annotations/v3/README.md

tiny-voca annotations v3: phoneme tokens and validated durations

Annotation layer for the recordings in this dataset at revision 93dfa15bf40ec278a3ea1b418294c5620d850a8f. It adds, for every accepted recording, the phoneme token sequence and the number of acoustic frames each token lasts. The audio shards are untouched; nothing here replaces them.

Annotation fingerprint: ebed758f71eb2af6d241a1badd9ee951ceb05e7605ad719507d77468a6341bdd (training checkpoints record it and refuse manifests from another run).

What was wrong before

• The old front end piped text to espeak-ng --stdin without a final newline. eSpeak NG 1.51 then drops the last character of the input, so the last phonemes of every clause were wrong ("lips" became l ɪ p, "forever" became f ɔː ɹ v). 294 of 300 sampled rows were affected.
• Tokens were the UTF-8 bytes of the IPA string: 2.5 byte tokens per phoneme, 3.3 frames per token, and each byte (including stress marks, length marks and spaces) had to own at least one frame.
• Durations came from a learned aligner that collapsed in all three attempts.

How these annotations were made

1. Text to phonemes. eSpeak NG 1.51, voice en-us, --ipa --sep=_, input terminated by a newline. One token per eSpeak phoneme (59 phones), stress as a separate 0/1/2 feature, explicit tokens for utterance edges, word boundaries and punctuation. 76 symbols in total; no unknown phoneme occurs in the corpus.
2. Forced alignment. A pretrained phoneme recogniser, facebook/wav2vec2-lv-60-espeak-cv-ft (revision ae45363bf3413b374fecd9dc8bc1df0e24c3b7f4, Apache-2.0), aligned each phoneme sequence to its recording with CTC Viterbi. It was used only to annotate this data. None of its weights or outputs are part of the TTS model. Its label inventory is the same eSpeak phoneme set, so one aligned label is one token.
3. Boundaries. This recogniser emits each label at the start of the phone. Measured against the pitch tracker's voicing flips on 300 recordings, a phone starts 19 ms before the start of its first emission frame (both voices). A phone lasts from its onset to the next phone's onset.
4. Pauses. Silence (frame level below the recording's loud level minus 45 dB) between two phones is given to the pause-capable token there: BOS, EOS, punctuation, or the word boundary. At least 75 ms of silence is needed, otherwise the gap stays with the phones (stop closures).
5. Frame grid. Durations are counted in 256-sample frames at 24 kHz, frame i centred on sample i * 256, the same grid as the stored mel and pitch features. They sum to the frame count exactly and every phoneme has at least one frame.

A second pass that re-timed boundaries with a small classifier trained from scratch on these labels was evaluated (refined-1, refined-2). It did not improve the timing probe below, so it is not used. The files are kept for reference.

Quality checks

Split	Accepted	Rejected	Hours accepted
train	8,186	40	10.02
validation	714	4	0.74
test	590	6	0.70

Rejected rows are listed with reasons in <split>.rejected.jsonl: 30 where the pitch tracker found voicing in under half of the vowel frames (creaky or breathy recordings), 19 with more than a quarter of phonemes weakly matched and 2 with low mean confidence. The weak matches are mostly numbers that eSpeak and the teacher TTS read differently ("1-800-555-0147", "In 1681", "12/09/2026"). Sentence-level split isolation is unchanged: no text occurs in two splits.

Label-free timing probe over all accepted rows: at vowel to voiceless-consonant and voiceless-fricative to vowel boundaries the voicing must flip.

Voice	Boundary type	Boundaries	Flip within 1 frame (10.7 ms)	within 2 frames
F2	vowel to voiceless	29,597	63.8%	85.6%
F2	fricative to vowel	10,625	66.3%	89.0%
M2	vowel to voiceless	29,617	58.0%	81.2%
M2	fricative to vowel	10,685	65.9%	85.6%

The probe includes the pitch tracker's own jitter, so these are lower bounds on boundary accuracy, not a comparison with hand labels (there are none).

Other statistics (summary.json has all of them, per voice and per phoneme): mean phone length 7.5 frames (F2) and 7.0 frames (M2); 0.2% of phones last one frame (97 to 99% did in the collapsed runs); a word boundary carries a pause in 0.8% of cases, a comma in 47%, a semicolon in 76%.

Files

File	Content
train.jsonl, validation.jsonl, test.jsonl	accepted rows: id, speaker, text, symbols, tokens, stress, durations, frames, qc, provenance, fingerprints
*.rejected.jsonl	rejected rows with reasons
annotations.json	identity of this run (front end, aligner revision, settings, source hashes), completion flag
summary.json	QC quantiles, timing probe, duration tables
phonemes.jsonl	front-end output per distinct text
raw/	per-recording alignment journal (resumable)
refined-1/, refined-2/	the evaluated, unused refinement passes

path in a row is relative to the acoustic feature directory on the training machine. Provenance fields (text_id, origin, source_id, source_license, source_url, teacher_model, reference_audio) are copied from the source rows; the dataset's LICENSE.md continues to apply.

Limits

• No hand-labelled boundaries exist for this corpus; accuracy is estimated from the voicing probe and from whether a TTS model trained on these durations speaks intelligibly.
• A pause inside an eSpeak word group has no token and stays with a phone.
• Where eSpeak and the teacher TTS pronounce a word differently (heteronyms, some numbers), the token is eSpeak's and the audio is the teacher's.

Reproduce on the training machine:

python -m tiny_voca.annotate --features <acoustic feature dir> \
  --output <new directory> --dataset-revision 93dfa15bf40ec278a3ea1b418294c5620d850a8f