Speech To Text
ElevenLabsOfficialPublished by ElevenLabs
- Skill
- Tool integration
- Automation and integrations
- MIT
- Medium risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/elevenlabs/speech-to-text/downloadUnzip 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_KEYisn't set for this project, runnpx -y @elevenlabs/cli@latest onboard initfrom 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_v2Models
| Model ID | Description | Best For |
|---|---|---|
scribe_v2 | State-of-the-art accuracy, 90+ languages | Batch transcription, subtitles, long-form audio |
scribe_v2_medical | Specialized recognition for medical and clinical audio, 90+ languages | Clinical documentation, medical dictation, patient calls |
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.
elevenlabs speech-to-text convert \
--file call.mp3 \
--model-id scribe_v2 \
--diarize true \
--detect-speaker-roles true \
--use-speaker-library trueMultichannel 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 wordspacing- 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
| 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 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
| Event | Description |
|---|---|
partial_transcript | Live interim results |
final_transcript | Stable segment result sent before the segment is committed |
final_transcript_with_timestamps | Delayed final result with timestamps and/or detected language |
committed_transcript | Final results after commit |
committed_transcript_with_timestamps | Final with word timing |
committed_transcript_entities | Entities detected in a committed segment |
edited_transcript | Original and edited text for a committed segment when transcript editing is enabled |
invalid_request | Connection parameters were rejected and the session closes |
error | Error occurred |
See real-time references for complete documentation.
References
Files
- ATTRIBUTION.mdAdded by AutoGineer812 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md10.6 KiB
- references/installation.md2.9 KiB
- references/realtime-client-side.md7.3 KiB
- references/realtime-commit-strategies.md4.6 KiB
- references/realtime-events.md9.0 KiB
- references/realtime-server-side.md11.5 KiB
- references/transcription-options.md10.7 KiB
references/realtime-client-side.md
Client-Side Real-Time Streaming
Stream audio from the browser directly to ElevenLabs for real-time transcription.
Installation
# React
npm install @elevenlabs/react@latest @elevenlabs/elevenlabs-js@latest
# JavaScript
npm install @elevenlabs/client@latest @elevenlabs/elevenlabs-js@latestWarning: Always use the
@elevenlabs/*namespace for client-side packages.
Token Generation
Client-side streaming requires a single-use token to protect your API key. Generate tokens on your backend:
import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
const elevenlabs = new ElevenLabsClient({
apiKey: process.env.ELEVENLABS_API_KEY,
});
app.get("/scribe-token", yourAuthMiddleware, async (req, res) => {
const token = await elevenlabs.tokens.singleUse.create("realtime_scribe");
res.json(token);
});Note: Single-use tokens expire after 15 minutes.
React Implementation
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
includeLanguageDetection: true,
onPartialTranscript: (data) => {
// Show live feedback as user speaks
console.log("Partial:", data.text);
},
onCommittedTranscript: (data) => {
// Final transcript for this segment
setTranscript((prev) => prev + data.text);
},
});
const startRecording = async () => {
const tokenResponse = await fetch("/scribe-token");
const { token } = await tokenResponse.json();
await scribe.connect({
token,
microphone: {
echoCancellation: true,
noiseSuppression: true,
autoGainControl: true,
},
});
};
const stopRecording = () => {
scribe.disconnect();
};
return (
<div>
<div>Status: {scribe.status}</div>
<button onClick={startRecording}>Start</button>
<button onClick={stopRecording}>Stop</button>
<p>{transcript}</p>
</div>
);
}Important: The default commit strategy is
CommitStrategy.MANUAL, which requires you to callscribe.commit()explicitly. For microphone input, always setCommitStrategy.VADso the server auto-commits when silence is detected. Without this, committed transcripts will never fire and the connection may drop.
scribe.status Values
| Status | Meaning |
|---|---|
"disconnected" | No active connection |
"connecting" | Connection is being established |
"connected" | Connected and ready to receive audio |
"transcribing" | Actively processing speech (transitions from "connected" when audio is detected or VAD commits) |
"error" | An error occurred |
Important: When checking if the session is active, always check for both
"connected"and"transcribing". The status transitions to"transcribing"during speech processing, so checking only"connected"will cause UI elements (buttons, waveforms, indicators) to incorrectly reset mid-session.
