Quick Start WhisperKit makes it easy to transcribe audio files on-device. This example shows how to get started with basic transcription.
Initialize WhisperKit import WhisperKit // Initialize WhisperKit with default settings let pipe = try await WhisperKit ()
WhisperKit automatically downloads the recommended model for your device on first run.
Transcribe an Audio File // Transcribe a local audio file let transcription = try await pipe. transcribe ( audioPath : "path/to/audio.wav" ) ? . text print (transcription)
Supported audio formats: .wav, .mp3, .m4a, .flac
Selecting a Model Using a Specific Model // Load a specific model let pipe = try await WhisperKit ( WhisperKitConfig ( model : "large-v3" ))
Using Wildcards // Use glob search to select a model let pipe = try await WhisperKit ( WhisperKitConfig ( model : "distil*large-v3" ))
The model search must return a single model from the source repo, otherwise an error will be thrown.
Available Models For a complete list of available models, see the HuggingFace repo .
Custom Model Repository If you’ve created your own fine-tuned model using whisperkittools , you can load it by specifying your repo:
let config = WhisperKitConfig ( model : "large-v3" , modelRepo : "username/your-model-repo" ) let pipe = try await WhisperKit (config)
Full Transcription Example Here’s a complete example with error handling:
import WhisperKit Task { do { // Initialize WhisperKit let pipe = try await WhisperKit () // Transcribe audio file guard let result = try await pipe. transcribe ( audioPath : "path/to/your/audio.wav" ) else { print ( "Transcription returned nil" ) return } // Print the transcription print ( "Transcription: \( result. text ) " ) // Access segments with timestamps for segment in result.segments { print ( "[ \( segment. start ) s - \( segment. end ) s]: \( segment. text ) " ) } } catch { print ( "Error: \( error ) " ) } }
Command Line Usage You can also use the WhisperKit CLI for quick testing:
# Install via Homebrew brew install whisperkit-cli # Transcribe an audio file whisperkit-cli transcribe --audio-path audio.wav
Download Models First If using the CLI from source:
# Clone the repository git clone https://github.com/argmaxinc/whisperkit.git cd whisperkit # Setup environment make setup # Download a specific model make download-model MODEL=large-v3 # Or download all models make download-models
Make sure git-lfs is installed before running download-model. Transcribe from Command Line # Transcribe a file swift run whisperkit-cli transcribe \ --model-path "Models/whisperkit-coreml/openai_whisper-large-v3" \ --audio-path "path/to/audio.wav"
Configuration Options Model Compute Options Optimize performance by selecting compute units:
let computeOptions = ModelComputeOptions ( audioEncoderCompute : . cpuAndNeuralEngine , textDecoderCompute : . cpuAndNeuralEngine ) let config = WhisperKitConfig ( model : "large-v3" , computeOptions : computeOptions ) let pipe = try await WhisperKit (config)
Decoding Options Customize the transcription behavior:
var decodingOptions = DecodingOptions () decodingOptions. task = . transcribe decodingOptions. language = "en" decodingOptions. temperature = 0.0 decodingOptions. wordTimestamps = true let result = try await pipe. transcribe ( audioPath : "audio.wav" , decodeOptions : decodingOptions )
Next Steps
Real-Time Streaming Learn how to transcribe audio in real-time from a microphone
Local Server Set up a local transcription server with API clients
