Opportunities › Open-source speech-to-text models and inference › Proposed app
Listenfair
Compare open speech-to-text models on your own audio, then ship the winner
- Score
- 52
- confidence 47
- Evidence
- 4
- items on the page
- Rivals
- 2
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2022
- Emerging market · AI estimate
ScreensConcept
Desktop app: ML engineers upload audio samples, compare model performance side by side, and review technical metrics and export configs—all sustained analysis work best done on a computer. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New comparison
Ready to run- Audio set
About this appAI proposal
Listenfair is a workbench for teams choosing a speech-to-text model. Upload a sample of your own recordings with correct transcripts, pick open-weight models and any cloud API you use today, and see word error rate, speed and hardware needs side by side on your audio, not on a public leaderboard.
It is built for developers and ML engineers who want self-hosted or on-premise speech recognition and want to avoid vendor lock-in and per-request costs. Streaming and batch runs are scored separately, so you can see what a lean GPU or CPU setup gives up.
When you decide, Listenfair exports a deployment starter: a container config, a client snippet and a report your team can read. It does not train models. It helps you pick one and ship it with evidence.
- Tagline
- Test open speech models on your own audio before you commit.
- Voice
- PlainPreciseCandid
- Why this name
- Listen plus fair: every model gets the same audio and the same scoring, so the comparison is honest. Two plain words, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Your audio, your score
Upload recordings with reference transcripts. Get word error rate per model, with errors grouped by names, numbers and accents.
No member yet
Open models next to cloud APIs
Run open-weight models and the cloud API you pay for today on the same files, with the same text normalisation.
No member yet
Streaming and batch scored apart
See latency to first word and finalised accuracy for streaming, and throughput for batch, so you pick for your real use.
No member yet
Hardware fit check
Enter your GPU, CPU or edge device and see which models run in real time and how much memory they need.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5C4APine · primary#F6F1E7Paper · surface#1F2A24Ink · text#D98A1FAmber · accent#3C6E9ESlate Blue · data
Typography
Aa
Listenfair
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Test open speech models on your own audio before you commit.
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, not a figure drawn from the market.
Free
Free
- 3 comparisons a month
- 30 minutes of audio per run
- Open models only
- Public report links
Team
$49 a month
- Unlimited comparisons
- 20 hours of audio per run
- Cloud API comparison with your key
- Private reports
- Deployment starter export
Business
$199 a month
- Shared workspace and roles
- Audio retention controls
- Custom model endpoints
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Each comparison produces a report link with scores, charts and the deployment starter. Engineers send it to teammates, managers and security reviewers, who see the Listenfair name on the page.
- Do they become customers?
- Viewers are often engineers or buyers who run their own evaluations, so some may sign up. Test how many report viewers start their own comparison, and whether they pay or only read.
- First customers
- ML and backend engineers at small teams of 5 to 50 building voice features such as call notes or captions, who currently pay a cloud speech API and are weighing self-hosting.
- Where to reach them
- Hacker News and Show HN: Post a public comparison of open models on a shared audio set. The topic drew 316 points and 81 comments on Hacker News.
- Hugging Face community: Share reproducible reports in model discussions and link to the free tier.
- Developer communities: Offer free comparisons in speech and local AI forums and ask for feedback.
- First milestone
- Fifty teams run a comparison on their own audio, and five pay for Team within 60 days.
Why people already pay for this
- VerifiedHacker News post received 316 points and 81 comments
- Public claimOpen-weight STT models achieving higher accuracy than OpenAI's Whisper Large v3, with lower word-error rates, are now viable.
- Public claimMarket already uses and validates STT models; competitive leaderboards show active development and comparison.
- Public claimStreaming STT capability with accuracy parity to closed models is now achievable with lean resource constraints.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · GPU inference workersOn-demand GPU job runners for open STT modelsComparison runs need GPUs that Vercel and Supabase do not provide; jobs run in a queue.
- This app · Audio storagePrivate object storage with expiryCustomer recordings are sensitive and need encryption and automatic deletion.
RisksAI inference
- CommercialTeams may run a one-off comparison and leave. Recurring value depends on new models arriving often and on teams re-testing after each release.
- DataCustomers upload recordings that may hold personal or regulated speech. Strict retention, encryption and clear terms are needed before teams trust it.
- TechnicalKeeping many open models running on GPUs with fair settings and the same text normalisation is ongoing work, and unfair setups would damage trust in scores.
- PlatformCloud API terms may limit benchmarking or publishing results, so those comparisons might need to use the customer's own keys and stay private.
- CommercialModel makers or free leaderboards could offer similar tools. Differentiation rests on scoring private audio and on the cost and deployment export.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.