smallest.ai @smallest_AI
Real-time Voice AI. Built for Scale. smallest.ai/?utm_source=tw… San Francisco Joined September 2023-
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Pulse hits 3.2% word error rate on Coval's STT benchmark! Coval is the eval platform built for voice AI agents. Their STT test uses diverse speakers, accents, and real-world conditions, not clean read speech. Ahead of: - Deepgram Nova-3 (4.2%) - AssemblyAI Universal Streaming (4.2%) - Speechmatics Enhanced (4.2%) Check out the full docs in 🧵
A 3-point gap in aggregate WER can hide a 13-point gap on the audio that actually breaks production. Heavy noise WER: - Pulse 18.29% - Assembly AI 25.61% - Deepgram Nova 3 31.29% Aggregate WER averages ten different noise conditions into a single number. The per-condition breakdown shows where a model actually breaks.
Pulse Pro is the #1 hosted STT API on the CodeSOTA leaderboard, and #3 overall across all models, hosted and open-source 👀 5.42% mean WER on the HF Open ASR Leaderboard's 8-dataset suite. A hosted API matching open-source frontier accuracy is rare. Doing it while shipping on-prem and air-gapped deployment is the position that matters for enterprise. Check out the full leaderboard 🧵
Word-Error-Rate doesn't matter if it isn't tested against real world call audio. On the Open ASR Leaderboard, Pulse STT wins on the datasets made of real customer audio. - AMI: noisy meetings - Earnings22: financial calls - SPGISpeech: compliance transcripts - VoxPopuli: mixed accent
We're hosting a fireside chat on Voice AI with Basia Sudol (Head of Enterprise Solutions at @DecagonAI), Sudarshan Kamath (Founder at @smallest_AI ), Varun Singh (CPTO at @trydaily), Steven Diaz (FDE Manager at Vapi), and Tyler D'Silva (Founding FDE at @retellai) next Thursday, June 4th! Don't miss it: luma.com/fmpyy1b6
Wednesday someone on the team asked if lightning v3 was good enough to do a real podcast. By friday we had podcast.smallest.ai. paste a url, two ai voices talk about it, flip the 3d toggle and watch them lip-sync the whole thing in your browser. It's Free.
Most STT vendors publish WER on audio that normalized to -10dBFS. But in the real world, audio doesn't come pre-normalized. On raw FLEURS English: - Grok jumps to 60% (claimed: 7.58%) - Deepgram jumps to 11.86% (claimed: 6.57%) - Pulse: 6.03%. Stable across raw and normalized
On WildASR, Pulse hits 9.63% word error rate. Deepgram Nova-3 hits 28.17%. Nearly 3x the WER, on the same audio. WildASR tests STT on real production conditions: far-field mics, reverb, phone codec compression, clipping, background noise. Check out all the benchmarks in our docs. Link below.
If you’re in Mumbai for the AWS Summit, come say hi to the team! Level 3, Jasmine Hall, Startup Zone
On FLEURS English streaming, Pulse STT hits 6.03% WER. Deepgram Nova-3: 11.59%. Nearly half the word error rate, on the most-cited multilingual STT benchmark.
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