Add dataset card

README.md

@@ -1,33 +1,130 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: speaker_id
-    dtype: int32
-  - name: codes
-    list:
-      list: int16
-  - name: n_frames
-    dtype: int32
-  - name: k_codebooks
-    dtype: int32
-  splits:
-  - name: train_clean_100
-    num_bytes: 79941329
-    num_examples: 28539
-  - name: train_clean_360
-    num_bytes: 289118508
-    num_examples: 104014
-  download_size: 247169030
-  dataset_size: 369059837
-configs:
-- config_name: default
-  data_files:
-  - split: train_clean_100
-    path: data/train_clean_100-*
-  - split: train_clean_360
-    path: data/train_clean_360-*
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
+tags:
+- mimi
+- neural-codec
+- speech-synthesis
+- speech-recognition
+- librispeech
+- audio-tokens
+pretty_name: LibriSpeech Mimi Codes
+size_categories:
+- 100K<n<1M
 ---
+
+# LibriSpeech — Mimi Codes
+
+Pre-extracted [Kyutai Mimi](https://huggingface.co/kyutai/mimi) neural-codec tokens for the
+[LibriSpeech](https://www.openslr.org/12) corpus — multi-speaker English audiobook readings
+from the LibriVox project.
+
+**This dataset contains codes only, not audio.** For waveforms, use any of the LibriSpeech
+mirrors (e.g. [openslr/librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr));
+these codes let you skip the ~hours of GPU extraction needed to train Mimi-based speech models.
+
+## Schema
+
+One row per utterance:
+
+| Column | Type | Notes |
+|---|---|---|
+| `id` | string | `{speaker_id}-{chapter_id}-{utterance_id:04d}`, e.g. `103-1240-0000` |
+| `text` | string | lowercased transcript |
+| `speaker_id` | int32 | LibriSpeech speaker ID |
+| `codes` | `int16[k=8][n_frames]` | Mimi codebook indices @ 12.5 fps |
+| `n_frames` | int32 | = `codes.shape[1]` |
+| `k_codebooks` | int32 | = 8 |
+
+## Extraction details
+
+- **Codec:** [`kyutai/mimi`](https://huggingface.co/kyutai/mimi) @ 24 kHz, 12.5 fps
+- **Codebooks:** all 8 extracted. Slice `codes[:k]` for fewer (Mimi's codebooks are ordered
+  by importance; the first few capture most of the signal).
+- **Codebook size:** 2048 per codebook → values stored as `int16`
+- **Transcripts:** sourced from LibriSpeech's `.trans.txt` files, **lowercased** (the raw
+  release is ALL-UPPER)
+
+## Splits
+
+Each standard LibriSpeech split is a separate HF split (hyphens replaced with underscores):
+
+| HF Split | Upstream | Approx. rows | Notes |
+|---|---|---|---|
+| `train_clean_100` | `train-clean-100` | ~28.5k | clean read speech, ~100 h |
+| `train_clean_360` | `train-clean-360` | ~104.0k | clean read speech, ~360 h |
+| `train_other_500` | `train-other-500` | ~148.7k | noisier/accented, ~500 h |
+| `dev_clean` | `dev-clean` | ~2.7k | dev set, clean |
+| `dev_other` | `dev-other` | ~2.9k | dev set, noisier |
+| `test_clean` | `test-clean` | ~2.6k | test set, clean |
+| `test_other` | `test-other` | ~2.9k | test set, noisier |
+
+Only splits the publisher had on disk are included — consult the "Files" tab for the exact
+subset in this release.
+
+## Usage
+
+```python
+from datasets import load_dataset
+import torch
+
+ds = load_dataset("shangeth/librispeech-mimi-codes", split="train_clean_100")
+
+ex    = ds[0]
+codes = torch.tensor(ex["codes"], dtype=torch.long)   # [8, n_frames]
+print(f"{ex['id']} (speaker {ex['speaker_id']}) → {ex['text'][:60]}")
+print("codes:", codes.shape, "duration:", codes.shape[1] / 12.5, "s")
+
+# Use only the first 3 codebooks:
+codes_3 = codes[:3]
+```
+
+Streaming (no full download):
+
+```python
+ds = load_dataset("shangeth/librispeech-mimi-codes", split="train_clean_360", streaming=True)
+for ex in ds.take(10):
+    print(ex["id"], len(ex["codes"]), "codebooks")
+```
+
+Decode to audio with the Mimi decoder:
+
+```python
+from transformers import MimiModel
+mimi = MimiModel.from_pretrained("kyutai/mimi").cuda().eval()
+with torch.no_grad():
+    wav = mimi.decode(codes.unsqueeze(0).cuda()).audio_values[0].cpu()
+# wav is [1, T] @ 24 kHz
+```
+
+## License & Attribution
+
+LibriSpeech is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
+The derived Mimi codes inherit this license — **attribution is required**. Please cite
+both the original corpus and this dataset when redistributing.
+
+## Citations
+
+```bibtex
+@inproceedings{panayotov2015librispeech,
+  title     = {Librispeech: an ASR corpus based on public domain audio books},
+  author    = {Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle = {ICASSP},
+  year      = {2015}
+}
+
+@article{defossez2024moshi,
+  title   = {Moshi: a speech-text foundation model for real-time dialogue},
+  author  = {D{\'e}fossez, Alexandre and others},
+  year    = {2024}
+}
+```
+
+## Related
+
+Used to train the [Wren](https://huggingface.co/shangeth/Wren-TTS-360M-v1) series of
+small speech LLMs.