🤗 Diarizers is a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem. It can be used to improve performance on both English and multilingual diarization datasets with simple example scripts, with as little as ten hours of labelled diarization data and just 5 minutes of GPU compute time.

• Installation
• Train
• Evaluation
• Inference
• Adding new datasets
• Acknowledgements
• Citation

First, clone the repository and install the dependencies:

git clone https://github.com/huggingface/diarizers.git
cd diarizers
pip install -e .

To load pre-trained diarization models from the Hub, you'll first need to accept the terms-of-use for the following two models:

1. pyannote/segmentation-3.0
2. pyannote/speaker-diarization-3.1

To load the CallHome dataset from the Hub, you'll also need to accept its terms of use:

1. diarizers-community/callhome

And subsequently use a Hugging Face authentication token to log in with:

huggingface-cli login

The script train_segmentation.py can be used to pre-process a diarization dataset and subsequently fine-tune the pyannote segmentation model. In the following example, we fine-tune the segmentation model on the Japanese subset of the CallHome dataset, a conversational dataset between native speakers:

python3 train_segmentation.py \
    --dataset_name=diarizers-community/callhome \
    --dataset_config_name=jpn \
    --split_on_subset=data \
    --model_name_or_path=pyannote/segmentation-3.0 \
    --output_dir=./speaker-segmentation-fine-tuned-callhome-jpn \
    --do_train \
    --do_eval \
    --learning_rate=1e-3 \
    --num_train_epochs=5 \
    --lr_scheduler_type=cosine \
    --per_device_train_batch_size=32 \
    --per_device_eval_batch_size=32 \
    --evaluation_strategy=epoch \
    --save_strategy=epoch \
    --preprocessing_num_workers=2 \
    --dataloader_num_workers=2 \
    --logging_steps=100 \
    --load_best_model_at_end \
    --push_to_hub

On a single NVIDIA RTX 24GB GPU, training takes approximately 5 minutes and improves the diarization error rate (DER) from 25% to 18%, representing a 28% relative improvement in performance. The final model will be pushed to the Hugging Face Hub, for example the checkpoint diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn.

We encourage you to swap the CallHome Japanese dataset for a dataset in your language of choice. The CallHome dataset provides splits for four additional languages, and there are a number of other diarization datasets available on the Hugging Face Hub. We also provide instructions for adding new datasets.

To train on a different dataset, simply change the arguments:

• dataset_name: Specify a dataset from the Hub on which to fine-tune your model.
• dataset_config_name: If the dataset contains multiple language subsets, select the language ID of the subset you want to train on.

If the data set doesn't contain a train and a validation split, you can automatically split it into train-val-test (90-10-10) by setting the argument:

• split_on_subset: Specify the subset of the dataset you want to split into train-val-set.

The script test_segmentation.py can be used to evaluate a fine-tuned model on a diarization dataset. In the following example, we evaluate the fine-tuned model from the previous step on the test split of the CallHome Japanese dataset:

python3 test_segmentation.py \
    --dataset_name=diarizers-community/callhome \
    --dataset_config_name=jpn \
    --split_on_subset=data \
    --test_split_name=test \
    --model_name_or_path=diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn \
    --preprocessing_num_workers=2 \
    --evaluate_with_pipeline \

The fine-tuned segmentation model can easily be loaded into the pyannote speaker diarization pipeline for inference. To do so, we load the pre-trained speaker diarization pipeline, and subsequently override the segmentation model with our fine-tuned checkpoint:

from diarizers import SegmentationModel
from pyannote.audio import Pipeline
from datasets import load_dataset
import torch

device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

# load the pre-trained pyannote pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
pipeline.to(device)

# replace the segmentation model with your fine-tuned one
model = SegmentationModel().from_pretrained("diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn")
model = model.to_pyannote_model()
pipeline._segmentation.model = model.to(device)

# load dataset example
dataset = load_dataset("diarizers-community/callhome", "jpn", split="data")
sample = dataset[0]["audio"]

# pre-process inputs
sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype)
sample["sample_rate"] = sample.pop("sampling_rate")

# perform inference
diarization = pipeline(sample)

# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
    diarization.write_rttm(rttm)

To apply the diarization pipeline directly to an audio file, simply call:

diarization = pipeline("audio.wav")

In order to be compatible with our training script, the Hugging Face dataset should contain the following features:

• audio: Audio feature.
• speakers: The list of audio speakers, with their order of appearance.
• timestamps_start: A list of timestamps indicating the start of each speaker segment.
• timestamps_end: A list of timestamps indicating the end of each speaker segment.

We added several speaker-diarization datasets to the hub in the diarizers-community organisation. These datasets have been generated using the scripts in datasets/spd_datasets.py : The idea is to convert any raw speaker diarization dataset containing <audio, annotation> pairs into a Hugging Face dataset.

See Adding a dataset for more details on how to add speaker diarization datasets to the hub.

This library builds on top of pyannote library as well as several Hugging Face libraries (transformers, datasets, accelerate). We would like to extend our warmest thanks to their developers!

If you found this repository useful, please consider citing this work and also the original pyannote paper:

@misc{akesbi-diarizers,
  author = {Kamil Akesbi and Sanchit Gandhi},
  title = {"Diarizers: A repository for fine-tuning speaker diarization models"},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/diarizers}}
}

@inproceedings{bredin-pyannote,
  author={Hervé Bredin},
  title={"pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe"},
  year={2023},
  booktitle={Proc. INTERSPEECH 2023},
}