Vaniq-Edge: A 34MB Local TTS Engine (~8.5M Params, 2.94% WER)
Article summary
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Building local conversational voice agents usually comes with a frustrating trade-off: multi-gigabyte models sound great but add 1–2 seconds of latency, while tiny micro-models often suffer from terrible Word Error Rates (skipping words or mumbling consonants). To see how much quality could fit into a micro footprint, I built Vaniq-Edge —an end-to-end, ~8.5M parameter standalone local TTS engine. Text goes in, 24kHz mono audio comes out. No external vocoders or second-stage models running in…
1Key Takeaways
- Building local conversational voice agents usually comes with a frustrating trade-off: multi-gigabyte models sound great but add 1–2 seconds of latency, while tiny micro-models often suffer from terrible Word Error Rates (skipping words or mumbling consonants).
- To see how much quality could fit into a micro footprint, I built Vaniq-Edge —an end-to-end, ~8.5M parameter standalone local TTS engine.
- Text goes in, 24kHz mono audio comes out.
- No external vocoders or second-stage models running in….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that building local conversational voice agents usually comes with a frustrating trade-off: multi-gigabyte models sound great but add 1–2 seconds of latency, while tiny micro-models often suffer from terrible Word Error Rates (skipping words or mumbling consonants).
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