nineninesix/diamond-benchmark

💎 Diamond Benchmark

750 real degraded speech recordings for evaluating restoration models

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These are real, genuinely degraded speech recordings — not synthetic, not clean audio re-degraded by a pipeline. Each clip carries the damage of an actual real-world capture: low-bitrate codec compression, narrow bandwidth, background noise, and clipping. Together they form a fixed benchmark for measuring how well a speech-restoration model recovers clean, studio-quality audio from real degradation.

Every clip ships with its reference transcript, so restoration can be scored on two axes at once — perceptual quality and content preservation.

What's inside

Clips	750 degraded .wav recordings
Language	English
Audio	real degraded speech (codec · band-limit · noise · clipping)
Reference	ground-truth transcript per clip

Structure

diamond-bench/
├── audio/
│   └── <set>_<id>.wav      # 750 degraded input clips
└── manifest.csv            # per-clip metadata

manifest.csv columns:

column	meaning
set	source subset the clip belongs to
id	clip id within its set
emolia_id	original recording id
audio_path	relative path under audio/
sample_rate	clip sample rate (Hz)
duration_sec	clip length (seconds)
speaker	speaker id
text	reference transcript (used for CER)

How to score

Two complementary metrics — one alone is not enough:

• DNSMOS-P.835 (sig_bak_ovr.onnx, raw windowed mean) — perceptual quality (SIG / BAK / OVRL). How good the audio sounds.
• CER (ASR vs. text) — content preservation. A generative restorer can sound clean while smearing or dropping words; DNSMOS is blind to that, CER is not.

A genuine gain means OVRL rises without CER rising.

Loading

import pandas as pd
import soundfile as sf
from huggingface_hub import snapshot_download

root = snapshot_download("nineninesix/diamond-benchmark", repo_type="dataset")
meta = pd.read_csv(f"{root}/manifest.csv")

row = meta.iloc[0]
wav, sr = sf.read(f"{root}/{row.audio_path}")
print(row.id, f"{row.duration_sec:.1f}s —", row.text)

Links

• Model: nineninesix/diamond-1.0
• Training: diamond-train
• Inference: diamond-inference