| --- |
| license: cc-by-nc-4.0 |
| base_model: BUT-FIT/diarizen-wavlm-large-s80-md-v2 |
| pipeline_tag: voice-activity-detection |
| language: |
| - as |
| - bn |
| - brx |
| - doi |
| - en |
| - gu |
| - hi |
| - kn |
| - ks |
| - kok |
| - mai |
| - ml |
| - mni |
| - mr |
| - ne |
| - or |
| - pa |
| - sa |
| - sat |
| - sd |
| - ta |
| - te |
| - ur |
| tags: |
| - speaker-diarization |
| - audio |
| - indic |
| - diarizen |
| - wavlm |
| - speech |
| - speaker |
| - speaker-segmentation |
| - overlapped-speech-detection |
| - conformer |
| - wespeaker |
| - vbx |
| - pytorch |
| - rust |
| metrics: |
| - der |
| model-index: |
| - name: Anuvani-Diarization |
| results: |
| - task: |
| name: Speaker Diarization |
| type: speaker-diarization |
| dataset: |
| name: IndicDiarBench, all 1,164 files (zero-shot) |
| type: sarvamai/indic-diarbench |
| revision: 92877bad8aab6e598167d91c6ee02aa8ca6ede09 |
| metrics: |
| - name: DER (micro, zero collar, overlap scored) |
| type: der |
| value: 17.11 |
| - name: DER (duration-weighted, zero collar, overlap scored) |
| type: der |
| value: 16.78 |
| --- |
| |
| <!-- markdownlint-disable first-line-h1 --> |
| <!-- markdownlint-disable html --> |
| <!-- markdownlint-disable no-duplicate-header --> |
|
|
| <div align="center"> |
| <img class="dark:hidden" src="figures/hero_light.png" width="100%" alt="Anuvani-Diarization" /> |
| <img class="hidden dark:block" src="figures/hero_dark.png" width="100%" alt="Anuvani-Diarization" /> |
| </div> |
| <hr> |
| <div align="center" style="line-height: 1;"> |
| <a href="https://www.ciil.org/" target="_blank" style="margin: 2px;"> |
| <img alt="Homepage" src="figures/badge.svg" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://colab.research.google.com/#fileId=https://huggingface.co/CIIL/Anuvani-Diarization/resolve/main/notebook.ipynb" target="_blank" style="margin: 2px;"> |
| <img alt="Open in Colab" src="https://img.shields.io/badge/Colab-Quick%20start-1a88ff?logo=googlecolab&logoColor=F9AB00&color=1a88ff" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://huggingface.co/CIIL" target="_blank" style="margin: 2px;"> |
| <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-CIIL-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://arxiv.org/abs/2610.20327" target="_blank" style="margin: 2px;"> |
| <img alt="arXiv paper" src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white&color=b31b1b" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
| <div align="center" style="line-height: 1;"> |
| <a href="https://huggingface.co/CIIL/Anuvani-Diarization/blob/main/triton_inference/README.md" target="_blank" style="margin: 2px;"> |
| <img alt="Serving kit" src="https://img.shields.io/badge/Triton-Serving%20kit-76b900?logo=nvidia&logoColor=white&color=76b900" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
| <div align="center" style="line-height: 1;"> |
| <a href="#licence-and-attribution" style="margin: 2px;"> |
| <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT%20OR%20Apache--2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="#licence-and-attribution" style="margin: 2px;"> |
| <img alt="Model License" src="https://img.shields.io/badge/Model_License-CC%20BY--NC%204.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
|
|
| <p align="center" style="text-wrap: balance;"><b>Who spoke when</b>, for multi-speaker audio in the 22 scheduled languages of India and English.</p> |
|
|
| Anuvani-Diarization finds who spoke when in recordings of conversations, calls and meetings: it splits the audio into |
