dsn-lid-whisper-base-1021-0007

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set: - evalloss: 0.8970 - evalmodelpreparationtime: 0.0018 - evalaccuracy: 0.87 - evalruntime: 443.4759 - evalsamplespersecond: 0.225 - evalstepspersecond: 0.029 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 10 - mixedprecisiontraining: Native AMP - labelsmoothingfactor: 0.1 - Transformers 5.16.1 - Pytorch 2.11.0+cpu - Datasets 2.19.0 - Tokenizers 0.23.1

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

ced-mini

Speech Team, Xiaomi MiLM Plus

CED are simple ViT-Transformer-based models for audio tagging, achieving sota performance on Audioset. Notable differences from other available models include: 1. Simplification for finetuning: Batchnormalization of Mel-Spectrograms. During finetuning one does not need to first compute mean/variance over the dataset, which is common for AST. 1. Support for variable length inputs. Most other models use a static time-frequency position embedding, which hinders the model's generalization to segments shorter than 10s. Many previous transformers simply pad their input to 10s in order to avoid the performance impact, which in turn slows down training/inference drastically. 1. Training/Inference…

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…