Datasets:

lance-format
/

librispeech-clean-lance

license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - audio-classification
  - text-retrieval
language:
  - en
tags:
  - librispeech
  - asr
  - audio
  - speech
  - lance
  - sentence-transformers
pretty_name: librispeech-clean-lance
size_categories:
  - 10K<n<100K

LibriSpeech `clean` (Lance Format)

Lance-formatted version of the LibriSpeech ASR clean configuration (sourced from openslr/librispeech_asr). Audio is stored inline as FLAC bytes (no re-encoding); transcripts are sentence-embedded so semantic transcript search works out of the box.

Splits

Split	Lance file	Rows	Description
`dev_clean.lance`	dev.clean	2,703	Standard ASR validation set
`test_clean.lance`	test.clean	2,620	Standard ASR test set
`train_clean_100.lance`	train.clean.100	28,539	100-hour clean training subset

The 360-hour and 500-hour LibriSpeech subsets (train.360, train.other.500) are not bundled here. To extend the dataset, point librispeech/dataprep.py at additional splits.

Schema

Column	Type	Notes
`id`	`string`	Utterance id (e.g. `1272-128104-0000`)
`audio`	`large_binary`	Inline FLAC bytes (16 kHz mono)
`sampling_rate`	`int32`	Always 16,000
`text`	`string`	Reference transcript
`speaker_id`	`int64`	LibriVox speaker id
`chapter_id`	`int64`	LibriVox chapter id
`num_chars`	`int32`	Length of `text` in characters
`text_emb`	`fixed_size_list<float32, 384>`	sentence-transformers `all-MiniLM-L6-v2` (cosine-normalized)

Pre-built indices

IVF_PQ on text_emb — metric=cosine
INVERTED (FTS) on text
BTREE on id, speaker_id, chapter_id

Quick start

import lance

ds = lance.dataset("hf://datasets/lance-format/librispeech-clean-lance/data/test_clean.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries. Each .lance file in data/ is a table — open by name (e.g., test_clean, train_clean_100).

import lancedb

db = lancedb.connect("hf://datasets/lance-format/librispeech-clean-lance/data")
tbl = db.open_table("test_clean")
print(f"LanceDB table opened with {len(tbl)} utterances")

Read one utterance and play it

from pathlib import Path
import lance

ds = lance.dataset("hf://datasets/lance-format/librispeech-clean-lance/data/test_clean.lance")
row = ds.take([0], columns=["id", "audio", "text", "speaker_id"]).to_pylist()[0]

Path(f"{row['id']}.flac").write_bytes(row["audio"])
print("speaker:", row["speaker_id"])
print("transcript:", row["text"])

You can decode the FLAC bytes in-memory with soundfile and feed them straight into a model:

import io
import soundfile as sf

samples, sr = sf.read(io.BytesIO(row["audio"]))
print(samples.shape, sr)

Semantic transcript retrieval

import lance
import pyarrow as pa
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q = encoder.encode(["a person talking about astronomy"], normalize_embeddings=True)[0]

ds = lance.dataset("hf://datasets/lance-format/librispeech-clean-lance/data/train_clean_100.lance")
emb_field = ds.schema.field("text_emb")
hits = ds.scanner(
    nearest={"column": "text_emb", "q": pa.array([q.tolist()], type=emb_field.type)[0], "k": 5},
    columns=["id", "speaker_id", "text"],
).to_table().to_pylist()
for h in hits:
    print(h)

LanceDB semantic transcript retrieval

import lancedb
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q = encoder.encode(["a person talking about astronomy"], normalize_embeddings=True)[0]

db = lancedb.connect("hf://datasets/lance-format/librispeech-clean-lance/data")
tbl = db.open_table("train_clean_100")

results = (
    tbl.search(q.tolist(), vector_column_name="text_emb")
    .metric("cosine")
    .select(["id", "speaker_id", "text"])
    .limit(5)
    .to_list()
)

Full-text and per-speaker filtering

ds = lance.dataset("hf://datasets/lance-format/librispeech-clean-lance/data/train_clean_100.lance")

# Word search via the FTS index.
hits = ds.scanner(full_text_query="universe stars", columns=["id", "text"], limit=10).to_table()

# All utterances by a given speaker.
sp = ds.scanner(filter="speaker_id = 1272", columns=["id", "chapter_id", "text"], limit=10).to_table()

LanceDB full-text search and per-speaker filtering

import lancedb

db = lancedb.connect("hf://datasets/lance-format/librispeech-clean-lance/data")
tbl = db.open_table("train_clean_100")

# Word search via the FTS index.
hits = (
    tbl.search("universe stars")
    .select(["id", "text"])
    .limit(10)
    .to_list()
)

# All utterances by a given speaker.
sp = (
    tbl.search()
    .where("speaker_id = 1272")
    .select(["id", "chapter_id", "text"])
    .limit(10)
    .to_list()
)

Why Lance?

One dataset for audio + transcripts + embeddings + indices — no parallel folder of FLAC files plus a transcript JSON.
On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
Schema evolution: add columns (alternate transcripts, speaker embeddings, model predictions) without rewriting the data.

Source & license

Converted from openslr/librispeech_asr. LibriSpeech is released under CC BY 4.0 and is built from the public-domain LibriVox audiobook corpus.

Citation

@inproceedings{panayotov2015librispeech,
  title={LibriSpeech: An ASR corpus based on public domain audiobooks},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year={2015}
}

LibriSpeech clean (Lance Format)