Moss Audio Sfx LoRA v4

Model Details

Training Data

Trained on laion/in-the-wild-soundscapes-gemini2.5-pro — 10,998 real-world soundscape recordings annotated by Gemini 2.5 Pro with timestamped sound event captions.

Each training sample consists of:

• Audio: Real-world soundscape (resampled to 16 kHz)
• Prompt: "Please describe all audio events in this audio together with start time, end time, and caption for {medium/short} segments that are {overlapping/not overlapping}."
• Target: JSON array of {"caption": "...", "start_time": float, "end_time": float}

Quick Start

Inference

import torch
from peft import PeftModel

# You need the MOSS-Audio source code:
# git clone https://github.com/OpenMOSS/MOSS-Audio
import sys; sys.path.insert(0, "MOSS-Audio")
from src.modeling_moss_audio import MossAudioModel
from src.processing_moss_audio import MossAudioProcessor
from src.audio_io import load_audio

BASE_MODEL = "OpenMOSS-Team/MOSS-Audio-8B-Instruct"
LORA_REPO = "laion/moss-audio-sfx-lora-v4"

# Load base model + LoRA
processor = MossAudioProcessor.from_pretrained(BASE_MODEL, trust_remote_code=True)
model = MossAudioModel.from_pretrained(
    BASE_MODEL, trust_remote_code=True,
    dtype=torch.bfloat16, device_map="cuda:0",
)
model = PeftModel.from_pretrained(model, LORA_REPO)
model = model.merge_and_unload()
model.eval()

# Run inference
prompt = "Please describe all audio events in this audio together with start time, end time, and caption for medium segments that are overlapping."
audio = load_audio("your_audio.wav", sample_rate=processor.config.mel_sr)

inputs = processor(text=prompt, audios=[audio], return_tensors="pt").to("cuda:0")
if inputs.get("audio_data") is not None:
    inputs["audio_data"] = inputs["audio_data"].to(torch.bfloat16)
inputs["audio_input_mask"] = inputs["input_ids"] == processor.audio_token_id

with torch.no_grad():
    gen = model.generate(**inputs, max_new_tokens=4096, do_sample=False, use_cache=True)

output = processor.decode(gen[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(output)

Example Output

[
  {"caption": "Birds chirping and singing in a forest setting", "start_time": 0.0, "end_time": 8.5},
  {"caption": "Wind rustling through leaves and branches", "start_time": 2.3, "end_time": 12.0},
  {"caption": "A dog barking twice in the distance", "start_time": 6.1, "end_time": 7.8},
  {"caption": "Car engine passing on a nearby road", "start_time": 9.0, "end_time": 13.2}
]

Prompt Variants

The model supports 4 prompt configurations via {segment_duration} and {overlapping}:

Recommended: medium + overlapping — produces broader predictions that a downstream model (e.g., MOSS-Audio-8B-Thinking) can refine into specific events.

Training

See train.py in this repository for the full training script. Key command:

accelerate launch \
    --num_processes 8 \
    --use_deepspeed \
    --deepspeed_config_file ds_config_zero2.json \
    train.py \
    --model_dir OpenMOSS-Team/MOSS-Audio-8B-Instruct \
    --data_path soundscapes_train/train.jsonl \
    --output_dir ./lora_output \
    --use_lora True \
    --lora_rank 128 \
    --lora_alpha 256 \
    --num_train_epochs 2 \
    --per_device_train_batch_size 1 \
    --gradient_accumulation_steps 1 \
    --learning_rate 5e-5 \
    --lr_scheduler_type cosine \
    --warmup_ratio 0.05 \
    --bf16 True \
    --gradient_checkpointing True \
    --max_len 8192

Training Hyperparameters

Version History

Use in the Universal Audio Annotation Pipeline

This LoRA adapter is a component of the Universal Audio Annotation Pipeline. In the full pipeline:

1. Three ASR systems transcribe speech (VibeVoice, Parakeet, Qwen3)
2. Whisper experts analyze voice attributes (emotion, timbre, style)
3. This LoRA model detects sound events (SFX, music, ambient sounds)
4. MOSS-Audio-8B-Thinking combines all upstream context into structured annotations

Citation

@misc{moss-audio-sfx-lora-v4,
  title={MOSS-Audio SFX LoRA v4: Sound Event Detection Adapter},
  author={LAION},
  year={2025},
  url={https://huggingface.co/laion/moss-audio-sfx-lora-v4},
}

License

Apache 2.0