speech-to-text

Files (7)

• SKILL.md
• 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 · references/

SKILL.md (first lines)

# ElevenLabs Speech-to-Text

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

> **Setup:** See [Installation Guide](references/installation.md). For JavaScript, use `@elevenlabs/*` packages only.

## Quick Start

### Python

```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

```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

```bash
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:

```python
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:

```python
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}")
```

## Keyterm Prompting

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

```python
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:

```python
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

```json
{
  "text": "The full transcription text",
  "language_code": "eng",
  "language_probability": 0.98,
  "words": [

From harshav167/ava, MIT.

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