| --- |
| license: other |
| license_name: cc-by-3.0-and-cc-by-4.0 |
| license_link: LICENSE |
| pretty_name: PolyPlex Word Timings |
| tags: |
| - forced-alignment |
| - word-timestamps |
| - speech |
| - multilingual |
| configs: |
| - config_name: fleurs |
| data_files: |
| - split: train |
| path: "fleurs/*.parquet" |
| - config_name: yodas2 |
| data_files: |
| - split: train |
| path: "yodas2/*.parquet" |
| --- |
| |
| # PolyPlex Word Timings |
|
|
| Word-level onset and end times for multilingual speech, obtained by forced alignment with Whisper large-v3. Each row is one |
| aligned piece of speech: its audio (16 kHz mono FLAC, playable in the dataset viewer), its transcript, and the onset and end |
| of every word. |
|
|
| The timings were made to measure, for the PolyPlex full-duplex speech model, how often a word's text tokens do not fit the |
| 80 ms frames before the next word starts. They are useful wherever word timings for real speech in many languages are needed. |
|
|
| ## Configurations |
|
|
| | Config | Source | Speech | License | |
| |---|---|---|---| |
| | `yodas2` | [YODAS2](https://huggingface.co/datasets/espnet/yodas2), user-uploaded subtitle subsets (`<lang>000`) | Real speech from YouTube videos under Creative Commons | CC BY 3.0 | |
| | `fleurs` | [FLEURS](https://huggingface.co/datasets/google/fleurs), first 200 utterances of `dev` | Read sentences | CC BY 4.0 | |
|
|
| ### `yodas2`: real speech, 84 languages |
|
|
| | Language | Source | Pieces | Words | |
| |---|---|---|---| |
| | Afrikaans | `af000` | 72 | 2,071 | |
| | Armenian | `hy000` | 421 | 7,031 | |
| | Assamese | `as000` | 104 | 946 | |
| | Bashkir | `ba000` | 14 | 73 | |
| | Basque | `eu000` | 1,000 | 33,176 | |
| | Belarusian | `be000` | 1,000 | 44,047 | |
| | Bengali | `bn000` | 472 | 8,901 | |
| | Bhojpuri | `bh000` | 1 | 48 | |
| | Bosnian | `bs000` | 801 | 28,728 | |
| | Bulgarian | `bg000` | 753 | 22,385 | |
| | Burmese | `my000` | 299 | 2,747 | |
| | Catalan | `ca000` | 1,000 | 38,587 | |
| | Croatian | `hr000` | 1,000 | 30,213 | |
| | Czech | `cs000` | 1,000 | 30,688 | |
| | Danish | `da000` | 1,000 | 40,163 | |
| | Dutch | `nl000` | 1,000 | 41,378 | |
| | Eastern Yiddish | `yi000` | 8 | 275 | |
| | English | `en000` | 1,000 | 43,379 | |
| | Estonian | `et000` | 1,000 | 31,981 | |
| | Faroese | `fo000` | 10 | 217 | |
| | Finnish | `fi000` | 1,000 | 29,147 | |
| | French | `fr000` | 1,000 | 46,976 | |
| | Galician | `gl000` | 519 | 17,362 | |
| | Georgian | `ka000` | 1,000 | 36,375 | |
| | German | `de000` | 785 | 25,801 | |
| | Greek | `el000` | 1,000 | 33,237 | |
| | Gujarati | `gu000` | 465 | 5,065 | |
| | Haitian | `ht000` | 143 | 3,655 | |
| | Hebrew | `iw000` | 1,000 | 41,621 | |
| | Hindi | `hi000` | 1,000 | 36,099 | |
| | Hungarian | `hu000` | 1,000 | 17,634 | |
| | Icelandic | `is000` | 654 | 24,036 | |
| | Indonesian | `id000` | 1,000 | 25,885 | |
| | Irish | `ga000` | 182 | 5,181 | |
| | Italian | `it000` | 1,000 | 38,902 | |
| | Japanese | `ja000` | 770 | 4,617 | |
