---
license: mit
task_categories:
  - text-to-speech
language:
  - en
tags:
  - tts
  - audio-tokens
  - codec-tokens
  - mintts
pretty_name: MintTTS Pre-tokenized Audio (somu9/expresso-conversational)
---

# MintTTS Pre-tokenized Audio Tokens

Pre-extracted audio codec tokens for TTS training.

## Source

- **Dataset**: `somu9/expresso-conversational`
- **Codec**: `MOSS-Audio-Tokenizer-Nano`
- **Codec sample rate**: 48,000 Hz (stereo)
- **Frame rate**: 12.5 Hz (1 frame = 80ms)

## Stats

| Metric | Value |
|--------|-------|
| Total samples | 29,487 |
| Total audio hours | 27.8h |
| Codebooks | 16 |
| Avg frames/sample | 42.5 |
| Avg duration | 3.4s |

## Format

JSONL file (`manifest.jsonl`) where each line is:

```json
{
  "text": "The transcribed text",
  "audio_codes": [[cb0, cb1, ..., cb15], ...],
  "n_frames": 125,
  "n_codebooks": 16
}
```

- `audio_codes`: List of `[n_frames, 16]` — each frame has 16 codebook tokens (0-1023)
- `n_frames`: Number of audio frames (duration = n_frames / 12.5 seconds)
- `n_codebooks`: Always 16

## Usage

```python
import json

with open("manifest.jsonl") as f:
    for line in f:
        sample = json.loads(line)
        text = sample["text"]
        codes = sample["audio_codes"]  # [n_frames, 16]
        print(f"{text[:50]}... | {len(codes)} frames ({len(codes)/12.5:.1f}s)")
```

## Codec

Tokens were extracted using [MOSS-Audio-Tokenizer-Nano](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano) (22M params).
To decode tokens back to audio, use:

```python
from transformers import AutoModel
import torch

codec = AutoModel.from_pretrained(
    "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano",
    trust_remote_code=True,
).eval()

codes = torch.tensor(sample["audio_codes"])  # [T, 16]
codes = codes.T.contiguous()                 # [16, T]
decoded = codec.batch_decode([codes], num_quantizers=16, chunk_duration=None)
waveform = decoded.audio[0]                  # [2, samples] at 48kHz
```