> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Migration guide: Deepgram to AssemblyAI

This guide walks through the process of migrating from Deepgram to AssemblyAI for transcribing pre-recorded audio.

### Get Started

Before we begin, make sure you have an AssemblyAI account and an API key. You can [sign up](https://assemblyai.com/dashboard/signup) for a free account and get your API key from your dashboard.

## Side-By-Side Code Comparison

Below is a side-by-side comparison of a basic snippet to transcribe a **local file** by Deepgram and AssemblyAI:

<Tabs groupId="language">
  <Tab language="dg" title="Deepgram">
    ```python expandable theme={null}
    from deepgram import (
         DeepgramClient,
         PrerecordedOptions,
         FileSource,
    )

    API_KEY = "YOUR_DG_API_KEY"

    AUDIO_FILE = "./example.wav"

    def main():
        try:
            deepgram = DeepgramClient(API_KEY)

            with open(AUDIO_FILE, "rb") as file:
                buffer_data = file.read()

                payload: FileSource = {
                    "buffer": buffer_data,
                }

                options = PrerecordedOptions(
                    model="nova-2",
                    smart_format=True,
                    diarize=True
                )

                response = deepgram.listen.prerecorded.v("1").transcribe_file(payload, options)

                print(response.to_json(indent=4))

        except Exception as e:
            print(f"Exception: {e}")

    if name == "main":
        main()
    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python expandable theme={null}
    import assemblyai as aai

    aai.settings.api_key = "YOUR-API-KEY"
    transcriber = aai.Transcriber()

    audio_file = "./example.wav"

    config = aai.TranscriptionConfig(
        speech_models=["universal-3-5-pro", "universal-2"],
        language_detection=True,
        speaker_labels=True,
    )
    transcript = transcriber.transcribe(audio_file, config)

    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        exit(1)

    print(transcript.text)

    for utterance in transcript.utterances:
        print(f"Speaker {utterance.speaker}: {utterance.text}")
    ```
  </Tab>
</Tabs>

Below is a side-by-side comparison of a basic snippet to transcribe a **publicly-accessible URL** by Deepgram and AssemblyAI:

<Tabs groupId="language">
  <Tab language="dg" title="Deepgram">
    ```python expandable theme={null}
    from deepgram import (
        DeepgramClient,
        PrerecordedOptions
    )

    API_KEY = "YOUR_DG_API_KEY"

    AUDIO_URL = {
        "url": "https://dpgr.am/spacewalk.wav"
    }

    def main():
        try:
            deepgram = DeepgramClient(API_KEY)

            options = PrerecordedOptions(
                model="nova-2",
                smart_format=True,
                diarize=True
            )

            response = deepgram.listen.prerecorded.v("1").transcribe_url(AUDIO_URL, options)

            print(response.to_json(indent=4))

        except Exception as e:
            print(f"Exception: {e}")

    if name == "main":
        main()
    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python expandable theme={null}
    import assemblyai as aai

    aai.settings.api_key = "YOUR-API-KEY"
    transcriber = aai.Transcriber()

    audio_file = (
        "https://assembly.ai/sports_injuries.mp3"
    )

    config = aai.TranscriptionConfig(
        speech_models=["universal-3-5-pro", "universal-2"],
        language_detection=True,
        speaker_labels=True,
    )
    transcript = transcriber.transcribe(audio_file, config)

    if transcript.status == aai.TranscriptStatus.error:
        print(f"Transcription failed: {transcript.error}")
        exit(1)

    print(transcript.text)

    for utterance in transcript.utterances:
        print(f"Speaker {utterance.speaker}: {utterance.text}")
    ```
  </Tab>
</Tabs>

Here are helpful things to know about our `transcribe` method:

* The SDK handles polling under the hood
* Transcript is directly accessible via `transcript.text`
* English is the default language. We recommend specifying `speech_models=["universal-3-5-pro", "universal-2"]` for the highest accuracy
* We have a [cookbook for error handling common errors](/pre-recorded-audio/guides/common_errors_and_solutions) when using our API.

