Datasets:

Namecoach
/

netjets-surname-phonetic-g2p-eval

grapheme stringlengths 1 23	phonetic stringlengths 2 26	alignment_margin float64 -0.13 1	alignment_method stringclasses 2 values	alignment_score float64 0.2 1	grapheme_key stringlengths 1 23	id stringlengths 30 32	observed_grapheme_count int64 1 36	observed_variant_count int64 1 15	observed_variant_probability float64 0.03 1	source_file stringclasses 72 values	source_name stringlengths 2 66	source_row_index int64 0 511
Harnisch	HAR-nish	0.504762	surname_prior	0.933333	harnisch	train-00000-of-00072.parquet:0	1	1	1	data/train-00000-of-00072.parquet	Heather Eileen Harnisch	0
Centanni	sen-TA-nee	0.339286	surname_prior	0.625	centanni	train-00000-of-00072.parquet:1	2	1	0.5	data/train-00000-of-00072.parquet	Joseph Centanni	1
Centanni	sin-TAW-nee	0.184874	surname_prior	0.470588	centanni	train-00000-of-00072.parquet:2	2	1	0.5	data/train-00000-of-00072.parquet	Sarah Melissa Centanni	2
Pelosi	puh-LOH-see	0.180392	surname_prior	0.533333	pelosi	train-00000-of-00072.parquet:3	1	1	1	data/train-00000-of-00072.parquet	Nicholas Pelosi	3
Antonelli	an-ti-NE-lee	0.380952	surname_prior	0.666667	antonelli	train-00000-of-00072.parquet:4	5	2	0.4	data/train-00000-of-00072.parquet	Brian Antonelli	4
Antonelli	an-toh-NE-lee	0.470175	surname_prior	0.736842	antonelli	train-00000-of-00072.parquet:5	5	2	0.4	data/train-00000-of-00072.parquet	Sarah Antonelli	5
Antonelli	an-toh-NE-lee	0.383901	surname_prior	0.736842	antonelli	train-00000-of-00072.parquet:6	5	2	0.4	data/train-00000-of-00072.parquet	Brielle Joy Antonelli	6
Antonelli	an-ti-NE-lee	0.133333	surname_prior	0.666667	antonelli	train-00000-of-00072.parquet:7	5	2	0.4	data/train-00000-of-00072.parquet	Angela Michelle Antonelli	7
Antonelli	ahn-toh-NE-lee	0.347059	surname_prior	0.7	antonelli	train-00000-of-00072.parquet:8	5	1	0.2	data/train-00000-of-00072.parquet	Pietro Achatz Antonelli	8
Arcuri	ar-KER-ee	0.061538	surname_prior	0.461538	arcuri	train-00000-of-00072.parquet:9	2	1	0.5	data/train-00000-of-00072.parquet	Carmella Arcuri	9
Arcuri	ar-KYER-ee	0.161905	surname_prior	0.428571	arcuri	train-00000-of-00072.parquet:10	2	1	0.5	data/train-00000-of-00072.parquet	Annibel Arcuri	10
Aronoff	AIR-i-nawf	0.033333	surname_prior	0.533333	aronoff	train-00000-of-00072.parquet:11	4	4	1	data/train-00000-of-00072.parquet	Harrison Aronoff	11
Aronoff	AIR-i-nawf	0.2	surname_prior	0.533333	aronoff	train-00000-of-00072.parquet:12	4	4	1	data/train-00000-of-00072.parquet	Nadav Idan Aronoff	12
Aronoff	AIR-i-nawf	0.247619	surname_prior	0.533333	aronoff	train-00000-of-00072.parquet:13	4	4	1	data/train-00000-of-00072.parquet	Daniel Aronoff	13
Aronoff	AIR-i-nawf	0.071795	surname_prior	0.533333	aronoff	train-00000-of-00072.parquet:14	4	4	1	data/train-00000-of-00072.parquet	Aidan Aronoff	14
Aronoff-Aspaturian	AR-i-nawf	0	surname_prior	0.333333	aronoff-aspaturian	train-00000-of-00072.parquet:15	1	1	1	data/train-00000-of-00072.parquet	Lilia Aronoff-Aspaturian	15
Blackford	BLAK-ferd	0.490196	surname_prior	0.823529	blackford	train-00000-of-00072.parquet:16	2	1	0.5	data/train-00000-of-00072.parquet	Mary Blackford	16
