qwen3_asr: scale decode budget for dense-script languages

src/arch/qwen3_asr/model.cpp

@@ -77,6 +77,13 @@ constexpr const char k_default_variant[] = "qwen3-asr";
 
 // Generation reserve: what the input gate keeps free, and the decode-budget floor.
 constexpr int k_gen_reserve = 256;
+// Qwen3-ASR's tokenizer fragments dense-script languages: measured 6.0
+// tokens/word for Bengali and 4.6 for Hindi, vs 1.2 for English. Fast speech
+// in those scripts reaches ~21 tokens/s, so the shared 12 tok/s estimate
+// truncates mid-clip (~25 s of continuous Bengali). 45 covers the fastest
+// measured speech with 2x margin; the context ceiling still clamps runaway
+// decodes.
+constexpr int k_transcript_tokens_per_sec = 45;
 
 // Effective decoder context ceiling, in tokens: the model's trained maximum,
 // optionally lowered — never raised — by the caller's session n_ctx knob.
@@ -853,7 +860,8 @@ transcribe_status run(transcribe_session *          session,
     }
 
     const int max_new = transcribe::pick_decode_budget(
-        transcribe::predict_transcript_tokens(T_enc, cm->limits.ms_per_audio_token), k_gen_reserve, T_prompt, ceiling);
+        transcribe::predict_transcript_tokens(T_enc, cm->limits.ms_per_audio_token, k_transcript_tokens_per_sec),
+        k_gen_reserve, T_prompt, ceiling);
 
     // KV cache init (grow-to-fit, clamped to the context ceiling). Size to
     // hold prompt + decode budget, rounded up to a power of two (the step
@@ -1739,7 +1747,8 @@ transcribe_status run_batch(transcribe_session *          session,
         return TRANSCRIBE_OK;
     }
     const int max_new =
-        transcribe::pick_decode_budget(transcribe::predict_transcript_tokens(max_T_enc, cm->limits.ms_per_audio_token),
+        transcribe::pick_decode_budget(transcribe::predict_transcript_tokens(max_T_enc, cm->limits.ms_per_audio_token,
+                                                                            k_transcript_tokens_per_sec),
                                        k_gen_reserve, max_T_prompt, ceiling);
     int max_n_kv = 1024;
     while (max_n_kv < max_T_prompt + max_new) {

src/transcribe-decode-budget.h

@@ -6,19 +6,25 @@
 
 namespace transcribe {
 
-// Conservative multilingual transcript estimate, in tokens per second.
+// Default transcript estimate, in tokens per second. Calibrated on
+// Latin-script languages: English realizes ~1.2 tokens per word (~2.6 tok/s
+// of speech), so 12 leaves generous headroom. Dense-script languages
+// fragment far more in multilingual tokenizers (measured on Qwen3-ASR's
+// tokenizer: Bengali 6.0 tokens/word, Hindi 4.6, vs English 1.2), so
+// families that transcribe them pass a higher rate.
 constexpr int k_transcript_tokens_per_sec = 12;
 
 // Fall back to the encoder-token count when its duration is unknown.
-inline int predict_transcript_tokens(int audio_tokens, double ms_per_audio_token) {
+inline int predict_transcript_tokens(int audio_tokens, double ms_per_audio_token,
+                                     int tokens_per_sec = k_transcript_tokens_per_sec) {
     if (audio_tokens <= 0) {
         return 0;
     }
     if (!(ms_per_audio_token > 0.0)) {
         return audio_tokens;
     }
     const double seconds   = static_cast<double>(audio_tokens) * ms_per_audio_token / 1000.0;
-    const double predicted = seconds * k_transcript_tokens_per_sec;
+    const double predicted = seconds * tokens_per_sec;
     if (predicted <= 0.0) {
         return 0;
     }

tests/decode_budget_unit.cpp

@@ -19,6 +19,16 @@ void check_predict(const char * what, int audio_tokens, double ms_per_audio_toke
     }
 }
 
+void check_predict_rate(const char * what, int audio_tokens, double ms_per_audio_token, int tokens_per_sec,
+                        int expected) {
+    const int got = transcribe::predict_transcript_tokens(audio_tokens, ms_per_audio_token, tokens_per_sec);
+    if (got != expected) {
+        std::fprintf(stderr, "FAIL %s: predict_transcript_tokens(%d, %.3f, %d) = %d, expected %d\n", what, audio_tokens,
+                     ms_per_audio_token, tokens_per_sec, got, expected);
+        ++g_failures;
+    }
+}
+
 void check_budget(const char * what, int predicted, int floor_tokens, int t_prompt, int ceiling, int expected) {
     const int got = transcribe::pick_decode_budget(predicted, floor_tokens, t_prompt, ceiling);
     if (got != expected) {
@@ -37,6 +47,15 @@ int main(void) {
     check_predict("480 ms LFR encoder, same 197 s", /*audio_tokens=*/410, /*ms_per_audio_token=*/480.0,
                   /*expected=*/2361);
 
+    // Families that transcribe dense-script languages pass a higher rate:
+    // Qwen3-ASR's tokenizer realizes ~6 tokens/word for Bengali and ~4.6 for
+    // Hindi vs ~1.2 for English, so fast speech in those scripts (~21 tok/s)
+    // truncates at the default 12 tok/s estimate (~25 s of continuous
+    // Bengali). 45 covers the fastest measured speech with 2x margin.
+    check_predict_rate("dense-script rate, 197 s", 2463, 80.0, 45, 8866);
+    check_predict_rate("26 s Bengali at the default rate truncates", 329, 80.0, 12, 315);
+    check_predict_rate("26 s Bengali at the dense-script rate completes", 329, 80.0, 45, 1184);
+
     // An unpublished rate falls back to the audio-token count.
     check_predict("unknown rate falls back", 2463, 0.0, 2463);
     check_predict("negative rate falls back", 2463, -1.0, 2463);