Supported languages and recognition models

A recognition model is a model trained to recognize speech in a particular language. The models are trained on datasets generated by Yandex services and applications. This allows us to continually improve speech recognition quality.

The main supported model for each recognition type is the general model. It recognizes speech on any topic in a given language, including short and long utterances, names, addresses, dates, and numbers.

Version tagsVersion tags

Three versions of the general model can be available at the same time. You can select the one you need by tags:

• general: The main version of the model.
• general:rc: The release candidate version available for testing.
• general:deprecated: The previous version of the model.

You can also use the deferred-general tag for asynchronous recognition with the API v2. Learn more about asynchronous recognition modes.

Supported recognition languagesSupported recognition languages

Use a recognition language code from the table below. All available code values are case insensitive.

Automatic language detectionAutomatic language detection

SpeechKit automatically detects language in each sentence during speech recognition.

To configure automatic language detection, set the language_code parameter of the LanguageRestrictionOptions() method to auto:

language_restriction=stt_pb2.LanguageRestrictionOptions(
      restriction_type=stt_pb2.LanguageRestrictionOptions.WHITELIST,
      language_code=['auto']
)

Along with recognition results, the service returns language labels containing the language code and probability of its correct detection:

language_code: "ru-RU" probability: 0.91582357883453369

If a sentence contains words in different languages, the language may be detected incorrectly. To improve results, provide a list of expected languages as a clue for the model. Here is an example:

...
      language_code=['auto', 'en-US', 'es-ES', 'fr-FR']
...

Examples

Recognition accuracyRecognition accuracy

In Yandex DataSphere, you can assess the recognition quality of a SpeechKit model yourself using your data.

Use casesUse cases

• Developing a Telegram bot for text recognition in images, audio synthesis and recognition
• Streaming speech recognition with auto language detection in the API v3
• Audio file streaming recognition using the API v3
• Microphone speech streaming recognition using the API v3

See alsoSee also

• Supported audio formats
• Streaming speech recognition
• Synchronous audio recognition
• Asynchronous recognition
• Extending a speech recognition model
• Streaming speech recognition with auto language detection in the API v3