Transcription

Learn how to turn spoken audio into text with ElevenLabs.

Overview

The ElevenLabs Speech to Text (STT) API turns spoken audio into text with state of the art accuracy. Our Scribe v2 model adapts to textual cues across 90+ languages and multiple voice styles. To try a live demo please visit our Speech to Text showcase page.

Products

Step-by-step guide for using speech to text in ElevenLabs.

Developers

Learn how to integrate the speech to text API into your application.

Realtime speech to text

Learn how to transcribe audio with ElevenLabs in realtime with WebSockets.

API reference

Full API reference for the Speech to Text endpoint.

Models

Scribe v2

State-of-the-art speech recognition model

Accurate transcription in 90+ languages

Keyterm prompting, up to 1000 terms

Entity detection, 65 entity types

Transcript editing with natural-language instructions

Precise word-level timestamps

Speaker diarization, up to 32 speakers

Dynamic audio tagging

Smart language detection

Scribe v2 Realtime

Real-time speech recognition model

Accurate transcription in 90+ languages

Real-time transcription

Low latency (~150ms†)

Precise word-level timestamps

Entity detection, 65 entity types

Transcript editing with natural-language instructions

Scribe v2 Medical

Speech recognition fine-tuned for clinical audio

35% fewer transcription errors on clinical audio than Scribe v2

Same accuracy on everyday speech as Scribe v2

Same features, languages, pricing, and API as Scribe v2

Explore all

Example API response

The following example shows the output of the Speech to Text API using the Scribe v2 model for a sample audio file.

View full JSON response

JavaScript

{
  "language_code": "en",
  "language_probability": 1,
  "text": "With a soft and whispery American accent, I'm the ideal choice for creating ASMR content, meditative guides, or adding an intimate feel to your narrative projects.",
  "words": [
    {
      "text": "With",
      "start": 0.119,
      "end": 0.259,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 0.239,
      "end": 0.299,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "a",
      "start": 0.279,
      "end": 0.359,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 0.339,
      "end": 0.499,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "soft",
      "start": 0.479,
      "end": 1.039,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 1.019,
      "end": 1.2,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "and",
      "start": 1.18,
      "end": 1.359,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 1.339,
      "end": 1.44,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "whispery",
      "start": 1.419,
      "end": 1.979,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 1.959,
      "end": 2.179,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "American",
      "start": 2.159,
      "end": 2.719,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 2.699,
      "end": 2.779,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "accent,",
      "start": 2.759,
      "end": 3.389,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 4.119,
      "end": 4.179,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "I'm",
      "start": 4.159,
      "end": 4.459,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 4.44,
      "end": 4.52,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "the",
      "start": 4.5,
      "end": 4.599,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 4.579,
      "end": 4.699,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "ideal",
      "start": 4.679,
      "end": 5.099,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 5.079,
      "end": 5.219,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "choice",
      "start": 5.199,
      "end": 5.719,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 5.699,
      "end": 6.099,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "for",
      "start": 6.099,
      "end": 6.199,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 6.179,
      "end": 6.279,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "creating",
      "start": 6.259,
      "end": 6.799,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 6.779,
      "end": 6.979,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "ASMR",
      "start": 6.959,
      "end": 7.739,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 7.719,
      "end": 7.859,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "content,",
      "start": 7.839,
      "end": 8.45,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 9,
      "end": 9.06,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "meditative",
      "start": 9.04,
      "end": 9.64,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 9.619,
      "end": 9.699,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "guides,",
      "start": 9.679,
      "end": 10.359,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 10.359,
      "end": 10.409,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "or",
      "start": 11.319,
      "end": 11.439,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 11.42,
      "end": 11.52,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "adding",
      "start": 11.5,
      "end": 11.879,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 11.859,
      "end": 12,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "an",
      "start": 11.979,
      "end": 12.079,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 12.059,
      "end": 12.179,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "intimate",
      "start": 12.179,
      "end": 12.579,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 12.559,
      "end": 12.699,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "feel",
      "start": 12.679,
      "end": 13.159,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 13.139,
      "end": 13.179,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "to",
      "start": 13.159,
      "end": 13.26,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 13.239,
      "end": 13.3,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "your",
      "start": 13.299,
      "end": 13.399,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 13.379,
      "end": 13.479,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "narrative",
      "start": 13.479,
      "end": 13.889,
      "type": "word",
      "speaker_id": "speaker_0"
    },
    {
      "text": " ",
      "start": 13.919,
      "end": 13.939,
      "type": "spacing",
      "speaker_id": "speaker_0"
    },
    {
      "text": "projects.",
      "start": 13.919,
      "end": 14.779,
      "type": "word",
      "speaker_id": "speaker_0"
    }
  ]
}

The output is classified in three category types:

word - A word in the language of the audio
spacing - The space between words, not applicable for languages that don’t use spaces like Japanese, Mandarin, Thai, Lao, Burmese and Cantonese
audio_event - Non-speech sounds like laughter or applause

Concurrency and priority

Concurrency is the concept of how many requests can be processed at the same time.

