speech-to-text

Q: How do I install speech-to-text?

Run npx skills add https://github.com/connorads/dotfiles --skill speech-to-text in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/speech-to-text/ (personal) or .claude/skills/speech-to-text/ (project).

by connoradsMedia & Creative

Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.

Installs: 63
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/connorads/dotfiles --skill speech-to-text

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads speech-to-text automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

ElevenLabs Speech-to-Text

Transcribe audio to text with Scribe v2 - supports 90+ languages, speaker diarization, and word-level timestamps.

Setup: See Installation Guide. For JavaScript, use @elevenlabs/* packages only.

Quick Start

Python

from elevenlabs import ElevenLabs

client = ElevenLabs()

with open("audio.mp3", "rb") as audio_file:
    result = client.speech_to_text.convert(file=audio_file, model_id="scribe_v2")

print(result.text)

JavaScript

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
import { createReadStream } from "fs";

const client = new ElevenLabsClient();
const result = await client.speechToText.convert({
  file: createReadStream("audio.mp3"),
  modelId: "scribe_v2",
});
console.log(result.text);

cURL

curl -X POST "https://api.elevenlabs.io/v1/speech-to-text" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" -F "file=@audio.mp3" -F "model_id=scribe_v2"

Models

Model ID	Description	Best For
`scribe_v2`	State-of-the-art accuracy, 90+ languages	Batch transcription, subtitles, long-form audio
`scribe_v2_realtime`	Low latency (~150ms)	Live transcription, voice agents

Transcription with Timestamps

Word-level timestamps include type classification and speaker identification:

result = client.speech_to_text.convert(
    file=audio_file, model_id="scribe_v2", timestamps_granularity="word"
)

for word in result.words:
    print(f"{word.text}: {word.start}s - {word.end}s (type: {word.type})")

Speaker Diarization

Identify WHO said WHAT - the model labels each word with a speaker ID, useful for meetings, interviews, or any multi-speaker audio:

result = client.speech_to_text.convert(
    file=audio_file,
    model_id="scribe_v2",
    diarize=True
)

for word in result.words:
    print(f"[{word.speaker_id}] {word.text}")

For call recordings, the batch API can label diarized speakers as agent and customer by setting detect_speaker_roles=true alongside diarize=true. This option is not compatible with use_multi_channel=true.

curl -X POST "https://api.elevenlabs.io/v1/speech-to-text" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" \
  -F "file=@call.mp3" \
  -F "model_id=scribe_v2" \
  -F "diarize=true" \
  -F "detect_speaker_roles=true"

Keyterm Prompting

Help the model recognize specific words it might otherwise mishear - product names, technical jargon, or unusual spellings (up to 100 terms):

result = client.speech_to_text.convert(
    file=audio_file,
    model_id="scribe_v2",
    keyterms=["ElevenLabs", "Scribe", "API"]
)

Language Detection

Automatic detection with optional language hint:

result = client.speech_to_text.convert(
    file=audio_file,
    model_id="scribe_v2",
    language_code="eng"  # ISO 639-1 or ISO 639-3 code
)

print(f"Detected: {result.language_code} ({result.language_probability:.0%})")

Supported Formats

Audio: MP3, WAV, M4A, FLAC, OGG, WebM, AAC, AIFF, Opus Video: MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP

Limits: Up to 3GB file size, 10 hours duration

Response Format

{
  "text": "The full transcription text",
  "language_code": "eng",
  "language_probability": 0.98,
  "words": [
    {"text": "The", "start": 0.0, "end": 0.15, "type": "word", "speaker_id": "speaker_0"},
    {"text": " ", "start": 0.15, "end": 0.16, "type": "spacing", "speaker_id": "speaker_0"}
  ]
}

Word types:

word - An actual spoken word
spacing - Whitespace between words (useful for precise timing)
audio_event - Non-speech sounds the model detected (laughter, applause, music, etc.)

