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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallChoose audio settings for the specific voice-AI endpoint and task—not by picking a supposedly universal “best” rate. Check whether the endpoint expects speech-recognition input, text-to-speech output, telephony audio, or a live stream, then match its required container, encoding, sample rate, channels, and framing. For recognition input you control, preserve a lossless source such as FLAC or LINEAR16 when supported; convert only when the receiving system requires it.
Start with the job the audio must do
Audio settings for speech recognition are not automatically right for text-to-speech or realtime voice. First identify the pipeline stage and the exact API, model, and request type. Then use that endpoint’s current documentation as the compatibility contract.
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- Speech recognition: Match an accepted input encoding and rate. If you control the original recording and the endpoint supports it, Google recommends FLAC or LINEAR16 rather than lossy encoding because lossy compression can affect recognition. This is Google’s guidance, not a guarantee about every provider or model. Google Cloud Speech-to-Text encoding guide.
- Text-to-speech: Check the output formats and rates for the particular TTS API and model. Output support does not establish what a speech-recognition endpoint accepts.
- Realtime or telephony: Check whether audio is exchanged as a complete file or as framed stream chunks, and whether the endpoint imposes codec or rate constraints.
Understand what “WAV” and “sample rate” tell you
WAV is a container, not a complete audio specification
A WAV file wraps audio data and metadata; its extension alone does not tell you the encoding, bit depth, channel count, or sample rate. A WAV may carry LINEAR16 or another encoding, so verify its header and contents rather than trusting the filename. Google Cloud documents that it can infer encoding and sample rate from WAV or FLAC headers when those values are omitted, and its guide describes WAV with LINEAR16 or μ-law. Google Cloud Speech-to-Text encoding guide.
FLAC is a lossless audio format. LINEAR16 is a linear PCM encoding commonly carried in WAV. Neither label removes the need to check whether the endpoint accepts the representation you are sending.
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Sample rate is a requirement, not a quality ranking
Sample rate describes how many audio samples are represented per second. A higher number is not automatically better for a given voice-AI task: the receiving model may require or produce a particular rate, and the encoding can have its own constraints. Google Cloud’s guide, for example, lists AMR at 8 kHz, AMR-WB at 16 kHz, and Opus at 8, 12, 16, 24, or 48 kHz. Follow the documented combination for the selected endpoint and codec rather than applying 16 kHz, 24 kHz, or 44.1 kHz as a blanket rule. Google Cloud Speech-to-Text encoding guide.
Compare formats by endpoint behavior
| Choice | What to verify | Practical guidance |
| Container and encoding | Whether the API expects WAV/FLAC or raw audio, and which codec or PCM representation it accepts | A file extension is not enough; match the actual encoded data and any header metadata. |
| Lossless or lossy | Whether the source is under your control and the endpoint accepts a lossless option | For recognition, Google advises FLAC or LINEAR16 when possible; do not generalize that advice into a universal accuracy guarantee. |
| Sample rate | The selected model’s accepted or produced rate and any codec-specific limits | Use the documented rate. Resample only when a downstream component requires a different one. |
| File or stream | Whether data includes a file header or arrives in chunks | Preserve the expected framing; raw stream chunks are not interchangeable with a complete WAV file. |
| Channels and bit depth | Accepted channel count and sample representation | Confirm both in the endpoint documentation and the actual audio data. |
Provider examples: the same settings do not apply everywhere
OpenAI speech output
The cited OpenAI audio API reference lists MP3, Opus, AAC, FLAC, WAV, and PCM for speech output, with MP3 as the default. That list describes the cited output endpoint; it does not establish which formats every OpenAI audio input endpoint accepts. Check the reference for the specific endpoint you are using. OpenAI API Reference: Audio.
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Google Gemini TTS
Google’s Gemini TTS documentation describes unary output as a complete WAV file: mono, 24 kHz, 16-bit signed little-endian PCM in a RIFF container. Its streaming output is headerless 24 kHz mono 16-bit PCM by default. Those are different representations even though their audio samples may otherwise correspond, so code that writes or combines streaming chunks must not treat them as complete WAV files without adding the appropriate file structure. The page also describes μ-law and A-law alternatives. Google AI for Developers: Gemini TTS.
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Google Cloud Speech-to-Text
The encoding guide lists several recognition encodings, including LINEAR16, FLAC, MULAW, AMR, AMR-WB, OGG_OPUS, and WEBM_OPUS. Their supported rates differ; for instance, the guide lists AMR at 8 kHz, AMR-WB at 16 kHz, and Opus at 8, 12, 16, 24, or 48 kHz. WAV and FLAC headers can provide encoding and rate information. Treat these as Google Cloud Speech-to-Text specifications, not cross-provider rules. Google Cloud Speech-to-Text encoding guide.
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Google Cloud Gemini TTS on Agent Platform
The cited Google Cloud documentation describes WAV/linear PCM at 24 kHz and μ-law/A-law at 8 kHz for the documented Gemini 3.8 TTS models. In that path, the sampleRate field is ignored; when a different rate is needed, Google advises client-side resampling. This is a model- and API-path-specific behavior, so verify it for the model and interface you select. Google Cloud Gemini TTS overview.
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Convert only when the receiving pipeline requires it
- Inspect the target requirements. Record the accepted container or raw representation, encoding, sample rate, channels, bit depth, and whether the request is a complete file or streaming data.
- Inspect the source audio. Check the real encoding and metadata rather than relying on its extension. If a WAV or FLAC header is present, ensure it accurately describes the audio.
- Keep a lossless source for recognition when practical. If the endpoint accepts it, use the original lossless audio or a lossless representation instead of introducing a lossy conversion first.
- Make one deliberate conversion. Resampling changes the sample rate; transcoding changes the encoding or container. Avoid unnecessary conversion chains that can discard information. If the needed rate differs from a TTS service’s fixed output rate, resample on the client where the provider’s documentation calls for it.
- Validate the actual request. Confirm that declared sample rate matches the samples, the channel count and encoding are accepted, and stream chunks have the framing expected by the endpoint.
Common mistakes to avoid
- Choosing 16 kHz or 24 kHz by habit: Neither rate is universally right. The endpoint, model, and encoding determine the compatible choice.
- Calling every WAV “lossless”: WAV is a container; check the encoding it contains.
- Assuming upsampling restores detail: Increasing a file’s sample rate does not recreate frequencies or audio information that were absent from the source.
- Using a TTS output list as an input-format list: Capabilities are endpoint-specific.
- Assuming a rate parameter is honored: Google’s cited Agent Platform Gemini TTS path ignores
sampleRatefor the documented models and formats. - Wrapping raw stream chunks as if they were a full file: Headerless PCM does not include the RIFF header required for a conventional WAV file.
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