Audio transcription

Pendra transcribes spoken audio into text using Whisper-class models — meetings, calls, voice notes, podcasts. It's OpenAI-compatible, so existing transcription code works against Pendra by swapping the base URL and key.

Transcribe a file

Upload an audio file and a model. Files can be up to 25 MB and must be wav, mp3, or flac — convert other formats first (e.g. ffmpeg -i input.m4a output.wav). An optional language hint (ISO 639-1, e.g. en, cy) improves accuracy.

from pendra import Pendra

client = Pendra()

with open("meeting.mp3", "rb") as f:
    result = client.audio.transcriptions.create(
        model="whisper-large-v3-turbo",
        file=f,
        language="en",
    )
print(result.text)

import Pendra from 'pendra';
import { readFileSync } from 'node:fs';

const client = new Pendra();

const result = await client.audio.transcriptions.create({
  file: { filename: 'meeting.mp3', content: readFileSync('meeting.mp3') },
  model: 'whisper-large-v3-turbo',
  language: 'en',
});
console.log(result.text);

Output formats

Choose the shape with response_format:

• json (default) — a simple { "text": ... } object.
• text — the raw transcript as a plain string.
• srt / vtt — time-coded subtitle files, ready to ship as captions.
• verbose_json — timing and language metadata, plus word- or segment-level timestamps when you set timestamp_granularities.

Available models

• whisper-large-v3-turbo — fast, multilingual, the balanced default (~1.6 GB).
• whisper-large-v3 — highest accuracy, larger and slower (~3.1 GB).

Transcribe live from your microphone

Prefer to talk instead of upload? The Playground in your Pendra dashboard can transcribe straight from your microphone in real time: open the Playground, pick a transcription model, and press Record — the transcript appears as you speak. It's the fastest way to try a model on your own voice before wiring up the API.