ModelsLab/midashenglm-gen-training-latents

Precomputed audio latents for fine-tuning mispeech/midashenglm-gen, paired with six-view prompts in the exact format the model was trained on.

This is not an audio dataset and not a caption dataset. Each record is the output of the model's frozen DashengTokenizer encoder — 768-dimensional latents at 25 Hz, stored float16 — next to the tagged prompt string built from the source metadata.

Why it exists

The encoder is frozen during fine-tuning, so its output for a given clip is a constant. Running it every epoch is waste, and precomputing keeps the mel front end and a 12-layer ViT out of the training step's memory budget. Building this set costs about 90 minutes, most of it a Whisper large-v3 pass over the MECAT speech categories, which carry no transcripts of their own.

Contents

source	clips	capability
LibriTTS-R	20,000	speech
MECAT 000	166	ambience
MECAT 00A	836	sfx
MECAT 0M0	2,593	music
MECAT 0MA	206	sfx
MECAT S00	1,500	speech
MECAT S0A	1,213	mixed
MECAT SM0	2,446	mixed
MECAT SMA	240	mixed

Total 29,200 clips, 59 shards, float16.

Format

Each *.pt is a torch.save of a list of dicts:

{
  "id":         "libritts:1234_56_000001_000000",   # or mecat:S0A:<key>
  "prompt":     "<|caption|> ... <|asr|> ... <|speech|> ... <|sfx|> ... <|music|> ... <|env|> ...",
  "latents":    np.ndarray,   # [T, 768] float16, 25 Hz
  "seconds":    4.31,
  "kind":       "speech" | "scene",
  "category":   "",           # MECAT bucket, empty for LibriTTS
  "capability": "speech" | "sfx" | "music" | "ambience" | "mixed",
  "text":       "the transcript",   # present where one exists
}

import torch
records = torch.load("libritts-00000.pt", weights_only=False)
print(records[0]["prompt"], records[0]["latents"].shape)

Prompts are built by the repo's own build_prompt, so the six tags come out in the order the endpoint sends and absent fields come out as <|unknown|>. The tag order is load-bearing: <|asr|> before <|speech|>, measured at 14.2% mean word error against 373% reversed.

How it was built

• Speech — LibriTTS-R, text_normalized (so the model learns "two hundred", not "200"), trimmed of leading and trailing silence and peak-normalised to 0.95 to match what the endpoint returns.
• Scenes — MECAT-Caption, whose long / speech / music / sound / environment views map onto the model's tags directly. MECAT carries no transcripts, so <|asr|> for the speech categories comes from Whisper large-v3; clips whose transcript returns non-English or low-confidence are dropped rather than guessed at, which is why the S0A and SMA buckets are smaller than their source.
• <|speech|> descriptors are measured off each waveform — median F0, words per second, spectral centroid — rather than looked up per speaker. Age, accent and texture cannot be measured honestly, so they are not invented.
• Clips outside 1-20 seconds are dropped, following the paper's training range.

Licence and attribution

Released CC-BY-4.0, and derived from two attribution-only sources that must be credited by anyone using it:

• LibriTTS-R (mythicinfinity/libritts_r), CC-BY-4.0 — Koizumi et al., LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus.
• MECAT-Caption (mispeech/MECAT-Caption), CC-BY-3.0 — the public slice of ACAVCaps, itself derived from ACAV100M.

Because the model's decoder reconstructs audio from these latents at parity with the source — log-mel L1 of 0.26 against controls at 3.3-4.0, and identical word error rate — treat this as a faithful encoding of that audio rather than as a derived feature set, and honour the source licences accordingly.

Base model mispeech/midashenglm-gen is Apache 2.0.

Provenance

Built by audio-scenegen/training/prepare_data.py.