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Speech To Text Agent Skill

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

12k tokens
context cost
the whole folder, loaded on every use
7
files
instructions only
0
copies elsewhere
how many repositories repackaged it
405
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/elevenlabs/skills --skill speech-to-text

What comes with it

37 832 bytes besides the instruction
references/installation.md
references/realtime-client-side.md
references/realtime-commit-strategies.md
references/realtime-events.md
references/realtime-server-side.md
references/transcription-options.md

The instruction itself

21 sections, as written by the author

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 "[email protected]" -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 |

| scribe_v2_realtime_turbo | Realtime transcription variant | Live transcription |

| scribe_v2_realtime_lite | Realtime transcription variant | Live transcription |

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.

If your workspace has registered speaker profiles, set use_speaker_library=true with diarize=true to match detected speakers against the speaker library.

curl -X POST "https://api.elevenlabs.io/v1/speech-to-text" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" \
  -F "[email protected]" \
  -F "model_id=scribe_v2" \
  -F "diarize=true" \
  -F "detect_speaker_roles=true" \
  -F "use_speaker_library=true"

Multichannel Audio

Use use_multi_channel=true when each speaker is isolated on a separate audio channel. By default, the API returns one transcript per channel under transcripts; set multichannel_output_style="combined" to receive one transcript merged by timestamp, with channel_index on each word.

result = client.speech_to_text.convert(
    file=audio_file,
    model_id="scribe_v2",
    use_multi_channel=True,
    multichannel_output_style="combined",
)

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 5.0GB 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,
        keyterms=["ElevenLabs", "Scribe"],
        no_verbatim=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
    keyterms: ["ElevenLabs", "Scribe"],
    noVerbatim: true,
    includeLanguageDetection: true,
    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 |

Set includeLanguageDetection: true to receive the detected language code on committed transcript

events that include timestamps.

// 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,
  keyterms: ["ElevenLabs", "Scribe"],
  noVerbatim: true,
  // 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",
  keyterms: ["ElevenLabs", "Scribe"],
  noVerbatim: true,
  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

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How to use it

Copy the folder

Take elevenlabs/speech-to-text from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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