Instructions to use Arsh9210/Nemotron-Labs-Audex-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Nemotron-Labs-Audex-2B / enhancement_VAE /README.md

Arsh9210

Added enhancement_VAE/README.md

fa44d96 verified 10 days ago

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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:

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:

python enhancement_VAE/enhance_audio_48k.py \
  --input tta_outputs \
  --output-dir tta_outputs_enhanced_48k

For a single WAV file:

python enhancement_VAE/enhance_audio_48k.py \
  --input tta_outputs/example.wav \
  --output-dir tta_outputs_enhanced_48k

Outputs are written as:

<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

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.