Datasets:

treadon
/

speech-dac-tokens-3cb

language:
  - en
license: cc-by-4.0
task_categories:
  - text-to-speech
  - audio-classification
tags:
  - dac
  - audio-tokens
  - speech
  - tts
  - codebook
  - descript-audio-codec
  - librispeech
pretty_name: Speech DAC Tokens (3 Codebooks)
size_categories:
  - 100K<n<1M

Speech DAC Tokens (3 Codebooks)

Pre-tokenized speech dataset using the Descript Audio Codec (DAC). Each audio clip has been encoded into discrete codebook tokens from DAC's first 3 residual vector quantization codebooks, paired with its text transcription.

Dataset Summary

Stat	Value
Total samples	241,451
Total audio	~780 hours
Language	English
Codebooks	3 (of DAC's 9)
Codebook size	1,024 entries each
DAC model	44kHz
Tokens per second	~258 (86 frames x 3 codebooks)
Token sequence length	219-4,096 (mean: 3,063)
Audio duration range	~0.8s-15.7s

Data Sources

Source	Split	Clips	License
LibriSpeech clean-100	train.100	~24,200	CC BY 4.0
LibriSpeech clean-360	train.360	~88,500	CC BY 4.0
LibriSpeech other-500	train.500	~128,750	CC BY 4.0

Format

Each row contains:

Column	Type	Description
`text`	string	Original text transcription
`prompt`	string	Full training prompt: `{text}<\|audio_start\|><\|c1_X\|><\|c2_Y\|><\|c3_Z\|>...<\|audio_end\|>`
`input_ids`	list[int]	Pre-tokenized 3-codebook prompt. Ready for training.
`input_ids_1cb`	list[int]	Pre-tokenized 1-codebook prompt (c1 only, shorter sequences).
`input_ids_2cb`	list[int]	Pre-tokenized 2-codebook prompt (c1+c2).
`attention_mask`	list[int]	All 1s, same length as input_ids (3cb).
`labels`	list[int]	Copy of input_ids (3cb). Used as training targets.
`n_audio_frames`	int	Number of DAC time frames
`n_tokens`	int	Total token count (text + audio tokens)

Audio tokens are interleaved per time frame: c1, c2, c3, c1, c2, c3, ... where:

c1 (codebook 1): Coarse structure - pitch, rhythm, broad spectral shape
c2 (codebook 2): Fine detail - residual from c1
c3 (codebook 3): Finest detail - residual from c1+c2

Use Cases

Text-to-Speech Training

Train a language model to predict DAC tokens from text input. The model learns to generate the audio token sequence, which is then decoded back to audio using DAC's decoder. No spectrogram or vocoder needed - just token prediction.

Input:  Hello world
Output: <|audio_start|><|c1_551|><|c2_118|><|c3_42|>...<|audio_end|>
-> DAC decoder -> audio waveform

Audio Language Modeling

Train unconditional or conditional audio generation models using discrete tokens, similar to how language models generate text.

Speech Understanding

Use the tokenized representation for speech classification, speaker identification, or other downstream tasks that benefit from discrete audio representations.

Codec Research

Study the information captured at different codebook levels, or compare DAC's tokenization against other codecs (EnCodec, SpeechTokenizer).

How to Decode Audio

import torch
import dac
from dac.utils import load_model
import re

# Load DAC decoder
dac_model = load_model(tag="latest", model_type="44khz")
dac_model.eval()

# Parse tokens from a prompt
prompt = dataset[0]["prompt"]
pattern = r'<\|c(\d+)_(\d+)\|>'
matches = re.findall(pattern, prompt)

# Group into frames (every 3 tokens = 1 frame)
frames = []
frame = [None, None, None]
for cb_str, val_str in matches:
    cb = int(cb_str) - 1
    frame[cb] = int(val_str)
    if cb == 2:
        frames.append(list(frame))
        frame = [None, None, None]

# Decode: pad to 9 codebooks (DAC expects all 9)
codes = torch.tensor(frames).T.unsqueeze(0).long()
full_codes = torch.zeros(1, 9, codes.shape[2], dtype=torch.long)
full_codes[:, :3, :] = codes

with torch.no_grad():
    z = dac_model.quantizer.from_codes(full_codes)
    audio = dac_model.decode(z)

# audio[0, 0] is the waveform at 44100 Hz

Training code: treadon/ri-tts on GitHub

Processing Details

Audio resampled from 16kHz (LibriSpeech native) to 44.1kHz (DAC native)
Clips exceeding 4,096 tokens were excluded (~17% of source data)
DAC encoding performed on Apple MPS (M4 Max) at ~2.4 clips/sec
No word-level alignment or prosodic features - raw text + DAC codes only
0 CPU fallback failures during encoding

Citation

If you use this dataset, please cite the original data sources:

@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{kumar2024high,
  title={High-fidelity audio compression with improved RVQGAN},
  author={Kumar, Rithesh and others},
  journal={NeurIPS},
  year={2024}
}