Blackford	BLAK-ford	0.77451	surname_prior	0.941176	blackford	train-00000-of-00072.parquet:17	2	1	0.5	data/train-00000-of-00072.parquet	Hope Blackford	17
Blinder	BLYN-der	0.590476	surname_prior	0.857143	blinder	train-00000-of-00072.parquet:18	3	1	0.333333	data/train-00000-of-00072.parquet	Benjamin Ethan Blinder	18
Blinder	BLEN-der	0.52381	surname_prior	0.857143	blinder	train-00000-of-00072.parquet:19	3	1	0.333333	data/train-00000-of-00072.parquet	Elana Blinder	19
Blinder	BLIN-der	0.833333	surname_prior	1	blinder	train-00000-of-00072.parquet:20	3	1	0.333333	data/train-00000-of-00072.parquet	Karen Blinder	20
Botha	BWE-tah	0.237762	surname_prior	0.545455	botha	train-00000-of-00072.parquet:21	4	1	0.25	data/train-00000-of-00072.parquet	Robynne Botha	21
Botha	BWE-dhah	0.333333	surname_prior	0.5	botha	train-00000-of-00072.parquet:22	4	1	0.25	data/train-00000-of-00072.parquet	Elize Botha	22
Botha	BOH-thuh	0.4	surname_prior	0.666667	botha	train-00000-of-00072.parquet:23	4	1	0.25	data/train-00000-of-00072.parquet	Collette Botha	23
Botha	BWE-thah	0.416667	surname_prior	0.666667	botha	train-00000-of-00072.parquet:24	4	1	0.25	data/train-00000-of-00072.parquet	Charlotte Botha	24
Bozarth	BOH-zarth	0.625641	surname_prior	0.933333	bozarth	train-00000-of-00072.parquet:25	1	1	1	data/train-00000-of-00072.parquet	Emilee Marie Bozarth	25
Breeden	BREE-din	0.690476	surname_prior	0.857143	breeden	train-00000-of-00072.parquet:26	2	2	1	data/train-00000-of-00072.parquet	Tessa Camille Breeden	26
Breyer	BRY-yer	0.833333	surname_prior	0.833333	breyer	train-00000-of-00072.parquet:27	1	1	1	data/train-00000-of-00072.parquet	Gillian Breyer	27
Brockway	BRAHK-way	0.416667	surname_prior	0.75	brockway	train-00000-of-00072.parquet:28	1	1	1	data/train-00000-of-00072.parquet	Anna Brockway	28
Burman	BER-min	0.5	surname_prior	0.666667	burman	train-00000-of-00072.parquet:29	2	1	0.5	data/train-00000-of-00072.parquet	Guneet Burman	29
Burman	BER-muhn	0.415385	surname_prior	0.615385	burman	train-00000-of-00072.parquet:30	2	1	0.5	data/train-00000-of-00072.parquet	Shourya Sonkar Roy Burman	30
Carlile	KAR-lyl	0.153846	surname_prior	0.615385	carlile	train-00000-of-00072.parquet:31	2	2	1	data/train-00000-of-00072.parquet	Karissa Carlile	31
Carlile	KAR-lyl	0.461538	surname_prior	0.615385	carlile	train-00000-of-00072.parquet:32	2	2	1	data/train-00000-of-00072.parquet	Matthew Carlile	32
Cawley	KAW-lee	0.066667	surname_prior	0.666667	cawley	train-00000-of-00072.parquet:33	1	1	1	data/train-00000-of-00072.parquet	Kate Cawley	33
Cremins	KRE-mins	0.703297	surname_prior	0.857143	cremins	train-00000-of-00072.parquet:34	2	1	0.5	data/train-00000-of-00072.parquet	Hannah Cremins	34
CUSHING	KUU-shing	0.492308	surname_prior	0.8	cushing	train-00000-of-00072.parquet:35	2	2	1	data/train-00000-of-00072.parquet	JULIA CUSHING	35
Cushing	KUU-shing	0.666667	surname_prior	0.8	cushing	train-00000-of-00072.parquet:36	2	2	1	data/train-00000-of-00072.parquet	Makayla Cushing	36
