This is not an officially supported Google product.
Overview
DiarizationLM model finetuned on the training subset of the Fisher corpus.
- Foundation model: unsloth/llama-3-8b-bnb-4bit
- Finetuning scripts: https://github.com/google/speaker-id/tree/master/DiarizationLM/unsloth
The difference between this model and google/DiarizationLM-8b-Fisher-v1:
- For this model, the loss is only computed on the completion tokens.
- For
google/DiarizationLM-8b-Fisher-v1, the loss is computed also on the prompt tokens.
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 671,088,640. With a batch size of 16, this model has been trained for 28800 steps, which is ~9 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 51,063 prompt-completion pairs in our training set.
The finetuning took more than 4 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
Fisher testing set
| System | WER (%) | WDER (%) | cpWER (%) |
|---|---|---|---|
| USM + turn-to-diarize baseline | 15.48 | 5.32 | 21.19 |
| + This model | - | 3.28 | 18.37 |
Callhome testing set
| System | WER (%) | WDER (%) | cpWER (%) |
|---|---|---|---|
| USM + turn-to-diarize baseline | 15.36 | 7.72 | 24.39 |
| + This model | - | 6.66 | 23.57 |
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, AutoTokenizer
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 = AutoTokenizer.from_pretrained("google/DiarizationLM-8b-Fisher-v2", device_map="cuda")
model = LlamaForCausalLM.from_pretrained("google/DiarizationLM-8b-Fisher-v2", 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...
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.32s/it]
generation_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 172/172 [00:00<00:00, 992kB/s]
Tokenizing input...
Generating completion...
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.
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: <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. [eod] [eod] <speaker:1
========================================
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}
}