Learning path
Accelerate multimodal Voice Assistant performance with KleidiAI and SME2
Build and benchmark a multimodal Android voice-assistant pipeline, then use KleidiAI and SME2 to accelerate its speech recognition and language-model components.
Evaluation Conditions
Text To Speech · vivo X300 · FP16 · MiniMaxAI/TTS-Multilingual-Test-Set (English subset)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 3878.00ms16877.00ms | −12999.00 ms | |
| End to end latency p90 | 3932.00ms17633.00ms | −13701.00 ms | |
| End to end latency p99 | 4089.00ms17809.00ms | −13720.00 ms | |
| Peak memory | 4727.20MB6718.00MB | −1990.80 MB | |
| Average memory | 2883.40MB5253.70MB | −2370.30 MB | |
| Real-time factor | 1.20×0.28× | +0.91 × |
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| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | MiniMaxAI/TTS-Multilingual-Test-Set (English subset) | MiniMaxAI/TTS-Multilingual-Test-Set (English subset) |
| Sample count | 99 | 99 |
| Batch size | 1 | 1 |
| Language | en | en |
| Output audio (s) | 4.8 | 4.64 |
| Sample rate (Hz) | 24000 | 24000 |
| Runs | 30 | 30 |
| Warmup runs | 5 | 5 |