lance-format/librispeech-clean-lance

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())

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)

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()

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}
}