You can also learn the content on this page from How to Transcribe Audio Files with Python on AssemblyAI’s YouTube channel.
If you’re using Python or TypeScript, see Transcribe an audio file.

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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. The entire source code of this guide can be viewed here.

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Step-by-step instructions​

  1. 1 Create a new file and import the necessary libraries for making an HTTP request.
  2. 2 Set up the API endpoint and headers. The headers should include your API key.
  3. 3 Upload your local file to the AssemblyAI API.
  4. 4 Use the upload_url returned by the AssemblyAI API to create a JSON payload containing the audio_url parameter. We delete uploaded files from our servers either after the transcription has completed, or 24 hours after you uploaded the file. After the file has been deleted, the corresponding upload_url is no longer valid.
  5. 5 Make a POST request to the AssemblyAI API endpoint with the payload and headers.
  6. 6 After making the request, you’ll receive an ID for the transcription. Use it to poll the API every few seconds to check the status of the transcript job. Once the status is completed, you can retrieve the transcript from the API response.

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Understanding the response​

The AssemblyAI API returns JSON-formatted output. Your transcription will be located in the text key. You’ll also find a timestamp and a confidence score for each word inside of the words key, as well as other parameters assigned by the API such as language_code and language_model.
Refer to the API reference for a breakdown of every element in your transcript output.

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Best practices​

When using the AssemblyAI API to transcribe audio files, we recommended using the polling technique to check for the status of the transcription. This means making a request every few seconds to check if the transcription is complete, as described above. Alternatively, you can also set up webhooks to receive notifications when the transcription is complete. This can help reduce the overhead of polling and make your application more efficient.

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Conclusion​

Transcription is our core API use case, and nearly all other AssemblyAI features leverage our transcription functionality. We’re constantly improving and updating the language models used by our transcription engine. Of course, higher quality audio generally produces better results. We’d love to hear about any new integrations or solutions that you build using our transcription API — you can find us on Twitter or apply to join our Creators Program. You can also try out the to experiment with our transcription features without needing to write any code! If you encounter any issues or have any questions, see FAQ or reach out to our Support team.