lab260/ProxyMos

ProxyMOS

ProxyMOS is a lightweight Mean Opinion Score (MOS) prediction model built on top of the OmniASR W2V 300M encoder (Wav2Vec2 architecture via fairseq2).

Given any audio file, the model returns a single scalar speech quality score on the MOS scale. It is the distilled model from the paper "ProxyMOS: Training Speech Quality Models via Ensemble-Derived MOS Targets" (Borodin & Trokunov, MTUCI, 2026).

Architecture

OmniASR-W2V-300M (encoder, fine-tuned with reduced lr)
        ↓
AttentiveStatsPooling  ->  [mean || std]  (dim: 2 x 1024 = 2048)
        ↓
Linear(2048 -> 1024) -> GELU -> Linear(1024 -> 1)
        ↓
     MOS score (scalar)

• —Encoder: omniASR_W2V_300M — a 300M-parameter Wav2Vec2-style encoder instantiated via fairseq2.
• —Weights: best_model_full.pt contains the trained ProxyMOS state dict, including the encoder and MOS head weights. No separate omniASR-W2V-300M.pt checkpoint is required.
• —Pooling: Attentive Statistics Pooling — computes attention-weighted mean and standard deviation over the time dimension.
• —Head: Two-layer MLP with GELU activation and a scalar output.

Repository files

Note: inference uses fairseq2 to instantiate the omniASR_W2V_300M architecture, then loads all trained weights from best_model_full.pt. You do not need a separate omniASR-W2V-300M.pt file.

Quick start

1. Clone the repository

git lfs install
git clone https://huggingface.co/lab260/ProxyMos
cd ProxyMos

2. Install dependencies

Requires Python 3.10+.

pip install torch torchaudio
pip install fairseq2
pip install accelerate scipy scikit-learn tqdm

GPU: if CUDA is available, the model will use it automatically.

3. Run inference

python inference_model.py /path/to/your/audio.wav

Example:

python inference_model.py /home/ae_samples_ae_finetuned_vocoder_segment_28.wav

Output:

MOS: 3.8721

Python API

import torch
from inference_model import load_model, predict_mos

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model("best_model_full.pt", device)

score = predict_mos(model, "/path/to/audio.wav", device)
print(f"MOS: {score:.4f}")

Batch processing

from pathlib import Path

audio_dir = Path("/path/to/audio_folder")

for wav_file in sorted(audio_dir.glob("*.wav")):
    score = predict_mos(model, str(wav_file), device)
    print(f"{wav_file.name}: {score:.4f}")

Model Performance

URGENT Dataset

MOS260 Dataset

Requirements

Technical details

• —Audio is automatically resampled to 16,000 Hz.
• —Stereo input is averaged to mono before processing.
• —Input is normalized to shape [B, T] internally.
• —Inference runs correctly on CPU when no GPU is available.

Results

ProxyMOS outperforms all individual teacher models (WhiSQA, DistillMOS, UTMOS, XLS-R) on both benchmarks.

Citation

@article{borodin2026proxymos,
  title     = {ProxyMOS: Training Speech Quality Models via Ensemble-Derived MOS Targets},
  author    = {Borodin, Kirill and Trokunov, Maksim},
  year      = {2026},
  institution = {MTUCI, Moscow}
}

License

Please refer to the fairseq2 and omnilingual-asr repositories for encoder licensing terms.

Contact

• —Email: kborodin.research@gmail.com
• —Telegram: @korallll_ai