sttlab-tech
3525e6b8fb
Go orchestrator: spawns audiotee for capture (system audio + mic, two tracks), segments with an energy-threshold VAD, transcribes each segment via qwen-asr (subprocess per segment, vendored as a pinned git submodule in third_party/qwen-asr), and serves the live transcript over SSE while also writing it durably to a text file. Includes a glossary/prompt-leakage guard (internal/asr/leak.go) that discards a segment if the model echoes the biasing prompt instead of transcribing. cmd/transcriptor-ai is the main orchestrator; cmd/transcript-tail is a minimal terminal SSE client. See CLAUDE.md for the full architecture and the reasoning behind each choice (Go over Python/Swift/Rust, SSE over WebSocket, why the VAD is a hand-rolled heuristic, etc). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
62 lines
2.1 KiB
Go
62 lines
2.1 KiB
Go
package asr
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import (
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"strings"
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"unicode"
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)
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// looksLikePromptLeak reports whether text appears to be the model echoing
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// the --prompt biasing content back verbatim instead of genuinely
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// transcribing the segment's audio — a known failure mode of
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// prompt-conditioned generation, confirmed via real testing against
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// qwen_asr (see transcriptor-ui/CLAUDE.md, "Glossary prompt-leakage bug"):
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// a whole segment came back as literally the glossary terms, twice, on two
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// separate 5-minute test runs, even after switching to the tool's
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// recommended "Preserve spelling: ..." prompt framing (which reduces but
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// does not eliminate the risk).
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//
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// Heuristic: if most of text's words also appear among prompt's words, and
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// there are enough of them to rule out a legitimate short mention of one
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// or two glossary terms in real speech, treat it as a leak. Both observed
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// real leaks were near-total reproductions of the prompt's term list, so a
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// strict threshold (80% word overlap, at least 4 words) catches them
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// without plausibly false-flagging a normal sentence that happens to
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// mention "Claude" or "Mistral" once — normal speech has enough other
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// words to keep the overlap ratio well below that.
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func looksLikePromptLeak(text, prompt string) bool {
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if prompt == "" || text == "" {
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return false
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}
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textWords := normalizeWords(text)
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if len(textWords) < 4 {
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return false
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}
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promptWords := wordSet(normalizeWords(prompt))
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matched := 0
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for _, w := range textWords {
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if promptWords[w] {
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matched++
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}
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}
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return float64(matched)/float64(len(textWords)) >= 0.8
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}
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// normalizeWords lowercases and splits on anything that isn't a letter or
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// digit — unicode.IsLetter (not an ASCII-only check) so accented
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// characters (French: é, è, ï, ç, ...) stay part of their word instead of
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// being split into fragments.
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func normalizeWords(s string) []string {
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return strings.FieldsFunc(strings.ToLower(s), func(r rune) bool {
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return !unicode.IsLetter(r) && !unicode.IsDigit(r)
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})
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}
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func wordSet(words []string) map[string]bool {
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set := make(map[string]bool, len(words))
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for _, w := range words {
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set[w] = true
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}
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return set
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}
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