Instructions to use selcukkubur/Audio8-ASR-Infinite-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use selcukkubur/Audio8-ASR-Infinite-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Audio8-ASR-Infinite-mlx selcukkubur/Audio8-ASR-Infinite-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Audio8-ASR-Infinite-mlx
Edge0/Audio8-ASR-Infinite in the layout mlx-audio-swift reads, at bfloat16. Made for OpenMeet.
Chinese and English only. That is the upstream model's own language list, not a limitation of this conversion.
The upstream model is published for Photon, which this is not. Same weights, different names.
What was changed
Nothing retrained or pruned. The weights are the upstream ones renamed, and the
8 semantic_vad_heads tensors are dropped โ they steer Photon's own endpointing
and transcription never reads them. 938 source tensors, 930 kept.
The audio tower needed no reimplementation: this checkpoint's audio_config
says model_type: voxtral_realtime_encoder and means it, down to the bias
pattern (query, value and output carry one; key does not) and the projector,
which is the encoder's own downsampleAndProject at the same 10240-wide input
(1280 ร 8). Only the Qwen2 decoder, the frame-length embedding and the decode
loop are new.
How it was checked
The inference loop does not ship with the model โ upstream publishes it for
Photon, and the HF module has forward and nothing that drives it. It was
reconstructed and validated in Python first, and that reference is what every
stage was checked against: mel, conv stem, encoder output, t_cond, and the
decoded tokens.
Transcribes word for word against that reference, at about 1.1ร real time on a 19-second clip:
The quarterly revenue forecast assumes a 17% increase in enterprise subscriptions, driven largely by the new authentication features we shipped in March. Sarah raised a concern about churn in the mid-market segment, and I think she is right that we should model a pessimistic scenario before committing to the hiring plan.
Requires a patched mlx-audio-swift
VoxtralRealtimeEncoderAttention upstream applies interleaved rotary embedding;
transformers applies split-half (rotate_half) there, and this checkpoint was
trained for split-half. With the interleaved rotation the model loads, runs at
full speed, and returns fluent-looking nonsense. The fix is in
selcukkubur/mlx-audio-swift.
Licence and credit
Apache-2.0, inherited. Credit to Edge0 for Audio8-ASR-Infinite, and to Mistral for the Voxtral Realtime audio architecture it builds on. This repository is a format conversion and claims nothing else.
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Base model
Edge0/Audio8-ASR-Infinite