Using Deepgram’s fully hosted Whisper Cloud instead of running your own version provides many benefits. Some of these benefits include:

• Pairing the Whisper model with Deepgram features that you can’t get using the OpenAI speech-to-text API, such as diarization and word timings.
• Support for all Whisper model sizes: tiny, base, small, medium, and large.
• Support for up to 5 concurrent requests for the Pay As You Go and Growth plans.

Deepgram hosts and maintains these Whisper models; they aren’t hosted or run by Open AI. Therefore, data sent through API requests for our Whisper models will not be sent to OpenAI.

Getting Started

In this guide, you’ll learn how to transcribe pre-recorded audio using Deepgram’s hosted Whisper API.

Create a Deepgram Account

Create a Deepgram API Key

Transcribe a Remote File

Transcribe a remote file using Deepgram’s Whisper API with the following request.

$
curl \
>
  --request POST \
>
  --header 'Authorization: Token YOUR_DEEPGRAM_API_KEY' \
>
  --header 'Content-Type: application/json' \
>
  --data '{"url":"https://static.deepgram.com/examples/Bueller-Life-moves-pretty-fast.wav}' \
>
  --url 'https://api.deepgram.com/v1/listen?model=whisper'

If you would like to use a Deepgram SDK to make the request, follow the steps in the Pre-Recorded speech-to-text guide, but change the model to whisper.

Analyze Response

1
{
2
  "metadata": {
3
    "transaction_key": "deprecated",
4
    "request_id": "6ba2879c...",
5
    "sha256": "6a7d98...",
6
    "created": "2023-04-12T20:33:53.620Z",
7
    "duration": 96.56319,
8
    "channels": 1,
9
    "models": [
10
      "e04910..."
11
    ],
12
    "model_info": {
13
      "e04910...": {
14
        "name": "medium-en-whisper",
15
        "version": "2022-09-21.4",
16
        "arch": "whisper"
17
      }
18
    }
19
  },
20
  "results": {
21
    "channels": [
22
      {
23
        "alternatives": [
24
          {
25
            "transcript": "another big problem in the speech analytics space when customers first bring the software on is that they are blown away by the fact that an engine can monitor hundreds of kpis ...",
26
            "confidence": 0.98273027,
27
            "words": [
28
              {
29
                "word": "another",
30
                "start": 0.06,
31
                "end": 0.56,
32
                "confidence": 0.34510013
33
              },
34
              {
35
                "word": "big",
36
                "start": 0.84,
37
                "end": 1.3399999,
38
                "confidence": 0.9840386
39
              },
40
              {
41
                "word": "problem",
42
                "start": 1.54,
43
                "end": 2.04,
44
                "confidence": 0.9970716
45
              },
46
            ...
47
            ]
48
          }
49
        ]
50
      }
51
    ]
52
  }
53
}

Enable Whisper Model and Sizes

To enable Deepgram’s Whisper API, add a model parameter in the query string and set it to model=whisper

$
https://api.deepgram.com/v1/listen?model=whisper

To enable a specific size of the Whisper model, set the model parameter to model=whisper-size.

$
https://api.deepgram.com/v1/listen?model=whisper-SIZE

These are the Deepgram Whisper Cloud models available:

• model=whisper (defaults to whisper-medium)
• model=whisper-tiny
• model=whisper-base
• model=whisper-small
• model=whisper-medium
• model=whisper-large (defaults to large-v2)

Other Features

Language Detection

Deepgram Whisper Cloud supports language detection, which means just by setting detect_language=true, your audio will be transcribed in the detected language.

Officially supported languages include Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. (Source: “Whisper API FAQ”)

Supported languages

Languages supported by whisper include: en, zh, de, es, ru, ko, fr, ja, pt, tr, pl, ca, nl, ar, sv, it, id, hi, fi, vi, he, uk, el, ms, cs, ro, da, hu, ta, no, th, ur, hr, bg, lt, la, mi, ml, cy, sk, te, fa, lv, bn, sr, az, sl, kn, et, mk, br, eu, is, hy, ne, mn, bs, kk, sq, sw, gl, mr, pa, si, km, sn, yo, so, af, oc, ka, be, tg, sd, gu, am, yi, lo, uz, fo, ht, ps, tk, nn, mt, sa, lb, my, bo, tl, mg, as, tt, haw, ln, ha, ba, jw, su.

If you would like to transcribe audio in a specific language, you can do so by setting the language parameter in the query string. You can pass in any language code supported by Whisper through our language parameter. To learn more about languages, see Language.

https://api.deepgram.com/v1/listen?model=whisper&language=en

Deepgram Features

This is a list of Deepgram Features and their current status for use with Deepgram Whisper Cloud:

Caveats

• It’s important to understand that Whisper models are less scalable than all other Deepgram models due to their inherent model architecture. Deepgram’s non-Whisper models will return results faster and scale to a higher load, so we recommend using a Deepgram model such as Nova if it can meet your needs.
• There is a 10 minute time out for all Deepgram models. Transcription requests that run longer than 10 minutes will return a 504 error.