ced-mini

Model · Audio classification

WeSpeaker-ResNet34-LM-MLX

Ivan

MLX-compatible weights for WeSpeaker ResNet34-LM, converted from the pyannote speaker embedding model with BatchNorm fused into Conv2d. WeSpeaker ResNet34-LM is a speaker embedding model (~6.6M params) that produces 256-dimensional L2-normalized speaker embeddings from audio. Trained on VoxCeleb for speaker verification and diarization. BatchNorm is fused into Conv2d at conversion time — no BN layers in the MLX model. Part of speech-swift. Converts the original pyannote/wespeaker-voxceleb-resnet34-LM checkpoint using a custom unpickler (no pyannote.audio dependency required). Key transformations: - Fuse BatchNorm into Conv2d: wfused = w × γ/√(σ²+ε), bfused = β − μ×γ/√(σ²+ε) - Transpose…

This repo contains the inference code to use pretrained human voice gender classifier. - You could also try Huggingface online demo. First, clone the original github repository and install the packages via pip. For those who need pretrained weights, please download it in here State-of-the-art speaker verification model already produces good representation of the speaker's gender. I used the pretrained ECAPA-TDNN from TaoRuijie's repository, added one linear layer to make two-class classifier, and finetuned the model with the VoxCeleb2 dev set. The model achieved 98.7% accuracy on the VoxCeleb1 identification test split. I would like to note the training dataset I've used for this model…

Model · Audio classification

Silero-VAD-v6.2.1-MLX

Ivan

MLX port of snakers4/silero-vad tag v6.2.1 for voice activity detection on Apple Silicon. Measured with speech-swift release tests on Apple Silicon using a 20 s 16 kHz speech fixture, 625 streaming chunks. Parity against the matching CoreML v6.2.1 export: The exported safetensors were also checked tensor-by-tensor against the upstream v6.2.1 JIT state dict after conversion; the maximum absolute tensor difference was 0. Converted from snakers4/silero-vad tag v6.2.1. The upstream project is MIT licensed. - speech-swift - Apple SDK - Docs - install and CLI docs - soniqo.audio - website - blog - blog

Model · Audio classification

ced-gguf

Mudler

GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark,...) into the 527-class AudioSet ontology. These files run with ced.cpp, a standalone C++/ggml port (no Python, no PyTorch at inference), and with LocalAI via the ced backend. Converted from the mispeech/ced- checkpoints (Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel spectrogram; the port is numerically equal to the PyTorch reference. One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size…

Model · Audio classification

lang-id-voxlingua107-ecapa

SpeechBrain

This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classifyfile if needed. The model can classify a speech utterance according to the language…

Model · Audio classification

accent-id-commonaccent_ecapa

Juan Pablo Zuluaga

Abstract: The recognition of accented speech still remains a dominant problem in Automatic Speech Recognition (ASR) systems. We approach the classification of accented English speech through the Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Network (ECAPA-TDNN) architecture which has been shown to perform well on a variety of speech tasks. Three models are proposed: one trained from scratch, another two models (one using data augmentation and a baseline model) fine-tuned from the checkpoints of speechbrain/spkrec-ecapa-voxceleb (VoxCeleb). Our results show that the model fine-tuned with data augmentation yield the best results. Most of the misclassifications…