For Speech to Text, files that are over 8 minutes long are transcribed in parallel internally in order to speed up processing. The audio is chunked into four segments to be transcribed concurrently.

You can calculate the concurrency limit with the following calculation:

C o n c u r r e n c y = min ⁡ ( 4 , round_up ( audio_duration_secs 480 ) ) Concurrency = \min(4, \text{round\_up}(\frac{\text{audio\_duration\_secs}}{480}))

For example, a 15 minute audio file will be transcribed with a concurrency of 2, while a 120 minute audio file will be transcribed with a concurrency of 4.

Advanced features

Keyterm prompting

Highlight words or phrases to bias the model towards transcribing them. This is useful for transcribing specific words or sentences that are not common in the audio, such as product names, names, or other specific terms. Keyterms are more powerful than biased keywords or customer vocabularies offered by other models, because it relies on the context to decide whether to transcribe that term or not. Batch supports up to 1000 keyterms (50 characters each), while realtime supports up to 50 keyterms (20 characters each).

To learn more about how to use keyterm prompting, see the keyterm prompting documentation.

No verbatim mode

When no_verbatim is enabled, the model removes filler words, false starts and disfluencies from the transcript. This produces a cleaner output suitable for subtitles, summaries, or any use case where readability is more important than capturing every spoken word.

Entity detection

Batch models and Scribe v2 Realtime can detect several categories of entities in the transcript, providing their exact timestamps. This is useful to highlight credit card numbers, names, medical conditions or SSNs.

For a full list of supported entities, see the entity detection documentation.

Transcript editing

Pass a natural-language instruction with the transcript_edit parameter and it will be applied to the finished transcript. The edited text is returned in edited_transcript alongside the original transcript, so timestamps and speaker labels stay intact. This is useful for normalizing how dates, times or units are written, applying a different style, or rewriting the transcript in a single request.

To learn more, see the transcript editing documentation and the realtime transcript editing guide.

Supported languages

Scribe v2 supports 90+ languages, including:

Afrikaans (afr), Amharic (amh), Arabic (ara), Armenian (hye), Assamese (asm), Asturian (ast), Azerbaijani (aze), Belarusian (bel), Bengali (ben), Bosnian (bos), Bulgarian (bul), Burmese (mya), Cantonese (yue), Catalan (cat), Cebuano (ceb), Chichewa (nya), Croatian (hrv), Czech (ces), Danish (dan), Dutch (nld), English (eng), Estonian (est), Filipino (fil), Finnish (fin), French (fra), Fulah (ful), Galician (glg), Ganda (lug), Georgian (kat), German (deu), Greek (ell), Gujarati (guj), Hausa (hau), Hebrew (heb), Hindi (hin), Hungarian (hun), Icelandic (isl), Igbo (ibo), Indonesian (ind), Irish (gle), Italian (ita), Japanese (jpn), Javanese (jav), Kabuverdianu (kea), Kannada (kan), Kazakh (kaz), Khmer (khm), Korean (kor), Kurdish (kur), Kyrgyz (kir), Lao (lao), Latvian (lav), Lingala (lin), Lithuanian (lit), Luo (luo), Luxembourgish (ltz), Macedonian (mkd), Malay (msa), Malayalam (mal), Maltese (mlt), Mandarin Chinese (zho), Māori (mri), Marathi (mar), Mongolian (mon), Nepali (nep), Northern Sotho (nso), Norwegian (nor), Occitan (oci), Odia (ori), Pashto (pus), Persian (fas), Polish (pol), Portuguese (por), Punjabi (pan), Romanian (ron), Russian (rus), Serbian (srp), Shona (sna), Sindhi (snd), Sinhala (sin), Slovak (slk), Slovenian (slv), Somali (som), Spanish (spa), Swahili (swa), Swedish (swe), Tamil (tam), Tajik (tgk), Telugu (tel), Thai (tha), Turkish (tur), Ukrainian (ukr), Umbundu (umb), Urdu (urd), Uzbek (uzb), Vietnamese (vie), Welsh (cym), Wolof (wol), Xhosa (xho) and Zulu (zul).

Breakdown of language support

Word Error Rate (WER) is a key metric used to evaluate the accuracy of transcription systems. It measures how many errors are present in a transcript compared to a reference transcript. Below is a breakdown of the WER for each language that Scribe v2 supports.

