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venice-audio-transcription

Transcribe audio files to text via POST /audio/transcriptions. Covers supported models (Parakeet, Whisper, Wizper, Scribe, xAI STT), supported formats (wav/flac/m4a/aac/mp4/mp3/ogg/webm), response formats (json/text), timestamps, and language hints. OpenAI-compatible multipart.

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https://deepseekmodel.com/api/download.php?id=veniceai-skills-skills-venice-audio-transcription-skill-md&format=skill
Download .skill Standard format with system_prompt and model_config, ready for any agent framework
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name venice-audio-transcription description Transcribe audio files to text via POST /audio/transcriptions. Covers supported models (Parakeet, Whisper, Wizper, Scribe, xAI STT), supported formats (wav/flac/m4a/aac/mp4/mp3/ogg/webm), response formats (json/text), timestamps, and language hints. OpenAI-compatible multipart. 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.
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The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
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.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
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