{"id":3096,"date":"2026-09-24T13:09:17","date_gmt":"2026-09-24T04:09:17","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/24\/edge0-audio8-asr-infinite-2\/"},"modified":"2026-09-24T13:09:17","modified_gmt":"2026-09-24T04:09:17","slug":"edge0-audio8-asr-infinite-2","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/24\/edge0-audio8-asr-infinite-2\/","title":{"rendered":"Edge0 Releases Audio8-ASR-Infinite Streaming Speech Model"},"content":{"rendered":"<p><em>Sample outputs are available on the <a href=\"https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite\">model card<\/a>.<\/em><\/p>\n<p><!-- lmw:facts --><\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Repository<\/td>\n<td><a href=\"https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite\">Edge0\/Audio8-ASR-Infinite<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-22<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>apache-2.0<\/td>\n<\/tr>\n<tr>\n<td>Formats<\/td>\n<td>safetensors<\/td>\n<\/tr>\n<tr>\n<td>Source type<\/td>\n<td>Primary source (the publisher itself)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>Values determined by this site&#8217;s code at collection time. Dates are JST.<\/em><\/p>\n<p><!-- \/lmw:facts --><\/p>\n<h2>Overview<\/h2>\n<p>Edge0 has released Audio8-ASR-Infinite, a native streaming speech recognition model designed for maximum responsiveness. This model is an Automatic Speech Recognition (ASR) model that converts audio input into text in real time, supporting bilingual Chinese and English.<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>Architecture: Inherits Voxtral realtime audio architecture and DSM-style streaming<\/li>\n<li>Audio tower: 32 layers, hidden 1280, 128 mel bins, sliding window 750 &#8211; Text decoder: 36 layers, hidden 2048, 16 query heads \/ 2 KV heads &#8211; Projector: max frame len 8 \u2192 projection size 10240, gelu<\/li>\n<li>Output specification: Chinese and English text<\/li>\n<li>Streaming settings:<\/li>\n<li>Selectable audio clock: 80ms \/ 120ms \/ 160ms &#8211; Configurable transcription delay (target_delay_ms): 240ms to 560ms (varies by clock setting)<\/li>\n<li>Recommended operating conditions (optimized points):<\/li>\n<li>At 80ms clock: frame_len 4, target_delay_ms 240 \/ 320 \/ 480 \/ 560 &#8211; At 120ms clock: frame_len 6, target_delay_ms 240 \/ 480 &#8211; At 160ms clock: frame_len 8, target_delay_ms 320 \/ 480<\/li>\n<li>License: Apache 2.0<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<p>Evaluation results at 480 ms Delay, 80ms frame length (Greedy decode, EOS suppressed) are as follows:<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>test set<\/th>\n<th>metric<\/th>\n<th>Audio8 ASR Infinite<\/th>\n<th>Voxtral-Mini-4B-Realtime-2602<\/th>\n<th>nemotron-3.5-asr-streaming-0.6b<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>aishell1\/test<\/td>\n<td>CER<\/td>\n<td><strong>1.750<\/strong><\/td>\n<td>16.795<\/td>\n<td>12.927@560ms<\/td>\n<\/tr>\n<tr>\n<td>aishell4\/test<\/td>\n<td>CER<\/td>\n<td><strong>2.893<\/strong><\/td>\n<td>16.456<\/td>\n<td>14.677@560ms<\/td>\n<\/tr>\n<tr>\n<td>librispeech test.clean<\/td>\n<td>WER<\/td>\n<td>3.042<\/td>\n<td><strong>2.210<\/strong><\/td>\n<td>3.353@560ms<\/td>\n<\/tr>\n<tr>\n<td>librispeech test.other<\/td>\n<td>WER<\/td>\n<td>6.808<\/td>\n<td><strong>5.552<\/strong><\/td>\n<td>7.140@560ms<\/td>\n<\/tr>\n<tr>\n<td><strong>average<\/strong><\/td>\n<td><\/td>\n<td><strong>3.623<\/strong><\/td>\n<td>10.253 (2 sets)<\/td>\n<td>9.524<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Audio8 ASR Infinite records significantly lower (better) CER (Character Error Rate) figures on aishell1\/test and aishell4\/test compared to the comparison models. On the other hand, for WER (Word Error Rate) on librispeech test.clean and test.other, it trails Voxtral-Mini-4B-Realtime-2602. In terms of the overall average, Audio8 ASR Infinite shows the best score.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<p>This model specializes in real-time speech transcription with top priority on responsiveness. It adopts a native streaming architecture, achieving extremely high response by decoding 12.5 times per second. Furthermore, by using Rolling KV Cache, it enables 24\/7 continuous operation (unlimited-length transcription) while keeping memory usage and latency constant.