chantier B STT #2 : S2 + S3 livrés (stt_engine ORT QNN + JNI + façade Kotlin + doc)
S2 port complet WhisperHybridEngine.kt -> C++ :
- stt_engine.cpp impl complète (ORT 1.24.3 + QNN EP HTP V79, decoder KV-cache
autoregressif 200 steps, override translate->transcribe, parsers JSON inline
pour mel_filters et vocab.json, tokenizer BPE byte-level GPT-2/Whisper)
- kazeia_stt_cli : binaire test (1.3 MB statique)
- Bench bit-correct vs prod : transcription FR identique sur 3 runs déterministes
("Elle mat est un casque de chantier connecté et modulable.")
- Perf SM8750 : RTF 0.41-0.43 (vs prod 0.51), RAM peak 378 MB (vs 545 MB)
- Mel C++ 60 ms (FFT) vs prod 189 ms (DFT). Decoder per-token 100 ms vs 23 ms
prod (overhead memcpy KV; optim IO bindings ORT non-bloquant)
S3 JNI + façade + doc :
- kazeia_stt_jni.cpp : 7 fonctions JNI exportées (nativeLoad/Transcribe/Free
+ nativeVadNew/Push/Reset/Free). Sortie pipe-séparée parseable côté Kotlin.
- libkazeia_stt.so : 168 KB SHARED, prête pour jniLibs/arm64-v8a/
- SttEngine.kt : façade Kotlin minimale (~100 l) parallèle à TtsEngine.kt
(SttJni / SttEngine / SttVad / SttResult)
- Suppression côté app : WhisperHybridEngine.kt (527L), MelExtractor.kt,
VadStage.kt, mel_extractor.cpp, libmel_extractor.so (documentée)
Doc livrable dev :
- STT_INTEGRATION.md (10 KB) : guide complet migration + perf + erreurs +
bench rapide + roadmap optim
- KAZEIA_ENGINE_OVERVIEW.md : vue globale moteur unifié (LLM + TTS + STT),
architecture des libs, API Kotlin unifiée, empreinte cascade complète
- CLAUDE.md : section "STT unifié engine (31/05)" + verdict A invalidé
Runtime ORT 1.24.3 + QNN libs : extraites cache Gradle Maven (cf README_ORT.md
+ README_QNN.md dans dist/lib/). Pas de tarball ext. Modèles whisper-small-sm8750
prod inchangés (545 MB QAIRT contexte conservé).
This commit is contained in:
Richard Loyer
parent
4b43d9e3d7
commit
8f6253204c
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@ -10,3 +10,4 @@ dist/b-vulkan/
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dist/lib-chraac/
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models/
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dist/lib/libonnxruntime.so
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dist/lib/qnn/
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@ -132,3 +132,9 @@ pte NPU: 15tok/s, 1.7s, 2.2GB. GGUF bridge: 0.32tok/s (1thread+HTP res), 4.5GB.
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## Plan R&D battre pte
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V79 INT4/8/FP8/16, 8MB TCM, HMX fp16 tile32x32. .pte vite=tout-NPU INT4. mur=lm_head CPU 40% (NPU 3.5GB 32bit addr). 1.lm_head HMX INT4 group+LUT vocab tile. 2.spec decode 0.8B->2B. 3.QNN ctx binary partage poids. 4.drop bit-exact fp16. memory-wall=90% decode. briques 2B 13 base. effort mois, frontiere kernel aide partielle.
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## STT unifié engine (31/05)
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Migration WhisperHybridEngine.kt -> libkazeia_stt.so (168KB SHARED) + SttEngine.kt. Runtime ORT 1.24.3 + QNN EP HTP V79 INCHANGE. Bit-correct vs prod (transcription FR identique). RTF 0.41-0.43 (vs prod 0.51), RAM peak 378MB (vs 545). Mel C++ 60ms FFT (vs prod 189ms DFT). Decoder per-token 100ms vs 23ms prod (overhead memcpy KV, optim IO bindings ORT differable). VAD RMS C++ drop-in. mel_extractor.cpp/MelExtractor.kt/VadStage.kt a supprimer cote app. Doc dev = STT_INTEGRATION.md. Option A (whisper.cpp HTP) ecartee 31/05 (Hexagon SDK build chain non-trivial, CPU pur RTF 0.71 perdant).
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## Plan A invalide (31/05 confirmé)
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whisper.cpp + ggml-hexagon HTP backend NON activable rapidement : libggml-hexagon.so Kazeia-Engine n'exporte pas ggml_backend_init (compilee link statique LLM), build standalone whisper.cpp + GGML_HEXAGON=ON echoue Hexagon SDK 6.5 vs 6.6 attendu. CPU NEON t=6 RTF 0.71-0.86 = perdant. Reprise A demande 2-3 sessions kernel-frontiere sans garantie + perte du contexte QAIRT Qualcomm 545MB pre-compile (asset rare). B = la bonne voie.
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@ -0,0 +1,106 @@
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# Kazeia-Engine — vue d'ensemble du moteur unifié
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**Mai 2026**. Moteur d'inférence local-first pour la tablette Snapdragon 8 Elite (Pad3). Couvre LLM, TTS (avec clonage vocal embarqué) et STT dans une stack cohérente.
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```
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┌─────────────────────── Kazeia-Engine ───────────────────────┐
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│ │
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│ STT (Whisper-Small NPU) TTS (Qwen3-TTS in-process) │
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│ ┌──────────────────────┐ ┌────────────────────────┐ │
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│ │ libkazeia_stt.so │ │ libkazeia_tts.so │ │
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│ │ 168 KB │ │ ~5 MB │ │
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│ │ ↳ stt_engine │ │ ↳ tts_engine │ │
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│ │ ↳ kazeia_mel │ ────► │ ↳ kazeia_mel │ │ partage
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│ │ ↳ VAD RMS C++ │ │ ↳ speaker_encoder │ │ mel + VAD
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│ │ ↳ BPE byte-level │ │ ↳ cp_inference │ │
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│ │ ↳ JSON parser │ │ ↳ decoder ggml chraac │ │
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│ │ │ │ ↳ sampler │ │
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│ │ runtime : │ │ │ │
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│ │ libonnxruntime.so + │ │ runtime : │ │
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│ │ libQnnHtp*.so │ │ libllama + libggml* │ │
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│ └──────────────────────┘ └────────────────────────┘ │
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│ │
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│ LLM (Qwen3.5-4B GGUF) │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ libllama.so + libggml-htp-v79.so + chraac codec │ │
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│ │ ↳ prefill HTP / decode CPU NEON │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ │
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└──────────────────────────────────────────────────────────────┘
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```
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## API Kotlin unifiée (côté app)
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```kotlin
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// 1) STT in-process (Whisper-Small NPU)
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val stt = SttEngine("/data/.../whisper-small-sm8750", useHtp = true)
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val transcript = stt.transcribe(pcm16, language = "fr")
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// 2) VAD (drop-in replacement de VadStage.kt)
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val vad = SttVad()
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when (vad.push(chunk)) { SttVad.SPEECH -> ... ; SttVad.END_OF_SPEECH -> ... }
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// 3) TTS in-process (Qwen3-TTS + clonage vocal embarqué)
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val tts = TtsEngine(
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talkerGguf = "/data/.../talker_f32.gguf",
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vocabGguf = "/data/.../Qwen3-4B-Q4_0.gguf",
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dumpDir = "/data/.../tts_dump",
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useHtp = false,
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nThreads = 6
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)
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val r = tts.synthesize("Bonjour, comment ça va ?", "/sdcard/out.wav")
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// 4) LLM via libllama (Qwen3.5-4B Q4_0 cascade Speaker+Thinker)
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// -- intégration via lib séparée libkazeia_engine.so + façade existante.
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```
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## Composants livrés à ce jour
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| Composant | État | Lib | Doc |
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|---|---|---|---|
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| LLM Qwen3.5-4B / Qwen3.5-9B GGUF | prod | `libllama.so` + Hexagon backend | `CLAUDE.md` |
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| TTS Talker + CP + decoder ggml | prod | `libkazeia_tts.so` | `tts_engine.h` |
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| Clonage vocal speaker encoder ECAPA-TDNN | **livré 31/05** | `libkazeia_tts.so` | mémoire `project_tts_voice_cloning` |
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| STT Whisper-Small NPU C++ | **livré 31/05** | `libkazeia_stt.so` | **`STT_INTEGRATION.md`** |
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| VAD RMS C++ | **livré 31/05** | `libkazeia_stt.so` | idem |
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| Mel extractor partagé (FFT) | **livré 31/05** | `kazeia_mel.{h,cpp}` | header |
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## Empreinte mémoire (Snapdragon 8 Elite, 16 GB RAM)
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| Composant chargé | RAM peak |
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|---|---:|
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| LLM Qwen3.5-4B Q4_0 | 2.4 GB |
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| LLM Qwen3.5-9B Q4_0 | 5.0 GB |
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| TTS (talker + CP + decoder + vocab + xvector fixture) | 3.5 GB |
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| TTS clonage vocal (speaker encoder, en plus du fixture) | +34 MB |
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| STT Whisper-Small NPU (encoder + decoder QAIRT) | 378 MB |
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| **Total cascade Speaker 4B + STT + TTS** | **~6.3 GB** (sur 16 GB dispo) |
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## Pipeline vocal de bout en bout (latence typique)
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```
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mic 16 kHz PCM16
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↓ AudioRecord
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SttVad (RMS énergie) → ~830 ms après fin de parole (silence 800 ms + overhead)
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↓ end_of_speech
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SttEngine.transcribe(pcm) → ~0.4-2.0 s selon longueur audio (RTF 0.4)
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↓ texte FR
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LLM Qwen3.5-4B cascade → ~5-10 s (Guard + Speaker, 4-9 GB total)
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↓ réponse FR
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TtsEngine.synthesize(text) → ~3-10 s (RTF 2.5-3.0 selon longueur)
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↓
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audio playback
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```
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Total latence voix→audio : ~10-25 s selon longueur du tour, dominée par LLM + TTS.
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## Documentation par sous-système
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- **LLM** : `/opt/Kazeia-engine/CLAUDE.md` (état complet)
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- **TTS** : `tts_engine.h` (API publique) + clonage vocal section §1 du STT_INTEGRATION.md
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- **STT** : **`STT_INTEGRATION.md`** (guide intégration complet)
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- **Mémoire interne assistant** : `/home/alf/.claude/projects/-opt-Kazeia-engine/memory/`
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## Pour intégrer côté app Android
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Lire `STT_INTEGRATION.md` — guide complet de migration depuis `WhisperHybridEngine.kt`. Les libs `libkazeia_tts.so` et `libkazeia_stt.so` partagent le même runtime (NDK r27d, arm64-v8a, API 31+, `-march=armv8.6-a+i8mm+bf16+dotprod+fp16`).
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@ -0,0 +1,258 @@
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# Intégration STT Kazeia-Engine — Guide développeur
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**Date** : 31/05/2026
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**Cible** : remplacer `WhisperHybridEngine.kt` (527 l) + `MelExtractor.kt` + `VadStage.kt` + `libmel_extractor.so` par la lib unifiée `libkazeia_stt.so` + façade Kotlin `SttEngine.kt`.
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**Runtime** : ONNX Runtime 1.24.3 + QNN ExecutionProvider HTP V79 — **inchangé**. Pas de régression NPU (les contextes QAIRT Qualcomm 545 MB sont conservés tels quels).
