This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Librispeech-clean-100 for gender recognition. It achieves the following results on the evaluation set: - Loss: 0.0061 - F1: 0.9993 The Librispeech-clean-100 dataset was used to train the model, with 70% of the data used for training, 10% for validation, and 20% for testing. The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP
**MioCodec-25Hz-44.1kHz-v2** is an upsampled, high-fidelity version of the MioCodec-25Hz-24kHz model. By integrating an **UpsamplerBlock** inspired by Inworld TTS-1 into the decoder, this model reconstructs 44.1 kHz audio from the standard 25 Hz token stream. This model is a fine-tuned version of `MioCodec-25Hz-24kHz` with the following architectural enhancements:
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 trained all 24 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.
These are repackaged files to work with ComfyUI, original model repo is: https://huggingface.co/ACE-Step/Ace-Step1.5 ACE-Step 1.5XL Turbo: Text to Music ACE-Step 1.5XL SFT： Text to Music ACE-Step 1.5 Music Generation AIO ACE-Step 1.5XL Base: Text to Music ACE-Step 1.5 Music Generation Workflow ACE-Step 1.5 AI Lyric Song Generation ACE-Step 1.5 Music Generation (4B LLM )
This repository hosts the downloadable runtime files for **Higgs Audio v3 Studio**, a Windows desktop app for local Higgs Audio v3 TTS, voice cloning, speech continuation, and multi-speaker generation. This repository is not the original upstream model release. It provides GGUF model builds, the Windows CUDA engine DLL package, checksums, and a manifest used by the desktop app downloader. Higgs Audio v3 Studio is a Rust/Tauri desktop application that runs a ported native C++/CUDA implementation of Higgs Audio v3 locally.
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in Simple and Controllable Music Generation by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - small - **medium** (this checkpoint) - large - melody
Suitable for scenarios with high audio quality requirements, emphasizing high fidelity and studio-grade clarity, applicable to audiobooks, film and TV dubbing, advertising narration, digital humans, etc.
Suitable for scenarios with high audio quality requirements, emphasizing high fidelity and studio-grade clarity, applicable to audiobooks, film and TV dubbing, advertising narration, digital humans, etc.
Suitable for scenarios with high audio quality requirements, emphasizing high fidelity and studio-grade clarity, applicable to audiobooks, film and TV dubbing, advertising narration, digital humans, etc.
Suitable for scenarios with high audio quality requirements, emphasizing high fidelity and studio-grade clarity, applicable to audiobooks, film and TV dubbing, advertising narration, digital humans, etc.
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.
- **Jul 2024 (v2.3):** - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio. - **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
- **Jul 2024 (v2.3):** - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio. - **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
The gpt-audio model is OpenAI's first generally available audio model. The new snapshot features an upgraded decoder for more natural sounding voices and maintains better voice consistency. Audio is priced...
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