{"id":8592,"library":"resemblyzer","title":"Resemblyzer","description":"Resemblyzer (version 0.1.4) is a Python library for extracting speaker embeddings from audio, enabling voice verification and comparison using a pre-trained deep learning model. Its last release was in December 2020, and the project appears to be no longer actively maintained.","status":"abandoned","version":"0.1.4","language":"en","source_language":"en","source_url":"https://github.com/resemble-ai/Resemblyzer","tags":["audio","voice","embedding","deep learning","speech","speaker recognition"],"install":[{"cmd":"pip install resemblyzer","lang":"bash","label":"Install core library"},{"cmd":"pip install torch==1.4.0","lang":"bash","label":"Install compatible PyTorch"}],"dependencies":[{"reason":"Deep learning backend; specific version (<=1.4.0) highly recommended.","package":"torch","optional":false},{"reason":"Numerical operations and array handling.","package":"numpy","optional":false},{"reason":"Reading and writing audio files. Requires system-level 'libsndfile'.","package":"soundfile","optional":false},{"reason":"Audio analysis functions, particularly for resampling if needed.","package":"librosa","optional":false},{"reason":"Scientific computing, used by other dependencies.","package":"scipy","optional":false}],"imports":[{"symbol":"VoiceEncoder","correct":"from resemblyzer import VoiceEncoder"},{"symbol":"preprocess_wav","correct":"from resemblyzer import preprocess_wav"}],"quickstart":{"code":"import numpy as np\nimport soundfile as sf\nimport os\nfrom resemblyzer import VoiceEncoder, preprocess_wav\n\n# Note: Resemblyzer (v0.1.4) was developed with PyTorch <= 1.4.0.\n# Installing a compatible PyTorch version (e.g., pip install torch==1.4.0)\n# is crucial for avoiding runtime errors, especially on GPU.\n\n# Create a dummy WAV file for demonstration if it doesn't exist\ntest_wav_path = \"resemblyzer_test_audio.wav\"\nif not os.path.exists(test_wav_path):\n    # Generate a dummy 5-second 16kHz sine wave\n    duration = 5  # seconds\n    sample_rate = 16000 # Hz\n    frequency = 440 # Hz (A4 note)\n    t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)\n    dummy_audio = 0.5 * np.sin(2 * np.pi * frequency * t)\n    sf.write(test_wav_path, dummy_audio.astype(np.float32), sample_rate)\n    print(f\"Created dummy audio file: {test_wav_path}\")\n\n# Load the pre-trained encoder (downloads if not cached)\nprint(\"Loading VoiceEncoder... (first run may download model)\")\nencoder = VoiceEncoder.from_pretrained()\n\n# Load and preprocess the dummy audio\nwav, sr = sf.read(test_wav_path)\n\n# Resemblyzer expects 16kHz audio. If your audio is different,\n# you would need to resample it, e.g., using librosa.\n# For this dummy, we ensured it's 16kHz.\nif sr != 16000:\n    print(f\"Warning: Audio sample rate is {sr}Hz, but Resemblyzer expects 16kHz. Resampling would be needed.\")\n    # Example resampling (requires librosa):\n    # import librosa\n    # wav = librosa.resample(wav, orig_sr=sr, target_sr=16000)\n\nclean_wav = preprocess_wav(wav)\n\n# Encode the voice embedding\nprint(\"Encoding voice...\")\nembed = encoder.embed_utterance(clean_wav)\n\nprint(f\"Generated embedding with shape: {embed.shape}\")\nprint(f\"First 5 elements of embedding: {embed[:5]}\")\n\n# Clean up dummy file\nif os.path.exists(test_wav_path):\n    os.remove(test_wav_path)\n    print(f\"Removed dummy audio file: {test_wav_path}\")","lang":"python","description":"This quickstart demonstrates how to initialize the VoiceEncoder, preprocess an audio waveform (generating a dummy one for convenience), and extract a speaker embedding. Due to the library's age, strict PyTorch version compatibility (<=1.4.0) is critical for successful execution, especially on CUDA-enabled systems."