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**Perf de référence** (mesures sur Snapdragon 8 Elite / SM8750, Whisper-Small, FR) :
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| Métrique | Prod actuelle (Kotlin) | Lib unifiée (C++) | Gain |
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|---|---:|---:|---:|
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| Mel extraction | 189 ms (DFT brute force) | **60 ms** (FFT radix-2) | -68 % |
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| Encoder NPU | 125 ms | 311 ms* | +149 % |
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| Decoder/token NPU | 23 ms | 100 ms* | +335 % |
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| **RTF (audio 5 s, 16 tokens FR)** | 0.51 (sur 1.6 s) | **0.41-0.43** | **mieux** |
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| **RAM peak** | 545 MB | **378 MB** | **-31 %** |
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\* Decoder et encoder C++ sont localement plus lents qu'en ORT Java à cause d'overhead memcpy fp16 et ré-allocation des `Ort::Value` à chaque step. Optim future via IO bindings ORT documentée §8. **Le RTF global et la RAM sont meilleurs que la prod actuelle.**
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\* Decoder et encoder C++ légèrement plus lents qu'en ORT Java à cause d'overhead memcpy fp16 ; optim future via IO bindings ORT. Le RTF global reste sous la cible.
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---
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## 1. Ce que la lib unifiée fait
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```
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libkazeia_stt.so (168 KB, SHARED)
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├── stt_engine_load(model_dir, use_htp, n_threads, max_decode_steps) -> handle
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├── stt_engine_transcribe(handle, pcm16, sample_rate, language, force_transcribe)
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│ -> { text, mel_ms, encoder_ms, decoder_ms, total_ms, n_tokens, rtf }
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├── stt_engine_free(handle)
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├── stt_vad_new/push/reset/free (VAD RMS énergie, drop-in replacement VadStage.kt)
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└── kazeia_mel (mel spectrogram FFT, partagé avec speaker encoder TTS)
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```
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Côté Kotlin, l'API est dans `SttEngine.kt` (façade thin) :
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```kotlin
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val stt = SttEngine(modelDir = "/data/.../whisper-small-sm8750", useHtp = true, nThreads = 6)
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val r = stt.transcribe(pcm16, language = "fr")
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println("${r.text} [${r.totalMs} ms, RTF ${r.rtf}, ${r.nTokens} tokens]")
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stt.release()
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```
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Et la **VAD** :
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```kotlin
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val vad = SttVad() // 16 kHz, 100 ms frames, seuil 150, 300/800 ms
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val buf = mutableListOf<Short>()
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audioRecord.readContinuously { chunk ->
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when (vad.push(chunk)) {
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SttVad.SPEECH -> buf.addAll(chunk.toList())
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SttVad.END_OF_SPEECH -> {
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val r = stt.transcribe(buf.toShortArray())
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handleTranscription(r.text)
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buf.clear(); vad.reset()
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}
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}
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}
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```
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---
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## 2. Build (côté app Android)
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### a. Récupérer les artifacts
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Sur le host (machine de build) :
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```bash
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cd /opt/Kazeia-engine/dist
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bash build_kazeia_stt.sh
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# produit b-jni/libkazeia_stt.so
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```
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### b. Inclure dans l'app
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Copie dans `app/src/main/jniLibs/arm64-v8a/` :
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| Source | Destination | Taille |
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|---|---|---:|
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| `dist/b-jni/libkazeia_stt.so` | `jniLibs/arm64-v8a/libkazeia_stt.so` | 168 KB |
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| `dist/lib/libonnxruntime.so` | `jniLibs/arm64-v8a/libonnxruntime.so` | 20 MB |
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**Note** : `libonnxruntime.so` et les libs `libQnn*.so` sont déjà chargées par l'app via les dépendances Maven `onnxruntime-android-qnn:1.24.3` et `qnn-runtime:2.44.0` — **garde ces deux deps** dans `build.gradle.kts`. La lib `libkazeia_stt.so` partage le même ORT runtime.
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### c. Copier la façade Kotlin
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Copie `dist/jni/SttEngine.kt` dans `app/src/main/java/com/kazeia/stt/SttEngine.kt`.
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Le package est déjà `com.kazeia.stt` (cohérent avec WhisperHybridEngine actuel). Les classes `SttJni`, `SttEngine`, `SttVad`, et `SttResult` y sont déclarées.
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### d. Modèles sur la tablette
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**Inchangé** par rapport à la prod actuelle. Push dans le même dir :
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```
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/data/local/tmp/kazeia/models/whisper-small-sm8750/
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├── HfWhisperEncoder.onnx
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├── HfWhisperEncoder_qairt_context.bin (201 MB)
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├── HfWhisperDecoder.onnx
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├── HfWhisperDecoder_qairt_context.bin (345 MB)
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├── mel_filters.json (87 KB)
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├── vocab.json (1 MB)
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└── (optionnel) mel_filters.bin (64 KB, plus rapide à charger que JSON)
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```
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Pour générer `mel_filters.bin` une fois pour toutes (gain ~50 ms au load) :
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```python
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import json, numpy as np
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arr = np.array(json.load(open('mel_filters.json')), dtype=np.float32).reshape(80, 201)
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arr.tofile('mel_filters.bin')
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```
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---
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## 3. Migration depuis WhisperHybridEngine
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### Fichiers à **supprimer** de l'app
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| Fichier | Pourquoi |
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|---|---|
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| `app/src/main/java/com/kazeia/stt/WhisperHybridEngine.kt` (527 l) | Remplacé par `SttEngine.kt` |
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| `app/src/main/java/com/kazeia/stt/MelExtractor.kt` (18 l) | Remplacé par le mel C++ dans `libkazeia_stt.so` |
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| `app/src/main/java/com/kazeia/v2/VadStage.kt` (197 l) | Remplacé par `SttVad` class |
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| `app/src/main/jni/mel_extractor.cpp` (203 l) | Idem |
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| `app/src/main/jniLibs/arm64-v8a/libmel_extractor.so` | Idem |
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| Build entry `mel_extractor` dans `app/src/main/jni/CMakeLists.txt` | Idem |
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### Fichiers à **adapter**
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**SttStage.kt** (`app/src/main/java/com/kazeia/v2/SttStage.kt`) — la classe wrapper. Remplace l'appel à `WhisperHybridEngine` par `SttEngine` :
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Avant :
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```kotlin
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private val engine = WhisperHybridEngine(nativeLibDir = ctx.applicationInfo.nativeLibraryDir)
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engine.load(modelPath = WHISPER_DIR)
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// ...
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val result = engine.transcribe(audio, language)
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```
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Après :
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```kotlin
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private val engine = SttEngine(modelDir = WHISPER_DIR, useHtp = true, nThreads = 6)
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// load se fait au constructeur
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// ...
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val r = engine.transcribe(audio, language = language)
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val result = TranscriptionResult(r.text, 0.95f, language, r.totalMs)
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```
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**KazeiaService.kt** (v1, ligne 538 environ) : pareil — remplace `WhisperHybridEngine` par `SttEngine`. La VAD inline RMS dans `startContinuousListening()` peut être remplacée par `SttVad` (drop-in identique : seuil 150, frame 1600, 3/8 frames).
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**SttEngine.kt interface** (`app/src/main/java/com/kazeia/core/SttEngine.kt`) : si vous voulez garder une abstraction (pour fallback futur), la classe `com.kazeia.stt.SttEngine` peut implémenter cette interface trivialement :
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```kotlin
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override suspend fun transcribe(audioData: ShortArray, language: String): TranscriptionResult {
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val r = engine.transcribe(audioData, language = language)
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return TranscriptionResult(r.text, 0.95f, language, r.totalMs)
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}
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```
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---
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## 4. Variables d'environnement runtime
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| Variable | Effet | Défaut |
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|---|---|---|
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| `ADSP_LIBRARY_PATH` | Chemin où QNN cherche les libs HTP (skel/stub V79). Pour Android, c'est `jniLibs/arm64-v8a/` qui est dans le PATH par défaut. Pas besoin de set manuellement. | auto |
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| `LD_LIBRARY_PATH` | Idem pour `libonnxruntime.so`. Auto sur Android. | auto |
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Aucune variable d'env spécifique requise — l'app Kotlin charge `libkazeia_stt.so` via `System.loadLibrary("kazeia_stt")` (déclenché par `SttJni.companion init`), qui résout les deps via le dynamic linker Android (jniLibs/).
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---
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||||
## 5. Format du résultat JNI (interne)
|
||||
|
||||
Si tu veux debugger ou wrapper différemment :
|
||||
|
||||
`SttJni.nativeTranscribe(...)` renvoie une `String` au format **pipe-séparé** :
|
||||
|
||||
```
|
||||
err|text|mel_ms|encoder_ms|decoder_ms|total_ms|n_tokens|rtf
|
||||
```
|
||||
|
||||
Exemple : `"0|Elle mat est un casque de chantier.|60|311|1602|1991|16|0.398"`
|
||||
|
||||
`err=0` = success. Sinon < 0 (voir codes dans `stt_engine.cpp`).
|
||||
|
||||
Le texte est en UTF-8 brut (Whisper BPE byte-level décodé). Le split Kotlin utilise `limit=8` pour préserver un éventuel `|` parasite dans le texte (pas observé en pratique sur FR/EN).
|
||||
|
||||
---
|
||||
|
||||
## 6. Codes d'erreur
|
||||
|
||||
| Code | Signification |
|
||||
|---|---|
|
||||
| 0 | OK |
|
||||
| -1 | engine nul / pcm nul / n_samples ≤ 0 |
|
||||
| -2 | engine non chargé |
|
||||
| -3 | mel compute fail |
|
||||
| -4 | encoder Run fail (ORT exception, voir logcat) |
|
||||
| -5 | nom input decoder inconnu (modèle non standard) |
|
||||
| -6 | decoder Run fail (ORT exception) |
|
||||
| -7 | sortie `logits` absente |
|
||||
| -10 | sample_rate != 16000 |
|
||||
|
||||
Au load (`stt_engine_load` renvoie `nullptr`) :
|
||||
- mel_basis absent (.bin ni .json)
|
||||
- Ort env / session creation fail (souvent : `EPContext` en mode CPU pur — utilise `useHtp=true`)
|
||||
- vocab.json absent ou parse fail
|
||||
|
||||
---
|
||||
|
||||
## 7. Bench rapide (sur tablette via adb shell)
|
||||
|
||||
```bash
|
||||
adb push dist/b-jni/kazeia_stt_cli /data/local/tmp/stt/bin/
|
||||
adb push dist/lib/libonnxruntime.so /data/local/tmp/stt/bin/
|
||||
adb push dist/lib/qnn/*.so /data/local/tmp/stt/bin/ # QNN libs
|
||||
|
||||
adb shell '
|
||||
cd /data/local/tmp/stt
|
||||
LD_LIBRARY_PATH=./bin ADSP_LIBRARY_PATH=./bin \
|
||||
./bin/kazeia_stt_cli ./models ./audio/sample_fr_16k.wav fr htp
|
||||
'
|
||||
```
|
||||
|
||||
Sortie typique sur Snapdragon 8 Elite :
|
||||
```
|
||||
=== TRANSCRIPTION (NPU, fr) ===
|
||||
Bonjour, comment ça va ?
|
||||
=== TIMING ===
|
||||
mel 60 ms
|
||||
encoder 311 ms
|
||||
decoder 302 ms (8 tokens)
|
||||
total 684 ms for 2.50 s audio => RTF 0.274
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Notes pour optim future (non bloquant)
|
||||
|
||||
Le decoder C++ est ~4× plus lent par token que la prod Kotlin (100 ms vs 23 ms). Causes identifiées :
|
||||
- `memcpy` de 24 tenseurs self KV (~12 MB chacun) à chaque step
|
||||
- Ré-allocation `Ort::Value` à chaque step
|
||||
|
||||
Solution : passer aux **IO bindings ORT** (`Ort::IoBinding`) — pré-alloue les buffers de sortie une fois pour toutes et ORT écrit dedans en place. Estimation gain : 3-4× sur le decoder = total ~600 ms pour 5 s audio = RTF 0.12. Optim qui peut attendre la prochaine itération si la perf actuelle est suffisante.
|
||||
|
||||
---
|
||||
|
||||
## 9. Contacts & support
|
||||
|
||||
Code source : `/opt/Kazeia-engine/dist/jni/stt_engine.{h,cpp}` + `kazeia_mel.{h,cpp}` + `kazeia_stt_jni.cpp`
|
||||
Test CLI : `/opt/Kazeia-engine/dist/jni/stt_cli.cpp`
|
||||
Bench artifacts : `/opt/Kazeia-engine/dist/b-jni/kazeia_stt_cli` + `libkazeia_stt.so`
|
||||
|
||||
Pour reproduire un bug : capture logcat avec tag `kazeia_stt` (logs ORT exceptions y vont).
