shangeth/expresso-mimi-codes

Expresso — Mimi Codes (k = 32)

Pre-extracted Kyutai Mimi tokens (all 32 codebooks) for both the read and conversational subsets of Expresso. Source audio + transcripts live in shangeth/expresso; this dataset publishes the discrete-token version for training Mimi-based speech models without re-extracting.

⚠️ License: CC-BY-NC-4.0 — non-commercial use only.

Why Expresso for Wren?

Expresso is the most directly relevant dataset for speech disentanglement research — the same speakers utter similar content across different expressive styles (happy, sad, confused, whispered, animal, etc.). Useful for studying:

• Style vs content vs speaker identity
• How expressive variation is encoded across Mimi's 32 codebooks (semantic + acoustic hierarchy)
• Controllable style transfer at fixed speaker identity

Configs

• read — ~11.6k mono utterances with human transcripts.
• conversational — ~15.9k mono per-utterance turns from stereo dialogues, transcribed with Whisper Large V3 Turbo (3.0% overall WER on a held-out human-transcribed set).

Why 32 codebooks?

Most published speech-codec datasets use only the first 8 of Mimi's 32 codebooks (1 semantic + 7 acoustic), which is enough for the original Moshi recipe. We publish all 32 so you can:

• Train models on more codebooks for higher resynthesis fidelity
• Study which codebooks carry which content (style/timbre/prosody)
• Slice codes[:k] at load time to use any prefix

k_codebooks is stored per row so the schema works for both k=32 and any subset you slice.

Schemas

read

Column	Type	Notes
id	string	e.g. ex01_confused_00001; longform: ex01_default_longform_00001 (full file in train)
text	string	human transcription (mixed case + punctuation); empty for chunked rows
speaker_id	int32	1–4
style	string	default, confused, enunciated, happy, laughing, narration, sad, whisper
substyle	string	finer label (e.g. default_emphasis, default_essentials, default_longform)
corpus	string	base or longform
start_s / end_s	float32	null for full-file rows
codes	int16[32][n_frames]	Mimi codebook indices @ 12.5 fps
n_frames	int32
k_codebooks	int32	32

conversational

Column	Type	Notes
id	string	e.g. ex01-ex02_default_001__ch1_23.88-28.14
text	string	Whisper Large V3 Turbo transcript
speaker_id	int32	this channel's speaker (1–4)
style	string	this channel's style
other_speaker_id	int32	partner's speaker id
other_style	string	partner's style
source_file_id	string	the parent stereo file
channel	int32	1 or 2
start_s / end_s	float32	turn boundaries within source file
codes	int16[32][n_frames]	Mimi codebook indices @ 12.5 fps
n_frames	int32
k_codebooks	int32	32

Extraction details

• Source audio: shangeth/expresso (the official Expresso tar, segmented and built into HF format)
• Codec: kyutai/mimi @ 24 kHz, 12.5 fps, codebook size 2048 (fits int16)
• Resampling: 48 kHz mono → 24 kHz before encoding
• Conversational text: machine-transcribed (Whisper Large V3 Turbo with anti-hallucination decoding)

Usage

from datasets import load_dataset
import torch

# Pick a config
read = load_dataset("shangeth/expresso-mimi-codes", "read",          split="train")
conv = load_dataset("shangeth/expresso-mimi-codes", "conversational", split="train")

ex = read[0]
codes = torch.tensor(ex["codes"], dtype=torch.long)  # [32, n_frames]
print(f"speaker={ex['speaker_id']} style={ex['style']} | {ex['text'][:60]}")
print(f"codes shape: {codes.shape}  ({codes.shape[1]/12.5:.2f}s @ 12.5 fps)")

# Use only the first 8 codebooks (Moshi-style)
codes8 = codes[:8]

# Decode back to 24 kHz audio
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()  # [1, T] @ 24 kHz

Links

• Audio dataset: shangeth/expresso
• Extraction code: github.com/shangeth/wren-datasets
• Wren research: github.com/shangeth/wren

Citation

@inproceedings{nguyen2023expresso,
  title     = {Expresso: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis},
  author    = {Nguyen, Tu Anh and Hsu, Wei-Ning and D'Avirro, Antony and Shi, Bowen and
               Gat, Itai and Fazel-Zarani, Maryam and Remez, Tal and Copet, Jade and
               Synnaeve, Gabriel and Hassid, Michael and Kreuk, Felix and Adi, Yossi and Dupoux, Emmanuel},
  booktitle = {Interspeech},
  year      = {2023}
}

@misc{wren2026,
  title  = {Wren: A Family of Small Open-Weight Models for Unified Speech-Text Modelling},
  author = {Shangeth Rajaa},
  year   = {2026},
  url    = {https://github.com/shangeth/wren}
}

License

CC-BY-NC-4.0 — non-commercial use only. See shangeth/expresso original_metadata/LICENSE.txt.