Technical Schedule : V0.1
✅ 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.
✅ 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.
✅ 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.
✅ 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.
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.
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.
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.
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.
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.
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.
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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