Datasets:

nineninesix
/

diamond-benchmark

license: other
task_categories:
  - audio-to-audio
language:
  - en
tags:
  - speech-restoration
  - speech-enhancement
  - benchmark
pretty_name: Diamond Benchmark — 750 real degraded speech clips
size_categories:
  - n<1K

💎 Diamond Benchmark

750 real degraded speech recordings for evaluating restoration models

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)