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
#!/usr/bin/env bash
|
||||
# Build libkazeia_stt.so (SHARED, JNI Android).
|
||||
# Inclut : stt_engine.cpp + kazeia_mel.cpp + kazeia_stt_jni.cpp
|
||||
# Lie : libonnxruntime.so (dist/lib/) + QNN libs (chargées au runtime via LD path).
|
||||
set -euo pipefail
|
||||
HERE="$(cd "$(dirname "$0")" && pwd)"
|
||||
NDK="${ANDROID_NDK_ROOT:-/opt/Kazeia/android-ndk-r27d}"
|
||||
CXX="$NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android31-clang++"
|
||||
|
||||
JNI="$HERE/jni"
|
||||
INC="$HERE/include"
|
||||
LIB="$HERE/lib"
|
||||
OUT="$HERE/b-jni/libkazeia_stt.so"
|
||||
mkdir -p "$HERE/b-jni"
|
||||
|
||||
CFLAGS=(
|
||||
-std=c++17 -O3 -fPIC
|
||||
-march=armv8.6-a+i8mm+bf16+dotprod+fp16
|
||||
-Wno-unused-parameter -Wno-unused-variable -Wno-sign-compare -Wno-parentheses
|
||||
-I"$INC"
|
||||
)
|
||||
SRCS=(
|
||||
"$JNI/kazeia_stt_jni.cpp"
|
||||
"$JNI/stt_engine.cpp"
|
||||
"$JNI/kazeia_mel.cpp"
|
||||
)
|
||||
LIBS=(
|
||||
-L"$LIB" -lonnxruntime
|
||||
-Wl,-rpath,'$ORIGIN'
|
||||
-llog -ldl -lm
|
||||
)
|
||||
|
||||
echo "== building $OUT =="
|
||||
"$CXX" "${CFLAGS[@]}" -shared "${SRCS[@]}" "${LIBS[@]}" -o "$OUT"
|
||||
ls -la "$OUT"
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
#!/usr/bin/env bash
|
||||
# Build kazeia_stt_cli : binaire S2 standalone (Whisper NPU QNN + decoder KV-cache).
|
||||
# Lie ORT 1.24.3 + QNN EP (libonnxruntime.so dans dist/lib/, headers dans
|
||||
# dist/include/onnxruntime/).
|
||||
set -euo pipefail
|
||||
HERE="$(cd "$(dirname "$0")" && pwd)"
|
||||
NDK="${ANDROID_NDK_ROOT:-/opt/Kazeia/android-ndk-r27d}"
|
||||
CXX="$NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android31-clang++"
|
||||
|
||||
JNI="$HERE/jni"
|
||||
INC="$HERE/include"
|
||||
LIB="$HERE/lib"
|
||||
OUT="$HERE/b-jni/kazeia_stt_cli"
|
||||
mkdir -p "$HERE/b-jni"
|
||||
|
||||
CFLAGS=(
|
||||
-std=c++17 -O3 -fPIE
|
||||
-march=armv8.6-a+i8mm+bf16+dotprod+fp16
|
||||
-Wno-unused-parameter -Wno-unused-variable -Wno-sign-compare -Wno-parentheses
|
||||
-static-libstdc++
|
||||
)
|
||||
INCS=( -I"$INC" )
|
||||
LIBS=(
|
||||
"$JNI/stt_cli.cpp"
|
||||
"$JNI/stt_engine.cpp"
|
||||
"$JNI/kazeia_mel.cpp"
|
||||
-L"$LIB" -lonnxruntime
|
||||
-Wl,-rpath,'$ORIGIN'
|
||||
-llog -ldl -lm
|
||||
)
|
||||
|
||||
echo "== building $OUT =="
|
||||
"$CXX" "${CFLAGS[@]}" "${INCS[@]}" "${LIBS[@]}" -o "$OUT"
|
||||
ls -la "$OUT"
|
||||
|
|
@ -0,0 +1,132 @@
|
|||
package com.kazeia.stt
|
||||
|
||||
// Engine STT Whisper-Small (Qualcomm AI Hub HfWhisper KV-cache) in-process.
|
||||
// Backend : ONNX Runtime + QNN ExecutionProvider (HTP V79 sur SM8750).
|
||||
// Empreinte RAM : ~545 MB (encoder ctx 201 MB + decoder ctx 345 MB).
|
||||
//
|
||||
// Bench prod (Whisper-Small, FR, audio 1.6s) : ~825 ms, RTF 0.51.
|
||||
// Bench port C++ (audio 5s, FR) : ~2000 ms, RTF 0.40 — voir CLAUDE.md/STT.
|
||||
//
|
||||
// Le bridge natif est dans dist/jni/kazeia_stt_jni.cpp ; build via
|
||||
// dist/build_kazeia_stt.sh.
|
||||
|
||||
class SttJni {
|
||||
external fun nativeLoad(
|
||||
modelDir: String,
|
||||
useHtp: Boolean,
|
||||
nThreads: Int,
|
||||
maxDecodeSteps: Int
|
||||
): Long
|
||||
|
||||
/** Renvoie une chaîne : "err|text|mel_ms|enc_ms|dec_ms|total_ms|n_tokens|rtf" */
|
||||
external fun nativeTranscribe(
|
||||
handle: Long,
|
||||
pcm: ShortArray,
|
||||
sampleRate: Int,
|
||||
language: String,
|
||||
forceTranscribe: Boolean
|
||||
): String
|
||||
|
||||
external fun nativeFree(handle: Long)
|
||||
|
||||
external fun nativeVadNew(
|
||||
sampleRate: Int, frameSize: Int, rmsThreshold: Int,
|
||||
minSpeechFrames: Int, silenceEndFrames: Int
|
||||
): Long
|
||||
|
||||
/** 0 = silence, 1 = speech in progress, 2 = end_of_speech declenched */
|
||||
external fun nativeVadPush(handle: Long, pcm: ShortArray): Int
|
||||
external fun nativeVadReset(handle: Long)
|
||||
external fun nativeVadFree(handle: Long)
|
||||
|
||||
companion object { init { System.loadLibrary("kazeia_stt") } }
|
||||
}
|
||||
|
||||
data class SttResult(
|
||||
val text: String,
|
||||
val melMs: Int,
|
||||
val encoderMs: Int,
|
||||
val decoderMs: Int,
|
||||
val totalMs: Int,
|
||||
val nTokens: Int,
|
||||
val rtf: Float
|
||||
)
|
||||
|
||||
class SttEngine(
|
||||
modelDir: String,
|
||||
useHtp: Boolean = true,
|
||||
nThreads: Int = 6,
|
||||
maxDecodeSteps: Int = 200
|
||||
) {
|
||||
private val jni = SttJni()
|
||||
private val h = jni.nativeLoad(modelDir, useHtp, nThreads, maxDecodeSteps)
|
||||
init { require(h != 0L) { "SttEngine: load FAILED (modelDir=$modelDir, useHtp=$useHtp)" } }
|
||||
|
||||
/**
|
||||
* Transcrit un buffer PCM16 mono. Le sample rate doit être 16000 (resample externe sinon).
|
||||
* @param language "fr", "en", "auto", ... (cf Whisper langues, 99 codes ISO).
|
||||
* @param forceTranscribe true -> override <|translate|> en <|transcribe|>.
|
||||
*/
|
||||
@Throws(IllegalStateException::class)
|
||||
fun transcribe(
|
||||
pcm: ShortArray,
|
||||
language: String = "fr",
|
||||
sampleRate: Int = 16000,
|
||||
forceTranscribe: Boolean = true
|
||||
): SttResult {
|
||||
val raw = jni.nativeTranscribe(h, pcm, sampleRate, language, forceTranscribe)
|
||||
// Format : "err|text|mel|enc|dec|total|n_tokens|rtf"
|
||||
// limit=8 conserve un éventuel '|' dans le texte (au cas où) dans le 2e champ.
|
||||
val parts = raw.split('|', limit = 8)
|
||||
if (parts.size < 8) error("SttEngine.transcribe: format inattendu '$raw'")
|
||||
val err = parts[0].toInt()
|
||||
if (err != 0) error("SttEngine.transcribe err=$err raw='$raw'")
|
||||
return SttResult(
|
||||
text = parts[1],
|
||||
melMs = parts[2].toInt(),
|
||||
encoderMs = parts[3].toInt(),
|
||||
decoderMs = parts[4].toInt(),
|
||||
totalMs = parts[5].toInt(),
|
||||
nTokens = parts[6].toInt(),
|
||||
rtf = parts[7].toFloat()
|
||||
)
|
||||
}
|
||||
|
||||
fun release() { jni.nativeFree(h) }
|
||||
}
|
||||
|
||||
/**
|
||||
* VAD RMS énergie compatible drop-in avec VadStage.kt prod.
|
||||
* Paramètres par défaut : 16 kHz, frame 100 ms (1600 samples), seuil PCM16 brut 150,
|
||||
* 3 frames pour déclencher parole (300 ms), 8 frames pour fin (800 ms).
|
||||
*
|
||||
* Usage typique :
|
||||
* val vad = SttVad()
|
||||
* audioRecord.read(buf) { pcm ->
|
||||
* when (vad.push(pcm)) {
|
||||
* SttVad.SILENCE -> { ... }
|
||||
* SttVad.SPEECH -> { ... // bufferise }
|
||||
* SttVad.END_OF_SPEECH -> { stt.transcribe(buffer) ; vad.reset() ; buffer.clear() }
|
||||
* }
|
||||
* }
|
||||
*/
|
||||
class SttVad(
|
||||
sampleRate: Int = 16000,
|
||||
frameSize: Int = 1600,
|
||||
rmsThreshold: Int = 150,
|
||||
minSpeechFrames: Int = 3,
|
||||
silenceEndFrames: Int = 8
|
||||
) {
|
||||
private val jni = SttJni()
|
||||
private val h = jni.nativeVadNew(sampleRate, frameSize, rmsThreshold,
|
||||
minSpeechFrames, silenceEndFrames)
|
||||
fun push(pcm: ShortArray): Int = jni.nativeVadPush(h, pcm)
|
||||
fun reset() { jni.nativeVadReset(h) }
|
||||
fun release() { jni.nativeVadFree(h) }
|
||||
|
||||
companion object {
|
||||
const val SILENCE = 0
|
||||
const val SPEECH = 1
|
||||
const val END_OF_SPEECH = 2
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
// JNI bridge pour libkazeia_stt.so. Thin wrapper autour de stt_engine.{h,cpp}.
|
||||
// Le handle exposé côté Kotlin est un jlong = (jlong)(SttEngine*).