Darnall	DAR-nuhl	0.406593	surname_prior	0.714286	darnall	train-00000-of-00072.parquet:37	3	1	0.333333	data/train-00000-of-00072.parquet	Sydney Darnall	37
Darnall	dar-NEL	0.483516	surname_prior	0.769231	darnall	train-00000-of-00072.parquet:38	3	2	0.666667	data/train-00000-of-00072.parquet	Samantha Lynn Darnall	38
DeCarlo	dee-KAR-loh	0.397059	surname_prior	0.75	decarlo	train-00000-of-00072.parquet:39	2	1	0.5	data/train-00000-of-00072.parquet	Caroline DeCarlo	39
DeCarlo	di-KAR-loh	0.358974	surname_prior	0.666667	decarlo	train-00000-of-00072.parquet:40	2	1	0.5	data/train-00000-of-00072.parquet	Renzo DeCarlo	40
DelFin	DEL-fin	0.6	surname_prior	1	delfin	train-00000-of-00072.parquet:41	1	1	1	data/train-00000-of-00072.parquet	Frenz Dave DelFin	41
Dewar	duh-WAR	0.727273	surname_prior	0.727273	dewar	train-00000-of-00072.parquet:42	5	1	0.2	data/train-00000-of-00072.parquet	Toni-Moi Dewar	42
Dewar	DEW-uhr	0.527273	surname_prior	0.727273	dewar	train-00000-of-00072.parquet:43	5	1	0.2	data/train-00000-of-00072.parquet	Cara Dewar	43
Dewar	JEW-er	0.418182	surname_prior	0.6	dewar	train-00000-of-00072.parquet:44	5	1	0.2	data/train-00000-of-00072.parquet	Carole Dewar	44
Dewar	DEW-er	0.4	surname_prior	0.8	dewar	train-00000-of-00072.parquet:45	5	2	0.4	data/train-00000-of-00072.parquet	Elena Dewar	45
Dewar	DEW-er	0.436364	surname_prior	0.8	dewar	train-00000-of-00072.parquet:46	5	2	0.4	data/train-00000-of-00072.parquet	Darien Dewar	46
D'razio	der-AH-zee-oh	0.25	surname_prior	0.625	d'razio	train-00000-of-00072.parquet:47	1	1	1	data/train-00000-of-00072.parquet	Justin Daniel D'razio.	47
D'Orazio	der-A-zee-oh	0.375	surname_prior	0.625	d'orazio	train-00000-of-00072.parquet:48	2	1	0.5	data/train-00000-of-00072.parquet	Dominic D'Orazio	48
D'Orazio	der-AH-zee-oh	0.463235	surname_prior	0.588235	d'orazio	train-00000-of-00072.parquet:49	2	1	0.5	data/train-00000-of-00072.parquet	Lianna D'Orazio	49
Duffey	DUH-fee	0.5	surname_prior	0.666667	duffey	train-00000-of-00072.parquet:50	1	1	1	data/train-00000-of-00072.parquet	Whitni Duffey	50
Easterly	EE-ster-lee	0.552036	surname_prior	0.705882	easterly	train-00000-of-00072.parquet:51	1	1	1	data/train-00000-of-00072.parquet	Mary Francis Easterly	51
Eberhard	E-ber-hard	0.714286	surname_prior	1	eberhard	train-00000-of-00072.parquet:52	2	2	1	data/train-00000-of-00072.parquet	Maddie Eberhard	52
Eberhard	E-ber-hard	0.857143	surname_prior	1	eberhard	train-00000-of-00072.parquet:53	2	2	1	data/train-00000-of-00072.parquet	Dalton Eberhard	53
Edens	EE-dins	0.527273	surname_prior	0.727273	edens	train-00000-of-00072.parquet:54	1	1	1	data/train-00000-of-00072.parquet	Abel Edens	54
Cvengros-Edens	EE-dins	0.526316	surname_prior	0.526316	cvengros-edens	train-00000-of-00072.parquet:55	1	1	1	data/train-00000-of-00072.parquet	Mark Cvengros-Edens	55
EISEMAN	YZ-min	0.083333	surname_prior	0.333333	eiseman	train-00000-of-00072.parquet:56	2	1	0.5	data/train-00000-of-00072.parquet	SAM EISEMAN	56
Eiseman	YZ-men	0	surname_prior	0.333333	eiseman	train-00000-of-00072.parquet:57	2	1	0.5	data/train-00000-of-00072.parquet	Brittani Ellen Eiseman	57
