Audio8 ASR Infinite streaming speech recognition model the native streaming architecture decodes 12.5 times per second a rolling KV Cache keeps both memory and latency constant, even in 24/7 operation one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost ML intern in huggingchat setup a gradio workflow to try it out: huggingchat: model:

Audio8 ASR Infinite keeps transcribing around the clock with bounded memory and latency. 🤖 🎙️ A rolling KV Cache enables unlimited-length Chinese and English transcription without accumulated drift. 🏆 At 480 ms delay, it reaches 1.75 CER on AISHELL-1, 2.89 on AISHELL-4, and 3.04/6.81 WER on LibriSpeech test-clean/test-other. ⚡ Select an 80, 120, or 160 ms audio clock and tune delay from 240 to 560 ms. Semantic VAD distinguishes pauses from real turn endings. 📜 Preview release under Apache 2.0, with continuous deployment through an adapted vLLM stack.

iFlytek has released Spark-ASR 2.0🚀 Gains show up where ASR usually breaks — code-switching, dialects, jargon, noise, low volume, fast speech, kids’ voices — plus cleaner, more readable transcripts. Inference cost is only ~10% above Spark-ASR-1.0. Rolling out to iFlytek Input Method tomorrow. Also available via the iFlytek Open Platform API, with planned deployments across AI glasses, smart office devices, and more.

🌍Introducing Hy ASR 3.0 preview, a speech recognition model from @TencentHunyuan built to understand, not just transcribe What we improved: - Cleaner on real-world audio: dialects, code-switching, and long-form content with less error accumulation over time - Context-aware correction: homophones and ambiguous phrases get resolved from context, not guessed in isolation - Hotword injection: drop in brand names, people, domain terms without retraining. Lowers integration cost for niche use cases - Built for noisy rooms: whisper, background noise, tricky acoustic conditions stay stable