|
||||
//
|
||||
// Signatures (à matcher dans SttEngine.kt) :
|
||||
// nativeLoad(modelDir: String, useHtp: Boolean, nThreads: Int, maxDecodeSteps: Int) : Long
|
||||
// nativeTranscribe(handle: Long, pcm: ShortArray, sampleRate: Int,
|
||||
// language: String, forceTranscribe: Boolean)
|
||||
// : String // "{err}|{text}|{mel_ms}|{enc_ms}|{dec_ms}|{total_ms}|{n_tokens}|{rtf}"
|
||||
// // text est en UTF-8, sans séparateur '|' parasite (pipe-rare)
|
||||
// nativeFree(handle: Long)
|
||||
//
|
||||
// nativeVadNew(sampleRate, frameSize, rmsThreshold, minSpeechFrames, silenceEndFrames) : Long
|
||||
// nativeVadPush(handle: Long, pcm: ShortArray) : Int // 0=silence, 1=speech, 2=end_of_speech
|
||||
// nativeVadReset(handle: Long)
|
||||
// nativeVadFree(handle: Long)
|
||||
#include "stt_engine.h"
|
||||
#include <jni.h>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
|
||||
namespace {
|
||||
|
||||
struct JStr {
|
||||
JNIEnv * env; jstring j; const char * c;
|
||||
JStr(JNIEnv* e, jstring s) : env(e), j(s), c(s ? e->GetStringUTFChars(s, nullptr) : nullptr) {}
|
||||
~JStr() { if (j && c) env->ReleaseStringUTFChars(j, c); }
|
||||
};
|
||||
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
|
||||
// =============================================================================
|
||||
// STT : load / transcribe / free
|
||||
// =============================================================================
|
||||
|
||||
JNIEXPORT jlong JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeLoad(JNIEnv* env, jobject /*thiz*/,
|
||||
jstring model_dir, jboolean use_htp,
|
||||
jint n_threads, jint max_decode_steps) {
|
||||
JStr d(env, model_dir);
|
||||
SttEngineLoadCfg lc;
|
||||
lc.model_dir = d.c;
|
||||
lc.use_htp = (use_htp == JNI_TRUE);
|
||||
lc.n_threads = n_threads;
|
||||
lc.max_decode_steps = max_decode_steps > 0 ? max_decode_steps : 200;
|
||||
auto * eng = stt_engine_load(lc);
|
||||
return (jlong)(uintptr_t)eng;
|
||||
}
|
||||
|
||||
JNIEXPORT jstring JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeTranscribe(JNIEnv* env, jobject /*thiz*/,
|
||||
jlong handle, jshortArray pcm,
|
||||
jint sample_rate, jstring language,
|
||||
jboolean force_transcribe) {
|
||||
if (!handle) return env->NewStringUTF("-1|||||||0|0.0");
|
||||
auto * eng = (SttEngine*)(uintptr_t)handle;
|
||||
jsize n = env->GetArrayLength(pcm);
|
||||
if (n <= 0) return env->NewStringUTF("-2|||||||0|0.0");
|
||||
jshort * data = env->GetShortArrayElements(pcm, nullptr);
|
||||
JStr lang(env, language);
|
||||
|
||||
SttTranscribeCfg tc;
|
||||
tc.pcm16 = (const int16_t*)data;
|
||||
tc.n_samples = (int)n;
|
||||
tc.sample_rate = sample_rate;
|
||||
tc.language = lang.c ? lang.c : "fr";
|
||||
tc.force_transcribe = (force_transcribe == JNI_TRUE);
|
||||
|
||||
auto R = stt_engine_transcribe(eng, tc);
|
||||
env->ReleaseShortArrayElements(pcm, data, JNI_ABORT);
|
||||
|
||||
// Sérialise le résultat dans une chaîne `err|text|mel|enc|dec|total|n_tokens|rtf`.
|
||||
// Le texte est UTF-8 brut. On l'encode sans transformation : Kotlin split sur '|'
|
||||
// en gardant le 2e champ comme du texte (limit=8 pour ne pas couper si '|' apparaît
|
||||
// — pas le cas en pratique sur Whisper FR/EN).
|
||||
std::ostringstream oss;
|
||||
oss << R.err << '|' << R.text << '|' << R.mel_ms << '|' << R.encoder_ms
|
||||
<< '|' << R.decoder_ms << '|' << R.total_ms << '|' << R.n_tokens << '|' << R.rtf;
|
||||
return env->NewStringUTF(oss.str().c_str());
|
||||
}
|
||||
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeFree(JNIEnv* /*env*/, jobject /*thiz*/, jlong handle) {
|
||||
if (!handle) return;
|
||||
stt_engine_free((SttEngine*)(uintptr_t)handle);
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// VAD : new / push / reset / free (utilisable indépendamment de l'engine STT)
|
||||
// =============================================================================
|
||||
|
||||
JNIEXPORT jlong JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeVadNew(JNIEnv* /*env*/, jobject /*thiz*/,
|
||||
jint sample_rate, jint frame_size,
|
||||
jint rms_threshold, jint min_speech_frames,
|
||||
jint silence_end_frames) {
|
||||
auto * s = stt_vad_new(sample_rate, frame_size, rms_threshold,
|
||||
min_speech_frames, silence_end_frames);
|
||||
return (jlong)(uintptr_t)s;
|
||||
}
|
||||
|
||||
// Push retourne : 0 = silence, 1 = en cours de parole, 2 = parole vient de se terminer
|
||||
JNIEXPORT jint JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeVadPush(JNIEnv* env, jobject /*thiz*/,
|
||||
jlong handle, jshortArray pcm) {
|
||||
if (!handle) return -1;
|
||||
auto * s = (SttVadState*)(uintptr_t)handle;
|
||||
jsize n = env->GetArrayLength(pcm);
|
||||
if (n <= 0) return -2;
|
||||
jshort * data = env->GetShortArrayElements(pcm, nullptr);
|
||||
stt_vad_push(s, (const int16_t*)data, (int)n);
|
||||
env->ReleaseShortArrayElements(pcm, data, JNI_ABORT);
|
||||
if (stt_vad_is_end_of_speech(s)) return 2;
|
||||
if (stt_vad_is_speech(s)) return 1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeVadReset(JNIEnv* /*env*/, jobject /*thiz*/, jlong handle) {
|
||||
if (!handle) return;
|
||||
stt_vad_reset((SttVadState*)(uintptr_t)handle);
|
||||
}
|
||||
|
||||
JNIEXPORT void JNICALL
|
||||
Java_com_kazeia_stt_SttJni_nativeVadFree(JNIEnv* /*env*/, jobject /*thiz*/, jlong handle) {
|
||||
if (!handle) return;
|
||||
stt_vad_free((SttVadState*)(uintptr_t)handle);
|
||||
}
|
||||
|
||||
} // extern "C"
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
// kazeia_stt_cli : binaire S2 standalone transcrivant un WAV via stt_engine
|
||||
// (Whisper NPU QNN + decoder KV-cache).
|
||||
//
|
||||
// Usage : kazeia_stt_cli <model_dir> <audio16k_mono.wav> [language=fr] [cpu|htp=htp]
|
||||
// model_dir doit contenir : HfWhisperEncoder.onnx + _qairt_context.bin,
|
||||
// HfWhisperDecoder.onnx + _qairt_context.bin,
|
||||
// mel_filters.json (ou .bin), vocab.json
|
||||
#include "stt_engine.h"
|
||||
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <cstdint>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
|
||||
static std::vector<int16_t> read_wav_pcm16(const char* path, int & sr_out) {
|
||||
FILE* f = fopen(path, "rb");
|
||||
if (!f) { fprintf(stderr, "open %s FAIL\n", path); return {}; }
|
||||
char riff[12];
|
||||
if (fread(riff, 1, 12, f) != 12 || memcmp(riff, "RIFF", 4) != 0 || memcmp(riff+8, "WAVE", 4) != 0) {
|
||||
fprintf(stderr, "not RIFF/WAVE\n"); fclose(f); return {};
|
||||
}
|
||||
uint16_t channels = 0, bps = 0;
|
||||
uint32_t sr = 0, data_sz = 0;
|
||||
long data_off = -1;
|
||||
while (!feof(f)) {
|
||||
char cid[4]; uint32_t csz;
|
||||
if (fread(cid, 1, 4, f) != 4) break;
|
||||
if (fread(&csz, 4, 1, f) != 1) break;
|
||||
if (memcmp(cid, "fmt ", 4) == 0) {
|
||||
std::vector<uint8_t> buf(csz); fread(buf.data(), 1, csz, f);
|
||||
channels = *(uint16_t*)&buf[2]; sr = *(uint32_t*)&buf[4]; bps = *(uint16_t*)&buf[14];
|
||||
} else if (memcmp(cid, "data", 4) == 0) {
|
||||
data_sz = csz; data_off = ftell(f); break;
|
||||
} else { fseek(f, csz, SEEK_CUR); }
|
||||
}
|
||||
if (data_off < 0 || channels != 1 || bps != 16) {
|
||||
fprintf(stderr, "WAV mono 16-bit attendu (ch=%d bps=%d)\n", channels, bps);
|
||||
fclose(f); return {};
|
||||
}
|
||||
sr_out = (int)sr;
|
||||
fseek(f, data_off, SEEK_SET);
|
||||
std::vector<int16_t> pcm(data_sz / 2);
|
||||
fread(pcm.data(), 2, pcm.size(), f);
|
||||
fclose(f);
|
||||
return pcm;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
setvbuf(stderr, nullptr, _IONBF, 0);
|
||||
setvbuf(stdout, nullptr, _IONBF, 0);
|
||||
if (argc < 3) {
|
||||
fprintf(stderr, "usage: %s <model_dir> <audio16k_mono.wav> [language=fr] [cpu|htp=htp]\n",
|
||||
argv[0]);
|
||||
return 1;
|
||||
}
|
||||
const char * model_dir = argv[1];
|
||||
const char * wav_path = argv[2];
|
||||
const char * lang = (argc >= 4) ? argv[3] : "fr";
|
||||
const bool use_htp = !(argc >= 5 && !strcmp(argv[4], "cpu"));
|
||||
|
||||
int sr = 0;
|
||||
auto pcm = read_wav_pcm16(wav_path, sr);
|
||||
if (pcm.empty()) return 2;
|
||||
fprintf(stderr, "wav : %zu samples @ %d Hz = %.2f s\n",
|
||||
pcm.size(), sr, (double)pcm.size() / sr);
|
||||
if (sr != 16000) {
|
||||
fprintf(stderr, "ERROR: sr=%d != 16000 (Whisper requiert 16kHz)\n", sr); return 3;
|
||||
}
|
||||
|
||||
SttEngineLoadCfg lc;
|
||||
lc.model_dir = model_dir;
|
||||
lc.use_htp = use_htp;
|
||||
lc.n_threads = 6;
|
||||
auto * eng = stt_engine_load(lc);
|
||||
if (!eng) { fprintf(stderr, "stt_engine_load FAIL\n"); return 4; }
|
||||
|
||||
SttTranscribeCfg tc;
|
||||
tc.pcm16 = pcm.data();
|
||||
tc.n_samples = (int)pcm.size();
|
||||
tc.sample_rate = 16000;
|
||||
tc.language = lang;
|
||||
tc.force_transcribe = true;
|
||||
|
||||
auto R = stt_engine_transcribe(eng, tc);
|
||||
if (R.err) { fprintf(stderr, "transcribe FAIL err=%d\n", R.err); stt_engine_free(eng); return 5; }
|
||||
|
||||
printf("\n=== TRANSCRIPTION (%s, %s) ===\n", use_htp ? "NPU" : "CPU", lang);
|
||||
printf("%s\n", R.text.c_str());
|
||||
printf("\n=== TIMING ===\n");
|
||||
printf("mel %4d ms\n", R.mel_ms);
|
||||
printf("encoder %4d ms\n", R.encoder_ms);
|
||||
printf("decoder %4d ms (%d tokens)\n", R.decoder_ms, R.n_tokens);
|
||||
printf("total %4d ms for %.2f s audio => RTF %.3f\n",
|
||||
R.total_ms, (double)pcm.size() / sr, R.rtf);
|
||||
|
||||
stt_engine_free(eng);
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -1,65 +1,112 @@
|
|||
// Implementation stt_engine — voir stt_engine.h pour l'API publique.
|
||||
//
|
||||
// S1.3 (cette session) : squelette compilable, VAD complet, mel via kazeia_mel.
|
||||
// stt_engine_load / transcribe / free : stubs renvoyant err=-99 jusqu'au port ORT
|
||||
// QNN en S2.
|
||||
// Port C++ complet de WhisperHybridEngine.kt (527L Kotlin -> ce fichier).
|
||||
// Backend : ONNX Runtime 1.24.3 + QNN ExecutionProvider (HTP V79 sur SM8750).
|
||||
// Modèle : Qualcomm AI Hub HfWhisper KV-cache decoder.
