_summary, +) -def _resolve_language(arg_language: str | None) -> str | None: - """Map the CLI ``--language`` value to a code or ``None`` (auto-detect).""" - language = arg_language if arg_language else DEFAULT_LANGUAGE - if language and language.lower() == "auto": - return None - return language - - -def main() -> None: +def _parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Download, transcribe, and summarize audio or video files " "on Apple Silicon (MLX)." @@ -49,24 +43,19 @@ def main() -> None: help="Language code for transcription (e.g. 'en', 'fr', 'es', " "or 'auto' for detection).", ) - args = parser.parse_args() + return parser.parse_args() - language = _resolve_language(args.language) + +def main() -> None: + args = _parse_args() + language = resolve_language(args.language) # --- Automatic model selection based on detected unified memory --- ram_gb, tier = select_models() - print("=" * 60) - print(f"Detected unified memory : {ram_gb:.1f} GB") - print(f"Selected hardware tier : >= {tier.min_ram_gb} GB") - print(f"STT model : {tier.stt.repo} ({tier.stt.engine})") - print(f"Summarization model : {tier.summarization_repo}") - print("=" * 60) + print_model_banner(ram_gb, tier) # --- Working directory for intermediate files --- - data_directory = Path("tmp") - if not data_directory.exists(): - data_directory.mkdir(parents=True) - print(f"Created directory: {data_directory}") + data_directory = ensure_directory("tmp") # --- Resolve input file --- if args.from_youtube: @@ -91,11 +80,7 @@ def main() -> None: # --- Summarization --- print("Generating summary...") summary = summarize_text(transcript, tier.summarization_repo) - - with open(args.output, "w") as md_file: - md_file.write("# Summary\n\n") - md_file.write(summary) - print(f"Summary written to {args.output}") + write_summary(args.output, summary) if __name__ == "__main__": diff --git a/src/audio_summary/models/__init__.py b/src/audio_summary/models/__init__.py new file mode 100644 index 0000000..a7ca230 --- /dev/null +++ b/src/audio_summary/models/__init__.py @@ -0,0 +1,13 @@ +"""Model configuration and hardware-based model selection.""" + +from .config import TIERS, STTModel, Tier +from .device import detect_ram_gb, select_models, select_tier + +__all__ = [ + "TIERS", + "STTModel", + "Tier", + "detect_ram_gb", + "select_models", + "select_tier", +] diff --git a/src/audio_summary/config.py b/src/audio_summary/models/config.py similarity index 100% rename from src/audio_summary/config.py rename to src/audio_summary/models/config.py diff --git a/src/audio_summary/device.py b/src/audio_summary/models/device.py similarity index 100% rename from src/audio_summary/device.py rename to src/audio_summary/models/device.py diff --git a/src/audio_summary/pipeline/__init__.py b/src/audio_summary/pipeline/__init__.py new file mode 100644 index 0000000..9dc58e7 --- /dev/null +++ b/src/audio_summary/pipeline/__init__.py @@ -0,0 +1,11 @@ +"""The download -> transcribe -> summarize processing pipeline.""" + +from .download import download_from_youtube +from .summarization import summarize_text +from .transcription import transcribe_file + +__all__ = [ + "download_from_youtube", + "summarize_text", + "transcribe_file", +] diff --git a/src/audio_summary/download.py b/src/audio_summary/pipeline/download.py similarity index 100% rename from src/audio_summary/download.py rename to src/audio_summary/pipeline/download.py diff --git a/src/audio_summary/summarization.py b/src/audio_summary/pipeline/summarization.py similarity index 100% rename from src/audio_summary/summarization.py rename to src/audio_summary/pipeline/summarization.py diff --git a/src/audio_summary/transcription.py b/src/audio_summary/pipeline/transcription.py similarity index 98% rename from src/audio_summary/transcription.py rename to src/audio_summary/pipeline/transcription.py index b4660a4..7cb2606 100644 --- a/src/audio_summary/transcription.py +++ b/src/audio_summary/pipeline/transcription.py @@ -7,7 +7,7 @@ Two engines