AssemblyAI Ruby SDK

[!IMPORTANT]
As of April 2025, AssemblyAI Ruby SDK has been discontinued and will no longer be maintained.

While the SDK will no longer be updated, any previously published releases will remain available.

Going forward, see the AssemblyAI API reference for information on how to integrate with our API directly.

We know this is a disruptive change. If you need help with this transition, reach out to our Support team and we'll help you in any way we can.

The AssemblyAI Ruby SDK provides an easy-to-use interface for interacting with the AssemblyAI API, which supports async,
audio intelligence models, as well as the latest LeMUR models.

The Ruby SDK does not support Streaming STT at this time.

Documentation

Visit the AssemblyAI documentation for step-by-step instructions and a lot more details about our AI models and API.

Quickstart

Install the gem and add to the application's Gemfile by executing:

bundle add assemblyai

If bundler is not being used to manage dependencies, install the gem by executing:

gem install assemblyai

Import the AssemblyAI package and create an AssemblyAI object with your API key:

require 'assemblyai'

client = AssemblyAI::Client.new(api_key: 'YOUR_API_KEY')

You can now use the client object to interact with the AssemblyAI API.

Speech-To-Text

<details open>
<summary>Transcribe an audio file with a public URL</summary>

transcript = client.transcripts.transcribe(
  audio_url: 'https://assembly.ai/espn.m4a',
)

transcribe queues a transcription job and polls it until the status is completed or error.

If you don't want to wait until the transcript is ready, you can use submit:

transcript = client.transcripts.submit(
  audio_url: 'https://assembly.ai/espn.m4a'
)

</details>
<details>
<summary>Transcribe a local audio file</summary>

uploaded_file = client.files.upload(file: '/path/to/your/file')
# You can also pass an IO object or base64 string
# uploaded_file = client.files.upload(file: File.new('/path/to/your/file'))

transcript = client.transcripts.transcribe(audio_url: uploaded_file.upload_url)
puts transcript.text

transcribe queues a transcription job and polls it until the status is completed or error.

If you don't want to wait until the transcript is ready, you can use submit:

transcript = client.transcripts.submit(audio_url: uploaded_file.upload_url)

</details>

<details>
<summary>Enable additional AI models</summary>

You can extract even more insights from the audio by enabling any of
our AI models using transcription options.
For example, here's how to
enable Speaker diarization model to detect who
said what.

transcript = client.transcripts.transcribe(
  audio_url: audio_url,
  speaker_labels: true
)

transcript.utterances.each do |utterance|
  printf('Speaker %<speaker>s: %<text>s', speaker: utterance.speaker, text: utterance.text)
end

</details>

<details>
<summary>Get a transcript</summary>

This will return the transcript object in its current state. If the transcript is still processing, the status field
will be queued or processing. Once the transcript is complete, the status field will be completed.

transcript = client.transcripts.get(transcript_id: transcript.id)

</details>

<details>
<summary>Get sentences and paragraphs</summary>

sentences = client.transcripts.get_sentences(transcript_id: transcript.id)
p sentences

paragraphs = client.transcripts.get_paragraphs(transcript_id: transcript.id)
p paragraphs

</details>

<details>
<summary>Get subtitles</summary>

srt = client.transcripts.get_subtitles(
  transcript_id: transcript.id,
  subtitle_format: AssemblyAI::Transcripts::SubtitleFormat::SRT
)
srt = client.transcripts.get_subtitles(
  transcript_id: transcript.id,
  subtitle_format: AssemblyAI::Transcripts::SubtitleFormat::SRT,
  chars_per_caption: 32
)

vtt = client.transcripts.get_subtitles(
  transcript_id: transcript.id,
  subtitle_format: AssemblyAI::Transcripts::SubtitleFormat::VTT
)
vtt = client.transcripts.get_subtitles(
  transcript_id: transcript.id,
  subtitle_format: AssemblyAI::Transcripts::SubtitleFormat::VTT,
  chars_per_caption: 32
)

</details>

<details>
<summary>List transcripts</summary>
This will return a page of transcripts you created.

page = client.transcripts.list

You can pass parameters to .list to filter the transcripts.
To paginate over all pages, subsequently, use the .list_by_url method.

loop do
  page = client.transcripts.list_by_url(url: page.page_details.prev_url)
  break if page.page_details.prev_url.nil?
end

</details>

<details>
<summary>Delete a transcript</summary>

response = client.transcripts.delete(transcript_id: transcript.id)

</details>

Apply LLMs to your audio with LeMUR

Call LeMUR endpoints to apply LLMs to your transcript.

<details open>
<summary>Prompt your audio with LeMUR</summary>

response = client.lemur.task(
  transcript_ids: ['0d295578-8c75-421a-885a-2c487f188927'],
  prompt: 'Write a haiku about this conversation.'
)

</details>

<details>
<summary>Summarize with LeMUR</summary>

response = client.lemur.summary(
  transcript_ids: ['0d295578-8c75-421a-885a-2c487f188927'],
  answer_format: 'one sentence',
  context: {
    'speakers': ['Alex', 'Bob']
  }
)

</details>

<details>
<summary>Ask questions</summary>

response = client.lemur.question_answer(
  transcript_ids: ['0d295578-8c75-421a-885a-2c487f188927'],
  questions: [
    {
      question: 'What are they discussing?',
      answer_format: 'text'
    }
  ]
)

</details>
<details>
<summary>Generate action items</summary>

response = client.lemur.action_items(
  transcript_ids: ['0d295578-8c75-421a-885a-2c487f188927']
)

</details>
<details>
<summary>Delete LeMUR request</summary>

response = client.lemur.task(...)
deletion_response = client.lemur.purge_request_data(request_id: response.request_id)

</details>