Migration guide: OpenAI to AssemblyAI

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.

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Get Started

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Side-By-Side Code Comparison

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)

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-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)

• 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.

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Installation

from openai import OpenAI

api_key = "YOUR_OPENAI_API_KEY"
client = OpenAI(api_key)

import assemblyai as aai

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

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

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Audio File Sources

client = OpenAI()

# Local Files

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

transcriber = aai.Transcriber()

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

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

• 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.

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Adding Features

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)

config = aai.TranscriptionConfig(
    speech_models=["universal-3-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}")

• 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.