// Correct - handles both active states
const isListening = scribe.status === "connected" || scribe.status === "transcribing";
// Wrong - will flicker/reset when VAD commits
const isListening = scribe.status === "connected";JavaScript Implementation
import { Scribe, RealtimeEvents } from "@elevenlabs/client";
async function startTranscription() {
const tokenResponse = await fetch("/scribe-token");
const { token } = await tokenResponse.json();
const connection = Scribe.connect({
token,
modelId: "scribe_v2_realtime",
includeTimestamps: true,
includeLanguageDetection: true,
keyterms: ["ElevenLabs", "Scribe"],
noVerbatim: true,
microphone: {
echoCancellation: true,
noiseSuppression: true,
autoGainControl: true,
},
});
connection.on(RealtimeEvents.OPEN, () => {
console.log("Connected");
});
connection.on(RealtimeEvents.PARTIAL_TRANSCRIPT, (data) => {
console.log("Partial:", data.text);
});
connection.on(RealtimeEvents.COMMITTED_TRANSCRIPT, (data) => {
console.log("Committed:", data.text);
});
connection.on(RealtimeEvents.COMMITTED_TRANSCRIPT_WITH_TIMESTAMPS, (data) => {
for (const word of data.words) {
console.log(`${word.text}: ${word.start}s - ${word.end}s`);
}
});
connection.on(RealtimeEvents.ERROR, (error) => {
console.error("Error:", error);
});
connection.on(RealtimeEvents.CLOSE, () => {
console.log("Disconnected");
});
return connection;
}keyterms biases realtime recognition toward important terms. noVerbatim removes filler words,
false starts, and disfluencies from committed transcripts. includeLanguageDetection returns the
detected language code in a delayed final transcript event.
Both Scribe.connect and useScribe accept secondaryLanguages for expected additional
languages, entityDetection for entity events, and filterBackgroundAudio to reduce false
activation from background speech and ambient noise. Do not combine filterBackgroundAudio with
includeTimestamps. Enterprise zero-retention sessions can set enableLogging: false.
To redact entities, pass entityRedaction (same values as entityDetection) and optionally
entityRedactionMode (redacted, entity_type, or enumerated_entity_type, the default). With
Scribe.connect, listen for RealtimeEvents.REDACTED_TRANSCRIPT; each event contains the original
text and the redacted_text. With useScribe, use the onRedactedTranscript callback or read
redactedText on the matching entry in committedTranscripts.
In microphone mode, both also accept previousText: text that precedes the audio, such as existing
document content. It is sent with the first audio chunk as context for casing, punctuation, and
sentence continuation.
Set transcriptEdit on Scribe.connect to apply a natural-language instruction of up to 2,000
characters to each committed segment. Listen for RealtimeEvents.EDITED_TRANSCRIPT; each event
contains the original text and the edited_text. Transcript editing cannot be combined with
entityDetection or entityRedaction and adds a 30% premium, billed for at least 10 seconds of audio per committed
segment.
Manual Audio Chunking
For file uploads or custom audio sources, encode to PCM-16 and send in chunks:
const chunkSize = 4096;
for (let offset = 0; offset < pcmData.length; offset += chunkSize) {
const chunk = pcmData.slice(offset, offset + chunkSize);
const bytes = new Uint8Array(chunk.buffer);
const base64 = btoa(String.fromCharCode(...bytes));
scribe.sendAudio(base64);
// Simulate real-time streaming
await new Promise((resolve) => setTimeout(resolve, 50));
}
// Finalize transcription
scribe.commit();Microphone Options
| Option | Description |
|---|---|
echoCancellation | Remove echo from speakers |
noiseSuppression | Filter background noise |
autoGainControl | Normalize volume levels |
Security
Never expose your API key to the client
Always generate single-use tokens on your backend
Use authentication middleware to protect token endpoints
For enterprise zero-retention sessions, set
enableLogging: falseinScribe.connectoruseScribe; this disables history features for the session
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)
- Synced
AutoGineer is not affiliated with or endorsed by ElevenLabs or ElevenLabs.
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