| speaker turns and labels each turn with an anonymous speaker, in a standard RTTM file. It is built for the 22 scheduled |
| languages of India and English. On Indic DiarBench, the public benchmark for those languages, it has the lowest |
| error of every system we ran, evaluated zero-shot: nothing in it was trained, tuned or selected on the benchmark. |
|
|
| **Release 1.0.0** comes with: |
|
|
| * **`anuvani_diar`**, a pip package that runs the model in a few lines of Python on a Rust engine, on every GPU you |
| give it (or the CPU). |
| * **`triton_inference/`**, a serving kit for many users: Triton Inference Server and a FastAPI service, on any number |
| of NVIDIA GPUs on one machine or several. |
| * **The original Python program** (`reference/`), whose outputs the engine reproduces. |
|
|
| Key capabilities: |
|
|
| * **The lowest error of every system we ran on Indic DiarBench:** 16.78 DER (duration-weighted) and 17.11 (pooled) on |
| all 1,164 recordings, zero-shot, against 17.18 to 39.41 for the nine other systems we ran; the most accurate |
| measured system in 14 of the 22 languages. |
| * **Any length, any number of speakers:** from seconds to many hours; up to 4 people talking at once within each |
| 16-second window, and no limit on the total number of speakers. |
| * **Runs on what you have:** any NVIDIA GPU from the Volta generation to Blackwell, as many as you have, on one |
| machine or several; the CPU works too, more slowly. By default it uses every GPU, and you can choose which. |
| * **Fast:** decoding, windowing, batching across GPUs and clustering run in Rust; only the two neural |
| networks run in PyTorch. |
| * **Reproducible:** on the setup the release's outputs were produced with, the output is byte-identical to the |
| original program; on any other GPU or PyTorch version every GPU checks its networks at start-up against the |
| release's own outputs. |
| * **Open source code** under MIT OR Apache-2.0; the weights are for non-commercial use (CC BY-NC 4.0). |
|
|
| ## Model overview |
|
|
| <div align="center"> |
|
|
| | Property | Value | |
| |:---|:---| |
| | **Task** | Speaker diarization: who spoke when; one RTTM file per recording | |
| | **Languages** | Built and evaluated for the 22 scheduled languages of India and English | |
| | **Segmentation network** | DiariZen's pruned WavLM-Large plus Conformer end-to-end network, fine-tuned (69.5M parameters) | |
| | **Voiceprint network** | WeSpeaker ResNet34 (6.6M parameters) | |
| | **Clustering** | VBx clustering with the base model's PLDA and default settings (AHC threshold 0.6, Fa 0.07, Fb 0.8); nothing after the network is tuned | |
| | **Total parameters** | 76.1M | |
| | **Analysis window** | 16 seconds, one every 1.6 seconds; up to 4 overlapping speakers per window | |
| | **Input** | any audio file libsndfile reads (WAV, FLAC, OGG, MP3), any length; converted to 16 kHz mono | |
| | **Output** | RTTM (speaker label, start, duration); in Python also a list of (start, end, speaker) | |
| | **Hardware** | any NVIDIA GPU from Volta to Blackwell with about 12 GB of memory free, any number of them, on one machine or several; or the CPU | |
| | **Licence** | code MIT OR Apache-2.0; weights CC BY-NC 4.0 (segmentation) and CC BY 4.0 (PLDA, voiceprints) | |
| | **Paper** | [arXiv 2610.20327](https://arxiv.org/abs/2610.20327) · [Hugging Face paper page](https://huggingface.co/papers/2610.20327) | |
|
|
| </div> |
|
|
| ## Benchmark results |
|
|
| All numbers are diarization error rates (DER, %): lower is better. Zero collar, overlap scored, NIST md-eval |
| semantics (identical to dscore `e02f949`). |
|
|
| ### Indic DiarBench |
|
|
