auro-rirum/audioforge-scratch-cnn-fsd50k

ScratchAudioCNN trained from random initialization on FSD50K for multi-label environmental sound event classification, produced by the AudioForge training pipeline.

Validation metrics (FSD50K)

Metric	Value
mAP	0.3020141498869784
micro F1	0.4260097201273672
macro F1	0.11832448567467674

Usage

This is a custom PyTorch architecture, not a native transformers class -- loading it requires the audioforge package:

import json
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from audioforge.models.scratch_cnn import create_scratch_cnn

config_path = hf_hub_download("auro-rirum/audioforge-scratch-cnn-fsd50k", "config.json")
weights_path = hf_hub_download("auro-rirum/audioforge-scratch-cnn-fsd50k", "model.safetensors")

with open(config_path, encoding="utf-8") as f:
    config = json.load(f)

model = create_scratch_cnn(**config)
model.load_state_dict(load_file(weights_path))
model.eval()

Input: log-mel spectrogram [batch, 1, n_mels, frames] (see audioforge.features.logmel). Output: raw logits [batch, num_labels]; apply torch.sigmoid for per-label probabilities.