| | Javanese | `jv000` | 202 | 3,217 | |
| | Kannada | `kn000` | 591 | 9,093 | |
| | Kazakh | `kk000` | 458 | 7,659 | |
| | Khmer | `km000` | 444 | 3,276 | |
| | Korean | `ko000` | 1,000 | 21,657 | |
| | Lao | `lo000` | 25 | 126 | |
| | Latvian | `lv000` | 328 | 10,414 | |
| | Lithuanian | `lt000` | 1,000 | 27,831 | |
| | Luxembourgish | `lb000` | 9 | 246 | |
| | Macedonian | `mk000` | 787 | 25,747 | |
| | Malay | `ms000` | 1,000 | 15,555 | |
| | Malayalam | `ml000` | 975 | 24,989 | |
| | Marathi | `mr000` | 575 | 9,364 | |
| | Nepali | `ne000` | 556 | 7,451 | |
| | North Azerbaijani | `az000` | 799 | 17,138 | |
| | Northern Uzbek | `uz000` | 830 | 19,177 | |
| | Norwegian Bokmal | `no000` | 838 | 35,707 | |
| | Occitan | `oc000` | 28 | 1,000 | |
| | Oriya | `or000` | 297 | 1,870 | |
| | Persian | `fa000` | 797 | 29,764 | |
| | Polish | `pl000` | 994 | 30,952 | |
| | Portuguese | `pt000` | 972 | 44,293 | |
| | Punjabi | `pa000` | 467 | 7,612 | |
| | Romanian | `ro000` | 1,000 | 32,751 | |
| | Russian | `ru000` | 1,000 | 30,844 | |
| | Sardinian | `sc000` | 2 | 40 | |
| | Serbian | `sr000` | 1,000 | 32,721 | |
| | Simplified Chinese | `zh000` | 860 | 7,567 | |
| | Sindhi | `sd000` | 13 | 155 | |
| | Sinhala | `si000` | 923 | 13,833 | |
| | Slovak | `sk000` | 1,000 | 26,622 | |
| | Slovenian | `sl000` | 1,000 | 24,210 | |
| | Spanish | `es000` | 766 | 27,778 | |
| | Standard Arabic | `ar000` | 1,000 | 23,562 | |
| | Sundanese | `su000` | 13 | 113 | |
| | Swahili | `sw000` | 633 | 14,926 | |
| | Swedish | `sv000` | 1,000 | 33,230 | |
| | Tajik | `tg000` | 47 | 1,128 | |
| | Tamil | `ta000` | 974 | 27,073 | |
| | Tatar | `tt000` | 2 | 3 | |
| | Telugu | `te000` | 698 | 9,096 | |
| | Thai | `th000` | 883 | 8,976 | |
| | Tosk Albanian | `sq000` | 573 | 20,474 | |
| | Turkish | `tr000` | 1,000 | 23,371 | |
| | Ukrainian | `uk000` | 913 | 32,020 | |
| | Urdu | `ur000` | 638 | 21,633 | |
| | Vietnamese | `vi000` | 837 | 32,817 | |
| | Welsh | `cy000` | 487 | 23,310 | |
|
|
| ### `fleurs`: read speech, 79 languages |
|
|
| | Language | Source | Pieces | Words | |
| |---|---|---|---| |
| | Afrikaans | `af_za` | 198 | 4,197 | |
| | Armenian | `hy_am` | 199 | 3,802 | |
| | Assamese | `as_in` | 192 | 3,283 | |
| | Asturian | `ast_es` | 200 | 4,143 | |
| | Belarusian | `be_by` | 199 | 4,039 | |
| | Bengali | `bn_in` | 191 | 3,291 | |
| | Bosnian | `bs_ba` | 199 | 3,803 | |
| | Bulgarian | `bg_bg` | 199 | 4,103 | |
| | Burmese | `my_mm` | 116 | 906 | |
| | Cantonese | `yue_hant_hk` | 199 | 313 | |
| | Catalan | `ca_es` | 199 | 4,624 | |
| | Cebuano | `ceb_ph` | 196 | 4,605 | |
| | Croatian | `hr_hr` | 200 | 3,487 | |
| | Czech | `cs_cz` | 200 | 3,459 | |
| | Danish | `da_dk` | 199 | 3,955 | |
| | Dutch | `nl_nl` | 171 | 3,695 | |
| | English | `en_us` | 200 | 3,884 | |
| | Estonian | `et_ee` | 200 | 3,050 | |
| | Finnish | `fi_fi` | 200 | 3,135 | |
| | French | `fr_fr` | 200 | 4,748 | |
| | Galician | `gl_es` | 200 | 4,468 | |
| | Georgian | `ka_ge` | 175 | 2,459 | |
| | German | `de_de` | 198 | 3,793 | |
