├── .gitignore ├── tests ├── test_clap.py └── conftest.py ├── pyproject.toml ├── .github └── workflows │ ├── test.yml │ └── publish.yml ├── README.md ├── llm_clap.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | venv 3 | llm_clap.egg-info 4 | -------------------------------------------------------------------------------- /tests/test_clap.py: -------------------------------------------------------------------------------- 1 | import llm 2 | 3 | 4 | def test_run_embedding(): 5 | model = llm.get_embedding_model("clap") 6 | result = model.embed("bunny") 7 | assert len(result) == 512 8 | assert isinstance(result[0], float) 9 | -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | 3 | 4 | @pytest.fixture 5 | def user_path(tmpdir): 6 | dir = tmpdir / "llm.datasette.io" 7 | dir.mkdir() 8 | return dir 9 | 10 | 11 | @pytest.fixture(autouse=True) 12 | def env_setup(monkeypatch, user_path): 13 | monkeypatch.setenv("LLM_USER_PATH", str(user_path)) 14 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "llm-clap" 3 | version = "0.2" 4 | description = "Generate embeddings for audio files using CLAP with llm" 5 | readme = "README.md" 6 | authors = [{name = "Evangelos Lamprou"}] 7 | license = {text = "Apache-2.0"} 8 | classifiers = [ 9 | "License :: OSI Approved :: Apache Software License" 10 | ] 11 | dependencies = [ 12 | "llm>=0.10", 13 | "transformers", 14 | "torch", 15 | "torchaudio", 16 | "librosa" 17 | ] 18 | 19 | [project.urls] 20 | Homepage = "https://github.com/vagos/llm-clap" 21 | Changelog = "https://github.com/vagos/llm-clap/releases" 22 | Issues = "https://github.com/vagos/llm-clap/issues" 23 | 24 | [project.entry-points.llm] 25 | clap = "llm_clap" 26 | 27 | [project.optional-dependencies] 28 | test = ["pytest"] 29 | -------------------------------------------------------------------------------- /.github/workflows/test.yml: -------------------------------------------------------------------------------- 1 | name: Test 2 | 3 | on: [push, pull_request] 4 | 5 | permissions: 6 | contents: read 7 | 8 | jobs: 9 | test: 10 | runs-on: ubuntu-22.04 11 | strategy: 12 | matrix: 13 | python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] 14 | steps: 15 | - uses: actions/checkout@v3 16 | - name: Set up Python ${{ matrix.python-version }} 17 | uses: actions/setup-python@v4 18 | with: 19 | python-version: ${{ matrix.python-version }} 20 | cache: pip 21 | cache-dependency-path: setup.py 22 | - name: Cache models 23 | uses: actions/cache@v3 24 | with: 25 | path: ~/.cache/torch 26 | key: ${{ runner.os }}-torch- 27 | - name: Install dependencies 28 | run: | 29 | pip install -e '.[test]' 30 | - name: Run tests 31 | run: | 32 | pytest -s 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # llm-clap 2 | 3 | [LLM](https://llm.datasette.io/) plugin for embedding audio files (music, speech, sounds) and text using [CLAP](https://huggingface.co/docs/transformers/main/en/model_doc/clap) 4 | 5 | ## Installation 6 | 7 | Install this plugin in the same environment as LLM. 8 | ```bash 9 | llm install llm-clap 10 | ``` 11 | 12 | ## Usage 13 | 14 | Once you have installed an embedding model you can use it to embed text like this: 15 | 16 | ```bash 17 | llm embed -m clap -c 'Hello world' 18 | ``` 19 | Or an audio file like this: 20 | ```bash 21 | llm embed -m clap --binary -i AUDIO_1431.wav 22 | ``` 23 | 24 | Embeddings are more useful if you store them in a database - see [the LLM documentation](https://llm.datasette.io/en/stable/embeddings/cli.html#storing-embeddings-in-sqlite) for details. 