Datasets:

AudioCC-Lab
/

Emilia_ERT

@@ -1,109 +1,150 @@
 ---
 language: [de, en, fr, ja, ko, zh]
 license: cc-by-nc-4.0
-pretty_name: Emilia Complete - Audio TAR and SCP
-tags: [audio, speech, emilia]
-configs:
-- config_name: default
-  default: true
-  data_files:
-  - split: train
-    path: audio/*.tar
 ---
-# Emilia_complete：音频 TAR + 单一 SCP
-本版本仅包含本地 `Emilia_complete` 的六语言全量音频，共 **9,310,186 条**。
-未应用中英文严格筛选清单，不包含 Emilia-YODAS 或其他数据集。
-`complete` 是本地目录名，不代表完整覆盖上游 Emilia。
 ```text
-emilia_complete_scp_release/
-├── audio/                 # 666 个原始 TAR 分片
 │   ├── DE-B000000.tar
 │   ├── EN-B000000.tar
 │   └── ...
-├── metrics.scp            # 唯一 SCP，含表头及全部六语言记录
-├── dataset_statistics.json
-├── SHA256SUMS             # metrics.scp 的 SHA-256
 ├── LICENSE
 └── README.md
 ```
-`metrics.scp` 是 UTF-8、TAB 分隔的 34 列文本表。所有原始字段、列顺序和数值
-字符串均保留，只将 `path` 改成相对于本目录的音频位置：
 ```text
 audio/DE-B000000.tar:512:154989
 ```
-含义依次是：**TAR 相对路径 : 音频内容起始字节偏移 : 音频内容字节数**。
-偏移指向 MP3 内容，不是 TAR 文件头；读取指定长度即可取得完整编码音频。
-TAR 内保留原有数字文件名，音频的原始 Emilia ID 由 SCP 的 `uid` 列给出。
-## 字段
-```text
-datasets uid fs path length(s) silerovad webrtcvad transcript confidence
-dnsmos scoreq squim_pesq squim_sisdr squim_stoi srmr vqscore wadasnr
-distillmos utmos dnsmos_pro nisqa sigmos_ovrl sigmos_col sigmos_disc
-sigmos_loud sigmos_noise sigmos_reverb sigmos_sig ced flexsed panns beats
-speaker_count overlap_ratio
-```
-上面为便于阅读以空格换行展示，实际 SCP 使用 TAB 分隔。
-`datasets` 均为 `Emilia_complete`；`fs` 为原清单采样率，`length(s)` 为已有时长，
-`transcript` 为已有转写。其余指标沿用源表，不重新计算、不改精度、不推测模型版本。
-保留指标字符串中的原始空值或其他标记；标注存在不等于经过人工确认。
-## 读取示例
-该 SCP 需要按表解析，不能直接使用空白字符 `split()`，因为转写中可能含空格。
 ```python
 import csv
 from pathlib import Path
-root = Path("/path/to/emilia_complete_scp_release")
 with (root / "metrics.scp").open(encoding="utf-8", newline="") as f:
-    rows = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-    row = next(rows)
 relative, offset, size = row["path"].rsplit(":", 2)
 with (root / relative).open("rb") as f:
     f.seek(int(offset))
     audio_bytes = f.read(int(size))
 assert len(audio_bytes) == int(size)
-print(row["uid"], row["transcript"], row["dnsmos"])
-# 可选：解码 MP3
-# import io, soundfile as sf
 # waveform, sample_rate = sf.read(io.BytesIO(audio_bytes))
 ```
-发布到 Hugging Face 后，下载该目录即可按以上方式使用。Hub 的普通 WebDataset
-加载器可以读取 TAR 音频，但**不会自动将这个自定义 SCP 的 34 列与音频合并**。
-完整转写和指标请按 SCP 的路径、偏移及长度读取，不要把 TAR 内的数字名当作原始 UID。
-如果需要音频和 JSON 标注原生配对的 WebDataset，可使用此前另行整理的版本。
-## 整理与核对
-- 六语言原始清单逐条核对 UID、采样率和原始路径/偏移/长度。
-- 合并原 `metrics_dataset` 当前六个 SCP 中 `datasets == Emilia_complete` 的记录。
-- 一条 UID 只允许出现一次，要求所有音频清单记录都有对应 SCP 行。
-- 完整回读输出 SCP，核对 34 列、行数、路径存在性、偏移边界和文件 SHA-256。
-- 音频不重采样、不转码、不解包重写。服务器上的 TAR 使用原始 TAR 的硬链接，
-  避免重复占用音频空间；应视为只读。如需修改 TAR，请先创建独立副本。
-  上传到 Hub 时会上传普通文件内容，不依赖服务器硬链接关系。