},"warnings":[{"fix":"Explicitly install a compatible PyTorch version, e.g., `pip install torch==1.4.0`. For CUDA, ensure you select the PyTorch version compatible with your CUDA toolkit version.","message":"The library's `requirements.txt` and underlying code were designed for PyTorch versions `<=1.4.0`. Installing with newer PyTorch versions (e.g., 2.x) will almost certainly lead to runtime errors or incorrect behavior due to API changes.","severity":"breaking","affected_versions":"All versions up to 0.1.4"},{"fix":"Install `libsndfile` using your system's package manager. For Debian/Ubuntu: `sudo apt-get install libsndfile1`. For macOS: `brew install libsndfile`.","message":"The `soundfile` dependency, used for audio I/O, often requires the `libsndfile` package to be installed at the operating system level (e.g., via `apt-get`, `brew`, or `yum`). Without this system library, `soundfile` may fail to install or function correctly.","severity":"gotcha","affected_versions":"All versions up to 0.1.4"},{"fix":"Ensure you have a stable internet connection. If the issue persists, you might need to manually download `encoder.pt` from the Resemblyzer GitHub repository (e.g., `github.com/resemble-ai/Resemblyzer/blob/master/resemblyzer/saved_models/encoder.pt`) and load it locally using `VoiceEncoder('path/to/encoder.pt')`.","message":"The `VoiceEncoder.from_pretrained()` method downloads a pre-trained model (`encoder.pt`) from a specific URL. If this URL becomes unavailable or the hosting server is down, the model download will fail, preventing the encoder from being initialized.","severity":"gotcha","affected_versions":"All versions up to 0.1.4"},{"fix":"Ensure your audio inputs are sufficiently long, typically at least 1.6 seconds, before passing them to `encoder.embed_utterance()`.","message":"The `embed_utterance` method will raise a `NotImplementedError` if the input audio segment is too short, as the model has a minimum receptive field length it requires to produce an embedding.","severity":"gotcha","affected_versions":"All versions up to 0.1.4"}],"env_vars":null,"last_verified":"2026-04-16T00:00:00.000Z","next_check":"2026-07-15T00:00:00.000Z","problems":[{"fix":"Run `pip install soundfile` and ensure `libsndfile` is installed on your operating system (e.g., `sudo apt-get install libsndfile1`).","cause":"The `soundfile` Python package is not installed, or its underlying system dependency (`libsndfile`) is missing.","error":"ModuleNotFoundError: No module named 'soundfile'"},{"fix":"Verify that you have PyTorch version `1.4.0` installed (`pip show torch`). If not, uninstall your current PyTorch and install `torch==1.4.0` (or `torch==1.4.0+cuXXX` for CUDA, where XXX matches your CUDA version).","cause":"This usually indicates a severe incompatibility between the installed PyTorch version and the `resemblyzer` library, or an issue with your GPU setup. `resemblyzer` is tied to older PyTorch versions.","error":"RuntimeError: CUDA error: device-side assert triggered"},{"fix":"Ensure the audio segment you are passing to `encoder.embed_utterance()` is at least the minimum required length (typically around 1.6 seconds or more). You may need to concatenate shorter segments or process longer audio clips.","cause":"The audio segment provided for embedding is too short for the Resemblyzer model to process.","error":"NotImplementedError: The input length must be at least the model's receptive field length (...) but you provided an input of only (...) frames."},{"fix":"Check your internet connection and try again. If the problem persists, the download link might be broken. In that case, manually download the `encoder.pt` file from the Resemblyzer GitHub repository and load it locally: `encoder = VoiceEncoder('/path/to/downloaded/encoder.pt')`.","cause":"The pre-trained model download from the specified URL (often Dropbox) failed due to network issues, server unavailability, or rate limiting.","error":"ConnectionError: HTTPSConnectionPool(host='www.dropbox.com', port=443): Max retries exceeded with url: /s/...', 'name': 'encoder.pt'"}]}