Whisper Api Stt

Name: Whisper Api Stt
Price: 5 USD
Availability: InStock
Author: Agensi

Transcribe audio through the OpenAI transcriptions endpoint with plain curl: pre-compress with ffmpeg, upload as multipart form data, pick whisper-1 or gpt-4o-t

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Works with the AI tools you already use

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See it in action

You say

Transcribe interview.mp3 using the diarization model and output it with speaker labels. The recording has specific jargon about 'Kubernetes clusters'.

Your agent does

Compressed interview.mp3 to 4.2MB (16kHz mono).
Sent to gpt-4o-transcribe-diarize. Output: Speaker A: How are the Kubernetes clusters performing? Speaker B: We scaled the pods but the ingress controller is lagging.

What you get

Compress and transcribe large audio files under the 25MB API limit.Extract speaker-labeled transcripts from multi-person interviews.Generate timestamped segments for video captioning workflows.Fix spelling of technical jargon using transcription prompt hints.

About this skill

The problem

Large audio files often exceed API limits or consume excessive bandwidth, while local transcription models lack the accuracy of specialized cloud endpoints. Managing multipart form data and parsing complex JSON segments manually is tedious and error-prone.

What it does

Pre-compresses audio to mono 16 kHz Opus/MP3 using ffmpeg to fit under 25 MB limits.
Executes authenticated multipart uploads to OpenAI transcription endpoints using curl.
Parses verbose_json responses into human-readable formats with jq.
Implements diarization models to extract speaker labels from recordings.
Optimizes accuracy for technical jargon and names using prompt-injection hints.

Frameworks & tools

OpenAI API, ffmpeg, curl, jq, and standard Unix shell environments.

Why this beats prompting it yourself

This skill handles the low-level infrastructure of audio processing that standard LLM prompts ignore. It ensures files are correctly downsampled for cost-efficiency and provides the specific jq filters needed to turn raw API responses into usable transcripts immediately.

Use cases

Transcribe long podcasts or meetings by compressing them for API compatibility.
Generate timestamped SRT or VTT files for video subtitles.
Identify different speakers in interview recordings using diarization models.
Improve transcription of technical documentation by biasing the model with specific jargon.

Known limitations

Requires an active OPENAI_API_KEY and local installations of ffmpeg and jq. Files that remain over 25 MB after compression must be manually chunked.