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Overview
DiarizationLM model finetuned on the training subset of the Fisher corpus.
- Foundation model: unsloth/llama-2-13b-bnb-4bit
- Finetuning scripts: https://github.com/google/speaker-id/tree/master/DiarizationLM/unsloth
Training config
This model is finetuned on the training subset of the Fisher corpus, using a LoRA adapter of rank 256. The total number of training parameters is 1,001,390,080. With a batch size of 16, this model has been trained for 12000 steps, which is ~4 epochs of the training data.
We use the mixed flavor during our training, meaning we combine data from hyp2ora and `deg2ref flavors. After the prompt builder, we have a total of 48,142 prompt-completion pairs in our training set.
The finetuning took more than 3 days on a Google Cloud VM instance that has one NVIDIA A100 GPU with 80GB memory.
The maximal length of the prompt to this model is 6000 characters, including the " --> " suffix. The maximal sequence length is 4096 tokens.
Metrics
Performance on the Fisher testing set:
| System | WER (%) | WDER (%) | cpWER (%) |
|---|---|---|---|
| USM + turn-to-diarize baseline | 15.48 | 5.32 | 21.19 |
| + This model | - | 3.65 | 18.92 |
Usage
First, you need to install two packages:
pip install transformers diarizationlm
On a machine with GPU and CUDA, you can use the model by running the following script:
from transformers import LlamaForCausalLM, LlamaTokenizer
from diarizationlm import utils
HYPOTHESIS = """<speaker:1> Hello, how are you doing <speaker:2> today? I am doing well. What about <speaker:1> you? I'm doing well, too. Thank you."""
print("Loading model...")
tokenizer = LlamaTokenizer.from_pretrained("google/DiarizationLM-13b-Fisher-v1", device_map="cuda")
model = LlamaForCausalLM.from_pretrained("google/DiarizationLM-13b-Fisher-v1", device_map="cuda")
print("Tokenizing input...")
inputs = tokenizer([HYPOTHESIS + " --> "], return_tensors = "pt").to("cuda")
print("Generating completion...")
outputs = model.generate(**inputs,
max_new_tokens = inputs.input_ids.shape[1] * 1.2,
use_cache = False)
print("Decoding completion...")
completion = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[1]:],
skip_special_tokens = True)[0]
print("Transferring completion to hypothesis text...")
transferred_completion = utils.transfer_llm_completion(completion, HYPOTHESIS)
print("========================================")
print("Hypothesis:", HYPOTHESIS)
print("========================================")
print("Completion:", completion)
print("========================================")
print("Transferred completion:", transferred_completion)
print("========================================")
The output will look like below:
Loading model...
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:17<00:00, 2.84s/it]
Tokenizing input...
Generating completion...
Decoding completion...
Transferring completion to hypothesis text...
========================================
Hypothesis: <speaker:1> Hello, how are you doing <speaker:2> today? I am doing well. What about <speaker:1> you? I'm doing well, too. Thank you.
========================================
Completion: 19:27 <speaker:1> hello, how are you doing today? <speaker:2> i am doing well. What about you? <speaker:1> i'm doing well, too. thank you. <speaker:2> my name
========================================
Transferred completion: <speaker:1> Hello, how are you doing today? <speaker:2> I am doing well. What about you? <speaker:1> I'm doing well, too. Thank you.
Citation
Our paper is cited as:
@article{wang2024diarizationlm,
title={{DiarizationLM: Speaker Diarization Post-Processing with Large Language Models}},
author={Quan Wang and Yiling Huang and Guanlong Zhao and Evan Clark and Wei Xia and Hank Liao},
journal={arXiv preprint arXiv:2401.03506},
year={2024}
}
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