Your local voice agent may not be waiting on its language model: audio capture, end-of-turn detection, resampling, format conversion, encoding, buffering, and transport can all add time before or after inference. But there is no evidence that encoding is usually the bottleneck. Time the whole path—from the end of your speech to the first audible reply—and inspect each stage before changing codecs or hardware.
What latency should you measure?
Measure the interval from the last captured frame of the user’s speech to the first audio the user can hear. Then timestamp the intermediate events so you can tell whether the delay is in turn detection, recognition, generation, synthesis, playback, or audio handling.
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NVIDIA recommends measuring both end-to-end latency and individual components. Its own example identifies LLM first-token latency as the largest share, a useful reminder that an encoder is only one possible bottleneck. NVIDIA recommends targeting under one second from the end of user speech to first synthesized audio; treat that as its product guidance, not a universal standard or guarantee.
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- Last captured speech frame.
- Voice activity detection (VAD) or end-of-turn decision.
- Any resampling, format conversion, encoding, or buffering, with start and finish times.
- Interim and final speech-recognition transcript.
- First language-model token.
- First text-to-speech (TTS) audio byte.
- First audio playback.
Use one consistent clock and define each start and finish the same way across runs. Record multiple turns with the same audio, settings, hardware, and concurrency. Compare typical and slow turns, and note warm-up, network conditions, and concurrent streams separately. This is a practical diagnostic protocol, not a formally standardized benchmark.
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Where can the delay come from?
A useful breakdown follows the audio from microphone to speaker. Some stages can overlap, so their individual durations do not always add up neatly to the end-to-end time; the timestamps reveal where waiting actually occurs.
| Stage | What to check | Why it matters |
|---|---|---|
| Capture and turn detection | Time from the last speech frame to the VAD or end-of-turn decision. | The agent may be waiting to decide that the user has finished. NVIDIA’s implementation guide estimates 200–500 ms for end-of-speech detection in its example; this is not a universal measurement. |
| Recognition (ASR) | Time to interim and final transcript, measured from the audio frames the recognizer receives. | Recognition can delay the request reaching the agent. NVIDIA reports roughly 80–160 ms from utterance end to final transcript for its Voice Agent Blueprint configuration. |
| Audio preparation and encoding | Time spent resampling, converting formats, encoding, and decoding. | These steps may add CPU work or wait for a complete frame or buffer. NVIDIA’s guide estimates 50–100 ms for audio post-processing in its example stack, not for encoding in every system. |
| Buffering and transport | Time waiting for chunks to fill, plus any network or inter-process transfer. | Larger buffers can improve stability but delay delivery; smaller ones may increase overhead or cause gaps. NVIDIA’s guide estimates 50–200 ms for audio buffering in its example. |
| Generation and synthesis | Time to first model token, first TTS audio byte, and first playback. | A slow first token or synthesis startup can dominate even when audio preparation is quick. NVIDIA reports 400–600 ms to first token for the Nano 30B LLM in its Blueprint example. |
Those figures are vendor-reported estimates for NVIDIA’s implementation, not measurements to apply to another local agent. NVIDIA reports about 0.79 seconds end-to-end with one concurrent stream for its Voice Agent Blueprint. It also reports 78 ms TTS time-to-first-byte on A100, and about 110 ms at 64 concurrent streams on H100. These are results for that vendor stack and configuration, not a general benchmark of local agents or codecs.
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Could the audio format or encoder be responsible?
Yes, but first verify what the audio actually is. A filename or container label does not identify its encoding: WAV is a container, and although it often contains linear PCM, it does not guarantee it. Google Cloud explicitly advises inspecting the WAV header rather than assuming a particular encoding. Ensure the receiving recognizer or service is configured for the actual codec, sample rate, and channel layout.
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There is no format that is fastest or best in every situation. Google Cloud recommends lossless FLAC or LINEAR16 when an application controls the source audio for its Speech-to-Text service. That is product-specific guidance, not a requirement for all local recognizers. Other supported encodings and their constraints are listed in Google Cloud’s audio-encoding documentation.
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Compression can reduce the data sent over a bandwidth-limited or unreliable connection, but the benefit depends on compatibility, codec processing, buffer behavior, and recognition quality. Microsoft gives an output-format example of 384 kbps for 24 kHz, 16-bit mono PCM versus 48 kbps for its 24 kHz, 48 kbps mono MP3 format. These are bitrate figures, not measured latency results; a smaller payload does not by itself prove a faster response.
Compare paths using the same audio and workload. Check time to first usable audio and total encode/decode time, payload size under your real network conditions, recognition quality, format and sample-rate compatibility, buffering behavior, and CPU or GPU cost at your expected concurrency. The cited guidance does not establish a controlled, universal local PCM-versus-Opus latency winner.
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How can streaming make replies feel faster?
Streaming can let later stages begin before earlier ones have finished. If the language model produces text incrementally, sending that text to synthesis as it becomes available can start audio sooner than waiting for the full response. Microsoft describes text streaming as enabling real-time text processing for rapid audio generation in its Speech SDK guidance. NVIDIA likewise describes overlapping TTS with LLM generation.
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This is an architecture technique, not a guaranteed speedup for every local setup. Measure time to first token, first TTS byte, and first playback separately. Streaming may improve perceived response time while leaving total completion time unchanged, and chunking or buffering can still hold back playback.
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How should you optimize without making audio worse?
- Establish a baseline. Record the stage timestamps over repeated turns and note warm-up, network conditions, and concurrency. Keep the audio and settings consistent.
- Find the largest avoidable wait. Compare time spent in end-of-turn detection, ASR, model generation, TTS, audio conversion, buffering, transport, and playback. Do not assume that the codec is responsible just because it appears in the pipeline.
- Change one variable at a time. Test frame or chunk size, resampling path, codec, buffer size, or streaming behavior separately. Re-run the same workload after each change.
- Check quality and reliability as well as speed. Confirm that recognition has not worsened and that playback has no clipping, jitter, or gaps. A lower timestamp is not an improvement if users hear broken audio or the recognizer misses words.
- Test expected concurrency gradually. Increase simultaneous streams in steps and watch latency and errors. Microsoft warns that a sudden concurrency increase in load testing can cause latency or throttling. NVIDIA discusses 20 ms Opus frames and buffering trade-offs in its example stack; those are not universal optimal settings.
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