| --- |
| language: |
| - en |
| task_categories: |
| - text-to-speech |
| tags: |
| - synthetic |
| - speech |
| - voice-cloning |
| - tts |
| size_categories: |
| - n<1K |
| --- |
| |
| # Synthetic single-speaker speech dataset (OmniVoice voice clone) |
|
|
| 40 utterances, 0.07 hours of audio, 24000 Hz, mono, PCM 16-bit WAV. |
|
|
| ## Voice-cloning disclosure |
| All audio is **synthetic speech** generated by a text-to-speech model that clones the voice of a reference |
| recording supplied by the dataset author. The speech is not a recording of a human reading these sentences. |
| Only use or redistribute it if the reference speaker has consented to this use and your use complies with the |
| upstream model licenses. Do not use it to impersonate people or to deceive. |
|
|
| ## How it was made |
| 1. **Text**: generated with `Qwen/Qwen2.5-0.5B-Instruct` (instruction prompts over varied topics, tones and forms; filtered for |
| length, digits, symbols, boilerplate, a coarse unsafe-word list and near-duplicates). The text is model-written |
| and may contain factual errors; it is intended as speech material, not as facts. |
| 2. **Fine-grained control markup** (added by code after the text was written, not by the LLM): |
| non-verbal tags (e.g. `[laughter]`, `[sigh]`) on about 35% of texts, balanced across tag types and |
| placed at commas or the end of the sentence; pronunciation overrides on about 25% of texts |
| (English: CMU phonemes for words with a single dictionary pronunciation; Chinese: pinyin with tone number). |
| Observed in this dataset: 14 clips with a non-verbal tag ({'[confirmation-en]': 3, '[dissatisfaction-hnn]': 2, '[laughter]': 3, '[sigh]': 2, '[surprise-yo]': 1, '[question-en]': 1, '[question-oh]': 1, '[surprise-wa]': 1}), 10 clips with pronunciation overrides. |
| `text_plain` is the same sentence without any markup. |
| 3. **Speech**: OmniVoice 0.6B (`OmniVoice-0.6B (omnivoice-f16.gguf via audio.cpp v0.9.1)`) run through the audio.cpp runtime v0.9.1, cloning one reference voice |
| (steps=20, guidance=2.0, speed=1.0, per-sample seed = 1234 + sample index). |
| 4. **Validation**: each clip is decoded, checked for duration, silence, NaNs and a characters-per-second plausibility window, |
| and stored together with its text. Duration is measured from the saved audio, not estimated. |
| Whether the model actually honoured each tag or pronunciation override is NOT verified automatically. |
|
|
| ## Layout |
| ``` |
| audio/000001.wav, ... |
| metadata.jsonl one JSON object per clip |
| texts.jsonl all generated texts (including texts not synthesized) |
| generation_errors.jsonl failed or retried jobs |
| config.json generation configuration (no secrets) |
| ``` |
|
|
| ## Schema (`metadata.jsonl`) |
| | field | meaning | |
| |---|---| |
| | id | stable 6-digit sample id (matches the text id in `texts.jsonl`) | |
| | file_name, audio_path | relative audio path (`file_name` is what `datasets` AudioFolder reads) | |
| | text | the string that was synthesized (may contain tags / pronunciation overrides) | |
| | text_plain | the same sentence with all markup removed (use for ASR-style transcripts) | |
| | control_tags | non-verbal tags present in `text` | |
| | pron_overrides | list of pronunciation overrides applied (word/char and phonemes/pinyin) | |
| | duration_seconds, sample_rate | measured from the saved file | |
| | language, speaker_id, voice_description | language code, constant speaker id, free-text voice description | |
| | style, topic, form | prompt attributes used when the text was generated | |
| | model_name, text_model, seed, tts_steps | generation provenance | |
| | generation_status | `ok` | |
| |
| ## Loading |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Shadow0482/tts-v2", split="train") # AudioFolder-style: metadata.jsonl + audio/ |
| print(ds[0]["text"], ds[0]["audio"]["sampling_rate"]) |
| ``` |
| (The loading snippet follows the standard `datasets` AudioFolder convention; it has not been executed by the generator.) |
| |
| ## Intended uses |
| Research and prototyping for TTS / voice-cloning pipelines, controllable-TTS experiments, speaker-adaptation studies. |
| |
| ## Limitations |
| * Single synthetic voice; prosody comes from a diffusion TTS model and may contain artifacts, mispronunciations or |
| occasional truncation that automated checks cannot fully catch. |
| * Tags and pronunciation overrides are requests to the TTS model; the effect strength varies and is not measured. |
| * Small-LLM text: limited diversity, simple vocabulary, occasional awkward phrasing. |
| * The unsafe-word filter is coarse; review before use in sensitive settings. |
| |
| ## Licensing |
| No license is declared by this card because none has been verified for the combined artifact. Upstream components: |
| * OmniVoice weights via the audio.cpp GGUF package (`audio-cpp/audio.cpp-gguf`; the repo's table lists Apache-2.0 for this package) - verify before redistribution. |
| * Text model `Qwen/Qwen2.5-0.5B-Instruct` (Qwen2.5 Instruct small models are published under Apache-2.0 on their model cards) - verify. |
| * audio.cpp runtime: Apache-2.0. |
| The reference speaker's rights are separate and are the responsibility of the dataset author. |
| |
| ## Generation summary |
| Valid audio: 0.068 h (target 0.05 h, delta +0.018 h); clips: 40; mean duration 6.123 s; this session: 0:05:28 wall time, 0.7x realtime aggregate. Failed/pruned records are in generation_errors.jsonl. |
|
|
