This model is a fine-tuned version of motheecreator/Deepfake-audio-detection on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0141 - Accuracy: 0.9973 The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5
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 speedup: 64-dimensional mel-filterbanks and 16x16 patches without overlap, leading to 248 patches from a 10s spectrogram. In comparison, AST uses 128 mel-filterbanks with 16x16 (10x10 overlap) convolution, leading to 1212 patches during training/inference. CED-Tiny runs on a common CPU as fast as a comparable MobileNetV3. 1. Performance: CED with 10M parameters outperforms the majority of previous approaches (~80M). `example_finetune_esc50.ipynb` demonstrates how to train a linear head on the ESC-50 dataset with the CED encoder frozen.
Dasheng (**D**eep **A**udio-**S**ignal **H**olistic **E**mbeddi**ng**s), or “大声” ("great sound"), is a general-purpose audio encoder trained on a large-scale self-supervised learning task. Dasheng is designed to capture rich audio information across various domains, including speech, music, and environmental sounds. The model is trained on 272,356 hours of diverse audio data with 1.2 billion parameters, and exhibits significant performance gains on the HEAR benchmark. Dasheng outperforms previous works on CREMA-D, LibriCount, Speech Commands, VoxLingua, and competes well in music and environmental sound classification tasks. `example_finetune_esc50.ipynb` demonstrates how to train a linear head on the ESC-50 dataset with the Dasheng encoder frozen.
Audio Spectrogram Transformer (AST) model fine-tuned on AudioSet. It was introduced in the paper AST: Audio Spectrogram Transformer by Gong et al. and first released in this repository. Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Audio Spectrogram Transformer is equivalent to ViT, but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.
This model is a fine-tuned version of mo-thecreator/wav2vec2-base-finetuned on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0829 - Accuracy: 0.9882 The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5
MAEST is a family of Transformer models based on PASST and focused on music analysis applications. The MAEST models are also available for inference in the Essentia library and for inference and training in the official repository. You can try the MAEST interactive demo on replicate. > Note: This model is available under CC BY-NC-SA 4.0 license for non-commercial applications and under proprietary license upon request. > Contact us for more information. > Note: The MAEST models rely on custom code. Set `trust_remote_code=True` to use them within the 🤗Transformers' `audio-classification` pipeline.
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 budget:
ONNX export of **Beat This!**, the JKU beat/downbeat tracker (checkpoint `final0`), packaged for the [`musetric`][musetric] `packages/ai` runtime on `onnxruntime-web`'s **WebGPU** execution provider. Feature extraction is the host's job, and `mel-filterbank.bin` is what it needs to do it: the host computes the log-mel spectrogram on the GPU (STFT + this filterbank + `log1p`) and feeds windows straight into the graph as GPU buffers, never round-tripping features through the CPU. **Feed one 1500-frame window per call** — the author's window, 30 s of audio. Longer tracks are covered by overlapping windows in the host (`chunkSize` 1500, `borderSize` 6). Batching every window into a single call materializes a huge time-attention tensor; one window per call is why the graph takes a window axis at all. Short clips must be padded to 1500 frames.