| All 1,164 recordings (107.7 hours, 22 Indian languages), dataset revision `92877bad`. |
|
|
| <div align="center"> |
|
|
| | # | System | Maker | DER, duration-weighted | DER, pooled | Source | |
| |---:|:---|:---|---:|---:|:---| |
| | 1 | Sarvam AI | Sarvam AI | 16.0 | | paper | |
| | 2 | **Anuvani-Diarization 1.0** | **CIIL** | **16.78** | **17.11** | **this model, zero-shot** | |
| | 3 | Precision-2 | pyannoteAI | 17.18 | 17.58 | run by us | |
| | 4 | DiariZen WavLM-Large s80 md v2 (the base model) | BUT Speech@FIT | 17.99 | 18.31 | run by us | |
| | 5 | Nemotron 3 Diarization | NVIDIA | 18.38 | 18.71 | run by us | |
| | 6 | speaker-diarization-community-1 | pyannote | 21.22 | 21.65 | run by us | |
| | 7 | Streaming Sortformer 4spk v2.1 | NVIDIA | 22.14 | 22.34 | run by us | |
| | 8 | DiCoW v3.3 | BUT Speech@FIT | 22.62 | 22.76 | run by us | |
| | 9 | AWS Transcribe | Amazon | 23.5 | | paper | |
| | 10 | 3D-Speaker CAM++ diarization | Alibaba Tongyi | 23.67 | 24.39 | run by us; 2 recordings failed | |
| | 11 | Deepgram Nova-3 | Deepgram | 32.0 | | paper | |
| | 12 | MOSS-Transcribe-Diarize 0.9B | OpenMOSS | 33.76 | 34.52 | run by us; 14 recordings failed | |
| | 13 | Azure STT | Microsoft | 34.8 | | paper | |
| | 14 | ElevenLabs Scribe | ElevenLabs | 35.0 | | paper | |
| | 15 | GPT-4o Transcribe | OpenAI | 36.2 | | paper | |
| | 16 | Sortformer 4spk v1 | NVIDIA | 39.41 | 39.66 | run by us; 40 recordings failed | |
| | 17 | AssemblyAI Universal-2 | AssemblyAI | 40.5 | | paper | |
| | 18 | Gemini 3 Pro | Google | 74.0 | | paper | |
|
|
| </div> |
|
|
| - **Run by us:** every system on the same 1,164 recordings with one scorer: 0 s collar, overlap scored, no |
| speaker-count hint; recordings a system failed on are scored as empty output. |
| - **Paper:** Indic DiarBench (Sarvam AI and AI4Bharat, arXiv 2607.23808), Table 3, duration-weighted, run as joint |
| transcription and diarization services. The paper does not name Sarvam AI's model. |
| - **This model:** evaluated zero-shot. Nothing in it was trained, tuned or selected on Indic DiarBench: the |
| segmentation network was adapted on other recordings, its recipe and checkpoint were fixed on non-benchmark |
| recordings, and clustering uses the base model's default settings. The system was frozen before it was first |
| scored on the benchmark. Earlier versions of this system were also scored on Indic DiarBench. |
| - **Pooled 95% confidence interval:** 15.80 to 18.45 (bootstrap over the 590 source recordings). The paired |
| differences against the three strongest systems we ran all exclude zero: -0.47 points against Precision-2, -1.21 |
| against DiariZen md-v2 and -1.60 against Nemotron 3 Diarization. |
| - **Scorer choice changes the number.** pyannote.metrics reads about 0.20 higher on Indic DiarBench, because it does |
| not merge the benchmark's overlapping same-speaker reference segments (13.7 minutes in 196 recordings). |
|
|
| ### Public benchmarks |
|
|
| This model against its base model on six international test sets, both run by us under the same conditions with the |
| base model's clustering. |
|
|
| <div align="center"> |
|
|
| | Test set | Files | DiariZen WavLM-Large s80 md v2 (the base model) | Anuvani-Diarization 1.0 | |
| |:---|---:|---:|---:| |
| | VoxConverse v0.3 test | 232 | **9.17** | 9.32 | |
| | NOTSOFAR-1 eval (one single-channel device per meeting) | 129 | 18.26 | **17.41** | |
| | MagicData-RAMC test | 43 | **10.97** | 11.13 | |
| | AMI test, single distant mic | 16 | **13.93** | 14.00 | |
| | AISHELL-4 test | 20 | 10.06 | **9.85** | |
| | AliMeeting test, far-field | 20 | 10.74 | **10.44** | |
