Shadow0482/Hagc

Maya1 expressive English TTS dataset (synthetic)

Samples: 37 | Total audio: 0.062 hours | Mean duration: 6.04 s | Format: 24000 Hz WAV

Purpose and generation method

Synthetic speech for TTS research. Each record was produced in two stages: a small instruction model (Qwen/Qwen3-0.6B) wrote an English utterance and inline-tag placement; the pipeline assigned controlled voice attributes and rendered them as a voice description. Maya1 (Q8_0 GGUF, audio-cpp/audio.cpp-gguf/Maya1-GGUF/maya1-q8_0.gguf) then ran through the audio.cpp runtime (v0.9.0, CUDA) to render the audio. Voice design uses a controlled attribute vocabulary (realistic: age, gender, accent, pitch, timbre, pacing, emotion and intensity, domain, role, register; creative: character plus the same base attributes) rendered into a fixed descriptive string passed via instruct=. The sampled attributes are stored in voice_attributes.

Scope

English only (default). 24 kHz WAV, sample rate and duration validated per file after generation.

Inline tag convention

Tags are angle-bracket tokens placed inline in text at the point they should affect delivery. Allowlist (20): , , , , , , , , , , , , , , , , , , , . Many records contain no tag. inline_tags lists the tags actually found in text.

Schema (metadata.jsonl)

id, text, audio (relative path), duration_seconds, sample_rate, channels, language, voice_description, voice_design_id, voice_attributes, topic, style, inline_tags, model_name, quantization, model_filename, runtime_version, qwen_model, voice_description_source, generation_settings, seed, generation_wall_seconds, generation_status, created_at. texts.jsonl holds all accepted text records (some may have no audio yet); generation_errors.jsonl logs failed attempts.

Loading

from huggingface_hub import snapshot_download
from datasets import load_dataset, Audio
root = snapshot_download("Shadow0482/test-v1", repo_type="dataset")
ds = load_dataset("json", data_files=root + "/metadata.jsonl", split="train")
ds = ds.map(lambda r: {"audio": root + "/" + r["audio"]}).cast_column("audio", Audio(sampling_rate=24000))
print(ds[0]["text"], ds[0]["voice_description"])

Known limitations

All text and audio are model-generated: pronunciation, prosody, accent and tag realisation can deviate from the written description; invented content is not factual. Automatic validation is heuristic (duration, silence, format), not human review. Near-duplicate filtering is lexical. Quality of a 0.6B-parameter text generator is limited.

Licenses and notices

Maya1 is Apache-2.0 and its bundled SNAC decoder is MIT (as stated in the task brief; confirm on the upstream model cards). See the model cards of Qwen/Qwen3-0.6B and the audio.cpp repository for their terms. No license has been declared for this dataset; review upstream model/output terms before choosing one.

Synthetic-data disclosure

This dataset consists entirely of synthetic speech and synthetic text. It does not contain recordings of real people.