Instructions to use TuKoResearch/AuriStream-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuKoResearch/AuriStream-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TuKoResearch/AuriStream-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TuKoResearch/AuriStream-base: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
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https://huggingface.co/TuKoResearch/AuriStream-base/resolve/main/README.md
- Command line
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hf download hf://TuKoResearch/AuriStream-base/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/TuKoResearch/AuriStream-base/resolve/main/README.md
1.84 kB
| license: apache-2.0 | |
| tags: | |
| - audio | |
| - speech | |
| - language-model | |
| - auristream | |
| library_name: transformers | |
| # AuriStream - Speech Language Model | |
| **AuriStream** is a speech language model by **Greta Tuckute** and **Klemen Kotar**. | |
| This repository contains the shared model code for AuriStream models. | |
| ## Overview | |
| AuriStream is a GPT-like transformer model for cochlear token prediction with optional | |
| multi-token prediction (MTP) heads. | |
| This model predicts cochlear tokens from a tokenizer such as [WavCochCausalV8192](https://huggingface.co/TuKoResearch/WavCochCausalV8192). | |
| ## Usage | |
| This repository is not meant to be used directly. Instead, use one of the checkpoint | |
| repositories that reference this base code: | |
| - [AuriStream7B_40Pred_BigAudioDataset_500k](https://huggingface.co/TuKoResearch/AuriStream7B_40Pred_BigAudioDataset_500k) | |
| To load a checkpoint: | |
| ```python | |
| from transformers import AutoModel, AutoConfig | |
| model = AutoModel.from_pretrained( | |
| "TuKoResearch/AuriStream7B_40Pred_BigAudioDataset_500k", | |
| trust_remote_code=True, | |
| ) | |
| ``` | |
| ## Model Architecture | |
| The AuriStream model includes: | |
| - RMSNorm for layer normalization | |
| - Rotary Position Embeddings (RoPE) | |
| - SiLU activation in MLP layers | |
| - Multi-token prediction heads | |
| ## Configuration Options | |
| | Parameter | Description | Default | | |
| |-----------|-------------|---------| | |
| | `vocab_size` | Number of cochlear tokens | 8192 | | |
| | `n_embd` | Hidden dimension | 768 | | |
| | `n_layer` | Number of transformer layers | 12 | | |
| | `n_head` | Number of attention heads | 12 | | |
| | `n_pred_steps` | Number of prediction steps (MTP) | 1 | | |
| ## Files | |
| - `configuration_auristream.py` - Configuration class | |
| - `modeling_auristream.py` - Model implementation | |
| ## Tokenizer | |
| This model uses cochlear tokens from [WavCochCausalV8192](https://huggingface.co/TuKoResearch/WavCochCausalV8192). | |