README.md

---
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`](https://huggingface.co/datasets/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

```python
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](https://lancedb.github.io/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`).

```python
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

```python
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:

```python
import io
import soundfile as sf

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

## Semantic transcript retrieval

```python
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

```python
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

```python
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

```python
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`](https://huggingface.co/datasets/openslr/librispeech_asr). LibriSpeech is released under [CC BY 4.0](https://creativecommons.org/licenses/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}
}
```