OpenAI has released two new state-of-the-art speech-to-text models, GPT-4 Transcribe and GPT-4 Mini Transcribe, that outperform their previous Whisper models on virtually every language tested. These new models are built on OpenAI's large speech model, trained on trillions of audio tokens, and leverage the latest technologies and architectures.

The GPT-4 Transcribe model offers exceptional accuracy, with a significantly lower word error rate compared to the previous Whisper models. The GPT-4 Mini Transcribe model, a smaller and more efficient version, retains excellent transcription capabilities while being faster and more cost-effective.

These new speech-to-text models are available through the OpenAI API, with GPT-4 Transcribe priced at $0.06 per minute and GPT-4 Mini Transcribe at $0.03 per minute, the same price as the Whisper models. Developers can take advantage of these powerful tools to build rich, human-like voice experiences, with features like noise cancellation and semantic voice activity detection included in the API.

The introduction of these advanced speech-to-text models from OpenAI represents a significant step forward in the field of voice-based AI interfaces, offering developers and businesses the opportunity to create highly accurate and cost-effective voice-enabled applications.

OpenAI has announced a new text-to-speech model, GPT-4 mini TTS, that allows developers to not only control what the model says, but also how it says it. This model introduces the ability to provide instructions on the desired tone, emphasis, and delivery of the generated speech, enabling more expressive and natural-sounding voice experiences.

The key features of this new text-to-speech model include:

1. Customizable Voices: Developers can choose from a variety of pre-generated voices, each with its own unique characteristics and personality.

2. Expressive Instructions: By providing additional instructions in a dedicated field, developers can specify how the model should deliver the text, such as in a "mad scientist" or "casual" tone.

3. Improved Realism: The model's ability to capture nuances in tone, emphasis, and cadence results in more natural and human-like speech output, bridging the gap between text and voice.

This advancement in text-to-speech technology is a significant step towards creating more engaging and personalized voice experiences for users. Developers can now build voice-based agents and interfaces that better convey the intended emotion and personality, making interactions feel more natural and human-like.

The availability of this new text-to-speech model, along with the previously announced speech-to-text models, provides developers with a comprehensive set of tools to build rich, voice-driven applications and experiences.

OpenAI has announced a suite of new models and tools designed to make it easier for developers to build rich, human-like voice experiences. The key highlights include:

1. New Speech-to-Text Models: OpenAI has released two new state-of-the-art speech-to-text models, GPT-4 Transcribe and GPT-4 Mini Transcribe, which outperform their previous Whisper models on a wide range of languages.

2. Advanced Text-to-Speech Model: The new GPT-4 Mini TTS model allows developers to not only control what the model says, but also how it says it, enabling more expressive and natural-sounding voice output.

3. Agents SDK Updates: OpenAI has updated their Agents SDK to make it easier to turn text-based agents into voice-powered experiences, with features like noise cancellation and semantic voice activity detection built-in.

4. Debugging and Tracing Tools: OpenAI has introduced a new tracing UI that allows developers to debug and analyze their voice-based agent interactions, including the ability to play back audio recordings.

These new capabilities make it simpler for developers to incorporate voice interfaces into their applications, leveraging OpenAI's advanced language models to create more natural and engaging user experiences. By bridging the gap between text and speech, these tools open up new possibilities for voice-first applications across a wide range of domains.

Open AI has introduced a new tracing UI that enables developers to easily debug and monitor their voice-powered applications. This tool provides a comprehensive view of the various events and interactions that occur during a conversation, allowing developers to gain deeper insights into the performance and behavior of their voice agents.

The tracing UI integrates seamlessly with the audio data, enabling developers to play back the recorded audio and correlate it with the corresponding events and metadata. This feature allows developers to identify and troubleshoot any issues that may arise, such as speech recognition errors, inappropriate responses, or unexpected user interactions.

The tracing UI offers a user-friendly interface that presents the conversation timeline, highlighting the different stages of the interaction. Developers can click on specific events to access detailed information, such as the input audio, the recognized text, and the agent's response. This level of visibility helps developers understand the decision-making process of their voice agents and identify areas for improvement.

By providing this comprehensive debugging and monitoring solution, Open AI aims to empower developers to build more robust and reliable voice-powered applications. The tracing UI streamlines the development and maintenance process, allowing developers to quickly identify and address any issues that may arise, ultimately delivering a better user experience.

The new speech-to-text and text-to-speech models announced by OpenAI represent significant advancements in voice AI capabilities. The GPT-4 Transcribe and GPT-4 Mini Transcribe models offer improved accuracy across multiple languages, while the GPT-4 Mini TTS model provides developers with greater control over the tone and delivery of synthesized speech.

The ability to easily integrate these models into existing text-based AI workflows through the updated Agents SDK is a valuable feature, allowing developers to quickly add voice interfaces to their applications. The provided debugging and tracing tools further enhance the developer experience, making it easier to monitor and optimize voice-based interactions.

Overall, these updates from OpenAI demonstrate the growing importance of voice as a natural interface for AI systems. By providing high-quality, cost-effective speech processing capabilities, the company is empowering developers to create more engaging and intuitive voice-driven experiences for users.