Venice Transcription (/audio/transcriptions)

POST /api/v1/audio/transcriptions takes an audio file and returns text. It's OpenAI-compatible with multipart/form-data — the OpenAI SDK's audio.transcriptions.create() works unchanged.

Use when

• You need STT (speech-to-text) for voice notes, meetings, podcasts, short audio.
• You need timestamps for subtitles / chapters.
• You want to pick between fast local-style models (Parakeet) and large multilingual ones (Whisper, Wizper, Scribe).

For long video / YouTube transcription, see venice-video's /video/transcriptions (takes a public video URL directly).

Minimal request

curl https://api.venice.ai/api/v1/audio/transcriptions \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -F "file=@./meeting.m4a" \
  -F "model=nvidia/parakeet-tdt-0.6b-v3" \
  -F "response_format=json" \
  -F "timestamps=false"

{ "text": "Alright everyone, let's kick off the meeting..." }

With timestamps=true, json format also returns segment/word timings (schema is model-specific).

Request (multipart/form-data)

Field	Type	Default	Notes
file	binary	—	Required. Audio file. Supported: wav, wave, flac, m4a, aac, mp4, mp3, ogg, webm. Base64 is not accepted — upload as a real file.
model	enum	nvidia/parakeet-tdt-0.6b-v3	See models below.
response_format	json / text	json	text returns text/plain body.
timestamps	bool	false	Include segment/word timestamps (JSON only).
language	string	—	ISO 639-1 hint (e.g. en, ja). Only Whisper-family models honor it; others auto-detect.

Models

Model ID	Notes
nvidia/parakeet-tdt-0.6b-v3	Default. Fast, English-first, great for real-time-ish flows.
openai/whisper-large-v3	Large multilingual, honors language hint.
fal-ai/wizper	Whisper variant, competitive on quality/latency tradeoff.
elevenlabs/scribe-v2	ElevenLabs Scribe, strong on noisy audio.
stt-xai-v1	xAI Speech-to-Text.

GET /models?type=asr returns the current catalog. ASR pricing is pricing.per_audio_second.usd — cost scales with audio duration.

OpenAI SDK

import OpenAI from 'openai'
import fs from 'node:fs'

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY,
  baseURL: 'https://api.venice.ai/api/v1',
})

const out = await client.audio.transcriptions.create({
  file: fs.createReadStream('meeting.m4a'),
  model: 'openai/whisper-large-v3',
  response_format: 'json',
  language: 'en',
  // @ts-expect-error — Venice-specific extra, passes through multipart
  timestamps: true,
})

console.log(out.text)

Batch / long files

Venice doesn't expose native chunking. For files > ~30 min, split client-side on silence with ffmpeg or pydub, transcribe each chunk, then concatenate with offset timestamps.

ffmpeg -i long.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3

Errors

Code	Meaning
400	Bad params, unsupported audio format, empty file, or file larger than 25 MB (this endpoint returns 400 with "Maximum size is 25MB", not 413).
401	Auth / Pro-only.
402	Insufficient balance.
415	Wrong Content-Type — must be multipart/form-data.
422	Validation / upstream ASR error (e.g. zero-length audio, upstream provider 422). Not a "content policy" code on this path.
429	Rate limited.
500 / 503	Transient; retry with jitter.

Gotchas

• file must be uploaded as a real multipart file part. JSON + base64 is not supported here.
• Timestamps are only surfaced in the JSON response shapes (json, verbose_json, srt, vtt). With response_format: text the handler returns a plain text/plain body containing just the transcript — you'll lose any timestamp data, so pick verbose_json / srt / vtt when you need timings.
• language is Whisper-specific. Parakeet / Scribe ignore it and auto-detect.
• Peak concurrency limits apply — on 429, back off; big batches should throttle to ~5 parallel requests.
• Content-policy rejection on the transcript is returned as 422 with an error string; it does not surface suggested_prompt on this path.