venice-audio-transcription: Workflow Automation for AI Agents | OpenAgentSkill

Overview

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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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.

View source ↗

File metadata

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.

View original text

---
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`](../venice-video/SKILL.md)'s `/video/transcriptions` (takes a public video URL directly).

## Minimal request

```bash
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"
```

```json
{ "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

```ts
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.

```bash
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.

Review the source

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Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: MIT
Version: 1.0.0
Last GitHub push: Aug 31, 2026
Registry updated: Sep 6, 2026
Instruction path: skills/venice-audio-transcription/SKILL.md @ be69bebc4703

Version reported in registry metadata; check source releases before relying on it.

Quality

69/100

Promising

Trust

65/100

Sandbox only

Audit

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Needs review

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More details

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    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20venice-audio-transcription%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20venice-audio-transcription%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/veniceai-venice-audio-transcription/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/veniceai-venice-audio-transcription"
  }
}

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Creator: veniceai
Source: veniceai/skills
Indexed by: OpenAgentSkill community index

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/veniceai-venice-audio-transcription?metric=listed&label=Listed)](https://www.openagentskill.com/skills/veniceai-venice-audio-transcription?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/veniceai-venice-audio-transcription?metric=trust&label=Trust)](https://www.openagentskill.com/skills/veniceai-venice-audio-transcription?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/veniceai-venice-audio-transcription?metric=audit&label=Audit)](https://www.openagentskill.com/skills/veniceai-venice-audio-transcription/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/veniceai-venice-audio-transcription?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/veniceai-venice-audio-transcription?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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