| | **Mean** | | 12.19 | **12.03** | |
|
|
| </div> |
|
|
| The Indic adaptation costs no accuracy on these sets on average: better on three, worse on three. |
|
|
| ### This model on every benchmark |
|
|
| <div align="center"> |
|
|
| | Benchmark | DER | |
| |:---|---:| |
| | Indic DiarBench, all 1,164 recordings, duration-weighted | **16.78** | |
| | Indic DiarBench, all 1,164 recordings, pooled | **17.11** | |
| | Indic DiarBench, near-field meetings, pooled | 19.05 | |
| | Indic DiarBench, far-field meetings, pooled | 18.64 | |
| | Indic DiarBench, in-the-wild video, pooled | 11.51 | |
| | VoxConverse v0.3 test | 9.32 | |
| | AISHELL-4 test | 9.85 | |
| | AliMeeting test, far-field | 10.44 | |
| | MagicData-RAMC test | 11.13 | |
| | AMI test, single distant mic | 14.00 | |
| | NOTSOFAR-1 eval (one single-channel device per meeting) | 17.41 | |
| | Average of the six public sets | 12.03 | |
|
|
| </div> |
|
|
| ## Getting started |
|
|
| <a href="https://colab.research.google.com/#fileId=https://huggingface.co/CIIL/Anuvani-Diarization/resolve/main/notebook.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"></a> |
| opens the quick-start notebook (`notebook.ipynb`) in Google Colab: choose a GPU runtime and run every cell. |
|
|
| On your own machine you need Linux x86_64 with glibc 2.28 or newer, Python 3.10 or newer and PyTorch 2.1 or newer; an |
| NVIDIA GPU is strongly recommended (the CPU works, much more slowly). |
| |
| ```bash |
| pip install -U huggingface_hub |
| hf download CIIL/Anuvani-Diarization wheels/anuvani_diar-1.0.0-cp310-abi3-manylinux_2_28_x86_64.whl --local-dir . |
| pip install wheels/anuvani_diar-1.0.0-cp310-abi3-manylinux_2_28_x86_64.whl |
| ``` |
| |
| ```python |
| from anuvani_diar import Diarizer |
| |
| # every visible GPU, or the CPU when there is none |
| diarize = Diarizer() |
| # any length; WAV, FLAC, OGG, MP3 |
| result = diarize("meeting.wav") |
| result.save_rttm("meeting.rttm") |
| # [(start, end, speaker), ...] |
| print(result.speakers, result.segments[:3]) |
|
|
| # many files; results arrive as each one finishes |
| for r in diarize.run(["a.wav", "b.flac"], out_dir="rttm/"): |
| print(r.uri, r.ok) |
| ``` |
| |
| Choosing devices: `Diarizer(devices=2)` uses the first two GPUs, `Diarizer(devices=[0, 3])` GPUs 0 and 3, |
| `Diarizer(devices="cpu")` the CPU. The engine spreads every file's windows over all of them. |
| |
| - **Command line:** `anuvani-diar --out rttm/ meeting.wav`, or `--in-dir recordings/`; `--devices 2`, `--devices 0,3` |
| or `--devices cpu` choose the devices. |
| - **What it downloads:** the first `Diarizer()` fetches `model/` (307 MB) once from this repository, at the release's |
| tag, and checks every file's sha256 against the package's own list. No account is needed. It also fetches the small |
| `config.json`, which the Hub counts as a download of the model; nothing else is sent. |
| - **Install check:** `anuvani-diar-check` diarizes the 12 check recordings and compares the results with the |
| release's fingerprints. |
| - **PyTorch:** installing the wheel into an environment without PyTorch pulls PyPI's default build; for PyTorch 2.14.1 |
| that is a CUDA 13.0 build, which needs a recent NVIDIA driver. With an older driver, install a CUDA 12 build first, |
| e.g. `pip install torch --index-url https://download.pytorch.org/whl/cu126`. Google Colab already has PyTorch. |
| |
| ## Serving at scale |
| |
| `triton_inference/` deploys the model for many users with Docker Compose: |
| - one Triton Inference Server per GPU holds the two networks; |