<\/p>\n<p>Main use cases and features include:<\/p>\n<ul>\n<li><strong>Bilingual Support<\/strong>: Supports Chinese and English transcription.<\/li>\n<li><strong>Flexible Streaming Control<\/strong>: Allows selection of audio clock (80\/120\/160 ms) and transcription delay (target_delay_ms), enabling adjustment of the balance between recognition granularity and resource cost.<\/li>\n<li><strong>Semantic VAD<\/strong>: Capable of distinguishing between thinking pauses, stuttering, and actual speech endings, which is difficult with conventional acoustic VAD (Voice Activity Detection).<\/li>\n<li><strong>Use Cases<\/strong>: Real-time subtitle generation, always-on voice interfaces, etc.<\/li>\n<\/ul>\n<p>It is currently a preview release providing base streaming transcription functionality. The roadmap lists implementing frame-level semantic recognition features using the same frame grid and acoustic forward path in the future.<\/p>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 4.1B parameters<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Your VRAM<\/th>\n<th>Quantization<\/th>\n<th>File size<\/th>\n<th>Est. memory needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>12GB (RTX 4070 \/ 3060 12GB, etc.)<\/td>\n<td>BF16<\/td>\n<td>7.6GB<\/td>\n<td>9.1GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>Inference engine support<\/strong> (architecture name matched against each project&#8217;s own model registry in its source code, checked 2026-09-23): llama.cpp: not registered, vLLM: not registered, MLX (mlx-lm): not registered. &#8220;Not registered&#8221; means the name is absent from that registry today, not that the model cannot run.<\/p>\n<p><em>Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model&#8217;s authors. Compare with other models in our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference<\/a>. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:hardware --><\/p>\n<h2>How to Get It<\/h2>\n<ul>\n<li><strong>Distribution format<\/strong>: safetensors<\/li>\n<li><strong>Repository<\/strong>: <a href=\"https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite\">Edge0\/Audio8-ASR-Infinite<\/a><\/li>\n<li><strong>Supported tools<\/strong>: <\/li>\n<li><code>transformers<\/code> library (requires <code>trust_remote_code=True<\/code>) &#8211; 24\/7 inference using <code>vLLM<\/code> (Docker Compose deployment is recommended) &#8211; Simulation streaming decoding via <code>torch<\/code><\/li>\n<\/ul>\n<p>Note that this model does not support adapter formats or partially converted weights; only fully merged weight directories are supported.<\/p>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/11\/edge0-35b-a3b-preview-sparse-moe\/\">Edge0-35B-A3B-Preview: Sparse MoE for Phone-Class Memory<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:related --><\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite\"><a href=\"https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite\">https:\/\/huggingface.co\/Edge0\/Audio8-ASR-Infinite<\/a><\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Edge0 has released Audio8-ASR-Infinite, a native streaming speech recognition model designed for bilingual Chinese-English transcription with high responsi<\/p>\n","protected":false},"author":1,"featured_media":3095,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[203],"tags":[2154,994,759,1547,2156,1017],"class_list":["post-3096","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-image-video-and-audio","tag-audio8-asr-infinite-en","tag-edge0-en","tag-transformers-en","tag-verified","tag--en"],"lang":"en","translations":{"en":3096,"ja":3094},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/3096","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=3096"}],"version-history":[{"count":0,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/3096\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/3095"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=3096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=3096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=3096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}