Error Handling

try:
    result = client.speech_to_text.convert(file=audio_file, model_id="scribe_v2")
except Exception as e:
    print(f"Transcription failed: {e}")

Common errors:

401: Invalid API key
422: Invalid parameters
429: Rate limit exceeded

Tracking Costs

Monitor usage via request-id response header:

response = client.speech_to_text.convert.with_raw_response(file=audio_file, model_id="scribe_v2")
result = response.parse()
print(f"Request ID: {response.headers.get('request-id')}")

Real-Time Streaming

For live transcription with ultra-low latency (~150ms), use the real-time API. The real-time API produces two types of transcripts:

Partial transcripts: Interim results that update frequently as audio is processed - use these for live feedback (e.g., showing text as the user speaks)
Committed transcripts: Final, stable results after you "commit" - use these as the source of truth for your application

A "commit" tells the model to finalize the current segment. You can commit manually (e.g., when the user pauses) or use Voice Activity Detection (VAD) to auto-commit on silence.

Python (Server-Side)

import asyncio
from elevenlabs import ElevenLabs

client = ElevenLabs()

async def transcribe_realtime():
    async with client.speech_to_text.realtime.connect(
        model_id="scribe_v2_realtime",
        include_timestamps=True,
    ) as connection:
        await connection.stream_url("https://example.com/audio.mp3")

        async for event in connection:
            if event.type == "partial_transcript":
                print(f"Partial: {event.text}")
            elif event.type == "committed_transcript":
                print(f"Final: {event.text}")

asyncio.run(transcribe_realtime())

JavaScript (Client-Side with React)

import { useScribe, CommitStrategy } from "@elevenlabs/react";

function TranscriptionComponent() {
  const [transcript, setTranscript] = useState("");

  const scribe = useScribe({
    modelId: "scribe_v2_realtime",
    commitStrategy: CommitStrategy.VAD, // Auto-commit on silence for mic input
    onPartialTranscript: (data) => console.log("Partial:", data.text),
    onCommittedTranscript: (data) => setTranscript((prev) => prev + data.text),
  });

  const start = async () => {
    // Get token from your backend (never expose API key to client)
    const { token } = await fetch("/scribe-token").then((r) => r.json());

    await scribe.connect({
      token,
      microphone: { echoCancellation: true, noiseSuppression: true },
    });
  };

  return <button onClick={start}>Start Recording</button>;
}

Commit Strategies

Strategy	Description
Manual	You call `commit()` when ready - use for file processing or when you control the audio segments
VAD	Voice Activity Detection auto-commits when silence is detected - use for live microphone input

// React: set commitStrategy on the hook (recommended for mic input)
import { useScribe, CommitStrategy } from "@elevenlabs/react";

const scribe = useScribe({
  modelId: "scribe_v2_realtime",
  commitStrategy: CommitStrategy.VAD,
  // Optional VAD tuning:
  vadSilenceThresholdSecs: 1.5,
  vadThreshold: 0.4,
});

// JavaScript client: pass vad config on connect
const connection = await client.speechToText.realtime.connect({
  modelId: "scribe_v2_realtime",
  vad: {
    silenceThresholdSecs: 1.5,
    threshold: 0.4,
  },
});

Event Types

Event	Description
`partial_transcript`	Live interim results
`committed_transcript`	Final results after commit
`committed_transcript_with_timestamps`	Final with word timing
`error`	Error occurred

See real-time references for complete documentation.

References

Installation Guide
Transcription Options
Real-Time Client-Side Streaming
Real-Time Server-Side Streaming
Commit Strategies
Real-Time Event Reference

FAQ

What does the speech-to-text skill do?

Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.

How do I install speech-to-text?

Run npx skills add https://github.com/connorads/dotfiles --skill speech-to-text in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/speech-to-text/ (personal) or .claude/skills/speech-to-text/ (project).

Which AI agents can use speech-to-text?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is speech-to-text safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains speech-to-text?

It is published by connorads in connorads/dotfiles. It has 63 recorded installs.

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speech-to-text

Install

Install

Describe the task

Review the result

SKILL.md

ElevenLabs Speech-to-Text

Quick Start

Python

JavaScript

cURL

Models

Transcription with Timestamps

Speaker Diarization

Keyterm Prompting

Language Detection

Supported Formats

Response Format

Error Handling

Tracking Costs

Real-Time Streaming

Python (Server-Side)

JavaScript (Client-Side with React)

Commit Strategies

Event Types

References

FAQ

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