| - a FastAPI service holds the Rust engine, and it is the only thing clients talk to (uploads, job ids, RTTMs, a |
| token, limits, metrics); |
| - it uses every GPU of the machine by default (`./diar up 2` or `./diar up --gpus 0,3` to choose), and the GPUs of other |
| machines too (`./diar up --tritons-only` on those, `./diar up --remote ...` on the service's machine). |
|
|
| ```bash |
| cd triton_inference && chmod +x diar |
| # the Triton image this machine's GPUs need, the service, every GPU |
| ./diar doctor && ./diar build && ./diar up |
| # the 12 check recordings through the service |
| ./diar gate |
| curl -s -H "Authorization: Bearer $(./diar token)" --data-binary @meeting.wav "localhost:8180/v1/diarize?name=meeting.wav" |
| ``` |
|
|
| The kit's default lane runs part of the networks in FP16 and TF32. It is not byte-identical to the release, and its |
| output can differ slightly from run to run. `./diar up --exact` runs the exact lane. |
| Details: `triton_inference/README.md`. |
|
|
| ## Best practices |
|
|
| ### 1. Devices |
|
|
| Leave `devices` on `"auto"` to use every GPU, or pass a number or a list to share a machine. One process per machine |
| is best: the engine already keeps every GPU busy and spreads each file over all of them. |
|
|
| ### 2. Many files |
|
|
| Give `run()` all the files at once rather than calling the model once per file: the engine decodes, schedules and |
| clusters them in parallel, and results arrive as each file finishes. |
|
|
| ### 3. Long recordings |
|
|
| Any length works, from seconds to many hours. A single long file finishes faster with more GPUs, because its windows |
| spread over all of them; decoding and the clustering run on the CPU before and after the networks. |
|
|
| ### 4. Speed and exactness |
|
|
| The pip package runs the exact lane (the release's FP32 networks). The serving kit defaults to the faster ffn16 |
| lane, which serves about 2.1 times as much audio per second as the exact lane on the same GPUs; `--exact` keeps the |
| exact lane. |
|
|
| ### 5. Audio formats |
|
|
| WAV, FLAC, OGG and MP3 are read directly; convert M4A, AAC and MP4 audio to WAV or FLAC first. |
|
|
| ### 6. Checking an installation |
|
|
| `anuvani-diar-check` (and `./diar gate` in the serving kit) diarize the 12 check recordings. Identical results |
| confirm the setup the release's outputs were produced with; anywhere else the networks pass a near-identical check |
| and the results may differ slightly. |
|
|
| ## Model data |
|
|
| ### Training data |
|
|
| - **Segmentation network:** DiariZen's WavLM-Large and Conformer network (`BUT-FIT/diarizen-wavlm-large-s80-md-v2`), |
| fine-tuned on Indian telephone conversations (2-person calls in 8 languages: as, bn, gu, hi, ml, mr, pa, ta) and |
| international meeting corpora. |
| - **Voiceprint network:** `pyannote/wespeaker-voxceleb-resnet34-LM`, unchanged. |
| - **PLDA model and clustering settings:** BUT Speech@FIT's, unchanged. |
| - **Indic DiarBench:** not used for training, tuning or selection; audio-fingerprint screening found no audio shared |
| between the training data and the benchmark. |
|
|
| ### Evaluation data |
|
|
| Indic DiarBench (Sarvam AI and AI4Bharat): 1,164 recordings, 107.7 hours, in 22 Indian languages and three recording |
| conditions, at the pinned dataset revision `92877bad`. The public sets are listed in the tables above. |
|
|
| ## Exactness and determinism |
|
|
| - **FP32, TF32 off, deterministic kernels,** seed 3407 reset per file. |
| - **Byte-identical to the research pipeline** the published numbers come from. |
