| --- |
| 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 |
| --- |
| |
| <div align="center"> |
|
|
| # 💎 Diamond Benchmark |
|
|
| ### 750 real degraded speech recordings for evaluating restoration models |
|
|
| [](.) |
| [](.) |
| [](.) |
| [](https://huggingface.co/nineninesix/diamond-1.0) |
|
|
| </div> |
|
|
| --- |
|
|
| 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 |
|
|
| ```python |
| 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`](https://huggingface.co/nineninesix/diamond-1.0) |
| - **Training:** [`diamond-train`](https://github.com/nineninesix-ai/diamond-train.git) |
| - **Inference:** [`diamond-inference`](https://github.com/nineninesix-ai/diamond-inference.git) |
|
|
