Lab-MSP
/

ChiReSSD

@@ -57,14 +57,11 @@ disorders.
 - **Not a medical device.** No diagnostic or treatment decision should rest on its output.
 - Not a replacement for assessment by a licensed speech-language pathologist.
-- Not validated for populations outside the training corpus — it was adapted to children aged
-  5–12 with a Central Scottish accent, and the phonemizer default (`en-gb`) reflects that.
-- Not a general-purpose voice cloning system.
 ## Training data
 Child speech from UltraSuite/CLP-derived recordings, obtained under their own data use
-agreements. **The corpus is not released here and is not redistributable**: it is identifiable
 child clinical speech. To fine-tune your own model,
 <https://huggingface.co/datasets/changelinglab/ultrasuite-benchmark> is a suitable starting
 point; see `DATA.md` in the code repository.
@@ -105,22 +102,10 @@ Two presets ship with the code:
 `alpha` is high on purpose. A low `alpha` leans on the reference acoustics, which is exactly
 where the disordered articulation lives.
-**Synthesis is stochastic.** The initial style latent is zeros rather than a Gaussian draw,
 but the ADPM2 sampler is ancestral and adds fresh noise at every step, so repeated calls
 differ in waveform and in duration. Pass a `seed` for reproducible output.
-## Evaluation
-See the paper.
-## Ethical considerations
-This model is trained on identifiable clinical recordings of children and is designed to
-reproduce a speaker's vocal identity accurately. High speaker similarity is the method's goal
-and therefore also its misuse vector: the same property that makes feedback usable in a
-child's own voice makes the model a voice-cloning tool for a minor. Use it only with
-appropriate consent and ethical oversight.
 ## Usage
 ```python
@@ -147,13 +132,6 @@ the `module.` prefix that DataParallel added to ten of the thirteen submodules,
 every tensor detached, CPU-resident and contiguous. Precision is unchanged (float32; no fp16
 cast, which would alter outputs).
-- 2258 MB → 767 MB; the 1491 MB removed is optimizer state, which inference never reads
-- `sha256` of `model.pth`: `6c5faf5b4967b4d26cb4c58d4517dcc477729a579039316741bd88b9bd0f16e0`
-- Verified bit-exact against the training checkpoint it was stripped from: under a fixed seed,
-  the two produce an identical 256-d style vector and identical waveform samples
-Full details in `manifest.json`.
 ## Citation
 Rosero, Yeo, Mortensen, Van't Slot, Hallac, and Busso. *Generative Reconstruction of Pediatric

 - **Not a medical device.** No diagnostic or treatment decision should rest on its output.
 - Not a replacement for assessment by a licensed speech-language pathologist.
 ## Training data
 Child speech from UltraSuite/CLP-derived recordings, obtained under their own data use
+agreements. The corpus is not released here and is not redistributable: it is identifiable
 child clinical speech. To fine-tune your own model,
 <https://huggingface.co/datasets/changelinglab/ultrasuite-benchmark> is a suitable starting
 point; see `DATA.md` in the code repository.
 `alpha` is high on purpose. A low `alpha` leans on the reference acoustics, which is exactly
 where the disordered articulation lives.
+Synthesis is stochastic. The initial style latent is zeros rather than a Gaussian draw,
 but the ADPM2 sampler is ancestral and adds fresh noise at every step, so repeated calls
 differ in waveform and in duration. Pass a `seed` for reproducible output.
 ## Usage
 ```python
 every tensor detached, CPU-resident and contiguous. Precision is unchanged (float32; no fp16
 cast, which would alter outputs).
 ## Citation
 Rosero, Yeo, Mortensen, Van't Slot, Hallac, and Busso. *Generative Reconstruction of Pediatric