| | Greek | `el_gr` | 200 | 4,474 | |
| | Gujarati | `gu_in` | 188 | 3,622 | |
| | Hebrew | `he_il` | 200 | 3,370 | |
| | Hindi | `hi_in` | 200 | 4,530 | |
| | Hungarian | `hu_hu` | 198 | 3,558 | |
| | Icelandic | `is_is` | 36 | 682 | |
| | Indonesian | `id_id` | 200 | 3,684 | |
| | Irish | `ga_ie` | 200 | 4,641 | |
| | Italian | `it_it` | 197 | 4,257 | |
| | Japanese | `ja_jp` | 200 | 203 | |
| | Javanese | `jv_id` | 199 | 3,807 | |
| | Kabuverdianu | `kea_cv` | 196 | 4,180 | |
| | Kannada | `kn_in` | 199 | 2,839 | |
| | Kazakh | `kk_kz` | 197 | 2,968 | |
| | Khmer | `km_kh` | 188 | 871 | |
| | Korean | `ko_kr` | 200 | 3,040 | |
| | Lao | `lo_la` | 161 | 674 | |
| | Latvian | `lv_lv` | 200 | 3,314 | |
| | Lithuanian | `lt_lt` | 200 | 3,249 | |
| | Luxembourgish | `lb_lu` | 200 | 3,994 | |
| | Macedonian | `mk_mk` | 200 | 3,829 | |
| | Malay | `ms_my` | 200 | 3,764 | |
| | Malayalam | `ml_in` | 181 | 2,288 | |
| | Maltese | `mt_mt` | 199 | 3,483 | |
| | Marathi | `mr_in` | 199 | 3,598 | |
| | Nepali | `ne_np` | 199 | 3,329 | |
| | North Azerbaijani | `az_az` | 198 | 3,562 | |
| | Northern Uzbek | `uz_uz` | 200 | 3,262 | |
| | Norwegian Bokmal | `nb_no` | 161 | 3,350 | |
| | Occitan | `oc_fr` | 199 | 4,352 | |
| | Oriya | `or_in` | 167 | 2,745 | |
| | Persian | `fa_ir` | 196 | 4,179 | |
| | Polish | `pl_pl` | 200 | 3,456 | |
| | Portuguese | `pt_br` | 200 | 4,323 | |
| | Punjabi | `pa_in` | 199 | 4,392 | |
| | Romanian | `ro_ro` | 200 | 4,514 | |
| | Russian | `ru_ru` | 200 | 3,601 | |
| | Serbian | `sr_rs` | 200 | 3,672 | |
| | Simplified Chinese | `cmn_hans_cn` | 200 | 403 | |
| | Sindhi | `sd_in` | 200 | 4,778 | |
| | Slovak | `sk_sk` | 200 | 3,651 | |
| | Slovenian | `sl_si` | 200 | 3,667 | |
| | Spanish | `es_419` | 200 | 4,735 | |
| | Standard Arabic | `ar_eg` | 199 | 3,617 | |
| | Swahili | `sw_ke` | 199 | 3,914 | |
| | Swedish | `sv_se` | 200 | 3,956 | |
| | Tagalog | `fil_ph` | 192 | 4,399 | |
| | Tajik | `tg_tj` | 195 | 4,132 | |
| | Tamil | `ta_in` | 197 | 2,869 | |
| | Telugu | `te_in` | 194 | 2,987 | |
| | Thai | `th_th` | 198 | 745 | |
| | Turkish | `tr_tr` | 200 | 3,133 | |
| | Ukrainian | `uk_ua` | 199 | 3,615 | |
| | Urdu | `ur_pk` | 200 | 5,090 | |
| | Vietnamese | `vi_vn` | 199 | 5,612 | |
| | Welsh | `cy_gb` | 198 | 4,338 | |
|
|
| ## Columns |
|
|
| | Column | Meaning | |
| |---|---| |
| | `audio` | The aligned audio, 16 kHz mono FLAC; word times count from its first sample | |
| | `language` | Language name | |
| | `subset` / `fleurs_config` | Source subset (YODAS2) or FLEURS configuration | |
| | `video_id`, `piece_start` | (`yodas2`) The source video and the start of the piece in it, in seconds | |
| | `id` | (`fleurs`) FLEURS utterance file name without extension | |
| | `text` | Transcript the alignment was forced to | |
| | `duration` | Length of `audio`, seconds | |
| | `words` | `text` split on whitespace | |
| | `word_starts`, `word_ends` | Onset and end of each word, seconds from the start of the piece | |
