Duplicate from FluidInference/ls-eend-coreml

Co-authored-by: Alex Weng <alexwengg@users.noreply.huggingface.co>

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LICENSE

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+MIT License
+
+Copyright (c) 2023 Audio-WestlakeU
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language:
+- en
+license: mit
+library_name: coreml
+pipeline_tag: audio-classification
+tags:
+- speaker-diarization
+- diarization
+- coreml
+- apple
+- streaming
+- audio
+- ls-eend
+- eend
+pretty_name: LS-EEND CoreML Models
+model-index:
+- name: LS-EEND CoreML Models
+  results: []
+---
+
+# LS-EEND CoreML Models
+
+CoreML exports of LS-EEND, a long-form streaming end-to-end neural diarization model with online attractor extraction.
+
+This repository contains non-quantized CoreML step models for four LS-EEND variants:
+
+- `AMI`
+- `CALLHOME`
+- `DIHARD II`
+- `DIHARD III`
+
+These models are intended for stateful streaming inference. Each package runs one LS-EEND step at a time with explicit recurrent/cache tensors, rather than processing an entire utterance in a single call.
+
+## Included files
+
+Each variant directory contains:
+
+- `*.mlpackage`: the CoreML model package
+- `*.json`: metadata needed by the runtime
+- `*.mlmodelc`: a compiled CoreML bundle generated locally for convenience
+
+Variant directories:
+
+- `AMI/`
+- `CALLHOME/`
+- `DIHARD II/`
+- `DIHARD III/`
+
+## Variants
+
+| Variant | Package | Configured max speakers | Model output capacity |
+| --- | --- | ---: | ---: |
+| AMI | `AMI/ls_eend_ami_step.mlpackage` | 4 | 6 |
+| CALLHOME | `CALLHOME/ls_eend_callhome_step.mlpackage` | 7 | 9 |
+| DIHARD II | `DIHARD II/ls_eend_dih2_step.mlpackage` | 10 | 12 |
+| DIHARD III | `DIHARD III/ls_eend_dih3_step.mlpackage` | 10 | 12 |
+
+The metadata JSON distinguishes between:
+
+- `max_speakers`: the dataset/config speaker setting from the LS-EEND infer YAML
+- `max_nspks`: the exported model's full decode/output capacity
+
+## Frontend and runtime assumptions
+
+All four non-quantized exports in this repo use the same frontend settings:
+
+- sample rate: `8000 Hz`
+- window length: `200` samples
+- hop length: `80` samples
+- FFT size: `1024`
+- mel bins: `23`
+- context receptive field: `7`
+- subsampling: `10`
+- feature type: `logmel23_cummn`
+- output frame rate: `10 Hz`
+- compute precision: `float32`
+
+These are step-wise streaming models. A runtime must maintain and feed the recurrent state tensors between calls:
+
+- `enc_ret_kv`
+- `enc_ret_scale`
+- `enc_conv_cache`
+- `dec_ret_kv`
+- `dec_ret_scale`
+- `top_buffer`
+
+The CoreML inputs and outputs follow the LS-EEND step export used by the reference Python and Swift runtimes.
+
+## Intended usage
+
+Use these packages with a runtime that:
+
+1. Resamples audio to mono `8 kHz`
+2. Extracts LS-EEND features with the settings above
+3. Preserves model state across step calls
+4. Uses `ingest`/`decode` control inputs to handle the encoder delay and final tail flush
+5. Applies postprocessing such as sigmoid, thresholding, optional median filtering, and RTTM conversion outside the CoreML graph
+
+This repository is not a drop-in replacement for generic Hugging Face `transformers` inference. It is meant for custom CoreML runtimes, such as:
+
+- the Python LS-EEND CoreML runtime from the FS-EEND project
+- the Swift/macOS runtime used for the LS-EEND CoreML microphone demo
+
+## Minimal metadata example
+
+Each variant ships a sidecar JSON with fields like:
+
+```json
+{
+  "sample_rate": 8000,
+  "win_length": 200,
+  "hop_length": 80,
+  "n_fft": 1024,
+  "n_mels": 23,
+  "context_recp": 7,
+  "subsampling": 10,
+  "feat_type": "logmel23_cummn",
+  "frame_hz": 10.0,
+  "max_speakers": 10,
+  "max_nspks": 12
+}
+```
+
+Check the variant-specific `*.json` file for the exact state tensor shapes and output dimensions.
+
+## Credits
+
+- **Base model**: [LS-EEND](https://github.com/Audio-WestlakeU/FS-EEND) by Di Liang & Xiaofei Li (Westlake University). Paper: [LS-EEND: Long-Form Streaming End-to-End Neural Diarization with Online Attractor Extraction](https://arxiv.org/abs/2410.06670) (IEEE TASLP 2025). The original model is not hosted on HuggingFace; pretrained weights are available on [GitHub](https://github.com/Audio-WestlakeU/FS-EEND).
+- **CoreML conversion**: [@GradientDescent2718](https://huggingface.co/GradientDescent2718). Original repo: [GradientDescent2718/ls-eend-coreml](https://huggingface.co/GradientDescent2718/ls-eend-coreml).
+
+## Source project
+
+These CoreML exports were produced from the LS-EEND code in the FS-EEND repository:
+
+- GitHub: [Audio-WestlakeU/FS-EEND](https://github.com/Audio-WestlakeU/FS-EEND)
+
+The export path is based on the LS-EEND CoreML step exporter and variant batch exporter in that project.
+
+## Training and evaluation context
+
+From the source project, the reported real-world diarization error rates are:
+
+| Dataset | DER (%) |
+| --- | ---: |
+| CALLHOME | 12.11 |
+| DIHARD II | 27.58 |
+| DIHARD III | 19.61 |
+| AMI Dev | 20.97 |
+| AMI Eval | 20.76 |
+
+These numbers come from the upstream LS-EEND project README and reflect the original training/evaluation setup, not a Hugging Face evaluation pipeline.
+
+## Limitations
+
+- These models are exported for Apple CoreML runtimes, not for PyTorch or ONNX consumers.
+- They are stateful streaming step models, so they require a custom driver loop.
+- They assume an 8 kHz LS-EEND frontend and will not produce matching results if you use a different spectrogram pipeline.
+- Speaker identities are output as activity tracks/slots and still require downstream diarization postprocessing and speaker-slot alignment where appropriate.
+
+## License and dataset constraints
+
+The upstream LS-EEND model/codebase used for these CoreML exports is MIT-licensed, and this repository is published as MIT accordingly.
+
+The underlying evaluation and fine-tuning datasets still have their own access and usage terms:
+
+- AMI
+- CALLHOME
+- DIHARD II
+- DIHARD III
+
+This repository redistributes CoreML exports of the LS-EEND model variants. Dataset licensing and access requirements remain governed by the original dataset providers.
+
+## Citation
+
+If you use LS-EEND, cite the original paper:
+
+```bibtex
+@ARTICLE{11122273,
+  author={Liang, Di and Li, Xiaofei},
+  journal={IEEE Transactions on Audio, Speech and Language Processing},
+  title={LS-EEND: Long-Form Streaming End-to-End Neural Diarization With Online Attractor Extraction},
+  year={2025},
+  volume={33},
+  pages={3568-3581},
+  doi={10.1109/TASLPRO.2025.3597446}
+}
+```

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