HttpEntity entity = response. HttpResponse response = httpclient.execute(request) HttpClient httpclient = HttpClients.createDefault() // This sample uses the Apache HTTP client from HTTP Components () "displayName": "Model with acoustic and language datasets" The maximum allowed key length is 64 characters, the maximum\r\nallowed value length is 256 characters and the count of allowed entries is 10.", "description": "The custom properties of this entity. "description": "The time-stamp when the object was created.\r\nThe time stamp is encoded as ISO 8601 date and time format\r\n(\"YYYY-MM-DDThh:mm:ssZ\", see ).", "description": "The status of the object.", Concepts include how to synchronize captions. Captioning is the process of converting the audio content of a television broadcast, webcast, film, video, live event, or other production into text, and then displaying the text on a screen, monitor, or other visual display system. "description": "The time-stamp when the current status was entered.\r\nThe time stamp is encoded as ISO 8601 date and time format\r\n(\"YYYY-MM-DDThh:mm:ssZ\", see ).", In this guide, you learn how to create captions with speech to text. "description": "The locale of the contained data.", "description": "Datasets used for adaptation.", "description": "The base model used for adaptation.", "description": "The text used to adapt this language model.", "description": "The description of the object.", "description": "The display name of the object.", "description": "The location of this entity.", "description": "Additional configuration options when creating a new model and additional metadata provided by the service.", "description": "The details of the error in case the entity is in a failed state.", "description": "The message for this error.", "description": "The code of this error.", "description": "The email address to send email notifications to in case the operation completes.\r\nThe value will be removed after successfully sending the email.", "description": "The date when features of this model become deprecated.", "description": "The links for additional actions or content related to this model.", "description": "The location to the model copy action.", "description": "The location to get a manifest for this model to be used in the on-prem container.", "description": "The project, the model is associated with.", "description": "The location of the referenced entity.", "description": "This is a language model" GET Get Supported Locales for Transcriptions.GET Get Supported Locales for Evaluations.GET Get Supported Locales for Endpoints.DELETE Delete Custom Model Endpoint Log.DELETE Delete All Custom Model Endpoint Logs.Upload data from Azure storage accounts by using a shared access signature (SAS) URI. Request the manifest of the models that you create, to set up on-premises containers. DELETE Delete All Base Model Endpoint Logs Speech-to-text REST API includes such features as: Get logs for each endpoint if logs have been requested for that endpoint.Of course, it will also develop better speech-to-text transcription software. “This will make Cortana more powerful, making a truly intelligent assistant possible,” says an excited Harry Shum, the executive vice president heading the Microsoft Artificial Intelligence and Research group. The applications for the new technology are bound to improve user experience for Microsoft's personal voice assistant for Windows and Xbox One. After that, it’s 0.035 per minute of speech processed rounded to the nearest 15. The first 5 hours of speech-to-text translation are free which is more than sufficient enough to test drive the API. The achievement was unlocked using the Computational Network Toolkit, Microsoft’s homegrown system for deep learning. When tighter controls are necessary, the on-premise solution allows you to run the speech-to-text engine in your own private instance. With Google's DeepMind making waves in speech and image recognition (and speaking like humans do), the technology is Microsoft's timely contribution to the fast-paced artificial intelligence (AI) research and development. The achievement comes decades after speech pattern recognition was first studied in the 1970s. The new technology uses neural language models that allow for more efficient generalization by grouping similar words together. “We’ve reached human parity,” says Xuedong Huang, Microsoft's chief speech scientist. The rate is the same as (or even lower than) the human professional transcriptionists who transcribed the same conversation. "t’s the lowest ever recorded against the industry standard Switchboard speech recognition task," Microsoft reports. The technology scored a word error rate (WER) of 5.9%, which was lower than the 6.3% WER reported just last month. A study published last Monday, heralded as an historic achievement by Microsoft, details a new speech recognition technology that's able to transcribe conversational speech as well as humans - or at least, as best as professional human transcriptionists (which is better than most humans).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |