Technical Schedule : V0.1

  1. Develop core API functionalities targeting <300ms latency, support for 30+ languages, and >90% accuracy.

    • Integrate advanced noise reduction and echo cancellation algorithms to improve accuracy and latency.

    • Utilize machine learning models trained on diverse datasets to ensure comprehensive language and dialect support.

    • Implement a microservices architecture to handle requests efficiently and scale dynamically based on demand.

  2. Begin the development of BlastAI Voices with a focus on human-like quality and low latency.

    • Employ state-of-the-art neural networks, such as convolutional and recurrent neural networks, to generate natural-sounding speech.

    • Optimize text-to-speech synthesis pathways for minimal delay, enabling real-time applications.

  3. Enhance speech recognition algorithms to lower WER by 22%.

    • Leverage deep learning techniques and continuous learning models to adapt and improve recognition accuracy over time.

    • Apply data augmentation and transfer learning to enhance model robustness across various speech contexts and environments.

  4. Optimize processing speeds to achieve up to 40x faster performance.

    • Implement LPU-accelerated computing and batch processing techniques for faster data processing.

    • Optimize algorithms for parallel processing to leverage multicore CPUs and distributed computing resources.

  5. Implement cost-reduction strategies to ensure 3-7x cost-effectiveness.

    • Adopt cloud-native technologies and auto-scaling groups to optimize resource usage and reduce operational costs.

    • Employ predictive analytics to manage resource allocation proactively, reducing wasted compute power.

  6. Execute rigorous internal testing across different languages, dialects, and use cases using real-world audio samples.

    • Develop a comprehensive testing suite that includes automated tests, stress tests, and scenario-based tests.

    • Source a diverse set of real-world audio samples and use cases to validate the system’s performance under various conditions.

  7. Start optimization for enterprise-scale applications for the Voices model.

    • Design the system architecture for high availability and failover to support enterprise-level SLAs.

    • Implement security measures, including data encryption in transit and at rest, to meet enterprise security requirements.

  8. Access to beta versions to a select group from diverse backgrounds for detailed feedback.

    • Create a feedback loop mechanism that allows beta users to report issues, suggest improvements, and provide usability feedback directly.

    • Organize virtual focus groups and one-on-one sessions with beta users to delve deeper into their experiences and gather qualitative insights.

  9. Analyze feedback and iterate on models and API interfaces accordingly.

    • Utilize data analytics tools to aggregate and analyze user feedback, identifying patterns and common issues.

    • Plan regular iteration cycles to address feedback, update models, and refine API interfaces, ensuring continuous improvement.

  10. Publicly announce and launch an open beta to validate the product at scale.

    • Develop a comprehensive communication plan to announce the open beta, including press releases, social media, and developer forums.

    • Implement a scalable infrastructure to support the influx of new users, ensuring the system remains stable and responsive during the open beta phase.

  11. Implement a comprehensive feedback system for efficient user insights collection and adjust in real-time based on feedback.

    • Integrate in-app feedback tools and external feedback channels (e.g., support tickets, community forums) to collect user insights.

    • Establish a rapid response team to address critical feedback and issues, ensuring user satisfaction and continuous product improvement.

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