Add dataset card

README.md

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+---
+language:
+  - en
+license: cc-by-4.0
+tags:
+  - audio
+  - text-to-speech
+  - mimi
+  - librispeech
+  - multi-speaker
+  - speech-synthesis
+  - codec
+task_categories:
+  - text-to-speech
+pretty_name: LibriSpeech ASR — Kyutai Mimi Encoded
+size_categories:
+  - 100K<n<1M
+---
+
+# LibriSpeech ASR — Kyutai Mimi Encoded
+
+[LibriSpeech ASR](https://www.openslr.org/12) (train.clean.100) pre-encoded with the [Kyutai Mimi](https://huggingface.co/kyutai/mimi) neural audio codec.
+
+Instead of raw waveforms, every utterance is stored as a compact matrix of discrete codec tokens. This format is ready to use directly in any language-model-style audio generation pipeline without needing a GPU encoder at training time.
+
+## What's inside
+
+```
+manifest.jsonl       # metadata — one JSON record per utterance
+spk_index.json       # { "speaker_id": [idx, idx, ...] } — speaker-to-utterance index
+shards/
+├── shard_0000.pt    # packed dict of { idx -> (8, L) int16 code tensor }
+├── shard_0001.pt
+└── ...
+```
+
+Each `manifest.jsonl` record:
+```json
+{
+  "idx": 0,
+  "text": "He was in a confused state of mind.",
+  "codes_file": "shards/shard_0000.pt:0",
+  "speaker_id": "1234",
+  "n_frames": 198
+}
+```
+
+`spk_index.json` maps each speaker ID to the list of utterance indices for that speaker, useful for sampling reference audio in speaker-conditioned tasks.
+
+## Dataset details
+
+| | |
+|---|---|
+| Source | [LibriSpeech ASR train.clean.100](https://www.openslr.org/12) |
+| Speakers | ~251 |
+| Utterances | ~28,000 |
+| Total duration | ~100 hours |
+| Codec | [Kyutai Mimi](https://huggingface.co/kyutai/mimi) |
+| Codec sample rate | 24,000 Hz |
+| Codec frame rate | 12.5 fps |
+| Codebooks | 8 |
+| Token dtype | int16 |
+| License | CC BY 4.0 |
+
+## What you can use this for
+
+- Multi-speaker / voice-cloning TTS research
+- Speaker-conditioned codec language models
+- Speaker representation learning
+- Audio tokenization benchmarks
+- Any task that benefits from a diverse, multi-speaker English speech corpus in discrete token form