-本次只整理存储布局；未额外进行全量音频解码、内容审查或评分。
-此前原生 WebDataset 整理已完整验证原始 TAR 成员与清单偏移、长度及音频内容。
-## 来源与许可
-来源：[原版 Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset)。
-上游对原版 Emilia 指定 **CC BY-NC 4.0**，并有额外访问条款；本数据不适用
-Emilia-YODAS 的 CC BY 4.0。权利归 Emilia 作者及原始录音权利人，未暗示上游认可本版本。
-本地筛选历史、上游下载版本和逐条再分发授权没有在现有清单中完整记录。
-技术格式整理不等于公开再分发授权已经确认；公开上传前须核实适用条款及授权。
-本次整理未创建 Hub 仓库或上传数据。

 ---
 language: [de, en, fr, ja, ko, zh]
 license: cc-by-nc-4.0
+pretty_name: Emilia ERT
+size_categories:
+- 1M<n<10M
+tags: [audio, speech, speech-quality, speech-metrics, emilia]
 ---
+# Emilia ERT
+## Dataset summary
+Emilia ERT contains **9,310,186 speech segments** in six languages, distributed as **666 audio TAR shards** and a single **34-column metadata table**, `metrics.scp`. It packages the entire locally retained `Emilia_complete` collection from a filtering workflow, with existing transcripts and automatic audio metrics.
+`Emilia_complete` identifies the local source collection; it does **not** mean this repository contains the complete upstream release. No additional English/Chinese-only selection was applied during release preparation. This is an independently prepared collection in the AudioCC-Lab namespace, not an official Amphion release.
+| Language | Code | Segments | TAR shards |
+| --- | --- | ---: | ---: |
+| German | de | 166,600 | 10 |
+| English | en | 4,619,380 | 333 |
+| French | fr | 78,751 | 12 |
+| Japanese | ja | 203,264 | 27 |
+| Korean | ko | 11,640 | 1 |
+| Chinese | zh | 4,230,551 | 283 |
+| **Total** | | **9,310,186** | **666** |
+Audio archives occupy 665,879,367,680 bytes; `metrics.scp` occupies 6,134,693,898 bytes. No held-out evaluation split is provided. Define appropriate speaker/source-disjoint splits for experiments.
+## Files and audio addressing
 ```text
+.
+├── audio/
 │   ├── DE-B000000.tar
 │   ├── EN-B000000.tar
 │   └── ...
+├── metrics.scp
+├── SHA256SUMS
 ├── LICENSE
 └── README.md
 ```
+`metrics.scp` is a UTF-8, TAB-separated table with a header, despite its extension. It is not a conventional two-column Kaldi SCP. Column order, values, precision, and source missing-value markers were preserved; only `path` was converted to a repository-relative locator:
 ```text
 audio/DE-B000000.tar:512:154989
 ```
+The components are **TAR path : byte offset : byte length**. The offset points to the encoded MP3 payload, not the TAR header. Archive members retain their local numeric names; `uid` supplies the original-style Emilia identifier. Archives were retained byte-for-byte without re-encoding during release preparation. Do not recompress or rebuild them while using these offsets.
+## Metadata fields
+Types describe valid values; all cells are stored as text. Handle missing or nonnumeric markers explicitly rather than converting them to zero.
