Resemblyzer
Analyze and compare voices with deep learning
Decision gist · record as of 2026-08-14
Yes, if you need voice similarity or speaker identification. The package is stable, permissively licensed, and has low install friction. Maintenance is dormant but the code is mature and no vulnerabilities are known. Choose it if your use case aligns with English-language speaker analysis; be aware that non-English performance is not guaranteed and the project is not actively developed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.5+.
- Runtime dependencies include torch, librosa, numpy, scipy, and webrtcvad; torch installation may require additional system setup depending on GPU availability.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2023-10-12 (1037 days) · last repo commit 2023-10-12 · 3,298 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 324,662 downloads/mo, #7,593 on PyPI
Alternatives
Verify before relying
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
fpath = Path("audio_file.wav")
wav = preprocess_wav(fpath)
encoder = VoiceEncoder()
embed = encoder.embed_utterance(wav)- Whether the pretrained model weights are downloaded automatically on first use or require manual setup.
- Current performance characteristics on non-English languages beyond the stated 'somewhat decently' claim.
- Whether GPU acceleration is automatic or requires explicit configuration.
What it is and what it does
Resemblyzer is a voice analysis library that uses a pretrained deep learning model to convert audio into a fixed-size embedding—a 256-dimensional vector that captures the essential characteristics of a speaker's voice. It wraps PyTorch and relies on librosa for audio processing, numpy for numerical operations, and webrtcvad for voice activity detection. The package is designed to work on CPU or GPU and handles noisy audio robustly.
The primary use case is voice-based identity and similarity tasks: you can compare two audio samples to determine how similar the speakers sound, verify that a new recording matches a reference voice profile, identify which speaker is talking at each moment in a multi-speaker recording, or detect whether speech is genuine or artificially generated. Developers can also use the embeddings as feature vectors for downstream machine learning tasks like accent analysis or voice cloning.
Use it for
- Speaker verification: build a voice profile from 5–30 seconds of reference audio and reject new recordings with low similarity scores.
- Speaker diarization: determine who is speaking when in a multi-speaker recording by comparing voice profiles.
- Fake speech detection: compare suspicious audio against known genuine samples to flag synthetic or spoofed speech.
- Voice similarity metric: compute a numerical similarity score between two audio samples for clustering or ranking.
- Feature extraction: use embeddings as input vectors for downstream ML tasks like gender or accent classification.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need voice similarity or speaker identification.
The package is stable, permissively licensed, and has low install friction. Maintenance is dormant but the code is mature and no vulnerabilities are known. Choose it if your use case aligns with English-language speaker analysis; be aware that non-English performance is not guaranteed and the project is not actively developed.
Install
resemblyzer on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance is dormant (last commit 2023-10-12, 1037 days ago), but the repository remains active with 3298 stars and no recent breaking changes indicated.
Requires Python 3.5+. Runtime dependencies include torch, librosa, numpy, scipy, and webrtcvad; torch installation may require additional system setup depending on GPU availability.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
from resemblyzer import VoiceEncoder, preprocess_wav
from pathlib import Path
fpath = Path("audio_file.wav")
wav = preprocess_wav(fpath)
encoder = VoiceEncoder()
embed = encoder.embed_utterance(wav)Verify before relying
- Whether the pretrained model weights are downloaded automatically on first use or require manual setup.
- Current performance characteristics on non-English languages beyond the stated 'somewhat decently' claim.
- Whether GPU acceleration is automatic or requires explicit configuration.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.5 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packageslibrosanumpywebrtcvadtorchscipytyping |
| Maintenance | Dormant 1,037 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 324,662 / month, #7,593 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: Resemblyzer-0.1.4-py3-none-any.whl
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See also pyannote-audio · whisperx · omnivoice · pyannote-metrics · chatterbox-tts · speechbrain · pyannote-core · sentence-transformers · voxcpm · coqui-tts