are supported, selected through ``config.STTModel.engine``: * ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs. """ -from .config import STTModel +from ..models import STTModel def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str: diff --git a/src/audio_summary/utils/__init__.py b/src/audio_summary/utils/__init__.py new file mode 100644 index 0000000..bfd0740 --- /dev/null +++ b/src/audio_summary/utils/__init__.py @@ -0,0 +1,15 @@ +"""Cross-cutting helpers (I/O, formatting, CLI argument resolution).""" + +from .helpers import ( + ensure_directory, + print_model_banner, + resolve_language, + write_summary, +) + +__all__ = [ + "ensure_directory", + "print_model_banner", + "resolve_language", + "write_summary", +] diff --git a/src/audio_summary/utils/helpers.py b/src/audio_summary/utils/helpers.py new file mode 100644 index 0000000..05ef5fe --- /dev/null +++ b/src/audio_summary/utils/helpers.py @@ -0,0 +1,46 @@ +"""Cross-cutting helpers: CLI argument resolution, I/O, and console output. + +These keep ``cli.py`` focused on orchestration and the ``pipeline`` modules +focused on the actual ML work. +""" + +from pathlib import Path + +from ..models import Tier + +DEFAULT_LANGUAGE = "en" # Use "auto" on the CLI for automatic detection. + + +def resolve_language(arg_language: str | None) -> str | None: + """Map the CLI ``--language`` value to a code or ``None`` (auto-detect).""" + language = arg_language if arg_language else DEFAULT_LANGUAGE + if language and language.lower() == "auto": + return None + return language + + +def ensure_directory(path: str | Path) -> Path: + """Create ``path`` (and parents) if missing and return it as a ``Path``.""" + directory = Path(path) + if not directory.exists(): + directory.mkdir(parents=True) + print(f"Created directory: {directory}") + return directory + + +def print_model_banner(ram_gb: float, tier: Tier) -> None: + """Print the detected hardware and the models selected for it.""" + print("=" * 60) + print(f"Detected unified memory : {ram_gb:.1f} GB") + print(f"Selected hardware tier : >= {tier.min_ram_gb} GB") + print(f"STT model : {tier.stt.repo} ({tier.stt.engine})") + print(f"Summarization model : {tier.summarization_repo}") + print("=" * 60) + + +def write_summary(output_path: str | Path, summary: str) -> None: + """Write ``summary`` to ``output_path`` as a titled markdown document.""" + with open(output_path, "w") as md_file: + md_file.write("# Summary\n\n") + md_file.write(summary) + print(f"Summary written to {output_path}") From 425d4db1e24f3b2ca5d22f872ca637de1431e3c3 Mon Sep 17 00:00:00 2001 From: Damien Date: Wed, 17 Jun 2026 10:12:08 +0200 Subject: [PATCH 4/7] Update README paths and add shell alias section --- README.md | 40 +++++++++++++++++++++++++++++----------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 40e1b5a..2d4593e 100644 --- a/README.md +++ b/README.md @@ -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 --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 +audio-summary --from-local --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. From 701f5dd56e0ca01e2cd645771cd1bab54005fce3 Mon Sep 17 00:00:00 2001 From: Damien Date: Wed, 17 Jun 2026 11:25:04 +0200 Subject: [PATCH 5/7] Update Voxtral STT model to non-realtime batch variant Use Voxtral-Mini-3B-2507-bf16 across all tiers and set appropriate max_tokens for full-file transcription --- src/audio_summary/models/config.py | 9 +++++---- src/audio_summary/pipeline/transcription.py | 18 +++++++++++++----- 2 files changed, 18 insertions(+), 9 deletions(-) diff --git a/src/audio_summary/models/config.py b/src/audio_summary/models/config.py index 5ae2709..4de4208 100644 --- a/src/audio_summary/models/config.py +++ b/src/audio_summary/models/config.py @@ -9,7 +9,8 @@ threshold is *less than or equal to* the detected memory is selected (see ``audio_summary.device.select_models``). STT engines: - * ``"voxtral"`` -> Mistral Voxtral via ``mlx-audio`` (streaming, long audio). + * ``"voxtral"`` -> Mistral