TEST_LOG.md

Release verification log: steertts-v1

Date: 2026-10-11. Everything ran on the CPU (4 threads, nice -n 19) of a shared 48-core workstation, with a 1-min load average of 20 to 40 from other jobs. The timings are lower bounds.

Weights

File	Source checkpoint	sha256 (first 16)
model.safetensors	acoustic model, step 312,500	adc11912871113e3
flow.safetensors	flow refiner (EMA), step 100,000	585ff2c931811c30
pflow.safetensors	prompted flow with prompt attention (EMA), step 40,000	877f81523dfaba50
prompt_adapter.safetensors	free-text → request adapter	aca34bf753456e46
speaker_vectors.safetensors	2,825 built-in voices (public names only)	edcabfd02dd4a520

These are the checkpoints behind the model card's numbers. The prompted flow is the one in the leaderboard cloning row (1.01 % / 37.4 and 2.73 % / 28.7).

1. Fresh-environment install

• A new Python 3.11 venv (uv) with the CPU torch wheel. Then pip install ".[test]" from a copy of the package code, holding no weights.
• Resolved versions: torch 2.11.0+cpu, torchaudio 2.11.0+cpu, numpy 2.4.6, safetensors 0.8.0, vocos 0.1.0, g2p_en 2.1.0, nltk 3.10.3, num2words 0.5.14, soundfile 0.14.0, soxr 1.1.0, transformers 5.19.0, praat-parselmouth 0.4.7, pytest 9.1.1, jsonschema 4.26.0.
• Not fresh: the Hugging Face hub cache and ~/nltk_data came from the host. A first run on a new machine downloads charactr/vocos-mel-24khz, plus microsoft/wavlm-base-plus-sv for cloning and sentence-transformers/all-MiniLM-L6-v2 for free-text prompts, and the NLTK tagger and CMUdict.

2. Test suite

STEERTTS_MODEL=<this dir> pytest from the fresh env gave 16 passed in 6 min 11 s. The suite covers:

• the Mamba-3 invariant suite;
• text normalisation and phonemes;
• the request schema and its examples;
• request validation errors;
• deterministic renders, both acoustic-only and through the flow refiner;
• pace and rate;
• Praat readback direction for loud, f0_med and h1h2;
• markup and streaming;
• cloning;
• prompt → request mapping.

3. Parity with the training code

The same inputs were rendered on the same device (CPU) by this package and by the training repository's steer.synth.Synth, given the source checkpoints above. Every case was bit-identical (max |Δ| = 0):

Case	Samples	Identical
default voice, acoustic only	73,472	yes
ctrl rate/f0_med/f0_end, acoustic only	68,096	yes
tag sadness + loud −1, guidance 1.5	85,760	yes
word_shift (pitch/energy/dur on one word)	37,376	yes
another built-in voice, flow refiner	73,216	yes
ctrl loud/f0_iqr, flow refiner	75,008	yes
cloning from a reference clip, prompted flow	51,456	yes

Cross-environment check: two renders (acoustic only, and flow + ctrl) were bit-identical between the fresh venv (torch 2.11.0+cpu) and the training environment (torch 2.11.0+cu128, run on the CPU). GPU renders are deterministic per device but are not bit-identical to CPU renders.

4. Speed on this package (CPU, 4 threads, load average about 22)

Path	RTFx (3 texts, 1.7–5 s)
Acoustic model + Vocos (flow=False)	4.35–5.59 (median 4.89)
+ flow refiner (default)	0.62–0.79 (median 0.71)
Streaming, first chunk (flow=False)	0.49 s

5. Hygiene

• No source-corpus speaker or clip identifiers appear in any file. The build fails on the pattern check in steer.release, and the check was re-run here with grep.
• VoxCeleb-derived speakers are excluded from the voice list.
• The schema $id is a URN (urn:steertts:request-schema:v1), not a URL.
• leaderboard/ names a placeholder Hugging Face repo id. It is not referenced by the package code.