25 | 26 | To embed every audio file in a folder and save them in a collection called "songs": 27 | 28 | ```bash 29 | llm embed-multi songs -m clap --binary --files songs/ '*.wav' 30 | ``` 31 | You can then search for songs of specific things like this: 32 | ```bash 33 | llm similar songs -c 'rap' 34 | ``` 35 | 36 | But you can also find songs/audio similar to a given audio file like this: 37 | ```bash 38 | llm similar songs --binary -i MYSONG.wav 39 | ``` 40 | -------------------------------------------------------------------------------- /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish Python Package 2 | 3 | on: 4 | release: 5 | types: [created] 6 | 7 | permissions: 8 | contents: read 9 | 10 | jobs: 11 | test: 12 | runs-on: ubuntu-latest 13 | strategy: 14 | matrix: 15 | python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"] 16 | steps: 17 | - uses: actions/checkout@v3 18 | - name: Set up Python ${{ matrix.python-version }} 19 | uses: actions/setup-python@v4 20 | with: 21 | python-version: ${{ matrix.python-version }} 22 | cache: pip 23 | cache-dependency-path: setup.py 24 | - name: Cache models 25 | uses: actions/cache@v3 26 | with: 27 | path: ~/.cache/torch 28 | key: ${{ runner.os }}-torch- 29 | - name: Install dependencies 30 | run: | 31 | pip install -e '.[test]' 32 | - name: Run tests 33 | run: | 34 | pytest -s 35 | deploy: 36 | runs-on: ubuntu-latest 37 | needs: [test] 38 | steps: 39 | - uses: actions/checkout@v3 40 | - name: Set up Python 41 | uses: actions/setup-python@v4 42 | with: 43 | python-version: "3.11" 44 | cache: pip 45 | cache-dependency-path: setup.py 46 | - name: Install dependencies 47 | run: | 48 | pip install setuptools wheel twine build 49 | - name: Publish 50 | env: 51 | TWINE_USERNAME: __token__ 52 | TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} 53 | run: | 54 | python -m build 55 | twine upload dist/* 56 | -------------------------------------------------------------------------------- /llm_clap.py: -------------------------------------------------------------------------------- 1 | from transformers import ClapModel, ClapProcessor, AutoTokenizer 2 | import io 3 | import librosa 4 | import llm 5 | 6 | 7 | @llm.hookimpl 8 | def register_embedding_models(register): 9 | register(ClapEmbeddingModel()) 10 | 11 | 12 | class ClapEmbeddingModel(llm.EmbeddingModel): 13 | model_id = "clap" 14 | supports_binary = True 15 | supports_text = True 16 | model_name = "laion/larger_clap_general" 17 | 18 | def __init__(self): 19 | self._model = None 20 | 21 | def embed_batch(self, items): 22 | # Embeds a mix of text strings and audio data 23 | if self._model is None: 24 | self._model = ClapModel.from_pretrained(self.model_name) 25 | 26 | embeddings = [] 27 | processor = ClapProcessor.from_pretrained(self.model_name) 28 | tokenizers = AutoTokenizer.from_pretrained(self.model_name) 29 | 30 | for item in items: 31 | if isinstance(item, bytes): 32 | # If the item is a byte string, treat it as audio data 33 | audio_array, _ = librosa.load(io.BytesIO(item), sr=48000) 34 | if len(audio_array.shape) > 1: 35 | audio_array = librosa.to_mono(audio_array) 36 | inputs = processor(audios=audio_array, sampling_rate=48000, return_tensors="pt") 37 | embedding = self._model.get_audio_features(**inputs) 38 | elif isinstance(item, str): 39 | # If the item is a string, embed it as text 40 | inputs = tokenizers(item, padding=True, return_tensors="pt") 41 | embedding = self._model.get_text_features(**inputs) 42 | else: 43 | raise ValueError(f"Cannot embed item of type: {type(item)}") 44 | 45 | yield [float(num) for num in embedding.flatten()] 46 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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