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 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:

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from openai import OpenAI
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api_key = "YOUR_OPENAI_API_KEY"
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client = OpenAI(api_key)
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audio_file = open("./example.wav", "rb")
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transcript = client.audio.transcriptions.create(
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    model = "whisper-1",
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    file = audio_file
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)
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print(transcript.text)

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-pro", "universal-2"] for the highest accuracy
• We have a cookbook for error handling common errors when using our API.

Installation

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from openai import OpenAI
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api_key = "YOUR_OPENAI_API_KEY"
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client = OpenAI(api_key)

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

Get your API key from your AssemblyAI dashboard
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

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

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
• The Whisper API only supports file sizes up to 25MB, AssemblyAI supports file sizes up to 5GB.

Adding Features

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transcript = client.audio.transcriptions.create(
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    file = audio_file,
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    prompt = "INSERT_PROMPT", # Optional text to guide the model's style
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    language = "en", # Set language code
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    model = "whisper-1",
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    response_format = "verbose_json",
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    timestamp_granularities = ["word"]
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)
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# Access word-level timestamps
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print(transcript.words)

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.
• Check our documentation 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 to apply the model to your transcribed audio files.