shira-audio 0.1.28

audio search/retrieval library

shira 🔖🎧

A simple audio search/retrieval library. (wip)

This is the audio version of ripple. Search through audio files/data with text queries or audio samples.
It's meant to be an neural encoded version of Shazam, but might just be for small scale/local usage.

Methodology

It's basically a semantic search library for audio.

The local audio data/files are indexed and embeddings are generated(with CLAP), then a FAISS vector index is created.
The files are retrieved based on cosine similarity between embeddings. (it could also be adapted for audio recommendation).

This process makes use of contrastively pretrained audio-language model, CLAP(like OpenAI CLIP for audio), specifically LAION's laion/larger_clap_music_and_speech checkpoint/model.

usage

• Install the library

pip install shira-audio

• For text-based search

from shira import AudioSearch, AudioEmbedding

embedder = AudioEmbedding(data_path='.') # init embedder class
audio_data_embeds = embedder.index_files() # create embeddings and index audio files

neural_search = AudioSearch() # init semantic search class

text_query = 'classical music' # text description for search

# get k similar audio w/probability score pairs 
matching_samples, scores = neural_search.text_search(text_query, audio_data_embeds, k_count=5)

matching_samples['path'][0], scores[0] # get file path for the top sample

Or you could use it from your terminal:

# -t for text query 
# --dir for [optional] target directory 
shira_text -t instrumental --dir downloads/music

• For audio-based search

from shira import AudioSearch, AudioEmbedding

embedder = AudioEmbedding(data_path='downloads') # init embedder class
audio_data_embeds = embedder.index_files() # create embeddings and index audio files

neural_search = AudioSearch() # init semantic search class

audiofile = 'beethoven_moonlight_sonata.mp3' # audio file for reference

# get k similar audio w/probability score pairs 
similar_samples, scores = neural_search.audio_search(audiofile, audio_data_embeds, k_count=4)

similar_samples['path'][0], scores[0] # get file path for the top sample

Or for terminal/cli use:

# -f reference audio file path 
# --dir for [optional] target directory 
shira -f sprinter.mp3 --dir downloads/

Acknowldgements

• CLAP: Learning Audio Concepts From Natural Language Supervision
• tinyCLAP: Distilling Contrastive Language-Audio Pretrained models
• Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation
• laion/larger_clap_music_and_speech model by LAION.