Emmons	E-mins	0.227273	surname_prior	0.727273	emmons	train-00000-of-00072.parquet:58	2	2	1	data/train-00000-of-00072.parquet	Madison Emmons	58
Emmons	E-mins	0.545455	surname_prior	0.727273	emmons	train-00000-of-00072.parquet:59	2	2	1	data/train-00000-of-00072.parquet	Rachel Emmons	59
Fetter	FE-der	0.363636	surname_prior	0.727273	fetter	train-00000-of-00072.parquet:60	1	1	1	data/train-00000-of-00072.parquet	Hayden Fetter	60
Fettig	FE-dig	0.505051	surname_prior	0.727273	fettig	train-00000-of-00072.parquet:61	1	1	1	data/train-00000-of-00072.parquet	Lyle Fettig	61
Frakes	FRAYKS	0.633333	surname_prior	0.833333	frakes	train-00000-of-00072.parquet:62	1	1	1	data/train-00000-of-00072.parquet	Elsa Frakes	62
Furst	FERST	0.577778	surname_prior	0.8	furst	train-00000-of-00072.parquet:63	1	1	1	data/train-00000-of-00072.parquet	Jake Furst	63
Geddes	GE-dis	0.527273	surname_prior	0.727273	geddes	train-00000-of-00072.parquet:64	3	2	0.666667	data/train-00000-of-00072.parquet	Sonja Geddes	64
Geddes	GE-dis	0.560606	surname_prior	0.727273	geddes	train-00000-of-00072.parquet:65	3	2	0.666667	data/train-00000-of-00072.parquet	Cameron Geddes	65
Geddes	GE-dees	0.651515	surname_prior	0.833333	geddes	train-00000-of-00072.parquet:66	3	1	0.333333	data/train-00000-of-00072.parquet	Kylie Geddes	66
Girardi	jer-AR-dee	0.247619	surname_prior	0.533333	girardi	train-00000-of-00072.parquet:67	2	1	0.5	data/train-00000-of-00072.parquet	Martin Girardi	67
GIRARDI	jee-RAR-dee	0.25	surname_prior	0.5	girardi	train-00000-of-00072.parquet:68	2	1	0.5	data/train-00000-of-00072.parquet	DANIELA GIRARDI	68
Gonzaga	gohn-SAH-gah	0.470588	surname_prior	0.705882	gonzaga	train-00000-of-00072.parquet:69	3	1	0.333333	data/train-00000-of-00072.parquet	Nereyda Gonzaga	69
GONZAGA	guhn-ZAH-guh	0.338235	best_unique_token	0.588235	gonzaga	train-00000-of-00072.parquet:70	3	1	0.333333	data/train-00000-of-00072.parquet	GONZAGA MENDEZ	70
Gonzaga	gohn-SAH-guh	0.477124	surname_prior	0.588235	gonzaga	train-00000-of-00072.parquet:71	3	1	0.333333	data/train-00000-of-00072.parquet	Jennifer Gonzaga	71
Grimshaw	GRIM-shaw	0.692308	surname_prior	1	grimshaw	train-00000-of-00072.parquet:72	1	1	1	data/train-00000-of-00072.parquet	Megan Grimshaw	72
Hallberg	HAWL-berg	0.741667	surname_prior	0.875	hallberg	train-00000-of-00072.parquet:73	1	1	1	data/train-00000-of-00072.parquet	Anthony Hallberg	73
Haring	HAIR-ing	0.589744	surname_prior	0.923077	haring	train-00000-of-00072.parquet:74	3	3	1	data/train-00000-of-00072.parquet	David Haring	74
Haring	HAIR-ing	0.589744	surname_prior	0.923077	haring	train-00000-of-00072.parquet:75	3	3	1	data/train-00000-of-00072.parquet	Troy Wayne Haring	75
Haughey	HAW-hee	0.415385	surname_prior	0.615385	haughey	train-00000-of-00072.parquet:76	3	1	0.333333	data/train-00000-of-00072.parquet	Sean Haughey	76
Haughey	HOW-ee	0	surname_prior	0.333333	haughey	train-00000-of-00072.parquet:77	3	1	0.333333	data/train-00000-of-00072.parquet	Matthew Ryan Haughey	77
Haughey	HOY	0.4	surname_prior	0.4	haughey	train-00000-of-00072.parquet:78	3	1	0.333333	data/train-00000-of-00072.parquet	Regan Haughey	78
