Setup feluda to match for exact matches on video and audio
dennyabrain opened this issue · comments
Overview
- Setup operators for exact matching of audio and video
- Setup operators for approximate matching of audio and video
- Setup operators for extracting additional metadata that might be useful for the tipline
- Identify throughput on various EC2 instances (cpu/gpu, single-core/multi-core)
Acceptance Criteria
- Publish API with documentation for indexing and search of video and audio content.
- Document the throughput for video and audio content wrt 2,3 EC2 instances on wiki
- Identify the Apropriate ec2 instance(s) type for our use case
Audio Matching
- https://stackoverflow.com/questions/71712529/compare-similarity-between-two-audio-signals-singing-recordings-in-python
- Audio Fragments to Consider - https://stackoverflow.com/questions/38971969/how-to-compare-audio-on-similarity-in-python
segment.cross_similarity- https://librosa.org/doc/main/generated/librosa.segment.cross_similarity.html#librosa.segment.cross_similarity- mp3 - pcm - FFT - correlation - https://stackoverflow.com/questions/3172911/compare-two-audio-files
Acoustic fingerprint- https://en.wikipedia.org/wiki/Acoustic_fingerprint- https://github.com/d4r3topk/comparing-audio-files-python/blob/master/mfcc.py
Spectral Analysis- librosa - Mel-frequency cepstral coefficients (MFCCs) - https://librosa.org/doc/main/generated/librosa.feature.mfcc.html- cross similarity - https://stackoverflow.com/questions/73849228/compare-two-audio-files-with-persons-speaking-and-compute-the-similarity-score
- cosine similarity?
- lot of open source libraries have been unmaintained for years.
- List of many methods - https://stackoverflow.com/questions/49895223/how-to-compare-match-two-non-identical-sound-clips
- Shazam Paper - https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf
- https://stackoverflow.com/questions/11705224/matching-two-audio-files
- https://medium.com/intrasonics/a-fingerprint-for-audio-3b337551a671
- https://emysound.com/blog/open-source/2020/06/12/how-audio-fingerprinting-works.html
- https://stackoverflow.com/questions/64161155/how-to-convert-an-audio-file-mp3-or-wav-or-any-other-to-an-unique-audio-id-u
Shazam Like App- https://github.com/MarwaAbdelAal/Shazam-like-app/blob/master/main.py
End of Week Deliverables after Status Check :
- Working Feluda Operators for audio and video exact match
- Documentation on how to use them
- Tests to run
- Status Update on "similarity match" of audio and video
Status of Audio Fingerprinting
We have an operator working that finds the fingerprint of an given audio file using signal processing.
It firsts finds a spectrogram of the audio file and then using it it finds the fingerprint by finding a list of (positive) frequencies (scaled to [0, 1]) at which the local periodogram has a peak
Limitations and TODO:
- Each audio file has to be an
.wavfile. - The fingerprint array doesn't have a fixed dimension.
Audio Embeddings using PANN (Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition)
[Article Link] [GitHub]
Given an audio file, this methods finds a vector of 2048 dimensions using PANNs. PANN is a CNN that is pre-trained on lot of audio files. They have been used for audio tagging and sound event detection. The PANNs have been used to fine-tune several audio pattern recognition tasks, and have outperformed several state-of-the-art systems.
Embeddings for vector audio search
Audio embeddings are often generated using spectrograms or other audio signal features. In the context of audio signal processing for machine learning, the process of feature extraction from spectrograms is a crucial step. Spectrograms are visual representations of the frequency content of audio signals over time. The identified features in this context encompass three specific types:
- Mel-frequency cepstral coefficients (MFCCs)
- Chroma features: Chroma features represent the 12 distinct pitch classes of the musical octave and are particularly useful in music-related tasks.
- Spectral contrast: Spectral contrast focuses on the perceptual brightness of different frequency bands within an audio signal.
Indexing and Searching Audio Vectors in Elasticsearch
All the audio files have to be of the .wav file format and once this operators process it, it will return an vector of dimension - 2048.
I index and search for this vector using curl commands listed below.
Step 1 - Create an index called "audio" with specific mappings
curl -X PUT "es:9200/audio" -H 'Content-Type: application/json' -d '{"mappings": {"_source": {"excludes": ["audio-embedding"]},"properties": {"audio-embedding": {"type": "dense_vector","dims": 2048,"index": true,"similarity": "cosine"},"path": {"type": "text","fields": {"keyword": {"type": "keyword","ignore_above": 256}}},"timestamp": {"type": "date"},"title": {"type": "text"},"genre": {"type": "text"}}}}'Step 2 - see a list of all the indices, check if the audio index is created
curl -X GET "http://es:9200/_cat/indices?v"Step 3 - Store a vector in the audio index
curl -X POST "es:9200/audio/_doc" -H 'Content-Type: application/json' -d '{"audio-embedding": [0.0, 0.0, 0.029310517013072968, 0.02595067210495472, 0.023528538644313812], "path": "path1", "timestamp": "2024-02-07T12:00:00", "title": "title1", "genre": "genre1"}'Step 4 - Search for the indexed vector. We use cosine similarity to search for the vector
curl -X GET "es:9200/audio/_search" -H 'Content-Type: application/json' -d '{"query": {"script_score": {"query": {"match_all": {}}, "script": {"source": "cosineSimilarity(params.query_vector, '"'"'audio-embedding'"'"') + 1.0", "params": {"query_vector": [0.0, 0.0, 0.029310517013072968, 0.02595067210495472, 0.023528538644313812]}}}}}'The pull request for this operators - tattle-made/feluda#59