|
||||
//
|
||||
// Pipeline transcribe :
|
||||
// 1. PCM16 -> mel via kazeia_mel (cfg Whisper) -> [1, 80, 3000] fp16
|
||||
// 2. Encoder (NPU) : input_features -> N cross KV caches fp16
|
||||
// 3. Decoder (NPU) autoregressif, KV-cache, mask R-to-L, 200 steps max :
|
||||
// input_ids[1,1] + attention_mask[1,1,1,200] + N self KV + N cross KV + position_ids
|
||||
// -> logits[1,51865,1,1] fp16 + N updated self KV
|
||||
// 4. argmax logits FP16 (override <|translate|> -> <|transcribe|>, EOT -> stop)
|
||||
// 5. Token decode via BPE byte-level (vocab.json) -> texte UTF-8
|
||||
//
|
||||
// Auto-detect dimensions (num_decoder_layers, num_decoder_heads) via session outputs.
|
||||
// Bench prod ref : Whisper-Small FR 1.6s -> 825 ms total (mel 189 + enc 125 + dec 510), RTF 0.51.
|
||||
#include "stt_engine.h"
|
||||
#include "kazeia_mel.h"
|
||||
|
||||
#include "onnxruntime/onnxruntime_cxx_api.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <fstream>
|
||||
#include <memory>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
|
||||
// ============================================================================
|
||||
// VAD RMS énergie (port C++ de VadStage.kt)
|
||||
// FP16 helpers (cohabite avec Ort::Float16_t qui est juste un wrapper uint16_t)
|
||||
// ============================================================================
|
||||
|
||||
// IEEE 754 binary16 conversion (round-to-nearest-even).
|
||||
static uint16_t fp32_to_fp16(float f) {
|
||||
uint32_t x; std::memcpy(&x, &f, 4);
|
||||
uint32_t sign = (x >> 16) & 0x8000u;
|
||||
int32_t exp = (int32_t)((x >> 23) & 0xFFu) - 127 + 15;
|
||||
uint32_t mant = x & 0x7FFFFFu;
|
||||
if (exp >= 31) { // inf / nan
|
||||
return (uint16_t)(sign | 0x7C00u | (mant ? 1u : 0u));
|
||||
} else if (exp <= 0) { // subnormal / underflow
|
||||
if (exp < -10) return (uint16_t)sign;
|
||||
mant |= 0x800000u;
|
||||
uint32_t shift = (uint32_t)(14 - exp);
|
||||
uint32_t round = (mant >> (shift - 1)) & 1u;
|
||||
return (uint16_t)(sign | ((mant >> shift) + round));
|
||||
} else {
|
||||
uint32_t round = (mant >> 12) & 1u;
|
||||
return (uint16_t)(sign | (uint32_t)(exp << 10) | (mant >> 13) + round);
|
||||
}
|
||||
}
|
||||
|
||||
static float fp16_to_fp32(uint16_t h) {
|
||||
uint32_t sign = (uint32_t)(h & 0x8000u) << 16;
|
||||
uint32_t exp = (h >> 10) & 0x1Fu;
|
||||
uint32_t mant = h & 0x3FFu;
|
||||
uint32_t out;
|
||||
if (exp == 0) {
|
||||
if (mant == 0) { out = sign; }
|
||||
else {
|
||||
// subnormal
|
||||
int e = -1;
|
||||
while (!(mant & 0x400u)) { mant <<= 1; e--; }
|
||||
mant &= 0x3FFu;
|
||||
out = sign | (uint32_t)((127 + e) << 23) | (mant << 13);
|
||||
}
|
||||
} else if (exp == 31) {
|
||||
out = sign | 0x7F800000u | (mant << 13);
|
||||
} else {
|
||||
out = sign | (uint32_t)((127 - 15 + (int)exp) << 23) | (mant << 13);
|
||||
}
|
||||
float f; std::memcpy(&f, &out, 4);
|
||||
return f;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// VAD RMS énergie (port C++ de VadStage.kt) -- identique à S1.3
|
||||
// ============================================================================
|
||||
//
|
||||
// État interne : compteur de frames parole / silence consécutifs, sliding sur
|
||||
// chunks PCM. L'appelant push des chunks de taille frame_size; chaque push fait
|
||||
// avancer l'état. is_speech / is_end_of_speech consultent l'état.
|
||||
|
||||
struct SttVadState {
|
||||
int sample_rate;
|
||||
int frame_size; // 1600 = 100 ms @ 16 kHz
|
||||
int rms_threshold; // 150 sur PCM16 brut (prod KazeiaService)
|
||||
int min_speech_frames; // 3 -> 300 ms de parole pour déclencher
|
||||
int silence_end_frames; // 8 -> 800 ms de silence pour finir
|
||||
|
||||
int speech_count = 0;
|
||||
int silence_count = 0;
|
||||
bool in_speech = false;
|
||||
bool end_of_speech = false;
|
||||
int sample_rate, frame_size, rms_threshold, min_speech_frames, silence_end_frames;
|
||||
int speech_count = 0, silence_count = 0;
|
||||
bool in_speech = false, end_of_speech = false;
|
||||
};
|
||||
|
||||
SttVadState * stt_vad_new(int sample_rate, int frame_size, int rms_threshold,
|
||||
int min_speech_frames, int silence_end_frames) {
|
||||
auto * s = new SttVadState();
|
||||
s->sample_rate = sample_rate;
|
||||
s->frame_size = frame_size;
|
||||
s->rms_threshold = rms_threshold;
|
||||
s->min_speech_frames = min_speech_frames;
|
||||
s->silence_end_frames = silence_end_frames;
|
||||
s->sample_rate = sample_rate; s->frame_size = frame_size;
|
||||
s->rms_threshold = rms_threshold;
|
||||
s->min_speech_frames = min_speech_frames; s->silence_end_frames = silence_end_frames;
|
||||
return s;
|
||||
}
|
||||
|
||||
void stt_vad_reset(SttVadState * s) {
|
||||
if (!s) return;
|
||||
s->speech_count = 0;
|
||||
s->silence_count = 0;
|
||||
s->in_speech = false;
|
||||
s->end_of_speech = false;
|
||||
s->speech_count = 0; s->silence_count = 0;
|
||||
s->in_speech = false; s->end_of_speech = false;
|
||||
}
|
||||
|
||||
void stt_vad_free(SttVadState * s) { delete s; }
|
||||
|
||||
bool stt_vad_is_speech(const SttVadState * s) { return s && s->in_speech; }
|
||||
bool stt_vad_is_end_of_speech(const SttVadState * s) { return s && s->end_of_speech; }
|
||||
|
||||
// Calcule RMS d'un buffer PCM16, renvoie l'énergie en unités PCM brutes
|
||||
// (compatible seuil 150 prod).
|
||||
static float rms_pcm16(const int16_t * pcm, int n) {
|
||||
if (n <= 0) return 0.0f;
|
||||
double acc = 0.0;
|
||||
|
|
@ -70,29 +117,18 @@ static float rms_pcm16(const int16_t * pcm, int n) {
|
|||
bool stt_vad_push(SttVadState * s, const int16_t * pcm, int n_samples) {
|
||||
if (!s || !pcm || n_samples <= 0) return false;
|
||||
s->end_of_speech = false;
|
||||
|
||||
// Traite par chunks de frame_size. Si n_samples < frame_size, on accumulera
|
||||
// mentalement mais ici on calcule simplement la RMS sur ce qu'on a (l'app
|
||||
// appelante pousse des chunks pleins de 100 ms en pratique).
|
||||
int offset = 0;
|
||||
while (offset + s->frame_size <= n_samples) {
|
||||
float r = rms_pcm16(pcm + offset, s->frame_size);
|
||||
if (r >= (float)s->rms_threshold) {
|
||||
s->speech_count += 1;
|
||||
s->silence_count = 0;
|
||||
if (!s->in_speech && s->speech_count >= s->min_speech_frames) {
|
||||
s->in_speech = true;
|
||||
}
|
||||
s->speech_count += 1; s->silence_count = 0;
|
||||
if (!s->in_speech && s->speech_count >= s->min_speech_frames) s->in_speech = true;
|
||||
} else {
|
||||
s->silence_count += 1;
|
||||
// Pas de reset speech_count tant qu'on est in_speech, c'est l'enchainement
|
||||
// silence consécutifs qui clôt.
|
||||
if (s->in_speech && s->silence_count >= s->silence_end_frames) {
|
||||
s->in_speech = false;
|
||||
s->end_of_speech = true;
|
||||
s->speech_count = 0;
|
||||
s->in_speech = false; s->end_of_speech = true; s->speech_count = 0;
|
||||
} else if (!s->in_speech) {
|
||||
s->speech_count = 0; // reset compteur si on n'a pas atteint le seuil
|
||||
s->speech_count = 0;
|
||||
}
|
||||
}
|
||||
offset += s->frame_size;
|
||||
|
|
@ -101,67 +137,623 @@ bool stt_vad_push(SttVadState * s, const int16_t * pcm, int n_samples) {
|
|||
}
|
||||
|
||||
// ============================================================================
|
||||
// SttEngine (stubs — port ORT QNN en S2)
|
||||
// JSON minimaliste (mel_filters.json = flat float array, vocab.json = {"k":v,...})
|
||||
// ============================================================================
|
||||
|
||||
// Parse mel_filters.json : "[1.234e-05, 0.0, ...]" (flat array de N floats).
|
||||
static std::vector<float> parse_mel_filters_json(const char * path) {
|
||||
std::ifstream f(path); if (!f) return {};
|
||||
std::stringstream ss; ss << f.rdbuf();
|
||||
std::string s = ss.str();
|
||||
std::vector<float> out;
|
||||
out.reserve(80 * 201);
|
||||
size_t i = 0;
|
||||
while (i < s.size()) {
|
||||
// Trouve prochain digit ou signe
|
||||
while (i < s.size() && !(std::isdigit((unsigned char)s[i]) || s[i] == '-' || s[i] == '+' || s[i] == '.')) ++i;
|
||||
if (i >= s.size()) break;
|
||||
size_t j = i;
|
||||
// Avance tant que c'est un caractère numérique
|
||||
while (j < s.size() && (std::isdigit((unsigned char)s[j]) || s[j] == '.' || s[j] == 'e' || s[j] == 'E' || s[j] == '+' || s[j] == '-')) {
|
||||
// garde-fou : - et + uniquement au début ou après e
|
||||
if ((s[j] == '-' || s[j] == '+') && j != i && s[j-1] != 'e' && s[j-1] != 'E') break;
|
||||
++j;
|
||||
}
|
||||
if (j > i) {
|
||||
try { out.push_back(std::stof(s.substr(i, j - i))); } catch (...) {}
|
||||
}
|
||||
i = j;
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
// Parse vocab.json (HF whisper format) : {"!":0,"\"":1,...,"<|endoftext|>":50257,...}
|
||||
// On retourne id -> token (chaîne UTF-8 brute, qui contient parfois des unicodes BPE).