Hendry	HEN-dree	0.615385	surname_prior	0.769231	hendry	train-00000-of-00072.parquet:79	2	2	1	data/train-00000-of-00072.parquet	Justin Hendry	79
Hendry	HEN-dree	0.602564	surname_prior	0.769231	hendry	train-00000-of-00072.parquet:80	2	2	1	data/train-00000-of-00072.parquet	Paige Hendry	80
Hepburn	HEP-bern	0.590476	surname_prior	0.857143	hepburn	train-00000-of-00072.parquet:81	2	2	1	data/train-00000-of-00072.parquet	Jeanette Hepburn	81
Hepburn	HEP-bern	0.549451	surname_prior	0.857143	hepburn	train-00000-of-00072.parquet:82	2	2	1	data/train-00000-of-00072.parquet	Sirena Hepburn	82
Hooley	HEW-lee	0.5	surname_prior	0.5	hooley	train-00000-of-00072.parquet:83	1	1	1	data/train-00000-of-00072.parquet	Nick Hooley	83
Kastner	KAST-ner	0.714286	surname_prior	1	kastner	train-00000-of-00072.parquet:84	3	1	0.333333	data/train-00000-of-00072.parquet	Mallory Kastner	84
Kastner	KAS-ner	0.923077	surname_prior	0.923077	kastner	train-00000-of-00072.parquet:85	3	1	0.333333	data/train-00000-of-00072.parquet	Phil Kastner	85
Kastner	KAHS-nair	0.333333	surname_prior	0.666667	kastner	train-00000-of-00072.parquet:86	3	1	0.333333	data/train-00000-of-00072.parquet	Arne Kastner	86
Keeton	KEE-tin	0.547619	surname_prior	0.833333	keeton	train-00000-of-00072.parquet:87	2	1	0.5	data/train-00000-of-00072.parquet	Caroline Keeton	87
Keeton	KEE(T)-in	0.5	best_unique_token	0.833333	keeton	train-00000-of-00072.parquet:88	2	1	0.5	data/train-00000-of-00072.parquet	Keeton Gibson	88
Kerin	KAIR-in	0.393939	surname_prior	0.727273	kerin	train-00000-of-00072.parquet:89	2	2	1	data/train-00000-of-00072.parquet	Athena Kerin	89
Kleinman	KLYN-min	0.2	surname_prior	0.533333	kleinman	train-00000-of-00072.parquet:90	2	1	0.5	data/train-00000-of-00072.parquet	Ellie Kleinman	90
Kleinman	KLYN-men	0.484848	surname_prior	0.666667	kleinman	train-00000-of-00072.parquet:91	2	1	0.5	data/train-00000-of-00072.parquet	Mary Kleinman	91
Knopp	kuh-NOHP	0.666667	surname_prior	0.666667	knopp	train-00000-of-00072.parquet:92	4	1	0.25	data/train-00000-of-00072.parquet	Gabrielle Knopp	92
Knopp	kuh-NAHP	0.346154	surname_prior	0.5	knopp	train-00000-of-00072.parquet:93	4	1	0.25	data/train-00000-of-00072.parquet	Colton Knopp	93
Knopp	ki-NAHP	0.237762	surname_prior	0.545455	knopp	train-00000-of-00072.parquet:94	4	1	0.25	data/train-00000-of-00072.parquet	William Knopp	94
Knopp	NAHP	0.290598	surname_prior	0.444444	knopp	train-00000-of-00072.parquet:95	4	1	0.25	data/train-00000-of-00072.parquet	Christian Knopp	95
Korman	KOR-min	0.433333	surname_prior	0.833333	korman	train-00000-of-00072.parquet:96	1	1	1	data/train-00000-of-00072.parquet	Amia Korman	96
Kravitz	KRA-vits	0.690476	surname_prior	0.857143	kravitz	train-00000-of-00072.parquet:97	1	1	1	data/train-00000-of-00072.parquet	Alexa Kravitz	97
Labonte	luh-BAHN-tee	0.377709	surname_prior	0.588235	labonte	train-00000-of-00072.parquet:98	2	1	0.5	data/train-00000-of-00072.parquet	Christian David Labonte	98
LaBonte	luh-BAHN-tay	0.352941	surname_prior	0.470588	labonte	train-00000-of-00072.parquet:99	2	1	0.5	data/train-00000-of-00072.parquet	Kristen LaBonte	99

