Gracias por enviar su consulta! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Gracias por enviar su reserva! Uno de los miembros de nuestro equipo se pondrá en contacto con usted en breve.
Programa del Curso
Overview of Speech Recognition Technologies
- History and evolution of speech recognition
- Acoustic models, language models, and decoding
- Modern architectures: RNNs, transformers, and Whisper
Audio Preprocessing and Transcription Basics
- Handling audio formats and sample rates
- Cleaning, trimming, and segmenting audio
- Generating text from audio: real-time vs batch
Hands-on with Whisper and Other APIs
- Installing and using OpenAI Whisper
- Calling cloud APIs (Google, Azure) for transcription
- Comparing performance, latency, and cost
Language, Accents, and Domain Adaptation
- Working with multiple languages and accents
- Custom vocabularies and noise tolerance
- Legal, medical, or technical language handling
Output Formatting and Integration
- Adding timestamps, punctuation, and speaker labels
- Exporting to text, SRT, or JSON formats
- Integrating transcriptions into apps or databases
Use Case Implementation Labs
- Transcribing meetings, interviews, or podcasts
- Voice-to-text command systems
- Real-time captions for video/audio streams
Evaluation, Limitations, and Ethics
- Accuracy metrics and model benchmarking
- Bias and fairness in speech models
- Privacy and compliance considerations
Summary and Next Steps
Requerimientos
- An understanding of general AI and machine learning concepts
- Familiarity with audio or media file formats and tools
Audience
- Data scientists and AI engineers working with voice data
- Software developers building transcription-based applications
- Organizations exploring speech recognition for automation
14 Horas
