ReDimNet2 B6 CoreML

--- library_name: coreml license: mit tags: - coreml - speaker-verification - speaker-embedding - diarization - redimnet - audio pipeline_tag: audio-classification --- # ReDimNet2-B6 Core ML Speaker Embeddings This directory contains a Core ML conversion of the ReDimNet2-B6 speaker embedding model from [`PalabraAI/redimnet2`](https://github.com/PalabraAI/redimnet2). The model is used by software to assign deterministic speaker labels inside each audio file and prefix transcriptions with markers such as: ```text {SPEAKER_1} Добрий день. {SPEAKER_2} Вітаю. ``` ## Files ```text ReDimNet2-B6.mlpackage/ ``` ## Model Details - Source model: ReDimNet2-B6 - Upstream repository: `PalabraAI/redimnet2` - Checkpoint: `b6-vb2+vox2_v0-lm.pt` - Task: speaker embedding extraction - Input: mono 16 kHz waveform - Output: L2-normalized speaker embedding - Core ML input name: `audio` - Core ML output name: `embedding` The converted package expects a fixed waveform input of `160320` samples, about `10.02s` at 16 kHz. The software pads shorter chunks and center-crops longer chunks before inference. ## Convert From the repository root: ```bash uv run --with torch --with torchaudio --with scipy --with coremltools \ scripts/convert_redimnet2_coreml.py \ --output Models/speaker/ReDimNet2-B6.mlpackage ``` ## Notes The model produces embeddings, not speaker IDs. The software performs per-file online cosine clustering over chunk embeddings. Speaker labels are deterministic within a source audio file, but `SPEAKER_1` in one file is not the same person as `SPEAKER_1` in another file.

Source context: 5 downloads · 0 likes · Pipeline audio-classification · Library coreml · Repo Yehor/ReDimNet2-B6-CoreML

Source context: 20 downloads · 0 likes · Pipeline audio-classification · Library coreml · Repo Yehor/ReDimNet2-B6-CoreML