SKILL.md
$2a
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,
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,
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,
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,
},
});