| - **The package's default (exact) lane and the kit's exact lane** give RTTMs byte-identical to the original program |
| on the setup the release's outputs were produced with (PyTorch 2.1.1 with CUDA 12.1): all 1,945 benchmark files from |
| cached outputs, and 1,973 audio inputs, including 1-, 3- and 6-hour files and damaged files, both locally and |
| through Triton. |
| - **Other setups** (other GPUs, other PyTorch versions) run the same FP32 networks with the same settings, but |
| cannot be byte-identical. At start-up, each GPU's outputs are checked against the release's own and must agree |
| within fixed bounds; the package then warns that its output is near-identical, and `Diarizer(strict=True)` refuses |
| instead. On other GPU models, rare near-ties in clustering could flip a file; the install checks show whether a |
| setup reproduces the release byte for byte. |
|
|
| ## Usage and limitations |
|
|
| ### Intended usage |
|
|
| - **Conversation and meeting analysis:** speaker turns for calls, interviews, panels, classrooms and meetings in |
| Indian languages and English. |
| - **Transcription pipelines:** attributing an ASR transcript's words to speakers, by aligning them with the RTTM. |
| - **Media and archives:** indexing radio, television and oral-history recordings by speaker turn. |
| - **Research:** a strong, reproducible baseline for speaker diarization in Indian languages. |
|
|
| ### Limitations |
|
|
| - **Overlap cap:** at most 4 people talking at the same moment within a 16-second window. There is no limit on the |
| total number of speakers. |
| - **Indian fine-tuning data is narrow:** 2-person telephone calls in 8 languages (as, bn, gu, hi, ml, mr, pa, ta). There |
| are no Indian multi-person meetings, and 14 of Indic DiarBench's 22 languages are unseen in fine-tuning. |
| - **Languages and conditions:** it trails the best other system we ran most in Maithili (+1.98 points), Sindhi (+1.80) |
| and Bodo (+1.56), and on in-the-wild video, where Precision-2 leads (10.78 against 11.51 pooled). |
| - **Annotation convention:** Indic DiarBench's references mark whole utterances, while this model, like other |
| diarizers, splits speech at pauses; part of its error on the benchmark comes from that difference. |
| - **Speaker identity** inside overlaps and very short turns is the largest remaining error source. |
| - **Single training seed.** |
| - **Evaluation history:** earlier versions of this system were also scored on Indic DiarBench. |
| - **Formats:** M4A/AAC/MP4 audio is not decoded; convert it to WAV or FLAC first. |
|
|
| ### Ethical considerations and risks |
|
|
| - **Privacy and consent:** diarization tells apart the voices in a recording. Use it on recordings whose speakers have |
| agreed to it, and follow the data protection law that applies to you. |
| - **No identities:** the speaker labels are anonymous clusters within one recording; the model does not identify |
| people and does not link speakers across recordings. Using its output to identify or track people needs separate |
| consent and a separate, appropriately evaluated system. |
| - **Uneven accuracy:** error rates vary with language, recording condition and the number of people talking; check |
| the results on your own audio before relying on them. |
| - **Licence:** the weights are for non-commercial use only (CC BY-NC 4.0, inherited from DiariZen). |
|
|
| ### Benefits |
|
|
| At release, the most accurate openly available speaker diarization for Indian languages on Indic DiarBench, with |
| reproducible results, open source code and a deployment that scales from one GPU to many machines. |
