> ## Documentation Index
> Fetch the complete documentation index at: https://docs.perceptron.inc/llms.txt
> Use this file to discover all available pages before exploring further.

# Audio transcription

> Request spoken text with explicit instructions for language, unclear words, and speaker labels.

Request a transcript when you need the spoken words rather than a summary. Specify whether to preserve the original language, include repetitions, and mark words that cannot be heard clearly. Review the result against the recording before relying on exact wording.

## Transcribe a local recording

Install `perceptron>=0.4.0`, set `PERCEPTRON_API_KEY`, and place a short WAV recording at `./recording.wav`. This example asks for a verbatim transcript in the original language:

```python theme={null}
import os

from perceptron import Client, audio

client = Client(
    provider="perceptron",
    api_key=os.environ["PERCEPTRON_API_KEY"],
)
response = client.chat.completions.create(
    model="perceptron-mk1.5",
    messages=[{
        "role": "user",
        "content": [
            audio("recording.wav"),
            {"type": "text", "text": (
                "Transcribe the speech verbatim in its original language. "
                "Preserve spoken repetitions and do not summarize or translate. "
                "Mark words you cannot hear clearly as [unclear] instead of guessing. "
                "If there is no intelligible speech, say so. Return only the transcript."
            )},
        ],
    }],
    reasoning_effort="none",
    max_completion_tokens=4096,
)
choice = response.choices[0]
if choice.finish_reason != "stop" or choice.message.tool_calls:
    raise RuntimeError(f"Expected a completed transcript, got: {choice.finish_reason}")
print(choice.message.content or "")
```

The transcript is returned in `choices[0].message.content`. A `length` finish reason means the output budget was exhausted; treat that transcript as incomplete. A `stop` finish reason indicates completion, not verified transcription accuracy.

The `audio()` helper detects the local recording's format and sends it inline. For other supported encodings, remote recordings, or uploaded files, use the [audio input forms](/perceptron-mk1.5/capabilities/audio#choose-an-audio-input).

## Specify the transcript you need

| Goal                                | Example instruction                                                                                                          |
| ----------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| Preserve spoken wording             | “Transcribe verbatim, including repetitions. Mark unclear words as \[unclear].”                                              |
| Produce readable text               | “Transcribe the speech with punctuation. Remove filler words without changing the meaning.”                                  |
| Separate transcript and translation | “First transcribe in the original language, then provide an English translation under a separate heading.”                   |
| Label speaker changes               | “Use Speaker 1, Speaker 2, and so on when voices can be distinguished. Mark uncertain speaker changes rather than guessing.” |

These are prompt instructions, not guarantees of word accuracy or speaker separation. Speaker labels describe turns within the recording; they do not establish a person's identity or a stable identity across recordings. Check labels and quotations against the audio when they matter to your application.

## Budget for the full transcript

A recording can fit the audio input limit while its transcript exceeds the requested output budget. Allow enough completion tokens for the spoken text, within the model's [context and output limits](/perceptron-mk1.5/models/perceptron-mk1.5#specifications). The example uses `reasoning_effort="none"` and a 4,096-token output budget; adjust that budget for your recording.

For a longer recording, split the audio into shorter excerpts before sending requests and retain their start times in your application. If you overlap excerpts to preserve context around a boundary, check for duplicated transcript text when joining results. Audio over the [per-item limit](/perceptron-mk1.5/capabilities/audio#understand-usage-and-limits) is rejected rather than automatically truncated.

Use [audio clipping](/perceptron-mk1.5/capabilities/audio-clipping) to ask for an interval containing a phrase or audible event. A clip is a temporal annotation; it does not provide automatic word-by-word alignment for a transcript. Use [audio Q\&A](/perceptron-mk1.5/capabilities/audio-qa) when you only need a specific answer from the recording.