The use case: how Rime deploys Weights & Biases
Rime logs and tracks every experiment for its text-to-speech models in W&B Models, including loss curves and sample audio as artifacts, to compare checkpoints and decide priorities. The team also uses Sweeps to automate hyperparameter tuning and Custom Views to compare runs.
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Figures published in Weights & Biases’s case study. They are not independently measured by usedby.
The technical performance of Arcana has also been impressive, delivering 200ms time-to-first audio token, as well as ~300ms public cloud latency.
Rime recently released Arcana, the world’s most realistic spoken language model now powering over 100 million phone calls per month at some of the world’s biggest enterprises.
In the customer’s words
For every model that we’ve ever trained, W&B has been a part of that process. I would say W&B is a crucial part of the experiment paradigm.Other AI tools used by Rime
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Based on Weights & Biases’s published case study. We check the public evidence; the results are reported by the vendor.
wandb.ai- Source published
- Jul 1, 2025
- Last checked
- Oct 8, 2026
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As a small but growing team of 12, with five machine learning engineers, W&B is our single source of truth for our team to make decisions about where our priorities lie,” continued Clifford. “Did this experiment go well? Let’s refer to W&B.