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Speech To Text

ElevenLabsOfficialPublished by ElevenLabs

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • Medium risk

From the author

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

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive interface for the ElevenLabs Scribe v2 speech-to-text engine. It supports batch processing of audio and video files, as well as low-latency real-time streaming for live applications. The tool includes advanced features such as speaker diarization, word-level timestamps, multi-channel audio processing, and keyterm boosting to improve recognition accuracy for specialized terminology.

What it does

  • Transcribes audio and video files into text with high accuracy.
  • Supports real-time streaming transcription with ultra-low latency.
  • Identifies individual speakers in multi-speaker audio recordings.
  • Provides word-level timestamps for precise timing and synchronization.
  • Allows keyterm boosting to improve recognition of specific jargon or names.
  • Supports automatic language detection across 90+ languages.

When to use it

  • When building applications that require live voice-to-text feedback.
  • When processing large volumes of meeting recordings or interviews.
  • When you need to generate accurate subtitles for video content.
  • When working with multi-channel audio where speakers are isolated.
  • When you need specialized transcription for medical or clinical audio.

Advantages

  • State-of-the-art transcription accuracy across 90+ languages.
  • Flexible integration options via Python, JavaScript, or CLI.
  • Robust real-time capabilities with configurable commit strategies.
  • Advanced features like speaker role detection and speaker library matching.
  • Comprehensive support for various audio and video file formats.

Usage examples

“Transcribe this audio file using the Scribe v2 model.”
The agent uses the ElevenLabs client to process the file and returns the full text transcription.
“Identify who is speaking in this meeting recording.”
The agent processes the audio with diarization enabled, returning a transcript labeled with speaker IDs.
“Set up a real-time transcription stream for a live microphone.”
The agent initializes a real-time connection using the VAD commit strategy to provide live text updates.
“Help the model recognize technical terms like 'ElevenLabs' and 'Scribe'.”
The agent includes the specified keyterms in the transcription request to improve accuracy for those specific words.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/elevenlabs/speech-to-text/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip speech-to-text.zip -d .agents/skills

Documentation by ElevenLabs

ElevenLabs Speech-to-Text

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

Setup, before writing any code: if ELEVENLABS_API_KEY isn't set for this project, run npx -y @elevenlabs/cli@latest onboard init from the app's folder first and follow what it prints: it signs the user in and stores a key for the project, so nobody copies one by hand. Otherwise see the 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);
CLI
elevenlabs speech-to-text convert --file audio.mp3 --model-id scribe_v2

Models

Model IDDescriptionBest For
scribe_v2State-of-the-art accuracy, 90+ languagesBatch transcription, subtitles, long-form audio
scribe_v2_medicalSpecialized recognition for medical and clinical audio, 90+ languagesClinical documentation, medical dictation, patient calls
scribe_v2_realtimeLow latency (~150ms)Live transcription, voice agents
scribe_v2_realtime_turboRealtime transcription variantLive transcription
scribe_v2_realtime_liteRealtime transcription variantLive 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.

elevenlabs speech-to-text convert \
  --file call.mp3 \
  --model-id scribe_v2 \
  --diarize true \
  --detect-speaker-roles true \
  --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.with_raw_response.convert(file=audio_file, model_id="scribe_v2")
result = response.data
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 CommitStrategy, ElevenLabs, RealtimeEvents, RealtimeUrlOptions

client = ElevenLabs()

async def transcribe_realtime():
    done = asyncio.Event()
    connection = await client.speech_to_text.realtime.connect(RealtimeUrlOptions(
        model_id="scribe_v2_realtime",
        url="https://npr-ice.streamguys1.com/live.mp3",
        commit_strategy=CommitStrategy.VAD,  # URL streams never commit on their own
        include_timestamps=True,
        keyterms=["ElevenLabs", "Scribe"],
        no_verbatim=True,
    ))

    def on_error(error):
        print(f"Error: {error}")
        done.set()

    connection.on(RealtimeEvents.PARTIAL_TRANSCRIPT, lambda data: print(f"Partial: {data.get('text', '')}"))
    connection.on(RealtimeEvents.COMMITTED_TRANSCRIPT, lambda data: print(f"Final: {data.get('text', '')}"))
    connection.on(RealtimeEvents.ERROR, on_error)
    connection.on(RealtimeEvents.CLOSE, done.set)

    try:
        await done.wait()
    finally:
        await connection.close()

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
StrategyDescription
ManualYou call commit() when ready - use for file processing or when you control the audio segments
VADVoice Activity Detection auto-commits when silence is detected - use for live microphone input

Set includeLanguageDetection: true to receive the detected language code in delayed final transcript events.

