Danny Waser
Multimodal Conversational Agent Developer
Lausanne, Vaud, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolDanny Waser is a multimodal conversational agent developer based in Lausanne with four years of experience building speech, NLP and multimodal systems. He pairs a Communication and Business background with hands-on CMS and multilingual translation expertise, which he leverages to design practical localization and content workflows for conversational products. At Mozilla and Coqui-AI he helped develop French speech recognition models and, as a DevOps/ML engineer, contributed to the widely used coqui-ai/STT project by improving CI and training pipelines, adding debugging and logging, and streamlining dependencies like ffmpeg. Through his own Waser Technologies and a short AI engineering stint at Synthflow, he delivers voice synthesis, transcription and multimodal integrations for web platforms. A Swiss entrepreneur at heart, he blends linguistic intuition, production-grade engineering and scalable data processing to advance conversational intelligence.
4 years of coding experience
1 year of employment as a software developer
Maturité Professionnelle Commerciale + Certificat Fédéral de Capacité, Communication et Commerce, 4.5/6, Maturité Professionnelle Commerciale + Certificat Fédéral de Capacité, Communication et Commerce, 4.5/6 at École de Culture Générale et de Commerce
French, English, Spanish, German
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (16)
speech-to-text10
ci-cd10
python10
github-action-workflow9
tensorflow9
github-actions-workflow9
github-ci9
bash9
github-actions-workflows9
tensorflow29
docker8
dockerce8
testing8
dockers8
django-cms6
Programming languages (9)
Github contributions (5)
coqui-ai/STT
Apr 2022 - Jan 2023
🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:54 reviews, 37 commits, 39 PRs in 8 months
Contributions summary:Danny primarily contributed to the continuous integration and training pipeline, as well as fixing issues related to the training scripts. They modified scripts to add debugging, logging, and skip batch testing. The user also fixed timer-related bugs, and made ffmpeg an external dependency. They also updated the documentation related to the building process.
voice-recognitionasrspeech-recognitiontrainingspeech-recognition-api
wasertech/STT
Nov 2021 - Feb 2022
The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
Contributions:2 PRs, 89 pushes, 24 branches in 2 months
pytorchdeployingdeep-learningneverspeech-to-text
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