|
|
| ## Repository layout |
|
|
| <div align="center"> |
|
|
| | Path | What | |
| |:---|:---| |
| | `model/` | the weights (safetensors) and `MANIFEST.json` (every file's sha256) | |
| | `model.safetensors.index.json` | the index of the tensors in `model/`'s two safetensors files (the Hub reads the parameter count from it) | |
| | `config.json` | the release's description (version, wheel, window, parameters, licences); the package fetches it with `model/` | |
| | `wheels/` | the pip package (`anuvani_diar-1.0.0-cp310-abi3-manylinux_2_28_x86_64.whl`) | |
| | `notebook.ipynb`, `notebooks/quickstart.ipynb` | install and diarize in a notebook (the same notebook; the first opens in Colab from this page) | |
| | `triton_inference/` | the serving kit: the `diar` command, the FastAPI service, the Triton images, Docker Compose, checks, a load test | |
| | `golden/audio/` | the 12 Indic DiarBench recordings of the install checks | |
| | `reference/` | the original Python program (`diarize.py`), its pickled weights and its install check | |
| | `src/` | the source of the pip package and the serving kit (Rust and Python), its tests and developer documents | |
| | `CHANGELOG.md` | release notes | |
| | `LICENSE-MIT`, `LICENSE-APACHE` | the code's licence (MIT OR Apache-2.0) | |
| | `THIRD_PARTY_NOTICES.md`, `LICENSES/` | third-party code, bundled libraries and Rust crates, with their licence texts | |
| | `third_party_sources/` | the source of the LGPL libraries the wheel bundles, and the script that built libsndfile | |
|
|
| </div> |
|
|
| ## Licence and attribution |
|
|
| - **Segmentation network:** a fine-tuned derivative of `BUT-FIT/diarizen-wavlm-large-s80-md-v2`, under **CC BY-NC 4.0 |
| (non-commercial)**. DiariZen's weights carry that licence because some of their training data is research-only. |
| - **PLDA model:** BUT Speech@FIT, CC BY 4.0. |
| - **Speaker-embedding model:** `pyannote/wespeaker-voxceleb-resnet34-LM`, CC BY 4.0. |
| - **Indic DiarBench audio** (`golden/audio`): derived from Sarvam AI's Indic DiarBench |
| (CC BY 4.0). Cite Mehendale et al., *Indic DiarBench*, Interspeech 2026 (arXiv 2607.23808). |
| - **DiariZen:** cite Han et al., ICASSP 2025, and arXiv 2505.24111 / 2506.18623. |
| - **Code:** MIT OR Apache-2.0, at your option ([`LICENSE-MIT`](LICENSE-MIT), [`LICENSE-APACHE`](LICENSE-APACHE)). |
| - It contains or ports code from DiariZen, torchaudio, pyannote.audio, WeSpeaker, VBx, SciPy, NumPy and |
| python-soundfile. |
| - The wheel bundles numpy 1.26.4's OpenBLAS (BSD-3) with libgfortran (GPL-3 with the GCC Runtime Library |
| Exception) and libquadmath (LGPL-2.1+), and libsndfile 1.2.2 (LGPL-2.1) with its codecs. |
| - Their licences and notices are in `THIRD_PARTY_NOTICES.md` and `LICENSES/`, and in the wheel. |
| - The source of the LGPL ones (libsndfile with its codecs, libquadmath) is in `third_party_sources/`. |
| |
| ## Acknowledgement |
|
|
| Anuvani-Diarization was conceived and developed by Stephen Fernandes under the leadership and guidance of Dr. Narayan Kumar Choudhary. We |
| thank Dr. Choudhary and the Central Institute of Indian Languages (CIIL) for their assistance, for the compute they |
| provided and for their continued support. |
|
|
| ## Citation |
|
|
| If you use this model, please cite the paper: |
|
|
| ```bibtex |
| @misc{fernandes2026anuvanidiarization, |
| title={Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages}, |
| author={Stephen Fernandes and Narayan Choudhary}, |
| year={2026}, |
| eprint={2610.20327}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2610.20327} |
| } |
| ``` |
|
|
