EAT-base (Epoch 20, Fine-tuned Checkpoint)
This is the fine-tuned version of the EAT-large (Epoch 20, Pre-trained Checkpoint), further trained on the AS-2M dataset. Compared to the pre-trained model, this version provides enhanced audio representations and typically yields better performance in downstream audio understanding tasks such as classification and captioning.
For more details on the EAT framework, please refer to the GitHub repository and our paper EAT: Self-Supervised Pre-Training with Efficient Audio Transformer.
🔧 Usage
You can load and use the model for feature extraction directly via Hugging Face Transformers:
import torchaudio
import torch
import soundfile as sf
import numpy as np
from transformers import AutoModel
model_id = "worstchan/EAT-large_epoch20_finetune_AS2M"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval().cuda()
source_file = "/path/to/input.wav"
target_file = "/path/to/output.npy"
target_length = 1024 # Recommended: 1024 for 10s audio
norm_mean = -4.268
norm_std = 4.569
# Load and resample audio
wav, sr = sf.read(source_file)
waveform = torch.tensor(wav).float().cuda()
if sr != 16000:
waveform = torchaudio.functional.resample(waveform, sr, 16000)
# Normalize and convert to mel-spectrogram
waveform = waveform - waveform.mean()
mel = torchaudio.compliance.kaldi.fbank(
waveform.unsqueeze(0),
htk_compat=True,
sample_frequency=16000,
use_energy=False,
window_type='hanning',
num_mel_bins=128,
dither=0.0,
frame_shift=10
).unsqueeze(0)
# Pad or truncate
n_frames = mel.shape[1]
if n_frames < target_length:
mel = torch.nn.ZeroPad2d((0, 0, 0, target_length - n_frames))(mel)
else:
mel = mel[:, :target_length, :]
# Normalize
mel = (mel - norm_mean) / (norm_std * 2)
mel = mel.unsqueeze(0).cuda() # shape: [1, 1, T, F]
# Extract features
with torch.no_grad():
feat = model.extract_features(mel)
feat = feat.squeeze(0).cpu().numpy()
np.save(target_file, feat)
print(f"Feature shape: {feat.shape}")
print(f"Saved to: {target_file}")
📌 Notes
The model supports both frame-level (~50Hz) and utterance-level (CLS token) representations. See the feature extraction guide for detailed instructions.
📚 Citation
If you find this model useful, please consider citing our paper:
@article{chen2024eat,
title={EAT: Self-supervised pre-training with efficient audio transformer},
author={Chen, Wenxi and Liang, Yuzhe and Ma, Ziyang and Zheng, Zhisheng and Chen, Xie},
journal={arXiv preprint arXiv:2401.03497},
year={2024}
}