| | `alignment_loss` | Mean teacher-forced cross entropy of the transcript tokens under Whisper; high values mean the transcript does not match the speech | |
|
|
| ## How it was made |
|
|
| - **Alignment.** The reference transcript is fed to the Whisper large-v3 decoder under teacher forcing. The cross attention |
| of the checkpoint's alignment heads is normalized per head and median filtered, and dynamic time warping over the token by |
| frame matrix gives each token the frame it starts on (20 ms resolution). Recognition errors do not move the words, because |
| the transcript is not decoded. |
| - **YODAS2 pieces.** For each language the subtitle file of four shards spread over the subset is read, and the audio shard is |
| streamed until 250 pieces per shard are aligned. Neighbouring subtitle segments of one video are joined into pieces of at |
| most 25 seconds, split at pauses over one second, with 0.3 seconds of margin on both sides. |
| - **Left out.** Clips longer than 30 seconds (Whisper's window) and transcripts longer than 448 decoder tokens. |
| - **Languages Whisper lacks** are prompted with a close language (Asturian as Spanish, Irish as English, Oriya as Bengali, |
| Cebuano as Tagalog, Kabuverdianu as Portuguese, Bhojpuri as Hindi, Sardinian as Italian). |
|
|
| ## Recommended filtering |
|
|
| YODAS2 subtitles are uploaded by users and are not always a verbatim transcript; a piece whose subtitle does not match the |
| speech gets compressed, wrong word times. Keep pieces with `alignment_loss <= 1.61`, the 95th percentile of the |
| loss on human-transcribed Korean conversation (KsponSpeech evaluation split). On Korean, this kept 82% of YODAS2 pieces and |
| brought the share of words cut at 12.5 Hz to 1.12%, against 0.89% on KsponSpeech and 8.44% before filtering. |
|
|
| ## Limitations |
|
|
| - Whisper attention alignment is not human-verified. It sometimes gives a short word almost no duration (onsets of two |
| words 0 to 60 ms apart). |
| - YODAS2 content is whatever the subset holds: conversation, vlogs, lectures, news reading. |
| - Scripts written without spaces (Chinese, Japanese, Thai, Lao, Khmer, Burmese) are aligned per whitespace unit, which is |
| often a whole phrase. |
|
|
| ## Licenses and attribution |
|
|
| - `yodas2` is derived from YODAS2 (CC BY 3.0) and carries pieces of its audio. The audio belongs to the creators of the |
| YouTube videos, which YODAS collected under Creative Commons licenses. Cite: Li et al., *YODAS: YouTube-Oriented Dataset for Audio and Speech*, |
| ASRU 2023. |
| - `fleurs` is derived from FLEURS (CC BY 4.0) and carries its audio. Cite: Conneau et al., *FLEURS: Few-shot Learning Evaluation of Universal |
| Representations of Speech*, SLT 2022. |
| - The word times added here are released under the license of their source configuration. |
| - Alignment model: Whisper large-v3 (MIT). |
|
|
| If you appear in a source video and want its rows removed, open a discussion on this repository; rows removed from YODAS2 |
| on request are removed here too. |
|
|