+| # | Field | Type / unit | Meaning |
+| ---: | --- | --- | --- |
+| 1 | `datasets` | string | Local source label; `Emilia_complete` throughout this release. |
+| 2 | `uid` | string | Segment identifier, e.g. `DE_B00008_S00023_W000000`; the prefix identifies the source language. |
+| 3 | `fs` | integer, Hz | Sample rate recorded in the source manifest. Scoring models may internally resample audio. |
+| 4 | `path` | string | Relative TAR path, payload offset, and payload length, separated by colons. |
+| 5 | `length(s)` | float, seconds | Segment duration from the source table. |
+| 6 | `silerovad` | float, fraction | Silero VAD detected-speech duration divided by total duration. |
+| 7 | `webrtcvad` | float, fraction | Speech fraction estimated by the WebRTC VAD workflow. |
+| 8 | `transcript` | string | Automatic transcript from the local WhisperX workflow; not a verified human transcription. |
+| 9 | `confidence` | float, score | Mean of available WhisperX aligned-word scores in the inspected implementation; not a calibrated probability of transcript correctness. |
+| 10 | `dnsmos` | float, predicted MOS | DNSMOS overall quality output (`DNSMOS_OVRL`). |
+| 11 | `scoreq` | float, model score | SCOREQ speech-quality estimate on the model's native scale. |
+| 12 | `squim_pesq` | float, predicted score | SQUIM estimate of PESQ without a clean reference. |
+| 13 | `squim_sisdr` | float, predicted dB | SQUIM estimate of scale-invariant signal-to-distortion ratio (SI-SDR). |
+| 14 | `squim_stoi` | float, predicted score | SQUIM estimate of short-time objective intelligibility (STOI). |
+| 15 | `srmr` | float, ratio | Speech-to-reverberation modulation energy ratio, a non-intrusive reverberation-related measure. |
+| 16 | `vqscore` | float, model score | VQScore non-intrusive speech-quality estimate. |
+| 17 | `wadasnr` | float, dB | Signal-to-noise ratio estimated using waveform amplitude distribution analysis (WADA-SNR). |
+| 18 | `distillmos` | float, predicted MOS | DistillMOS speech-quality estimate. |
+| 19 | `utmos` | float, predicted MOS | UTMOS speech-quality estimate. |
+| 20 | `dnsmos_pro` | float, model score | DNSMOS Pro quality output retained from the local scoring workflow. |
+| 21 | `nisqa` | float, predicted MOS | NISQA overall quality estimate (`NISQA_MOS`). |
+| 22 | `sigmos_ovrl` | float, predicted rating | SIGMOS overall quality rating. |
+| 23 | `sigmos_col` | float, predicted rating | SIGMOS coloration-related quality rating. |
+| 24 | `sigmos_disc` | float, predicted rating | SIGMOS discontinuity-related quality rating. |
+| 25 | `sigmos_loud` | float, predicted rating | SIGMOS loudness-related quality rating. |
+| 26 | `sigmos_noise` | float, predicted rating | SIGMOS background-noise-related quality rating. |
+| 27 | `sigmos_reverb` | float, predicted rating | SIGMOS reverberation-related quality rating. |
+| 28 | `sigmos_sig` | float, predicted rating | SIGMOS speech-signal quality rating. |
+| 29 | `ced` | float, probability score | Maximum CED probability over the workflow's selected noise/event classes (AudioSet indices 72 onward). |
+| 30 | `flexsed` | float, probability score | Maximum FlexSED probability over selected noise/event prompts and time frames in the inspected workflow. |
+| 31 | `panns` | float, probability score | Maximum PANNs clip-level probability over selected classes at AudioSet indices 72 onward. |
+| 32 | `beats` | string | BEATs highest-scoring selected non-speech event label followed by its score, e.g. `Inside, small room 0.0067`. Split from the right to extract the final numeric score. |
+| 33 | `speaker_count` | integer, count | Number of Sortformer speaker streams meeting minimum active-duration criteria; not a verified count of distinct people. |
+| 34 | `overlap_ratio` | float, fraction | Fraction of analyzed frames with at least two active speaker streams after thresholding and short-run removal. The denominator includes all analyzed frames, not only speech. |
+Meanings were checked against available local assembly and scoring scripts. Exact historical checkpoints, command-line settings, and per-row generation versions are not fully recorded. Current script defaults are not guaranteed historical settings.
