| --- |
| license: mit |
| tags: |
| - mlx |
| - voice-activity-detection |
| - speaker-segmentation |
| - speaker-diarization |
| - pyannote |
| - apple-silicon |
| base_model: pyannote/segmentation-3.0 |
| library_name: mlx |
| pipeline_tag: voice-activity-detection |
| --- |
| |
| # Pyannote Segmentation 3.0 — MLX |
|
|
| MLX-compatible weights for [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) (PyanNet), converted from the official PyTorch Lightning checkpoint with pre-computed SincNet filters. |
|
|
| ## Model |
|
|
| PyanNet is a speaker segmentation model (~1.5M params) that processes 10-second audio windows and outputs 7-class powerset probabilities for up to 3 simultaneous speakers. Used for both voice activity detection (binary) and speaker diarization (per-speaker). |
|
|
| **Architecture:** SincNet → BiLSTM(4 layers) → Linear(2 layers) → 7-class softmax |
|
|
| **Output classes:** non-speech, spk1, spk2, spk3, spk1+2, spk1+3, spk2+3 |
|
|
| ## Usage (Swift / MLX) |
|
|
| ```swift |
| import SpeechVAD |
| |
| // Voice Activity Detection |
| let vad = try await PyannoteVADModel.fromPretrained() |
| let segments = vad.detectSpeech(audio: samples, sampleRate: 16000) |
| for seg in segments { |
| print("Speech: \(seg.startTime)s - \(seg.endTime)s") |
| } |
| |
| // Speaker Diarization (with WeSpeaker embeddings) |
| let pipeline = try await DiarizationPipeline.fromPretrained() |
| let result = pipeline.diarize(audio: samples, sampleRate: 16000) |
| for seg in result.segments { |
| print("Speaker \(seg.speakerId): \(seg.startTime)s - \(seg.endTime)s") |
| } |
| ``` |
|
|
| Part of [qwen3-asr-swift](https://github.com/ivan-digital/qwen3-asr-swift). |
|
|
| ## Conversion |
|
|
| ```bash |
| python3 scripts/convert_pyannote.py --token YOUR_HF_TOKEN --upload |
| ``` |
|
|
| Converts the gated pyannote/segmentation-3.0 checkpoint using a custom unpickler (no pyannote.audio dependency required). Key transformations: |
|
|
| - **SincNet**: pre-compute 80 sinc bandpass filters (40 cos + 40 sin) from 40 learned `(low_hz, band_hz)` parameter pairs |
| - **Conv1d**: transpose weights `[O, I, K]` → `[O, K, I]` for MLX channels-last |
| - **BiLSTM**: split into forward/backward stacks, sum `bias_ih + bias_hh` |
| - **Linear/classifier**: kept as-is |
|
|
| ## Weight Mapping |
|
|
| | PyTorch Key | MLX Key | Shape | |
| |-------------|---------|-------| |
| | `sincnet.conv1d.0.filterbank.*` (computed) | `sincnet.conv.0.weight` | [80, 251, 1] | |
| | `sincnet.conv1d.{1,2}.weight` | `sincnet.conv.{1,2}.weight` | [O, K, I] | |
| | `sincnet.norm1d.{0-2}.*` | `sincnet.norm.{0-2}.*` | varies | |
| | `lstm.weight_ih_l{i}` | `lstm_fwd.layers.{i}.Wx` | [512, I] | |
| | `lstm.weight_hh_l{i}` | `lstm_fwd.layers.{i}.Wh` | [512, 128] | |
| | `lstm.bias_ih_l{i} + bias_hh_l{i}` | `lstm_fwd.layers.{i}.bias` | [512] | |
| | `lstm.*_reverse` | `lstm_bwd.layers.{i}.*` | same | |
| | `linear.{0,1}.*` | `linear.{0,1}.*` | varies | |
| | `classifier.*` | `classifier.*` | [7, 128] | |
|
|
| ## License |
|
|
| The original pyannote segmentation model is released under the [MIT License](https://github.com/pyannote/pyannote-audio/blob/develop/LICENSE). |
|
|
