Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation
Authors: Weiting Tan, Jingyu Zhang, Lingfeng Shen, Daniel Khashabi, Philipp Koehn
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our strategies result in a notable improvement of about +7 ASRBLEU for English-Spanish (En-Es) and +2 ASR-BLEU for English-French (En-Fr) translations on the CVSS benchmark, while attaining over 14 speedup for En-Es and 5 speedup for En-Fr translations compared to autoregressive baselines. |
| Researcher Affiliation | Academia | Department of Computer Science Johns Hopkins University EMAIL |
| Pseudocode | Yes | Algorithm 1 Latent Diffusion Model Training; Algorithm 2 Normalized Units Construction |
| Open Source Code | Yes | Code available at: https://github.com/steventan0110/DiffNorm. |
| Open Datasets | Yes | We perform experiments using the established CVSS-C datasets [24], which are created from COVOST2 by employing advanced text-tospeech models to synthesize translation texts into speech [59]. CVSS-C comprises aligned speech in multiple languages along with their respective transcriptions. |
| Dataset Splits | Yes | Split En-Es En-Fr Size Length Size Length Train 79,012 256 207,364 228 Valid 13,212 296 14,759 264 Test 13,216 308 14,759 283 Table 1: Data statistics for CVSS benchmarks. |
| Hardware Specification | Yes | We train the VAE model using a learning rate of 5e-4 with distributed data-parallel (DDP) on 4 A100 GPUs, where we set the maximum batch token to be 15000. |
| Software Dependencies | No | The paper mentions software like Fairseq, Adam optimizer, Hifi GAN, WAV2VEC2.0, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For optimization, we use the Adam [26] optimizer with betas (0.9, 0.98) and we apply gradient clipping by setting clip-norm=2.0. During training, we apply dropout with a probability of 0.1. We train the VAE model using a learning rate of 5e-4 with distributed data-parallel (DDP) on 4 A100 GPUs, where we set the maximum batch token to be 15000. |