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 | 6772.00ms16323.00ms | −9551.00 ms | |
| End to end latency p90 |
7596.00ms16671.00ms |
| −9075.00 ms |
| End to end latency p99 | 7679.00ms17235.00ms | −9556.00 ms |
| Peak memory | 5213.40MB6799.00MB | −1585.60 MB |
| Average memory | 3542.20MB5397.20MB | −1855.00 MB |
| Real-time factor | 0.72×0.27× | +0.45 × |
| 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 |
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| Language | en | en |
| Output audio (s) | 4.38 | 4.86 |
| Sample rate (Hz) | 24000 | 24000 |
| Runs | 30 | 30 |
| Warmup runs | 5 | 5 |