Tag: #text-to-speech
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 4 posts
Choosing Speech Models: Practical Criteria for STT and TTS
Unlike text models, speech models are chosen after the language coverage, audio length constraints, real-time requirement, and diarization need are already fixed. This post lays out the parameters, licenses, language cov
2026-08-12 · 7 min read #ai#huggingface#open-source-llm#speech-to-text#text-to-speechSpeech Recognition and Synthesis Technical Reports: What to Read, and What a Single WER Hides
Ten speech recognition and synthesis technical reports, each verified by opening the arXiv abstract page directly. Whisper, Omnilingual ASR, Qwen3-ASR, the Open ASR Leaderboard, Seed-TTS and F5-TTS, CosyVoice 2, Qwen3-TT
2026-08-12 · 7 min read #ai-papers#paper-review#technical-report#speech-recognition#text-to-speechtts-bench: comparing local TTS models when quality is subjective
tts-bench is a local benchmark by 5uck1ess for comparing 55 text-to-speech models on hardware you own. It splits evaluation into three lenses: Speed (TTFA, RTF, memory), Listen (every model on every prompt, judged by ear
2026-07-11 · 5 min read #tts#text-to-speech#benchmark#local-ai#evaluationSOTA Speech Recognition and Synthesis — From Whisper to Codec Language Models
We survey recent trends in speech recognition (ASR) and synthesis (TTS). From HMM to CTC/attention, Whisper large-scale weak supervision, and from Tacotron to neural vocoders and codec language models, we trace the linea
2026-06-30 · 15 min read #ai-papers#speech-recognition#text-to-speech#whisper#neural-codec