FLEURS-R metadata, source audio and precomputed NeuCodec speech tokens for 102
locales, plus a speaker label FLEURS itself does not ship.
data/{locale}-{split}.parquet — metadata, one row per utterance (this is what the
viewer shows).audio/{locale}-{split}.zip — source FLEURS-R audio, 24kHz mono PCM16 WAV, members
named audio/{locale}/{split}/{id}.wav (the path column).neucodec/{locale}-{split}-rank{N}.zip — NeuCodec tokens, members named
neucodec/{locale}/{split}/{id}.pt (the neucodec_path column), each a
{'source': str, 'codes': IntTensor[1, 1, T]} torch file at 50 tokens/s.| column | what |
|---|---|
id | FLEURS utterance id |
path | audio member path inside audio/{locale}-{split}.zip |
neucodec_path | token member path inside the matching neucodec/ zip |
filename | bare {id}.wav |
sentence | raw transcription |
normalized_text | lowercased, punctuation-stripped transcription |
characters | space-separated character transcription |
locale, split | e.g. en_us, train |
gender | FLEURS speaker gender label |
speaker | derived voice identity, {locale}_spkNN (see below) |
num_samples | sample count of the original 16kHz FLEURS audio |
FLEURS and FLEURS-R ship gender but no speaker identity. speaker here is derived:
every utterance is embedded with TitaNet-L (titanet-vectors-fp16),
and the vectors are clustered per locale with agglomerative clustering
(cosine distance, average linkage, threshold 0.4) within each gender, so a
cluster never merges voices FLEURS already labels apart. Clusters smaller than
5 utterances are folded into their nearest centroid, and ids are numbered by
descending cluster size, so {locale}_spk00 is the most-recorded voice of that locale.
They are clusters, not ground truth: treat them as consistent voice tags for conditioning, not as verified speaker identities.
Across 102 locales / 279,496 utterances: 5–43 speakers per locale (median 11), mean silhouette 0.60.
from datasets import load_dataset
ds = load_dataset("malaysia-ai/fleurs-r-neucodec-all-languages") # all locales
ds = load_dataset("malaysia-ai/fleurs-r-neucodec-all-languages", "en_us") # one locale
Audio and tokens are the zips above — path and neucodec_path name the member inside
audio/{locale}-{split}.zip and neucodec/{locale}-{split}-rank*.zip.
google/fleurs-r (FLEURS-R, CC BY 4.0), the restored variant of google/fleurs.
FLEURS-R metadata, source audio and precomputed NeuCodec speech tokens for 102
locales, plus a speaker label FLEURS itself does not ship.
data/{locale}-{split}.parquet — metadata, one row per utterance (this is what the
viewer shows).audio/{locale}-{split}.zip — source FLEURS-R audio, 24kHz mono PCM16 WAV, members
named audio/{locale}/{split}/{id}.wav (the path column).neucodec/{locale}-{split}-rank{N}.zip — NeuCodec tokens, members named
neucodec/{locale}/{split}/{id}.pt (the neucodec_path column), each a
{'source': str, 'codes': IntTensor[1, 1, T]} torch file at 50 tokens/s.| column | what |
|---|---|
id | FLEURS utterance id |
path | audio member path inside audio/{locale}-{split}.zip |
neucodec_path | token member path inside the matching neucodec/ zip |
filename | bare {id}.wav |
sentence | raw transcription |
normalized_text | lowercased, punctuation-stripped transcription |
characters | space-separated character transcription |
locale, split | e.g. en_us, train |
gender | FLEURS speaker gender label |
speaker | derived voice identity, {locale}_spkNN (see below) |
num_samples | sample count of the original 16kHz FLEURS audio |
FLEURS and FLEURS-R ship gender but no speaker identity. speaker here is derived:
every utterance is embedded with TitaNet-L (titanet-vectors-fp16),
and the vectors are clustered per locale with agglomerative clustering
(cosine distance, average linkage, threshold 0.4) within each gender, so a
cluster never merges voices FLEURS already labels apart. Clusters smaller than
5 utterances are folded into their nearest centroid, and ids are numbered by
descending cluster size, so {locale}_spk00 is the most-recorded voice of that locale.
They are clusters, not ground truth: treat them as consistent voice tags for conditioning, not as verified speaker identities.
Across 102 locales / 279,496 utterances: 5–43 speakers per locale (median 11), mean silhouette 0.60.
from datasets import load_dataset
ds = load_dataset("malaysia-ai/fleurs-r-neucodec-all-languages") # all locales
ds = load_dataset("malaysia-ai/fleurs-r-neucodec-all-languages", "en_us") # one locale
Audio and tokens are the zips above — path and neucodec_path name the member inside
audio/{locale}-{split}.zip and neucodec/{locale}-{split}-rank*.zip.
google/fleurs-r (FLEURS-R, CC BY 4.0), the restored variant of google/fleurs.