| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train/*.parquet |
| - split: test |
| path: test/*.parquet |
| --- |
| |
| # AVSpeech Video + Audio |
|
|
| This repository is a media-bearing reconstruction of the public AVSpeech |
| annotations. Each row represents an already-trimmed segment and keeps the |
| original source-video timing and target-face-center metadata. |
|
|
| ## Dataset structure |
|
|
| - `clip_id`: identifier derived as |
| `{youtube_id}_{start_sec:.3f}_{end_sec:.3f}`. |
| - `avspeech_metadata`: JSON containing `youtube_id`, `start_sec`, `end_sec`, |
| `x_center`, and `y_center` from the AVSpeech annotation. |
| - `video`: video-only stream, or null when the source segment could not be |
| materialized. |
| - `audio`: audio-only stream, or null when the source segment could not be |
| materialized. |
|
|
| The `video` field is already trimmed. `start_sec` and `end_sec` refer to the |
| original YouTube-video timeline and must not be used to seek again within this |
| clip. AVSpeech defines `(x_center, y_center)` as the normalized center of the |
| speaker's face in the frame at the beginning of the segment, with `(0, 0)` at |
| the top left. |
|
|
| ## Audited snapshot and known publication gap |
|
|
| The following figures describe revision |
| `efdceb2a0b9d81a6aec76f10668cca49e8209e37`: |
|
|
| | Split | Rows | Rows with both media | Rows without both media | Parquet files | Encoded size | |
| | --- | ---: | ---: | ---: | ---: | ---: | |
| | train | 2,621,845 | 1,589,842 | 1,032,003 | 5,142 | 1,404,473,032,806 bytes | |
| | test | 183,273 | 98,605 | 84,668 | 359 | 88,861,772,006 bytes | |
| | total | 2,805,118 | 1,688,447 | 1,116,671 | 5,501 | 1,493,334,804,812 bytes | |
|
|
| The train states were verified by an exhaustive read-only scan of all 5,142 |
| train Parquet files and 2,621,845 rows. Of the 1,032,003 train rows without both |
| media streams, 1,032,000 have both media null, 3 are video-only, and 0 are |
| audio-only. The test split was not audited at that row-state granularity, so |
| the table reports only its aggregate count without both streams. |
|
|
| The completed exporter expected 1,589,942 paired train occurrences, while this |
| snapshot contains 1,589,842, an aggregate gap of 100. Surviving non-media |
| evidence supports high-confidence assignment of 49 of those occurrence slots: |
|
|
| - 46 both-null occurrences have a paired sibling at the pinned revision. |
| - 3 video-only occurrences retain a published `video.path` while `audio.path` |
| is null, showing that the exporter reached archive-member processing. |
|
|
| The remaining 51 occurrence slots cannot be assigned to exact rows or |
| `clip_id` values without the original expected-pair manifest or historical |
| ID-to-archive map. Their ambiguity remains within a pool of 4,103 |
| metadata-resolved both-null occurrences across 403 YouTube IDs. “Unresolved” |
| does not mean that these rows were verified unavailable. |
|
|
| The audit selected only `clip_id`, `avspeech_metadata`, `video.path`, and |
| `audio.path`. It did not select or materialize embedded media bytes, and its |
| range guards recorded zero intersections with media-byte column chunks. No |
| media recovery, YouTube retrieval, torrent-media transfer, row repair, or |
| Hugging Face mutation was attempted as part of that audit. |
|
|
| ## Bounded loading |
|
|
| Do not use `snapshot_download` for routine training: the repository is about |
| 1.49 TB. Stream rows, keep media decoding disabled at the dataset layer, skip |
| any row without both media streams, and materialize only one bounded work unit |
| at a time. Preserve the official split and stable row provenance, and report |
| pre-filter and retained denominators plus exclusions by reason. |
|
|
| ```python |
| from datasets import Audio, Video, load_dataset |
| |
| revision = "efdceb2a0b9d81a6aec76f10668cca49e8209e37" |
| rows = load_dataset( |
| "ProgramComputer/avspeech-visual-audio", |
| split="train", |
| revision=revision, |
| streaming=True, |
| ) |
| rows = rows.cast_column("video", Video(decode=False)) |
| rows = rows.cast_column("audio", Audio(decode=False)) |
| |
| for row in rows: |
| if row["video"] is None or row["audio"] is None: |
| continue |
| # Materialize/process this row in bounded temporary storage. |
| ``` |
|
|
| The official AVSpeech page states that its supplied train and test annotations |
| use disjoint speakers. This reconstruction preserves those source split labels. |
| It does not add person identities, and a YouTube video ID must not be described |
| as a speaker identity. |
|
|
| ## Intended use and limitations |
|
|
| This dataset is intended for research on audio-visual speech and related |
| representation-learning tasks. It is derived from public Internet video and is |
| not demographically balanced. Availability, codecs, media quality, language, |
| pose, lighting, and annotation accuracy vary. Missing rows are not necessarily |
| random, so filtering to paired media may introduce additional selection bias. |
| The unresolved 51-slot publication gap is aggregate provenance information, |
| not a verified unavailable-row list, and must not be converted into invented |
| row-level labels. |
|
|
| The face-center coordinate is a point hint at the beginning of the segment, |
| not a bounding box, persistent track, verified identity label, or consent |
| signal. Downstream systems must validate the associated detected face and must |
| not use this dataset for identification, surveillance, or consequential |
| decisions. |
|
|
| ## License and provenance review |
|
|
| The official AVSpeech download page provides train/test annotation CSVs and |
| states that “this data” is available under CC BY 4.0. This repository also |
| redistributes media derived from YouTube videos. The maintainer has not yet |
| documented a legal review establishing that the same license statement covers |
| redistribution of every embedded media stream or that all upstream platform |
| and uploader terms are satisfied. Therefore this card deliberately does not |
| assert a Hugging Face `license` tag for the media-bearing reconstruction. |
|
|
| Before continued public redistribution, document the source acquisition |
| process, takedown procedure, upstream terms, and the basis for redistributing |
| the embedded audio/video. This note is a publication safeguard, not legal |
| advice. |
|
|
| ## Citation |
|
|
| If you use the data, cite the original AVSpeech work: |
|
|
| ```bibtex |
| @article{ephrat2018looking, |
| title={Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation}, |
| author={Ephrat, Ariel and Mosseri, Inbar and Lang, Oran and Dekel, Tali and Wilson, Kevin and Hassidim, Avinatan and Freeman, William T. and Rubinstein, Michael}, |
| journal={ACM Transactions on Graphics}, |
| year={2018} |
| } |
| ``` |
|
|
| Official AVSpeech project and download page: |
| <https://looking-to-listen.github.io/avspeech/download.html>. |
|
|