Voxtral (non-realtime batch model) via ``mlx-audio``. + Honors the requested language and transcribes whole files in one pass. * ``"whisper"`` -> ``mlx-whisper`` (lightweight fallback for low-RAM Macs). """ @@ -42,17 +43,17 @@ TIERS: list[Tier] = [ ), Tier( min_ram_gb=16, - stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-4bit"), + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"), summarization_repo="mlx-community/Qwen3-8B-4bit", ), Tier( min_ram_gb=24, - stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"), + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"), summarization_repo="mlx-community/Qwen3-8B-8bit", ), Tier( min_ram_gb=32, - stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"), + stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"), summarization_repo="mlx-community/Qwen3-30B-A3B-4bit", ), ] diff --git a/src/audio_summary/pipeline/transcription.py b/src/audio_summary/pipeline/transcription.py index 7cb2606..aefe4c4 100644 --- a/src/audio_summary/pipeline/transcription.py +++ b/src/audio_summary/pipeline/transcription.py @@ -2,22 +2,30 @@ Two engines are supported, selected through ``config.STTModel.engine``: -* ``voxtral`` -> Mistral Voxtral via ``mlx-audio``. Outperforms Whisper - large-v3 on most benchmarks and natively handles long audio with streaming. +* ``voxtral`` -> Mistral Voxtral (non-realtime batch model) via ``mlx-audio``. + Outperforms Whisper large-v3 on most benchmarks. The *realtime* Voxtral + variant is deliberately not used here: it ignores the requested language and + truncates when run one-shot on a full file. * ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs. """ from ..models import STTModel +# Upper bound on generated tokens for a full-file transcription. The non-realtime +# Voxtral ``generate`` defaults to only 128 tokens, which would truncate any real +# recording, so we raise it well above the length of a long-form transcript. +_VOXTRAL_MAX_TOKENS = 32768 + def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str: """Transcribe using Mistral Voxtral through mlx-audio.""" from mlx_audio.stt.utils import load model = load(model_repo) - # Voxtral handles long audio natively; ``generate`` returns an object with a - # ``.text`` attribute. ``language`` is optional (auto-detected when None). - kwargs: dict = {} + # The non-realtime Voxtral model transcribes the whole file in one pass and + # honors ``language``. ``generate`` returns an object with a ``.text`` + # attribute. ``language`` is optional (auto-detected when None). + kwargs: dict = {"max_tokens": _VOXTRAL_MAX_TOKENS} if language: kwargs["language"] = language result = model.generate(file_path, **kwargs) From 02ca896808188a26a760ce0bad1135660244b4b4 Mon Sep 17 00:00:00 2001 From: Damien Date: Wed, 17 Jun 2026 11:51:23 +0200 Subject: [PATCH 6/7] Add Voxtral support with audio preprocessing --- pyproject.toml | 2 + src/audio_summary/pipeline/transcription.py | 44 ++- uv.lock | 386 +++++++++++++++++++- 3 files changed, 428 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 087060e..a180788 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,8 @@ dependencies = [ "mlx-whisper>=0.4.0", "psutil>=6.0.0", "yt-dlp>=2025.3.27", + "librosa>=0.11.0", + "mistral-common>=1.11.3", ] [project.scripts] audio-summary = "audio_summary.cli:main" diff --git a/src/audio_summary/pipeline/transcription.py b/src/audio_summary/pipeline/transcription.py index aefe4c4..2a0487c 100644 --- a/src/audio_summary/pipeline/transcription.py +++ b/src/audio_summary/pipeline/transcription.py @@ -9,6 +9,10 @@ Two engines are supported, selected through ``config.STTModel.engine``: * ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs. """ +import os +import subprocess +import tempfile + from ..models import STTModel # Upper bound on generated tokens for a full-file transcription. The non-realtime @@ -16,6 +20,38 @@ from ..models import STTModel # recording, so we raise it well above the length of a long-form