Instructions to use bklynhlth/WillisDiarize-v1-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bklynhlth/WillisDiarize-v1-quantized with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bklynhlth/WillisDiarize-v1-quantized")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bklynhlth/WillisDiarize-v1-quantized")
model = AutoModelForCausalLM.from_pretrained("bklynhlth/WillisDiarize-v1-quantized")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bklynhlth/WillisDiarize-v1-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bklynhlth/WillisDiarize-v1-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bklynhlth/WillisDiarize-v1-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bklynhlth/WillisDiarize-v1-quantized

SGLang

How to use bklynhlth/WillisDiarize-v1-quantized with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bklynhlth/WillisDiarize-v1-quantized" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bklynhlth/WillisDiarize-v1-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bklynhlth/WillisDiarize-v1-quantized" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bklynhlth/WillisDiarize-v1-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bklynhlth/WillisDiarize-v1-quantized with Docker Model Runner:
```
docker model run hf.co/bklynhlth/WillisDiarize-v1-quantized
```

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WillisDiarize-v1-quantized

Description

WillisDiarize-v1-quantized is an ensemble model for diarization correction as a post-processing step. It was fine-tuned using the Mistral-7B-Instruct v0.2 foundational model. This is the quantized version of the original model.

During fine-tuning, three separate automated speech recognition tools (namely AWS, Azure, and WhisperX) were used to generate the transcripts used. All fine-tuning was done on the Fisher corpus, a dataset of approximately 12,000 recorded conversations and their transcripts. For a full description of model development and performance testing, please read our preprint, Efstathiadis et al. (2024).

WillisDiarize is free to use for non-commercial purposes; see here for the full license text. If you are interested in using this model commercially, please getintouch@brooklyn.health.

Usage

Install OpenWillis to easily access this model on both AWS cloud or any high-performance GPU machine. Follow the installation steps here.

Citation

@article{bklynhlth/WillisDiarize,
  title={LLM-based speaker diarization correction: A generalizable approach},
  author={Efstathiadis et al.},
  journal={arXiv preprint arXiv:2406.04927},
  year={2024}
}

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Safetensors

Model size

7B params

Tensor type

I32

F16

Paper for bklynhlth/WillisDiarize-v1-quantized

LLM-based speaker diarization correction: A generalizable approach

Paper • 2406.04927 • Published Jun 7, 2024