From a8005cce506fae4578d01b2aeeb66be72933abb3 Mon Sep 17 00:00:00 2001 From: darnodo Date: Fri, 28 Mar 2025 12:58:39 +0100 Subject: [PATCH] Add support pour GPU (MPS and CUDA) Migrate to `uv` --- .gitignore | 5 +- README.md | 184 ++++++-- pyproject.toml | 109 +++++ src/requirements.txt | 6 - src/summary.py | 71 ++- uv.lock | 1006 ++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 1336 insertions(+), 45 deletions(-) create mode 100644 pyproject.toml delete mode 100644 src/requirements.txt create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 63e4486..ab876c3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,9 @@ +audio_summary_with_local_LLM.egg-info/ +.ruff_cache/ # Virtual Env .venv # Local data .DS_Store -tmp \ No newline at end of file +tmp +summary.md \ No newline at end of file diff --git a/README.md b/README.md index 882045f..edde61d 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# Audio Summary with local LLM +# Audio Summary with Local LLM -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 Mistral AI (Ollama) for generating summaries. +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. > [!TIP] > It is possible to change the model you wish to use. @@ -9,45 +9,47 @@ This tool is designed to provide a quick and concise summary of audio and video ## Features - **YouTube Integration**: Download and summarize content directly from YouTube. -- **Local File Support**: Summarize audio files available on your local disk. +- **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 Mistral AI (Ollama). +- **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). ## Prerequisites Before you start using this tool, you need to install the following dependencies: -- Python 3.8 or higher -- `pytube` for downloading videos from YouTube. -- `pathlib` for local file handling -- `openai-whisper` for audio transcription. -- [Ollama](https://ollama.com) for LLM model management. -- `ffmpeg` (required for whisper) +- Python 3.12 and lower than 3.13 +- [Ollama](https://ollama.com) for LLM model management +- `ffmpeg` (required for audio processing) +- [uv](https://docs.astral.sh/uv/getting-started/installation/) for package management ## Installation -### Python Requirements +### Using uv -Clone the repository and install the required Python packages: +Clone the repository and install the required Python packages using [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 -pip install -r src/requirements.txt + +# 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 Mistral model: +Download and use the Llama3 model: ```bash -ollama pull mistral +ollama pull llama3 ## Test the access: -ollama run mistral "tell me a joke" +ollama run llama3 "tell me a joke" ``` ## Usage @@ -56,51 +58,175 @@ The tool can be executed with the following command line options: - `--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. -- `--transcript-only`: To only transcribe the audio content without generating a summary. This option must be used with either `--from-youtube` or `--from-local`. +- `--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) ### Examples 1. **Summarizing a YouTube video:** ```bash - python src/summary.py --from-youtube + uv run python src/summary.py --from-youtube ``` 2. **Summarizing a local audio file:** ```bash - python src/summary.py --from-local + uv run python src/summary.py --from-local ``` 3. **Transcribing a YouTube video without summarizing:** ```bash - python src/summary.py --from-youtube --transcript-only + uv run python src/summary.py --from-youtube --transcript-only ``` 4. **Transcribing a local audio file without summarizing:** ```bash - python src/summary.py --from-local --transcript-only + 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. ## Output -The summarized content is saved as a markdown file named `summary.md` in the current working directory. This file includes the transcribed text and its corresponding summary. If `--transcript-only` is used, only the transcription will be saved in the temporary directory. +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. + +## Hardware Acceleration + +The tool automatically detects and uses the best available hardware: + +- MPS (Metal Performance Shaders) for Apple Silicon Macs +- CUDA for NVIDIA GPUs +- Falls back to CPU when neither is available + +### Handling Longer Audio Files + +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 + +This approach allows for efficient processing of longer content while managing memory usage. However, be aware that: + +- Longer files will take proportionally more time to process +- Very long files (>30 minutes) may require significant processing time, especially on CPU +- For extremely long content, consider splitting the audio file into smaller segments before processing + +If you encounter memory issues with very long files, you can try: + +1. Using a smaller Whisper model by changing `WHISPER_MODEL` to "openai/whisper-base" +2. Reducing the `chunk_length_s` parameter in the `transcribe_file` function +3. Processing the file in separate parts and combining the summaries afterward ## Sources - [YouTube Video Summarizer with OpenAI Whisper and GPT](https://github.com/mirabdullahyaser/Summarizing-Youtube-Videos-with-OpenAI-Whisper-and-GPT-3/tree/master) -- [Mistral Python Client](https://github.com/mistralai/client-python) -- [Ollama : Installez LLama 2 et Code LLama en quelques secondes !](https://www.geeek.org/tutoriel-installation-llama-2-et-code-llama/) +- [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) -## Known Issues +## Troubleshooting -```python -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. If reading from a remote URL, ensure that the URL is the full address to **download** the audio file. +### ffmpeg not found + +If you encounter this error:: + +```bash +yt_dlp.utils.DownloadError: ERROR: Postprocessing: ffprobe and ffmpeg not found. Please install or provide the path using --ffmpeg-location ``` -To fix it : -`ffmpeg -i my_file.mp4 -movflags faststart my_file_fixed.mp4` +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) + +### Audio Format Issues + +If you encounter this error: + +```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. +``` + +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). diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..6ed6010 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,109 @@ +[project] +name = "audio-summary-with-local-LLM" +dynamic = ["version"] +description = 'Sum up your local or remote files with a local LLM' +keywords = ["audio", "summary", "local-llm", "ollama", "whisper"] +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", + "yt-dlp>=2025.3.27", +] +[tool.setuptools] +py-modules = [] + +[tool.ruff] +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".ipynb_checkpoints", + ".mypy_cache", + ".nox", + ".pants.d", + ".pyenv", + ".pytest_cache", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + ".vscode", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "site-packages", + "venv", +] + +# Same as Black. +line-length = 88 +indent-width = 4 + +# Assume Python 3.8 +target-version = "py38" + +[tool.ruff.lint] +# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. +# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or +# McCabe complexity (`C901`) by default. +select = ["E4", "E7", "E9", "F"] +ignore = [] + +# Allow fix for all enabled rules (when `--fix`) is provided. +fixable = ["ALL"] +unfixable = [] + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +[tool.ruff.format] +# Like Black, use double quotes for strings. +quote-style = "double" + +# Like Black, indent with spaces, rather than tabs. +indent-style = "space" + +# Like Black, respect magic trailing commas. +skip-magic-trailing-comma = false + +# Like Black, automatically detect the appropriate line ending. +line-ending = "auto" + +# Enable auto-formatting of code examples in docstrings. Markdown, +# reStructuredText code/literal blocks and doctests are all supported. +# +# This is currently disabled by default, but it is planned for this +# to be opt-out in the future. +docstring-code-format = false + +# Set the line length limit used when formatting code snippets in +# docstrings. +# +# This only has an effect when the `docstring-code-format` setting is +# enabled. +docstring-code-line-length = "dynamic" + +[dependency-groups] +lint = [ + "ruff>=0.0.17", +] +dev = [ + "ipython>=5.10.0", +] diff --git a/src/requirements.txt b/src/requirements.txt deleted file mode 100644 index 94d3d95..0000000 --- a/src/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -openai-whisper==20231117 -ollama==0.1.8 -torch==2.5.0.dev20240712 -torchaudio==2.4.0.dev20240712 -torchvision==0.20.0.dev20240712 -transformers==4.42.4 \ No newline at end of file diff --git a/src/summary.py b/src/summary.py index d7dbd22..6ef7372 100644 --- a/src/summary.py +++ b/src/summary.py @@ -3,8 +3,11 @@ 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): @@ -20,26 +23,70 @@ def download_from_youtube(url: str, path: str): 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) -> str: - # Load the pipeline model for automatic speech recognition with MPS - transcriber_gpu = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device="mps") +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 - transcribe = transcriber_gpu(file_path) + # 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(transcribe["text"]) + tmp_file.write(full_text) print(f"Transcription saved to file: {output_file}") # Return the transcribed text - return transcribe["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 = f"I would like for you to assume the role of a Technical Expert" + 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} @@ -73,9 +120,15 @@ def main(): 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 @@ -94,7 +147,7 @@ def main(): print(f"Transcribing file: {file_path}") # Transcribe the audio file - transcript = transcribe_file(str(file_path), data_directory / "transcript.txt") + transcript = transcribe_file(str(file_path), data_directory / "transcript.txt", language) if args.transcript_only: print("Transcription complete. 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