+MOS fields are model predictions, not listening-test ratings. SQUIM outputs are predictions rather than reference-based measurements. SIGMOS dimensions describe quality, not physical noise power or reverberation time. The SED class selection is a workflow convention; event scores are not human-confirmed noise labels. Different models' scores are not interchangeable, and this release defines no universal quality threshold.
+## Usage
+Download `metrics.scp` and the required shards, then parse with an explicit TAB delimiter. Plain whitespace splitting would corrupt transcripts and event labels.
 ```python
 import csv
 from pathlib import Path
+root = Path("/path/to/Emilia_ERT")
 with (root / "metrics.scp").open(encoding="utf-8", newline="") as f:
+    row = next(csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE))
 relative, offset, size = row["path"].rsplit(":", 2)
 with (root / relative).open("rb") as f:
     f.seek(int(offset))
     audio_bytes = f.read(int(size))
 assert len(audio_bytes) == int(size)
+print(row["uid"], row["transcript"])
+# Optional MP3 decoding with a compatible libsndfile installation:
+# import io
+# import soundfile as sf
 # waveform, sample_rate = sf.read(io.BytesIO(audio_bytes))
 ```
+This custom TAR/SCP layout requires a table-driven reader. An automatic WebDataset loader does not join SCP annotations to archive members. For efficient training, group reads by shard and reuse file handles. `SHA256SUMS` records the SHA-256 of `metrics.scp`.
+## Provenance and preparation
+The upstream dataset is [Emilia, by Haorui He and collaborators / Amphion](https://huggingface.co/datasets/amphion/Emilia-Dataset). Local records identify the original Emilia branch; no Emilia-YODAS collection was included.
+Immediate inputs were the six-language `30wh_filter/data/Emilia_complete` audio collection, its language manifests, and corresponding rows in six `metrics_dataset.*.scp` tables. Preparation consolidated those rows, preserved audio bytes and metric values, and replaced local addresses with relative TAR locators. It did not rerun ASR, scoring, diarization, or filtering. Record IDs, source locators, and all non-path fields were checked during preparation.
+The exact upstream download revision, complete earlier filtering history, original media URLs, and per-record rights records are not supplied. Local consistency checks do not establish a complete upstream provenance or permission chain.
+## License and access review
+Reviewed on **2026-09-18**. The [upstream card and access terms](https://huggingface.co/datasets/amphion/Emilia-Dataset) distinguish original Emilia (**CC BY-NC 4.0**) from Emilia-YODAS (**CC BY 4.0**). This repository retains `cc-by-nc-4.0`; the combined upstream repository's license badge should not be used to relabel original Emilia. Upstream states that recording copyright remains with the original owners.
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode.en) permits noncommercial sharing and adaptation within the licensor's rights, subject to attribution, a license reference, retention of supplied notices, and indication of changes. It does not independently clear third-party rights. See [LICENSE](LICENSE) for the attribution notice. This repackaging claims no upstream endorsement or new ownership of recordings.
+**Unresolved release condition:** upstream access terms require associates or colleagues receiving access to agree to those terms first. Available records do not establish how unrestricted public mirroring satisfies this condition or document permission for that distribution arrangement. README text alone does not enforce acceptance. Compliance of unrestricted redistribution is therefore **not confirmed**. The maintainer should obtain upstream clarification/authorization and establish the appropriate access arrangement before treating this release as cleared for unrestricted distribution. Gating alone would not establish recording-level rights.
+The license label records the upstream designation, not certification that every recording or annotation has been cleared. Provenance and licensing of annotation-generating models/checkpoints are not fully documented here either.
+## Limitations and responsible use
+Languages are strongly imbalanced. Automatic transcripts, speaker estimates, and quality/event scores can be wrong and may behave differently across languages and recording conditions. No guarantee of absence of personal information or sensitive content is made. Evaluate suitability and rights for the intended use; metadata scores are not human approval or authorization to impersonate speakers.
+For dataset questions or rights concerns, contact maintainers through this repository's Community page. Avoid posting personal information publicly.
+## Citation
+Credit the original Emilia authors and cite their [2024 paper](https://arxiv.org/abs/2407.05361) and, where relevant, the [2025 extended paper](https://arxiv.org/abs/2501.15907). Also identify this repository and the commit used to distinguish its local selection and added metadata from upstream Emilia.