Migration guide: OpenAI to AssemblyAI

This guide walks through the process of migrating from OpenAI to AssemblyAI.

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 and Best is the default speech model if none is specified
• 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
Check our documentation for the full list of available SDKs

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 audio intelligence 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 the LLM, you can use LeMUR Task and apply the model to your transcribed audio files.