Excellent (≤ 5% WER)

Belarusian (bel), Bosnian (bos), Bulgarian (bul), Catalan (cat), Croatian (hrv), Czech (ces), Danish (dan), Dutch (nld), English (eng), Estonian (est), Finnish (fin), French (fra), Galician (glg), German (deu), Greek (ell), Hungarian (hun), Icelandic (isl), Indonesian (ind), Italian (ita), Japanese (jpn), Kannada (kan), Latvian (lav), Macedonian (mkd), Malay (msa), Malayalam (mal), Norwegian (nor), Polish (pol), Portuguese (por), Romanian (ron), Russian (rus), Slovak (slk), Spanish (spa), Swedish (swe), Turkish (tur), Ukrainian (ukr) and Vietnamese (vie).

High Accuracy (>5% to ≤10% WER)

Armenian (hye), Azerbaijani (aze), Bengali (ben), Cantonese (yue), Filipino (fil), Georgian (kat), Gujarati (guj), Hindi (hin), Kazakh (kaz), Lithuanian (lit), Maltese (mlt), Mandarin (cmn), Marathi (mar), Nepali (nep), Odia (ori), Persian (fas), Serbian (srp), Slovenian (slv), Swahili (swa), Tamil (tam) and Telugu (tel)

Good (>10% to ≤20% WER)

Afrikaans (afr), Arabic (ara), Assamese (asm), Asturian (ast), Burmese (mya), Hausa (hau), Hebrew (heb), Javanese (jav), Korean (kor), Kyrgyz (kir), Luxembourgish (ltz), Māori (mri), Occitan (oci), Punjabi (pan), Tajik (tgk), Thai (tha), Uzbek (uzb) and Welsh (cym).

Moderate (>25% to ≤50% WER)

Amharic (amh), Ganda (lug), Igbo (ibo), Irish (gle), Khmer (khm), Kurdish (kur), Lao (lao), Mongolian (mon), Northern Sotho (nso), Pashto (pus), Shona (sna), Sindhi (snd), Sinhala (sin), Somali (som), Urdu (urd), Wolof (wol), Xhosa (xho), Yoruba (yor) and Zulu (zul).

FAQ

Can I use speech to text API with video files?

Yes, the API supports uploading both audio and video files for transcription.

What are the file size and duration limits for the Speech to Text API?

Files up to 3 GB in size are supported. Duration limits depend on the transcription mode:

Standard mode (use_multi_channel=false): Up to 10 hours
Multi-channel mode (use_multi_channel=true): The combined duration of all channels must be less than 10 hours

Which audio and video formats are supported in the API?

The API supports the following audio and video formats:

audio/aac
audio/x-aac
audio/x-aiff
audio/ogg
audio/mpeg
audio/mp3
audio/mpeg3
audio/x-mpeg-3
audio/opus
audio/wav
audio/x-wav
audio/webm
audio/flac
audio/x-flac
audio/mp4
audio/aiff
audio/x-m4a

Supported video formats include:

video/mp4
video/x-msvideo
video/x-matroska
video/quicktime
video/x-ms-wmv
video/x-flv
video/webm
video/mpeg
video/3gpp

When will you support more languages?

ElevenLabs is constantly expanding the number of languages supported by our models. Please check back frequently for updates.

Does speech to text API support webhooks?

Yes, asynchronous transcription results can be sent to webhooks configured in webhook settings in the UI. Learn more in the webhooks cookbook.

Is a multichannel transcription mode supported in the API?

Yes, the multichannel STT feature allows you to transcribe audio where each channel is processed independently and assigned a speaker ID based on its channel number. This feature supports up to 5 channels. Learn more in the multichannel transcription cookbook.

How does billing work for the speech to text API?

ElevenLabs charges for speech to text based on the duration of the audio sent for transcription. Billing is calculated per hour of audio, with rates varying by tier and model. See the API pricing page for detailed pricing information.

Key facts

Supported input: Both audio and video files are accepted
Maximum file size: 3 GB
Maximum duration: 10 hours (standard mode), 1 hour (multichannel mode)
Multichannel mode: Up to 5 channels; each processed independently with a speaker ID assigned by channel number
Webhooks: Asynchronous transcription results can be delivered to a webhook — configure in workspace settings
Supported audio formats: AAC, AIFF, OGG, MP3, OPUS, WAV, FLAC, M4A, WebM
Supported video formats: MP4, AVI, MKV, MOV, WMV, FLV, WebM, MPEG, 3GPP

Search documentation

Administration

Transcription

Overview

Models

Example API response

Concurrency and priority

Advanced features

Keyterm prompting

No verbatim mode

Entity detection

Transcript editing

Supported languages

Breakdown of language support

FAQ

Key facts

Administration