Text Generation
Transformers
Safetensors
English
nemotron_labs_audex
nvidia
nemotron-labs-audex
reasoning
general-purpose
SFT
audio-language-modeling
audio-understanding
text-to-speech
text-to-audio
speech-recognition
speech-translation
Instructions to use Arsh9210/Nemotron-Labs-Audex-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arsh9210/Nemotron-Labs-Audex-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arsh9210/Nemotron-Labs-Audex-2B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Arsh9210/Nemotron-Labs-Audex-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Arsh9210/Nemotron-Labs-Audex-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arsh9210/Nemotron-Labs-Audex-2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arsh9210/Nemotron-Labs-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arsh9210/Nemotron-Labs-Audex-2B
- SGLang
How to use Arsh9210/Nemotron-Labs-Audex-2B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Arsh9210/Nemotron-Labs-Audex-2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arsh9210/Nemotron-Labs-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Arsh9210/Nemotron-Labs-Audex-2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arsh9210/Nemotron-Labs-Audex-2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arsh9210/Nemotron-Labs-Audex-2B with Docker Model Runner:
docker model run hf.co/Arsh9210/Nemotron-Labs-Audex-2B
| # Audex Enhancement VAE | |
| This directory contains the Audex enhancement VAE used to convert | |
| XCodec1-decoded 16 kHz mono WAV audio into enhanced 48 kHz mono WAV audio. | |
| The enhancement model is a postprocessor for Audex text-to-audio generation: | |
| ```text | |
| Audex generation -> XCodec1 decode -> 16 kHz WAV -> Enhancement VAE -> 48 kHz WAV | |
| ``` | |
| The model is intended for XCodec1-decoded Audex audio as inputs. It is not a general | |
| purpose enhancer for arbitrary audio. | |
| ## Requirements | |
| - `torch, numpy, scipy` | |
| ## Command Line Usage | |
| For a folder of outputs: | |
| ```bash | |
| python enhancement_VAE/enhance_audio_48k.py \ | |
| --input tta_outputs \ | |
| --output-dir tta_outputs_enhanced_48k | |
| ``` | |
| For a single WAV file: | |
| ```bash | |
| python enhancement_VAE/enhance_audio_48k.py \ | |
| --input tta_outputs/example.wav \ | |
| --output-dir tta_outputs_enhanced_48k | |
| ``` | |
| Outputs are written as: | |
| ```text | |
| <input_stem>_enhanced_48k.wav | |
| ``` | |
| Supported options: | |
| - `--device`: inference device. Defaults to `cuda` when available, otherwise `cpu`. | |
| - `--seed`: torch seed for stochastic VAE sampling. Defaults to `0`. | |
| - `--deterministic`: use the posterior mean instead of VAE sampling. | |
| ## Python API | |
| ```python | |
| from pathlib import Path | |
| import torch | |
| from enhancement_VAE.enhancement_vae import enhance_file, load_model | |
| root = Path("enhancement_VAE") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = load_model( | |
| checkpoint_path=root / "XCodec_RVQ4_mono_causal_fp32.safetensors", | |
| config_path=root / "config.json", | |
| device=device, | |
| ) | |
| enhance_file( | |
| model=model, | |
| input_path=Path("input_16k.wav"), | |
| output_path=Path("input_16k_enhanced_48k.wav"), | |
| deterministic=False, | |
| ) | |
| ``` | |
| ## Input and Output | |
| Input: | |
| - XCodec1-decoded Mono 16 kHz WAV file | |
| Output: | |
| - Mono 48 kHz audio WAV file | |
| If a directory is passed to `--input`, all `.wav` files directly inside that | |
| directory are processed. Directory traversal is not recursive. | |