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NetJets surname phonetic G2P evaluation

Source: Namecoach/netjets-name-asr, Hub revision dd9ca45ff76c1cf5017d03bbd3558f625420928f, train split.

This benchmark presents a written surname grapheme and expects one of the observed custom written phonetic spellings for that surname. The phonetic strings are retained exactly after outer whitespace trimming; capitalization encodes stress and hyphens/parentheses are meaningful notation.

Files

data/eval-00000-of-00001.parquet: viewer-backed eval table with grapheme and phonetic as the first two columns.
rows.jsonl: one row per retained source occurrence. Do not deduplicate this file: its repeated rows preserve the source pronunciation frequency.
metadata/groups.jsonl.txt: one row per grapheme with its observed variant counts and probabilities. Use this for distributional evaluation and allowed-output validation.
metadata/excluded.jsonl.txt: source rows that could not be mapped to one auditable grapheme token, with the reason and candidate scores.
manifest.json: source hash, counts, extraction policy, and frequency policy.

Row contract

Each row contains grapheme, phonetic, a stable source id, source provenance, and alignment metadata. A prediction is valid only if it exactly equals one of the phonetic variants listed for that row's grapheme_key in metadata/groups.jsonl.txt.

Alignment policy

The dataset's name field is an ASR/spoken reconstruction and can contain extra or missing tokens, so the final whitespace token is not always the labeled surname. The builder uses a documented token-to-phonetic character-similarity heuristic with a surname prior, removes common suffixes such as Jr/III, and excludes ambiguous or unalignable rows instead of fabricating grapheme labels.

Counts

Source rows: 36,704
Retained eval occurrences: 35,891
Unique graphemes: 15,938
Unique grapheme/phonetic pairs: 27,644
Excluded rows: 813

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