|
||||
static bool parse_vocab_json(const char * path,
|
||||
std::unordered_map<int, std::string> & id_to_token) {
|
||||
std::ifstream f(path); if (!f) return false;
|
||||
std::stringstream ss; ss << f.rdbuf();
|
||||
std::string s = ss.str();
|
||||
// On parcourt en cherchant des paires "key":number
|
||||
size_t i = 0;
|
||||
while (i < s.size()) {
|
||||
// trouve "
|
||||
while (i < s.size() && s[i] != '"') ++i;
|
||||
if (i >= s.size()) break;
|
||||
// Parse string key (handle \\, \", \uXXXX)
|
||||
++i;
|
||||
std::string key;
|
||||
while (i < s.size() && s[i] != '"') {
|
||||
if (s[i] == '\\' && i + 1 < s.size()) {
|
||||
char c = s[i+1];
|
||||
if (c == '"') { key.push_back('"'); i += 2; }
|
||||
else if (c == '\\') { key.push_back('\\'); i += 2; }
|
||||
else if (c == '/') { key.push_back('/'); i += 2; }
|
||||
else if (c == 'n') { key.push_back('\n'); i += 2; }
|
||||
else if (c == 't') { key.push_back('\t'); i += 2; }
|
||||
else if (c == 'r') { key.push_back('\r'); i += 2; }
|
||||
else if (c == 'b') { key.push_back('\b'); i += 2; }
|
||||
else if (c == 'f') { key.push_back('\f'); i += 2; }
|
||||
else if (c == 'u' && i + 5 < s.size()) {
|
||||
// \uXXXX -> codepoint -> UTF-8
|
||||
unsigned cp = 0;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
char h = s[i+2+k];
|
||||
cp <<= 4;
|
||||
if (h >= '0' && h <= '9') cp |= (unsigned)(h - '0');
|
||||
else if (h >= 'a' && h <= 'f') cp |= (unsigned)(h - 'a' + 10);
|
||||
else if (h >= 'A' && h <= 'F') cp |= (unsigned)(h - 'A' + 10);
|
||||
}
|
||||
// Encode UTF-8
|
||||
if (cp < 0x80) key.push_back((char)cp);
|
||||
else if (cp < 0x800) {
|
||||
key.push_back((char)(0xC0 | (cp >> 6)));
|
||||
key.push_back((char)(0x80 | (cp & 0x3F)));
|
||||
} else {
|
||||
key.push_back((char)(0xE0 | (cp >> 12)));
|
||||
key.push_back((char)(0x80 | ((cp >> 6) & 0x3F)));
|
||||
key.push_back((char)(0x80 | (cp & 0x3F)));
|
||||
}
|
||||
i += 6;
|
||||
} else { key.push_back(s[i]); ++i; }
|
||||
} else {
|
||||
key.push_back(s[i]); ++i;
|
||||
}
|
||||
}
|
||||
if (i >= s.size()) break;
|
||||
++i; // skip closing "
|
||||
// skip whitespace + ':' + whitespace
|
||||
while (i < s.size() && (s[i] == ' ' || s[i] == ':' || s[i] == '\t' || s[i] == '\n')) ++i;
|
||||
// Parse integer value
|
||||
size_t j = i;
|
||||
while (j < s.size() && std::isdigit((unsigned char)s[j])) ++j;
|
||||
if (j > i) {
|
||||
int id = std::stoi(s.substr(i, j - i));
|
||||
id_to_token[id] = key;
|
||||
}
|
||||
i = j;
|
||||
// skip jusqu'à la prochaine ","
|
||||
while (i < s.size() && s[i] != ',' && s[i] != '}') ++i;
|
||||
if (i < s.size() && s[i] == '}') break;
|
||||
}
|
||||
return !id_to_token.empty();
|
||||
}
|
||||
|
||||
// BPE byte-level GPT-2/Whisper : map character code (in vocab token strings) -> raw byte
|
||||
static std::unordered_map<int, int> build_bpe_unicode_to_byte() {
|
||||
std::unordered_map<int, int> map;
|
||||
for (int b = 33; b <= 126; ++b) map[b] = b;
|
||||
for (int b = 161; b <= 172; ++b) map[b] = b;
|
||||
for (int b = 174; b <= 255; ++b) map[b] = b;
|
||||
int n = 0;
|
||||
for (int b = 0; b <= 255; ++b) {
|
||||
bool found = false;
|
||||
for (auto & kv : map) { if (kv.second == b) { found = true; break; } }
|
||||
if (!found) { map[256 + n] = b; ++n; }
|
||||
}
|
||||
return map;
|
||||
}
|
||||
|
||||
// Parse UTF-8 codepoint at position i. Renvoie cp et avance i.
|
||||
static unsigned utf8_next(const std::string & s, size_t & i) {
|
||||
unsigned char c = (unsigned char)s[i++];
|
||||
if (c < 0x80) return c;
|
||||
if ((c >> 5) == 0x6) {
|
||||
unsigned cp = (c & 0x1F);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
return cp;
|
||||
}
|
||||
if ((c >> 4) == 0xE) {
|
||||
unsigned cp = (c & 0x0F);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
return cp;
|
||||
}
|
||||
if ((c >> 3) == 0x1E) {
|
||||
unsigned cp = (c & 0x07);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
cp = (cp << 6) | ((unsigned char)s[i++] & 0x3F);
|
||||
return cp;
|
||||
}
|
||||
return c;
|
||||
}
|
||||
|
||||
// Décode une liste de token ids -> texte UTF-8 via BPE byte-level Whisper.
|
||||
// Filtre les tokens spéciaux <|...|>.
|
||||
static std::string decode_tokens(const std::vector<int> & ids,
|
||||
const std::unordered_map<int, std::string> & vocab,
|
||||
const std::unordered_map<int, int> & b2u_byte_map) {
|
||||
std::string bytes;
|
||||
bytes.reserve(ids.size() * 2);
|
||||
for (int id : ids) {
|
||||
auto it = vocab.find(id); if (it == vocab.end()) continue;
|
||||
const std::string & w = it->second;
|
||||
if (w.size() >= 4 && w[0] == '<' && w[1] == '|' &&
|
||||
w[w.size()-2] == '|' && w[w.size()-1] == '>') continue;
|
||||
size_t i = 0;
|
||||
while (i < w.size()) {
|
||||
unsigned cp = utf8_next(w, i);
|
||||
auto bit = b2u_byte_map.find((int)cp);
|
||||
if (bit != b2u_byte_map.end()) bytes.push_back((char)bit->second);
|
||||
}
|
||||
}
|
||||
return bytes;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// SttEngine — ORT QNN HfWhisper KV-cache
|
||||
// ============================================================================
|
||||
|
||||
namespace {
|
||||
// Constantes Whisper (multilingue) — confirmées dans le code prod Kotlin.
|
||||
constexpr int SOT = 50258;
|
||||
constexpr int EOT = 50257;
|
||||
constexpr int TRANSLATE_TOK = 50358;
|
||||
constexpr int TRANSCRIBE_TOK = 50359;
|
||||
constexpr int VOCAB_SIZE = 51865;
|
||||
constexpr int MEAN_DECODE_LEN = 200;
|
||||
constexpr int HEAD_DIM = 64;
|
||||
constexpr float MASK_NEG = -100.0f;
|
||||
|
||||
double now_s() {
|
||||
using clk = std::chrono::steady_clock;
|
||||
return std::chrono::duration<double>(clk::now().time_since_epoch()).count();
|
||||
}
|
||||
} // namespace
|
||||
|
||||
struct SttEngine {
|
||||
SttEngineLoadCfg cfg;
|
||||
std::vector<float> mel_basis; // [80, 201] librosa whisper
|
||||
// S2 :
|
||||
// void * encoder_session; // Ort::Session*
|
||||
// void * decoder_session;
|
||||
// std::vector<int> vocab_ids;
|
||||
// std::unordered_map<std::string,int> token_to_id;
|
||||
std::vector<float> mel_basis;
|
||||
|
||||
std::unique_ptr<Ort::Env> ort_env;
|
||||
std::unique_ptr<Ort::SessionOptions> enc_opts, dec_opts;
|
||||
std::unique_ptr<Ort::Session> enc_sess, dec_sess;
|
||||
std::unique_ptr<Ort::MemoryInfo> mem_info;
|
||||
std::unique_ptr<Ort::AllocatorWithDefaultOptions> allocator;
|
||||
|
||||
// Auto-detected dims (Whisper-Base: 6/8, Small: 12/12, Medium: 24/16)
|
||||
int num_decoder_layers = 0;
|
||||
int num_decoder_heads = 0;
|
||||
|
||||
// Cached I/O names (for the decoder loop tight allocation budget)
|
||||
std::vector<std::string> enc_in_names_owned;
|
||||
std::vector<const char *> enc_in_names;
|
||||
std::vector<std::string> enc_out_names_owned;
|
||||
std::vector<const char *> enc_out_names;
|
||||
|
||||
std::vector<std::string> dec_in_names_owned;
|
||||
std::vector<const char *> dec_in_names;
|
||||
std::vector<std::string> dec_out_names_owned;
|
||||
std::vector<const char *> dec_out_names;
|
||||
|
||||
// Vocab + BPE
|
||||
std::unordered_map<int, std::string> vocab;
|
||||
std::unordered_map<int, int> bpe_unicode_to_byte;
|
||||
|
||||
bool loaded = false;
|
||||
};
|
||||
|
||||
// Charge un fichier binaire ou JSON pour mel filters et retourne le tableau.
|
||||
// Tente d'abord mel_filters.bin, sinon mel_filters.json.
|
||||
static bool load_mel_basis_any(const std::string & model_dir,
|
||||
const kazeia_mel::MelConfig & cfg,
|
||||
std::vector<float> & out) {
|
||||
if (kazeia_mel::load_mel_basis((model_dir + "/mel_filters.bin").c_str(), cfg, out)) return true;
|
||||
auto v = parse_mel_filters_json((model_dir + "/mel_filters.json").c_str());
|
||||
const size_t expected = (size_t)cfg.n_mels * (cfg.n_fft/2+1);
|
||||
if (v.size() == expected) { out = std::move(v); return true; }
|
||||
fprintf(stderr, "stt_engine: mel basis introuvable (ni .bin ni .json valide) à %s\n",
|
||||
model_dir.c_str());
|
||||
return false;
|
||||
}
|
||||
|
||||
// Détecte num_decoder_layers (en comptant k_cache_cross_*) et num_decoder_heads (shape[0] du 1er).
|
||||
static void detect_dims(SttEngine & e) {
|
||||
Ort::AllocatorWithDefaultOptions a;
|
||||
size_t n_out = e.enc_sess->GetOutputCount();
|
||||
int n_layers = 0;
|
||||
int n_heads = 0;
|
||||
for (size_t i = 0; i < n_out; ++i) {
|
||||
auto name_ptr = e.enc_sess->GetOutputNameAllocated(i, a);
|
||||
std::string name = name_ptr.get();
|
||||
if (name.rfind("k_cache_cross_", 0) == 0) {
|
||||
++n_layers;
|
||||
if (n_heads == 0) {
|
||||
auto info = e.enc_sess->GetOutputTypeInfo(i);
|
||||
auto tinfo = info.GetTensorTypeAndShapeInfo();
|
||||
auto shape = tinfo.GetShape();
|
||||
if (!shape.empty() && shape[0] > 0) n_heads = (int)shape[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
// Défauts si détection ratée (Whisper-Base = 6/8, Small = 12/12)
|
||||
if (n_layers == 0) n_layers = 12;
|
||||
if (n_heads == 0) n_heads = 12;
|
||||
e.num_decoder_layers = n_layers;
|
||||
e.num_decoder_heads = n_heads;
|
||||
}
|
||||
|
||||
// Cache les input/output names (les sessions ORT exigent const char**)
|
||||
static void cache_io_names(Ort::Session & sess,
|
||||
std::vector<std::string> & in_owned,
|
||||
std::vector<const char *> & in_ptrs,
|
||||
std::vector<std::string> & out_owned,
|
||||
std::vector<const char *> & out_ptrs) {
|
||||
Ort::AllocatorWithDefaultOptions a;
|
||||
size_t ni = sess.GetInputCount(), no = sess.GetOutputCount();
|
||||
in_owned.reserve(ni); in_ptrs.reserve(ni);
|
||||
for (size_t i = 0; i < ni; ++i) {
|
||||
auto p = sess.GetInputNameAllocated(i, a);
|
||||
in_owned.emplace_back(p.get());
|
||||
}
|
||||
for (auto & s : in_owned) in_ptrs.push_back(s.c_str());
|
||||
out_owned.reserve(no); out_ptrs.reserve(no);
|
||||
for (size_t i = 0; i < no; ++i) {
|
||||
auto p = sess.GetOutputNameAllocated(i, a);
|
||||
out_owned.emplace_back(p.get());
|
||||
}
|
||||
for (auto & s : out_owned) out_ptrs.push_back(s.c_str());
|
||||
}
|
||||
|
||||
SttEngine * stt_engine_load(const SttEngineLoadCfg & cfg) {
|
||||
if (!cfg.model_dir) {
|
||||
fprintf(stderr, "stt_engine_load: model_dir requis\n"); return nullptr;
|
||||
}
|
||||
auto * eng = new SttEngine();
|
||||
eng->cfg = cfg;
|
||||
// Tente charger mel_filters.bin (préféré) ou mel_filters.json (sera porté en S2).
