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Speech emotion recognition for Russian over seven classes: `anger`, `disgust`, `enthusiasm`, `fear`, `happiness`, `neutral`, `sadness`. Audio resampled to 16 kHz mono, clips capped at 12 s, normalized per utterance, padding masked. UA is macro-averaged recall, WA is accuracy, F1 is macro-averaged. All three test sets went through the same harness, so the rows are comparable to each other. The RESD split matches fold 1 of EmoBox bit for bit. The top entry there is WavLM-large at WA 56.47 / UA 55.87 / F1 55.82. These numbers are higher, but the training protocol differs — EmoBox freezes the encoder and trains a probe, this is a full fine-tune — so read it as a different recipe on the same split, not as a like-for-like win.

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**MioCodec-25Hz-24kHz** is a lightweight and fast neural audio codec designed for efficient spoken language modeling. Based on the Kanade-Tokenizer implementation, this model features an integrated wave decoder (iSTFTHead) that directly synthesizes waveforms without requiring an external vocoder. 1. **Content Tokens:** Discrete representations that primarily capture linguistic information and phonetic content ("what" is being said) at a low frame rate (25 Hz). 2. **Global Embeddings:** A continuous vector representing broad acoustic characteristics ("how")—including speaker identity, recording environment, and microphone traits. MioCodec allows you to swap speaker identities by combining the content tokens of a source with the global embedding of a reference.

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Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with audEERING. The model expects a raw audio signal as input, and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on MSP-Podcast (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An ONNX export of the model is available from doi:10.5281/zenodo.6221127. Further details are given in the associated paper and tutorial.

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The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we only trained the first six transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.

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GGUF weights for native inference with audio.cpp. RE-USE restores degraded speech while preserving its input sample rate. It uses a convolutional encoder/decoder and 30 bidirectional time-frequency Mamba blocks. The checkpoint and reference implementation are pinned to nvidia/RE-USE, revision 022e920d727347a64d6c21fbf0628f2a5f37ad78. Each GGUF includes the model weights, configuration, and audio.cpp model spec. No separate encoder or tokenizer is required. Packages are converted directly from the original safetensors, not from another GGUF. Only F32 is provided. F16 and Q8 were rejected because their small disk savings did not improve speed or peak VRAM in testing, while introducing waveform drift and additional packaging complexity.

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GGUF weights for native inference with audio.cpp. SAM Audio separates a sound described by text, a masked image or video, or positive and negative time spans from an input recording. These packages use the original Meta checkpoints, not the separate `-tv` variants. The official SAM Audio implementation is pinned to bb4c699.

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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. Converts the original pyannote/wespeaker-voxceleb-resnet34-LM checkpoint using a custom unpickler (no pyannote.audio dependency required). Key transformations:

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The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction. .hf-button { display: inline-flex; align-items: center; gap: 6px; padding: 6px 12px; font-size: 14px; font-weight: bold; color: white; border-radius: 6px; text-decoration: none; } .hf-button img { height: 18px; }

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