> ## Documentation Index > Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Batch Audio Transcription > Transcribe audio and video files at scale using the Runcrate Whisper API. Python, TypeScript, and curl examples. export const RuncrateStyles = () => { if (typeof document !== 'undefined' && !document.getElementById('runcrate-overrides')) { const s = document.createElement('style'); s.id = 'runcrate-overrides'; s.textContent = ` /* Match Runcrate's rounding scale (--radius: 0.75rem) */ .rounded-sm { border-radius: 0.5rem !important; } /* 8px */ .rounded-md { border-radius: 0.625rem !important; } /* 10px */ .rounded-lg { border-radius: 0.75rem !important; } /* 12px */ .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; } .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; } .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; } .rounded-r-md { border-top-right-radius: 0.625rem !important; border-bottom-right-radius: 0.625rem !important; } .rounded-l-lg { border-top-left-radius: 0.75rem !important; border-bottom-left-radius: 0.75rem !important; } .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; } /* Cards: never pure white in light mode */ .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; } html.dark .card { background-color: #141414 !important; } /* Docs hero box */ .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; } html.dark .rc-hero { background-color: #141414; border-color: #242424; } html.dark .rc-hero h1 { color: #f5f5f5; } /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */ ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; } ::-webkit-scrollbar-track { background-color: transparent; } ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; } ::-webkit-scrollbar-thumb:hover { background-color: rgba(155, 155, 155, 0.7); } *:hover::-webkit-scrollbar-thumb, *:focus::-webkit-scrollbar-thumb, *:active::-webkit-scrollbar-thumb { opacity: 1; } * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; } `; document.head.appendChild(s); } return null; }; Transcribe meetings, podcasts, interviews, and customer calls at scale using the Runcrate Whisper API. This guide covers single-file transcription, batch processing across a folder, and output format options. ## What you'll build A pipeline that transcribes multiple audio files using Whisper large-v3 through the Runcrate API. The pipeline reads a folder of recordings, transcribes each one, and writes the results as plain text, SRT subtitles, or structured JSON — your choice. *** ## Single file transcription ```bash curl theme={"theme":"github-dark"} curl https://api.runcrate.ai/v1/audio/transcriptions \ -H "Authorization: Bearer rc_live_YOUR_API_KEY" \ -F model="openai/whisper-large-v3" \ -F file=@meeting-2025-05-19.mp3 ``` ```python Python (OpenAI SDK) theme={"theme":"github-dark"} from openai import OpenAI client = OpenAI( base_url="https://api.runcrate.ai/v1", api_key="rc_live_YOUR_API_KEY", ) transcript = client.audio.transcriptions.create( model="openai/whisper-large-v3", file=open("meeting-2025-05-19.mp3", "rb"), ) print(transcript.text) ``` ```typescript TypeScript (OpenAI SDK) theme={"theme":"github-dark"} import OpenAI from "openai"; import fs from "fs"; const client = new OpenAI({ baseURL: "https://api.runcrate.ai/v1", apiKey: "rc_live_YOUR_API_KEY", }); const transcript = await client.audio.transcriptions.create({ model: "openai/whisper-large-v3", file: fs.createReadStream("meeting-2025-05-19.mp3"), }); console.log(transcript.text); ``` ```python Python (Runcrate SDK) theme={"theme":"github-dark"} from runcrate import Runcrate client = Runcrate(api_key="rc_live_YOUR_API_KEY") with open("meeting-2025-05-19.mp3", "rb") as f: result = client.models.transcribe( model="openai/whisper-large-v3", file=f, filename="meeting-2025-05-19.mp3", ) print(result.text) ``` *** ## Batch processing Transcribe every audio file in a folder and write the results to disk. This example uses the Runcrate Python SDK and outputs SRT subtitle files. ```python theme={"theme":"github-dark"} import os from pathlib import Path from runcrate import Runcrate client = Runcrate(api_key="rc_live_YOUR_API_KEY") audio_dir = Path("./recordings") output_dir = Path("./transcripts") output_dir.mkdir(exist_ok=True) SUPPORTED = {".mp3", ".wav", ".m4a", ".flac", ".ogg", ".webm"} for audio_file in sorted(audio_dir.iterdir()): if audio_file.suffix.lower() not in SUPPORTED: continue with open(audio_file, "rb") as f: result = client.models.transcribe( model="openai/whisper-large-v3", file=f, filename=audio_file.name, response_format="srt", ) srt_path = output_dir / f"{audio_file.stem}.srt" srt_path.write_text(result.text) print(f"Transcribed: {audio_file.name} → {srt_path.name}") ``` ### Batch processing with the OpenAI SDK The same pattern works with the OpenAI Python SDK pointed at the Runcrate API: ```python theme={"theme":"github-dark"} import os from pathlib import Path from openai import OpenAI client = OpenAI( base_url="https://api.runcrate.ai/v1", api_key="rc_live_YOUR_API_KEY", ) audio_dir = Path("./recordings") output_dir = Path("./transcripts") output_dir.mkdir(exist_ok=True) for audio_file in sorted(audio_dir.glob("*.mp3")): transcript = client.audio.transcriptions.create( model="openai/whisper-large-v3", file=open(audio_file, "rb"), ) txt_path = output_dir / f"{audio_file.stem}.txt" txt_path.write_text(transcript.text) print(f"Transcribed: {audio_file.name} → {txt_path.name}") ``` *** ## Output formats | Format | Extension | Use case | | ------ | --------- | -------------------------------------------------------------- | | `text` | `.txt` | Plain transcript — search, summarization, RAG ingestion | | `json` | `.json` | Structured output with word-level timestamps | | `srt` | `.srt` | Subtitle file for video editors (Premiere, DaVinci, Final Cut) | | `vtt` | `.vtt` | Web video subtitles (HTML5 `` element) | Pass the format via the `response_format` parameter: ```python theme={"theme":"github-dark"} result = client.models.transcribe( model="openai/whisper-large-v3", file=f, filename="episode-42.mp3", response_format="srt", # or "text", "json", "vtt" ) ``` *** ## Language hints Whisper auto-detects the spoken language, but you can improve accuracy on non-English audio by passing a language hint: ```python theme={"theme":"github-dark"} result = client.models.transcribe( model="openai/whisper-large-v3", file=f, filename="interview-tokyo.mp3", language="ja", # ISO 639-1 code ) ``` *** ## Use cases * **Meeting recordings** — transcribe and feed into an LLM for searchable notes and action items. * **Podcast episodes** — generate full transcripts for show notes, blog posts, and SEO. * **Customer support calls** — bulk-transcribe for quality analysis and compliance review. * **Lecture recordings** — produce study materials and make content accessible. * **Video content** — generate SRT/VTT subtitle files for automatic captioning. *** ## Tips * **Supported formats.** MP3, WAV, M4A, FLAC, OGG, and WebM. * **Large files.** For recordings longer than \~2 hours, split into chunks before uploading. Tools like `ffmpeg` make this easy: `ffmpeg -i long-meeting.mp3 -f segment -segment_time 1800 -c copy chunk_%03d.mp3`. * **SRT for video editing.** SRT is the most widely supported subtitle format across video editors and media players. Use VTT only if you need web-native `` elements. * **Combine with chat models.** Pipe transcripts into a Runcrate chat model for summarization, action-item extraction, or translation — see the [RAG Pipeline](/examples/rag-pipeline) guide for the pattern.