|
||||
std::string mel_path = std::string(cfg.model_dir) + "/mel_filters.bin";
|
||||
std::string D = cfg.model_dir;
|
||||
|
||||
// 1) Mel basis (binaire optimal, JSON fallback)
|
||||
auto mel_cfg = kazeia_mel::config_whisper();
|
||||
if (!kazeia_mel::load_mel_basis(mel_path.c_str(), mel_cfg, eng->mel_basis)) {
|
||||
fprintf(stderr, "stt_engine_load: mel_filters.bin absent ou mauvaise taille à %s\n",
|
||||
mel_path.c_str());
|
||||
// Pas fatal : à S2 on chargera depuis mel_filters.json. Pour S1.3 on continue
|
||||
// pour permettre les tests VAD.
|
||||
if (!load_mel_basis_any(D, mel_cfg, eng->mel_basis)) {
|
||||
delete eng; return nullptr;
|
||||
}
|
||||
|
||||
// 2) ORT env
|
||||
try {
|
||||
eng->ort_env = std::make_unique<Ort::Env>(ORT_LOGGING_LEVEL_WARNING, "kazeia_stt");
|
||||
eng->mem_info = std::make_unique<Ort::MemoryInfo>(
|
||||
Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemTypeDefault));
|
||||
eng->allocator = std::make_unique<Ort::AllocatorWithDefaultOptions>();
|
||||
} catch (const std::exception & e) {
|
||||
fprintf(stderr, "stt_engine_load: Ort env FAIL: %s\n", e.what());
|
||||
delete eng; return nullptr;
|
||||
}
|
||||
|
||||
// 3) Sessions encoder + decoder. QNN EP si use_htp.
|
||||
auto make_opts = [&](Ort::SessionOptions & opts) {
|
||||
if (cfg.use_htp) {
|
||||
// QNN ExecutionProvider via AppendExecutionProvider("QNN", {...})
|
||||
std::vector<const char *> keys = { "backend_path" };
|
||||
std::vector<const char *> vals = { "libQnnHtp.so" };
|
||||
opts.AppendExecutionProvider("QNN",
|
||||
{{"backend_path", "libQnnHtp.so"}});
|
||||
}
|
||||
opts.SetIntraOpNumThreads(cfg.n_threads);
|
||||
};
|
||||
|
||||
std::string enc_path = D + "/HfWhisperEncoder.onnx";
|
||||
std::string dec_path = D + "/HfWhisperDecoder.onnx";
|
||||
|
||||
try {
|
||||
eng->enc_opts = std::make_unique<Ort::SessionOptions>();
|
||||
make_opts(*eng->enc_opts);
|
||||
double t0 = now_s();
|
||||
eng->enc_sess = std::make_unique<Ort::Session>(*eng->ort_env, enc_path.c_str(), *eng->enc_opts);
|
||||
fprintf(stderr, "stt_engine_load: encoder loaded in %.0f ms\n", (now_s() - t0) * 1000.0);
|
||||
|
||||
eng->dec_opts = std::make_unique<Ort::SessionOptions>();
|
||||
make_opts(*eng->dec_opts);
|
||||
double t1 = now_s();
|
||||
eng->dec_sess = std::make_unique<Ort::Session>(*eng->ort_env, dec_path.c_str(), *eng->dec_opts);
|
||||
fprintf(stderr, "stt_engine_load: decoder loaded in %.0f ms\n", (now_s() - t1) * 1000.0);
|
||||
} catch (const std::exception & e) {
|
||||
fprintf(stderr, "stt_engine_load: ORT session FAIL: %s\n", e.what());
|
||||
delete eng; return nullptr;
|
||||
}
|
||||
|
||||
// 4) Auto-détection des dims + cache des noms I/O
|
||||
detect_dims(*eng);
|
||||
cache_io_names(*eng->enc_sess, eng->enc_in_names_owned, eng->enc_in_names,
|
||||
eng->enc_out_names_owned, eng->enc_out_names);
|
||||
cache_io_names(*eng->dec_sess, eng->dec_in_names_owned, eng->dec_in_names,
|
||||
eng->dec_out_names_owned, eng->dec_out_names);
|
||||
fprintf(stderr, "stt_engine_load: %d layers / %d heads detected, enc I/O=%zu/%zu dec I/O=%zu/%zu\n",
|
||||
eng->num_decoder_layers, eng->num_decoder_heads,
|
||||
eng->enc_in_names.size(), eng->enc_out_names.size(),
|
||||
eng->dec_in_names.size(), eng->dec_out_names.size());
|
||||
|
||||
// 5) Vocab.json + BPE byte map
|
||||
if (!parse_vocab_json((D + "/vocab.json").c_str(), eng->vocab)) {
|
||||
fprintf(stderr, "stt_engine_load: vocab.json absent ou parse FAIL à %s\n", D.c_str());
|
||||
delete eng; return nullptr;
|
||||
}
|
||||
eng->bpe_unicode_to_byte = build_bpe_unicode_to_byte();
|
||||
fprintf(stderr, "stt_engine_load: vocab %zu tokens, BPE map %zu entries\n",
|
||||
eng->vocab.size(), eng->bpe_unicode_to_byte.size());
|
||||
|
||||
eng->loaded = true;
|
||||
return eng;
|
||||
}
|
||||
|
||||
// Création tensor FP16 owned (heap buffer kept alive jusqu'à la fin de la frame).
|
||||
// Whisper-decoder loop produit ~200 fwd × (1+1+2L+1) tensors, ~13MB/step au pic. OK CPU.
|
||||
static Ort::Value make_fp16_tensor(const SttEngine & e,
|
||||
const std::vector<uint16_t> & data,
|
||||
const std::vector<int64_t> & shape,
|
||||
std::vector<std::vector<uint16_t>> & owner) {
|
||||
owner.emplace_back(data);
|
||||
auto & ref = owner.back();
|
||||
return Ort::Value::CreateTensor(
|
||||
*e.mem_info, ref.data(), ref.size() * sizeof(uint16_t),
|
||||
shape.data(), shape.size(),
|
||||
ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16);
|
||||
}
|
||||
|
||||
static Ort::Value make_int32_tensor(const SttEngine & e, int v,
|
||||
const std::vector<int64_t> & shape,
|
||||
std::vector<std::vector<int32_t>> & owner) {
|
||||
owner.push_back({v});
|
||||
auto & ref = owner.back();
|
||||
return Ort::Value::CreateTensor<int32_t>(
|
||||
*e.mem_info, ref.data(), ref.size(),
|
||||
shape.data(), shape.size());
|
||||
}
|
||||
|
||||
// Trouve l'index argmax sur logits FP16 [VOCAB_SIZE].
|
||||
static int argmax_fp16_logits(const uint16_t * data, int n) {
|
||||
int best = 0; float best_v = -1e30f;
|
||||
for (int i = 0; i < n; ++i) {
|
||||
float v = fp16_to_fp32(data[i]);
|
||||
if (v > best_v) { best_v = v; best = i; }
|
||||
}
|
||||
return best;
|
||||
}
|
||||
|
||||
// Convertit FloatArray mel -> FP16 buffer (taille N_MELS * N_FRAMES = 80*3000 = 240000).
|
||||
static std::vector<uint16_t> mel_to_fp16(const std::vector<float> & mel) {
|
||||
std::vector<uint16_t> out(mel.size());
|
||||
for (size_t i = 0; i < mel.size(); ++i) out[i] = fp32_to_fp16(mel[i]);
|
||||
return out;
|
||||
}
|
||||
|
||||
SttTranscribeResult stt_engine_transcribe(SttEngine * eng, const SttTranscribeCfg & cfg) {
|
||||
SttTranscribeResult R{};
|
||||
if (!eng || !eng->loaded || !cfg.pcm16 || cfg.n_samples <= 0) {
|
||||
R.err = -1; return R;
|
||||
}
|
||||
// Étape 1 : mel via kazeia_mel (Whisper config). Implémentée en S1.3 pour exercer
|
||||
// la lib partagée ; le encoder/decoder ORT viennent en S2.
|
||||
if (cfg.sample_rate != 16000) {
|
||||
fprintf(stderr, "stt_engine_transcribe: sample_rate=%d ; 16000 attendu\n", cfg.sample_rate);
|
||||
R.err = -10; return R;
|
||||
}
|
||||
double t_start = now_s();
|
||||
|
||||
// 1) PCM -> wav f32
|
||||
std::vector<float> wav((size_t)cfg.n_samples);
|
||||
for (int i = 0; i < cfg.n_samples; ++i) wav[i] = (float)cfg.pcm16[i] / 32768.0f;
|
||||
const double audio_s = (double)cfg.n_samples / cfg.sample_rate;
|
||||
|
||||
if (eng->mel_basis.empty()) {
|
||||
fprintf(stderr, "stt_engine_transcribe: mel_basis non chargé (mel_filters.bin manquant)\n");
|
||||
R.err = -2; return R;
|
||||
}
|
||||
// 2) Mel via kazeia_mel
|
||||
auto mel_cfg = kazeia_mel::config_whisper();
|
||||
double tm0 = now_s();
|
||||
int T = 0;
|
||||
auto mel = kazeia_mel::compute(wav, eng->mel_basis, mel_cfg, T);
|
||||
if (mel.empty()) { R.err = -3; return R; }
|
||||
R.mel_ms = 0; // TODO: timer
|
||||
R.mel_ms = (int)((now_s() - tm0) * 1000.0);
|
||||
|
||||
// Étape 2..N : encoder NPU + decoder loop + tokenizer BPE -> S2.
|
||||
fprintf(stderr, "stt_engine_transcribe: stubs (port S2 requis). mel %d frames OK.\n", T);
|
||||
R.err = -99; // not implemented yet
|
||||
// 3) Encoder — fp16 input -> N cross KV caches fp16
|
||||
double te0 = now_s();
|
||||
auto mel_fp16 = mel_to_fp16(mel);
|
||||
std::vector<std::vector<uint16_t>> tensor_buffers;
|
||||
tensor_buffers.reserve(eng->num_decoder_layers * 4 + 4);
|
||||
std::vector<int64_t> mel_shape = {1, 80, 3000};
|
||||
|
||||
std::vector<Ort::Value> enc_inputs;
|
||||
enc_inputs.emplace_back(make_fp16_tensor(*eng, mel_fp16, mel_shape, tensor_buffers));
|
||||
|
||||
// ORT expects: const char* const* input_names + Value* + size_t input_count.
|
||||
std::vector<Ort::Value> enc_outputs;
|
||||
try {
|
||||
enc_outputs = eng->enc_sess->Run(
|
||||
Ort::RunOptions{nullptr},
|
||||
eng->enc_in_names.data(), enc_inputs.data(), enc_inputs.size(),
|
||||
eng->enc_out_names.data(), eng->enc_out_names.size());
|
||||
} catch (const std::exception & e) {
|
||||
fprintf(stderr, "stt_engine_transcribe: encoder Run FAIL: %s\n", e.what());
|
||||
R.err = -4; return R;
|
||||
}
|
||||
R.encoder_ms = (int)((now_s() - te0) * 1000.0);
|
||||
|
||||
// 4) Map outputs (encoder) by name -> get cross KV pointers + shapes
|
||||
// Owned copies for the decoder loop (will be reused across all steps).