// 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
EventDescription
partial_transcriptLive interim results
final_transcriptStable segment result sent before the segment is committed
final_transcript_with_timestampsDelayed final result with timestamps and/or detected language
committed_transcriptFinal results after commit
committed_transcript_with_timestampsFinal with word timing
committed_transcript_entitiesEntities detected in a committed segment
edited_transcriptOriginal and edited text for a committed segment when transcript editing is enabled
invalid_requestConnection parameters were rejected and the session closes
errorError occurred

See real-time references for complete documentation.

References

Files

references/realtime-commit-strategies.md

Transcripts and Commit Strategies

Control when and how transcripts are finalized in real-time streaming.

Why Commits Matter

In real-time transcription, the model continuously refines its understanding as more audio arrives. A word that sounds like "their" might become "there" or "they're" once more context is heard. The commit mechanism lets you decide when to "lock in" the transcript.

Transcript Types

TypeDescription
PartialInterim "best guess" results that update frequently as audio is processed. Use for live feedback (showing text as the user speaks), but don't save these - they may change.
CommittedFinal, stable results after a commit occurs. Use these as the source of truth for your application - they won't change.
Committed with TimestampsSame as committed, but includes word-level timing data for subtitles, karaoke, or lip-sync.

Manual Commit (Default)

You explicitly control when transcript segments finalize.

Python
from elevenlabs import AudioFormat, CommitStrategy, RealtimeAudioOptions

connection = await client.speech_to_text.realtime.connect(RealtimeAudioOptions(
    model_id="scribe_v2_realtime",
    audio_format=AudioFormat.PCM_16000,
    sample_rate=16000,
    commit_strategy=CommitStrategy.MANUAL,
))

# Send audio
await connection.send({"audio_base_64": audio_base_64})

# Commit when ready (e.g., pause in speech, end of sentence)
await connection.commit()
JavaScript
const connection = await client.speechToText.realtime.connect({
  modelId: "scribe_v2_realtime",
});

// Send audio
connection.send({
  audioBase64: audioBase64,
  sampleRate: 16000,
});

// Commit when ready
connection.commit();
Best Practices
  • Commit every 20-30 seconds for optimal performance

  • Commit during silence or logical breaks (end of sentence, speaker change)

  • Auto-commit at 90 seconds if no manual commit is sent

Providing Context

Send previous text with the first audio chunk to help the model:

await connection.send({
    "audio_base_64": first_chunk,
    "previous_text": "So as I was saying,"  # Keep under 50 characters
})

This helps with:

  • Continuing conversations after reconnection

  • Providing context for better accuracy

  • Handling sentence fragments

Voice Activity Detection (VAD)

VAD listens for silence and automatically commits when the speaker pauses. This creates natural transcript segments that match how people actually speak - pausing between sentences and thoughts. Recommended for live microphone input.

Configuration
React (useScribe)
import { useScribe, CommitStrategy } from "@elevenlabs/react";

const scribe = useScribe({
  modelId: "scribe_v2_realtime",
  commitStrategy: CommitStrategy.VAD,
  // Optional VAD tuning:
  vadSilenceThresholdSecs: 1.5,    // Silence duration before commit
  vadThreshold: 0.4,               // Speech detection sensitivity (0-1)
  minSpeechDurationMs: 100,        // Minimum speech length required
  minSilenceDurationMs: 100,       // Minimum silence length required
});

Important: The default is CommitStrategy.MANUAL. For microphone input, always set CommitStrategy.VAD — without it, committed transcripts will never fire and the connection may drop.

JavaScript client
const connection = await client.speechToText.realtime.connect({
  modelId: "scribe_v2_realtime",
  vad: {
    silenceThresholdSecs: 1.5,    // Silence duration before commit
    threshold: 0.4,               // Speech detection sensitivity (0-1)
    minSpeechDurationMs: 100,     // Minimum speech length required
    minSilenceDurationMs: 100,    // Minimum silence length required
  },
});
Parameters
ParameterDescriptionDefault
silenceThresholdSecsSeconds of silence before auto-commit1.5
thresholdSpeech detection sensitivity (lower = more sensitive)0.4
minSpeechDurationMsIgnore speech shorter than this100
minSilenceDurationMsIgnore silence shorter than this100
When to Use VAD
  • Live microphone input

  • Conversational applications

  • When natural speech boundaries are preferred

  • Client-side implementations

When to Use Manual Commit
  • Processing audio files

  • Known segment boundaries

  • Maximum control over timing

  • Server-side batch processing

Supported Audio Formats

FormatSample RateNotes
PCM 16-bit16kHzRecommended, best balance
PCM 16-bit8kHz - 48kHzSupported range
μ-law 8-bit8kHzTelephony compatibility

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python, Needs Node.js

The author says: Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).

Security

What our scanner noticed· Medium risk

  • Instructions that use the network
  • Downloads piped into a shell

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
ElevenLabs
Publisher
ElevenLabs (Official)
Source
elevenlabs/skills · speech-to-text · commit 9763c39
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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