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README.md

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+---
+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())
+```
+
+## 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)
+```
+
+## 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()
+```
+
+## 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}
+}
+```