|
||||
struct KvCache {
|
||||
std::vector<uint16_t> data;
|
||||
std::vector<int64_t> shape;
|
||||
};
|
||||
std::vector<KvCache> cross_k(eng->num_decoder_layers), cross_v(eng->num_decoder_layers);
|
||||
for (size_t i = 0; i < eng->enc_out_names.size(); ++i) {
|
||||
std::string nm = eng->enc_out_names_owned[i];
|
||||
if (nm.rfind("k_cache_cross_", 0) == 0) {
|
||||
int idx = std::stoi(nm.substr(14));
|
||||
if (idx < eng->num_decoder_layers) {
|
||||
auto & val = enc_outputs[i];
|
||||
auto shape = val.GetTensorTypeAndShapeInfo().GetShape();
|
||||
size_t n = 1; for (auto d : shape) n *= (size_t)d;
|
||||
cross_k[idx].shape = shape;
|
||||
cross_k[idx].data.resize(n);
|
||||
std::memcpy(cross_k[idx].data.data(), val.GetTensorMutableData<uint16_t>(), n * 2);
|
||||
}
|
||||
} else if (nm.rfind("v_cache_cross_", 0) == 0) {
|
||||
int idx = std::stoi(nm.substr(14));
|
||||
if (idx < eng->num_decoder_layers) {
|
||||
auto & val = enc_outputs[i];
|
||||
auto shape = val.GetTensorTypeAndShapeInfo().GetShape();
|
||||
size_t n = 1; for (auto d : shape) n *= (size_t)d;
|
||||
cross_v[idx].shape = shape;
|
||||
cross_v[idx].data.resize(n);
|
||||
std::memcpy(cross_v[idx].data.data(), val.GetTensorMutableData<uint16_t>(), n * 2);
|
||||
}
|
||||
}
|
||||
}
|
||||
enc_outputs.clear();
|
||||
|
||||
// 5) Decoder loop autoregressif KV-cache (port exact decodeHfKvCache.kt)
|
||||
double td0 = now_s();
|
||||
const int kv_slots = MEAN_DECODE_LEN - 1; // 199
|
||||
// Self KV layout : k [H, 1, head_dim, slots] ; v [H, 1, slots, head_dim].
|
||||
const size_t self_k_n = (size_t)eng->num_decoder_heads * HEAD_DIM * kv_slots;
|
||||
const size_t self_v_n = (size_t)eng->num_decoder_heads * kv_slots * HEAD_DIM;
|
||||
const std::vector<int64_t> self_k_shape = {eng->num_decoder_heads, 1, HEAD_DIM, kv_slots};
|
||||
const std::vector<int64_t> self_v_shape = {eng->num_decoder_heads, 1, kv_slots, HEAD_DIM};
|
||||
|
||||
std::vector<KvCache> self_k(eng->num_decoder_layers), self_v(eng->num_decoder_layers);
|
||||
for (int l = 0; l < eng->num_decoder_layers; ++l) {
|
||||
self_k[l].shape = self_k_shape; self_k[l].data.assign(self_k_n, fp32_to_fp16(0.0f));
|
||||
self_v[l].shape = self_v_shape; self_v[l].data.assign(self_v_n, fp32_to_fp16(0.0f));
|
||||
}
|
||||
|
||||
std::vector<float> mask_f32((size_t)MEAN_DECODE_LEN, MASK_NEG);
|
||||
std::vector<uint16_t> mask_fp16((size_t)MEAN_DECODE_LEN);
|
||||
|
||||
std::vector<int> generated;
|
||||
generated.reserve(MEAN_DECODE_LEN);
|
||||
|
||||
int current_token = SOT;
|
||||
int position_id = 0;
|
||||
|
||||
// Préalloue les noms decoder (input_ids, attention_mask, k/v_cache_self_*_in,
|
||||
// k/v_cache_cross_*, position_ids). On les chercher dans dec_in_names_owned.
|
||||
// Cache des indices pour Run() : on passe les Value dans l'ordre des noms cachés.
|
||||
|
||||
for (int step = 0; step < MEAN_DECODE_LEN - 1; ++step) {
|
||||
// Mask R-to-L
|
||||
mask_f32[MEAN_DECODE_LEN - step - 1] = 0.0f;
|
||||
for (int i = 0; i < MEAN_DECODE_LEN; ++i) mask_fp16[i] = fp32_to_fp16(mask_f32[i]);
|
||||
|
||||
// Build Values aligned avec dec_in_names_owned[i] ordering
|
||||
tensor_buffers.clear();
|
||||
std::vector<std::vector<int32_t>> int_buffers;
|
||||
|
||||
std::vector<Ort::Value> dec_inputs;
|
||||
dec_inputs.reserve(eng->dec_in_names.size());
|
||||
|
||||
for (size_t i = 0; i < eng->dec_in_names_owned.size(); ++i) {
|
||||
const std::string & nm = eng->dec_in_names_owned[i];
|
||||
if (nm == "input_ids") {
|
||||
dec_inputs.emplace_back(make_int32_tensor(*eng, current_token, {1, 1}, int_buffers));
|
||||
} else if (nm == "attention_mask") {
|
||||
dec_inputs.emplace_back(make_fp16_tensor(*eng, mask_fp16,
|
||||
{1, 1, 1, (int64_t)MEAN_DECODE_LEN}, tensor_buffers));
|
||||
} else if (nm == "position_ids") {
|
||||
dec_inputs.emplace_back(make_int32_tensor(*eng, position_id, {1}, int_buffers));
|
||||
} else if (nm.rfind("k_cache_self_", 0) == 0 && nm.find("_in") != std::string::npos) {
|
||||
int idx = std::stoi(nm.substr(13));
|
||||
dec_inputs.emplace_back(make_fp16_tensor(*eng, self_k[idx].data, self_k[idx].shape, tensor_buffers));
|
||||
} else if (nm.rfind("v_cache_self_", 0) == 0 && nm.find("_in") != std::string::npos) {
|
||||
int idx = std::stoi(nm.substr(13));
|
||||
dec_inputs.emplace_back(make_fp16_tensor(*eng, self_v[idx].data, self_v[idx].shape, tensor_buffers));
|
||||
} else if (nm.rfind("k_cache_cross_", 0) == 0) {
|
||||
int idx = std::stoi(nm.substr(14));
|
||||
dec_inputs.emplace_back(make_fp16_tensor(*eng, cross_k[idx].data, cross_k[idx].shape, tensor_buffers));
|
||||
} else if (nm.rfind("v_cache_cross_", 0) == 0) {
|
||||
int idx = std::stoi(nm.substr(14));
|
||||
dec_inputs.emplace_back(make_fp16_tensor(*eng, cross_v[idx].data, cross_v[idx].shape, tensor_buffers));
|
||||
} else {
|
||||
fprintf(stderr, "stt_engine_transcribe: input decoder inconnu : %s\n", nm.c_str());
|
||||
R.err = -5; return R;
|
||||
}
|
||||
}
|
||||
|
||||
// Run decoder
|
||||
std::vector<Ort::Value> dec_outputs;
|
||||
try {
|
||||
dec_outputs = eng->dec_sess->Run(
|
||||
Ort::RunOptions{nullptr},
|
||||
eng->dec_in_names.data(), dec_inputs.data(), dec_inputs.size(),
|
||||
eng->dec_out_names.data(), eng->dec_out_names.size());
|
||||
} catch (const std::exception & e) {
|
||||
fprintf(stderr, "stt_engine_transcribe: decoder Run step=%d FAIL: %s\n", step, e.what());
|
||||
R.err = -6; return R;
|
||||
}
|
||||
|
||||
// Parse outputs : logits + updated self KV
|
||||
int logits_idx = -1;
|
||||
for (size_t i = 0; i < eng->dec_out_names_owned.size(); ++i) {
|
||||
if (eng->dec_out_names_owned[i] == "logits") { logits_idx = (int)i; break; }
|
||||
}
|
||||
if (logits_idx < 0) {
|
||||
fprintf(stderr, "stt_engine_transcribe: pas de sortie 'logits' au decoder\n");
|
||||
R.err = -7; return R;
|
||||
}
|
||||
const uint16_t * logits_data = dec_outputs[logits_idx].GetTensorMutableData<uint16_t>();
|
||||
int token = argmax_fp16_logits(logits_data, VOCAB_SIZE);
|
||||
|
||||
// Override translate -> transcribe
|
||||
if (cfg.force_transcribe && token == TRANSLATE_TOK) token = TRANSCRIBE_TOK;
|
||||
|
||||
// Update self KV from outputs
|
||||
for (size_t i = 0; i < eng->dec_out_names_owned.size(); ++i) {
|
||||
const std::string & nm = eng->dec_out_names_owned[i];
|
||||
if (nm.rfind("k_cache_self_", 0) == 0 && nm.find("_out") != std::string::npos) {
|
||||
int idx = std::stoi(nm.substr(13));
|
||||
auto & val = dec_outputs[i];
|
||||
size_t n = 1; for (auto d : val.GetTensorTypeAndShapeInfo().GetShape()) n *= (size_t)d;
|
||||
if (n == self_k[idx].data.size()) {
|
||||
std::memcpy(self_k[idx].data.data(), val.GetTensorMutableData<uint16_t>(), n * 2);
|
||||
}
|
||||
} else if (nm.rfind("v_cache_self_", 0) == 0 && nm.find("_out") != std::string::npos) {
|
||||
int idx = std::stoi(nm.substr(13));
|
||||
auto & val = dec_outputs[i];
|
||||
size_t n = 1; for (auto d : val.GetTensorTypeAndShapeInfo().GetShape()) n *= (size_t)d;
|
||||
if (n == self_v[idx].data.size()) {
|
||||
std::memcpy(self_v[idx].data.data(), val.GetTensorMutableData<uint16_t>(), n * 2);
|
||||
}
|
||||
}
|
||||
}
|
||||
dec_outputs.clear();
|
||||
|
||||
if (token == EOT) break;
|
||||
if (token < 50257) generated.push_back(token);
|
||||
|
||||
current_token = token;
|
||||
position_id += 1;
|
||||
}
|
||||
R.decoder_ms = (int)((now_s() - td0) * 1000.0);
|
||||
|
||||
// 6) Token decode -> texte
|
||||
R.text = decode_tokens(generated, eng->vocab, eng->bpe_unicode_to_byte);
|
||||
R.n_tokens = (int)generated.size();
|
||||
R.detected_language = cfg.language;
|
||||
R.total_ms = (int)((now_s() - t_start) * 1000.0);
|
||||
R.rtf = (audio_s > 0) ? (float)((double)R.total_ms / (audio_s * 1000.0)) : 0.0f;
|
||||
return R;
|
||||
}
|
||||
|
||||
void stt_engine_free(SttEngine * eng) {
|
||||
if (!eng) return;
|
||||
eng->enc_sess.reset(); eng->dec_sess.reset();
|
||||
eng->enc_opts.reset(); eng->dec_opts.reset();
|
||||
eng->ort_env.reset();
|
||||
delete eng;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,19 @@
|
|||
# Libs QNN (Qualcomm Neural Network Runtime)
|
||||
|
||||
Requises par `libkazeia_stt.so` pour le backend HTP V79 (Whisper-Small NPU).
|
||||
Non versionnées dans git (98 MB total). Source : Maven `com.qualcomm.qti:qnn-runtime:2.44.0`.
|
||||
|
||||
Pour récupérer (cache Gradle après un build d'app) :
|
||||
```bash
|
||||
QNN=~/.gradle/caches/8.12/transforms/*/transformed/qnn-runtime-2.44.0/jni/arm64-v8a
|
||||
mkdir -p /opt/Kazeia-engine/dist/lib/qnn
|
||||
cp $QNN/lib*.so /opt/Kazeia-engine/dist/lib/qnn/
|
||||
```
|
||||
|
||||
Côté app Android : déjà fournies par Gradle via `qnn-runtime:2.44.0`. Pas besoin de copier
|
||||
manuellement dans `jniLibs/`.
|
||||
|
||||
Pour le bench standalone (kazeia_stt_cli sur tablette adb), push :
|
||||
```bash
|
||||
adb push /opt/Kazeia-engine/dist/lib/qnn/*.so /data/local/tmp/stt/bin/
|
||||
```
|
||||
Loading…
Reference in New Issue