AudioCC-Lab/Emilia_ERT

Emilia ERT

Dataset summary

Emilia ERT contains 9,310,186 speech segments totaling 26,133.85 hours in six languages, distributed as 666 audio TAR shards and a single 34-column metadata table, metrics.scp. The data originate from Emilia dataset and were selected using the SQA+SED filtering strategy described in the accompanying paper, combining speech quality assessment (SQA) and sound event detection (SED). Existing transcripts and automatic audio metrics are included.

Language	Code	Segments	Duration (hours)	TAR shards
German	de	166,600	433.62	10
English	en	4,619,380	13,370.38	333
French	fr	78,751	191.89	12
Japanese	ja	203,264	391.90	27
Korean	ko	11,640	30.20	1
Chinese	zh	4,230,551	11,715.86	283
Total		9,310,186	26,133.85	666

Durations are sums of the length(s) values in the released metrics.scp, converted to hours and rounded to two decimal places. All 9,310,186 records have numeric duration values.

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

.
├── 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:

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. Related outputs are grouped into a single metric entry below; column numbers refer to the unchanged 34-column SCP schema.

#	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.
12–14	squim_pesq, squim_sisdr, squim_stoi	float; PESQ score, SI-SDR in dB, STOI score	Non-intrusive estimates of perceptual speech quality (PESQ), scale-invariant signal-to-distortion ratio (SI-SDR), and short-time objective intelligibility (STOI), respectively. These are model predictions without a clean reference.
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).
22–28	sigmos_ovrl, sigmos_col, sigmos_disc, sigmos_loud, sigmos_noise, sigmos_reverb, sigmos_sig	float, predicted quality ratings	Ratings for overall quality, coloration, discontinuity, loudness, background noise, reverberation, and speech-signal quality, respectively. These are predicted quality dimensions, not physical impairment measurements.
29	ced	float, probability score	Maximum CED probability over the workflow's selected noise/event classes.
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.
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.
33–34	speaker_count, overlap_ratio	integer count; float fraction	Both are derived from Sortformer diarization outputs. speaker_count respents the number of speakers in the audio and overlap_ratio represents the degree of overlap between the speakers.

Usage

Download metrics.scp and the required shards, then parse with an explicit TAB delimiter. Plain whitespace splitting would corrupt transcripts and event labels.

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))

License and access review

CC BY-NC 4.0 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 for the attribution notice.