## Installation

<Tabs groupId="language">
  <Tab language="dg" title="Deepgram">
    ```python theme={null}
    from deepgram import (
        DeepgramClient,
        PrerecordedOptions,
        FileSource,
    )

    API_KEY = "YOUR_DG_API_KEY"
    deepgram = DeepgramClient(API_KEY)
    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python theme={null}
    import assemblyai as aai

    aai.settings.api_key = "YOUR-API-KEY"
    transcriber = aai.Transcriber()
    ```
  </Tab>
</Tabs>

When migrating from Deepgram to AssemblyAI, you'll first need to handle authentication and SDK setup:

Get your API key from your [AssemblyAI dashboard](https://www.assemblyai.com/dashboard/home) \
To follow this guide, install AssemblyAI's Python SDK by typing this code into your terminal: \
`pip install assemblyai`

Things to know:

* Store your API key securely in an environment variable
* API key authentication works the same across all AssemblyAI SDKs

## Audio File Sources

<Tabs groupId="language">
  <Tab language="dg" title="Deepgram">
    ```python expandable theme={null}
    # Local Files
    AUDIO_FILE = "example.wav"
    with open(AUDIO_FILE, "rb") as file:
        buffer_data = file.read()

    payload: FileSource = {
        "buffer": buffer_data,
    }

    options = PrerecordedOptions(
        smart_format=True,
        summarize="v2",
    )

    file_response = deepgram.listen.rest.v("1").transcribe_file(payload, options)

    json = file_response.to_json()

    #Public URLs
    AUDIO_URL = {
        "url": "https://static.deepgram.com/examples/Bueller-Life-moves-pretty-fast.wav"
    }

    options = PrerecordedOptions(
        smart_format=True,
        summarize="v2"
    )

    url_response = deepgram.listen.rest.v("1").transcribe_url(AUDIO_URL, options)

    json = url_response.to_json()
    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python theme={null}
    transcriber = aai.Transcriber()

    # Local Files
    transcript = transcriber.transcribe("./audio.mp3")

    # Public URLs
    transcript = transcriber.transcribe("https://example.com/audio.mp3")

    # S3 files (using pre-signed URLs)
    s3_client = boto3.client('s3')
    presigned_url = s3_client.generate_presigned_url(
        'get_object',
        Params={'Bucket': 'my-bucket', 'Key': 'audio.mp3'},
        ExpiresIn=3600
    )

    transcript = transcriber.transcribe(presigned_url)
    ```
  </Tab>
</Tabs>

Here are helpful things to know when migrating your audio input handling:

* There's no need to specify the audio format to AssemblyAI - it's auto-detected. AssemblyAI accepts almost every audio/video file type: [here is a full list of all our supported file types](/faq/what-audio-and-video-file-types-are-supported-by-your-api)
* Our SDK handles file upload and transcription automatically in one step
* For S3 files, you'll need to generate pre-signed URLs ([see example in cookbook](/pre-recorded-audio/guides/transcribe_from_s3))

## Adding Features

<Tabs groupId="language">
  <Tab language="dg" title="Deepgram">
    ```python theme={null}
    options = PrerecordedOptions(
       model="nova-2",
       smart_format=True,
       diarize=True,
       detect_entities=True
    )

    response = deepgram.listen.prerecorded.v("1").transcribe_url(AUDIO_URL, options)

    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python theme={null}
    config = aai.TranscriptionConfig(
        speech_models=["universal-3-5-pro", "universal-2"],
        language_detection=True,
        speaker_labels=True, # Speaker diarization
        auto_chapters=True, # Auto chapter detection
        entity_detection=True, # Named entity detection
    )

    transcript = transcriber.transcribe(audio_url, config)

    # Access speaker labels
    for utterance in transcript.utterances:
        print(f"Speaker {utterance.speaker}: {utterance.text}")
    ```
  </Tab>
</Tabs>

Key differences:

* Use `aai.TranscriptionConfig` to specify any extra features that you wish to use
* The results for Speaker Diarization are stored in `transcript.utterances`. To see the full transcript response object, refer to our [API Reference](/api-reference).
* Check our [documentation](/speech-understanding/getting-started) for our full list of available features and their parameters