> ## 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: OpenAI to AssemblyAI

This guide walks through the process of migrating from OpenAI 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 OpenAI and AssemblyAI:

<Tabs groupId="language">
  <Tab language="openai" title="OpenAI">
    ```python theme={null}
    from openai import OpenAI

    api_key = "YOUR_OPENAI_API_KEY"
    client = OpenAI(api_key)

    audio_file = open("./example.wav", "rb")

    transcript = client.audio.transcriptions.create(
        model = "whisper-1",
        file = audio_file
    )

    print(transcript.text)
    ```
  </Tab>

  <Tab language="aai" title="AssemblyAI">
    ```python 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,
    )
    transcript = transcriber.transcribe(audio_file, config)

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

    print(transcript.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="openai" title="OpenAI">
    ```python theme={null}
    from openai import OpenAI

    api_key = "YOUR_OPENAI_API_KEY"
    client = OpenAI(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 OpenAI 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="openai" title="OpenAI">
    ```python theme={null}
    client = OpenAI()

    # Local Files

    audio_file = open("./example.wav", "rb")
    transcript = client.audio.transcriptions.create(
        model = "whisper-1",
        file = audio_file
    )

    ```
  </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")
    ```
  </Tab>
</Tabs>

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

* AssemblyAI natively supports transcribing publicly accessible audio URLs (for example, S3 URLs), the Whisper API only natively supports transcribing local files.
* 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)
* The Whisper API only supports file sizes up to 25MB, AssemblyAI supports file sizes up to 5GB.

## Adding Features

<Tabs groupId="language">
  <Tab language="openai" title="OpenAI">
    ```python theme={null}
    transcript = client.audio.transcriptions.create(
        file = audio_file,
        prompt = "INSERT_PROMPT", # Optional text to guide the model's style
        language = "en", # Set language code
        model = "whisper-1",
        response_format = "verbose_json",
        timestamp_granularities = ["word"]
    )

    # Access word-level timestamps

    print(transcript.words)
    ```
  </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
        sentiment_analysis=True, # Sentiment Analysis
        entity_detection = True, # Named entity detection
    )

    transcript = transcriber.transcribe(audio_url, config)

    # Access word-level timestamps
    print(transcript.words)

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

Key differences:

* OpenAI does not offer speech understanding features for their speech-to-text API
* Use `aai.TranscriptionConfig` to specify any extra features that you wish to use
* With AssemblyAI, timestamp granularity is word-level by default
* 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
* If you want to send a custom prompt to an LLM, you can use [LLM Gateway](/llm-gateway/quickstart) to apply the model to your transcribed audio files.