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Edge0-AI / Audio8-ASR-Infinite

git clone https://github.com/Edge0-AI/Audio8-ASR-Infinite.git

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2026-10-20 Β· 119 β˜… 2026-10-22 Β· 121 β˜…
LanguagePython
LicenseApache-2.0
Created2026-09-21 (1mo ago)
Last push29d ago
Watchers1
OwnerOrganization

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README excerpt
# Audio8 ASR Infinite
English | δΈ­ζ–‡
**Audio8 ASR Infinite** is a native streaming speech recognition model built to be
as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and
a transcription delay (240–560 ms). 
With our adapted vLLM build it transcribes unlimited-length audio **24/7** without drifting.
## Highlights
- **Super responsive** β€” the native streaming architecture decodes 12.5 times per second.
- **Unlimited-length transcription** β€” a rolling KV cache keeps memory and
 latency bounded, even in **24/7 operation**.
- **Selectable streaming clock** β€” one text token per clock step
 (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost.
- **Configurable transcription delay** β€” set how much delay to trade for accuracy.
- **Semantic VAD** β€” distinguishes thinking pauses, stuttering and real end of turn, where traditional acoustic VAD fails.
- **Bilingual** β€” Chinese and English.
## Roadmap
This is the **preview release**: it delivers the transcription base. Realtime
semantic perception is being built on the same frame grid and the same acoustic
forward pass.
| Stage | Status | Scope |
| --- | --- | --- |
| **Preview β€” ASR base** | βœ… done | Streaming Chinese/English transcription: selectable 80/120/160 ms clock, configurable `target_delay_ms`, unlimited-length rolling KV window |
| **Formal release** | πŸƒin progress | Frame-level semantic perception on the same grid, beyond transcription |
## Architecture
Inherits the Voxtral realtime audio architecture and DSM-style streaming.
| Component | Initial weights | Trained |
| --- | --- | --- |
| Causal Audio Tower | Voxtral Realtime 4B | βœ… |
| Audio Projector | random initialisation | βœ… |
| Frame Length Embedding | random initialisation | βœ… |
| Decoder | Qwen2.5-3B-Instruct | βœ… |
| LM Head | Qwen2.5-3B-Instruct | βœ… |
## Optimized operation points
The following combinations of frame length and delay are post-trained. Other combinations can be used but performance may not be optimum.
| audio clock | `frame_len` | `streaming_n_left_pad_tokens` | selectable `target_delay_ms` |
| --- | --- | --- | --- |
| 80 ms | 4 | 18 | 240 / 320 / 480 / 560 |
| 120 ms | 6 | 12 | 240 / 480 |
| 160 ms | 8 | 9 | 320 / 480 |
`target_delay_ms` must be an integer multiple of the selected clock, so longer
delays stay available at every clock even when they are not listed above.
## Evaluation
### 480 ms Delay, 80ms frame length
| test set | metric | Audio8 ASR Infinite | Voxtral-Mini-4B-Realtime-2602 | nemotron-3.5-asr-streaming-0.6b |
| --- | --- | --- | --- | --- |
| aishell1/test | CER | **1.750** | 16.795 | 12.927@560ms |
| aishell4/test | CER | **2.893** | 16.456 | 14.677@560ms |
| librispeech test.clean | WER | 3.042 | **2.210** | 3.353@560ms |
| librispeech test.other | WER | 6.808 | **5.552** | 7.140@560ms |
| **average** | | **3.623** | 10.253 (2 sets) | 9.524 |
## 24/7 inference with vLLM
Docker compose is the canonical deployment path; it also serves t