speech-to-text

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---
name: speech-to-text
description: Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.
license: MIT
compatibility: Requires internet access and an ElevenLabs API key (ELEVENLABS_API_KEY).
metadata: {"openclaw": {"requires": {"env": ["ELEVENLABS_API_KEY"]}, "primaryEnv": "ELEVENLABS_API_KEY"}}
---

# 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);
```

### CLI

```bash
elevenlabs speech-to-text convert --file audio.mp3 --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:

```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}")
```

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.

```bash
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.

```python
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):

```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="scr