transcript. _VOXTRAL_MAX_TOKENS = 32768 +# Sample rate Voxtral expects; we resample to it during the WAV conversion below. +_VOXTRAL_SAMPLE_RATE = 16000 + + +def _to_wav_16k_mono(src: str) -> str: + """Transcode ``src`` to a temporary 16 kHz mono WAV via ffmpeg. + + Voxtral's processor loads audio through soundfile/libsndfile, which cannot + decode AAC/M4A (and several other compressed formats). ffmpeg (a project + prerequisite) handles those, so we normalize the input first. The caller is + responsible for deleting the returned path. + """ + fd, wav_path = tempfile.mkstemp(suffix=".wav") + os.close(fd) + subprocess.run( + [ + "ffmpeg", + "-y", + "-i", + src, + "-ac", + "1", + "-ar", + str(_VOXTRAL_SAMPLE_RATE), + wav_path, + ], + check=True, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + return wav_path + def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str: """Transcribe using Mistral Voxtral through mlx-audio.""" @@ -28,7 +64,13 @@ def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) - kwargs: dict = {"max_tokens": _VOXTRAL_MAX_TOKENS} if language: kwargs["language"] = language - result = model.generate(file_path, **kwargs) + + # Hand Voxtral a soundfile-readable WAV regardless of the source container. + wav_path = _to_wav_16k_mono(file_path) + try: + result = model.generate(wav_path, **kwargs) + finally: + os.remove(wav_path) return result.text.strip() diff --git a/uv.lock 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deletions(-) diff --git a/src/audio_summary/pipeline/summarization.py b/src/audio_summary/pipeline/summarization.py index 4620c72..b994b1a 100644 --- a/src/audio_summary/pipeline/summarization.py +++ b/src/audio_summary/pipeline/summarization.py @@ -2,14 +2,36 @@ from mlx_lm import generate, load -SYSTEM_PROMPT = "I would like for you to assume the role of a Technical Expert." +SYSTEM_PROMPT = ( + "You are an expert technical analyst who writes clear, faithful summaries of " + "transcribed audio. Rules you always follow:\n" + "- Write the summary in the SAME language as the transcript.\n" + "- Only use information present in the transcript; never invent facts, names, " + "or numbers. If something is unclear or cut off, say so rather than guessing.\n" + "- Preserve technical terms, product names, and acronyms exactly as spoken.\n" + "- The text is raw speech-to-text: ignore filler, repetitions, and transcription " + "noise, and focus on the substance." +) -USER_PROMPT_TEMPLATE = """Generate a concise summary of the text below. -Text : {text} -Add a title to the summary. -Make sure your summary has useful and true information about the main points of the topic. -Begin with a short introduction explaining the topic. If you can, use bullet points to list important details, -and finish your summary with a concluding sentence.""" +USER_PROMPT_TEMPLATE = """Summarize the transcript below as a Markdown document with this exact structure: + +# + +**TL;DR:** <2-3 sentence overview a busy reader could read alone> + +## Key points +- + +## Details that matter +- + +## Takeaways +- + +Keep it concise and skimmable. Omit any section that the transcript genuinely does not support. + +Transcript: +{text}""" def summarize_text(text: str, model_repo: str, max_tokens: int = 2048) -> str: diff --git a/src/audio_summary/utils/helpers.py b/src/audio_summary/utils/helpers.py index 05ef5fe..dc2ebdb 100644 --- a/src/audio_summary/utils/helpers.py +++ b/src/audio_summary/utils/helpers.py @@ -39,8 +39,11 @@ def print_model_banner(ram_gb: float, tier: Tier) -> None: def write_summary(output_path: str | Path, summary: str) -> None: - """Write ``summary`` to ``output_path`` as a titled markdown document.""" + """Write ``summary`` to ``output_path``. + + The summarization prompt already makes the model emit its own ``#`` title and + Markdown structure, so the text is written verbatim. + """ with open(output_path, "w") as md_file: - md_file.write("# Summary\n\n") md_file.write(summary) print(f"Summary written to {output_path}")