From speech analytics to video subtitles, speech-to-text software brings new ideas to life. Leopard Speech-to-Text has created even more opportunities with cloud-level accuracy on the device, combining the best of the cloud and on-device processing.
Leopard Speech-to-Text, now officially supports seven new languages: French, German, Italian, Japanese, Korean, Portuguese, and Spanish. More developers can build private, reliable, cost-effective transcription software with production-ready, easy-to-use, and cross-platform Leopard Speech-to-Text.
Try It
The web demo below converts voice data to text in eight languages: English, French, German, Italian, Japanese, Korean, Portuguese, and Spanish. The demo runs within your web browser, meaning the audio is processed locally without using 3rd party cloud services. Transcribe an existing audio file or record a new one to see it yourself.
Start Building Now
Building with intuitive and cross-platform Leopard Speech-to-Text SDKs doesn’t require any experience in Machine Learning. Anyone can start transcribing with a few lines of code.
leopard = pvleopard.create(access_key)transcript, words = leopard.process_file(path)Build with Python
const leopard = new Leopard(accessKey)const { transcript, words } = leopard.processFile(path)Build with NodeJS
Leopard leopard = new Leopard.Builder().setAccessKey(accessKey).setModelPath(modelPath).build(appContext);LeopardTranscript result = leopard.processFile(path);Build with Android
let leopard = Leopard(accessKey: accessKey,modelPath: modelPath)let result = leopard.processFile(path)Build with iOS
leopard = NewLeopard(accessKey)err := leopard.Init()transcript, words, err := leopard.ProcessFile(path)Build with Go
Leopard leopard = new Leopard.Builder().setAccessKey(accessKey).build();LeopardTranscript result = leopard.processFile(path);Build with Java
Leopard leopard = Leopard.Create(accessKey);LeopardTranscript result = leopard.ProcessFile(path);Build with .NET
let leopard: Leopard = LeopardBuilder::new().access_key(access_key).init().expect("");if let Ok(result) = leopard.process_file(path) { }Build with Rust
Leopard leopard = await Leopard.create(accessKey,modelPath);LeopardTranscript result = await leopard.processFile(path);Build with Flutter
const leopard = await Leopard.create(accessKey,modelPath)const {transcript, words} = await leopard.processFile(path)Build with React Native
pv_leopard_t *leopard = NULL;pv_leopard_init(access_key,model_path,enable_automatic_punctuation,&leopard);char *transcript = NULL;int32_t num_words = 0;pv_word_t *words = NULL;pv_leopard_process_file(leopard,path,&transcript,&num_words,&words);Build with C
const leopard = await LeopardWorker.fromPublicDirectory(accessKey,modelPath);const { transcript, words } = await leopard.process(pcm);Build with Web
Train Use-case Specific Speech Models
Leopard Speech-to-Text offers cloud-level accuracy out-of-the-box. However, some use cases and industries, such as healthcare, finance, or legal, have special terminology that cannot be accurately predicted by generic speech models.
Custom Vocabulary & Keyword Boosting features allow developers to customize speech-to-text models specific to their application on the no-code Picovoice Console .
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