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Model: google/DiarizationLM-8b-Fisher-v1 Source: Original Platform
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README.md
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README.md
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---
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license: llama3
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---
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**This is not an officially supported Google product.**
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## Overview
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Note: This model is outdated. Please use [google/DiarizationLM-8b-Fisher-v2](https://huggingface.co/google/DiarizationLM-8b-Fisher-v2) instead.
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[DiarizationLM](https://arxiv.org/abs/2401.03506) model finetuned
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on the training subset of the Fisher corpus.
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* Foundation model: [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)
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* Finetuning scripts: https://github.com/google/speaker-id/tree/master/DiarizationLM/unsloth
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## Training config
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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 25400 steps, which is ~8 epochs of the training data.
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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.
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The finetuning took more than 4 days on a Google Cloud VM instance that has one NVIDIA A100 GPU with 80GB memory.
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The maximal length of the prompt to this model is 6000 characters, including the " --> " suffix. The maximal sequence length is 4096 tokens.
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## Metrics
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### Fisher testing set
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| System | WER (%) | WDER (%) | cpWER (%) |
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| ------- | ------- | -------- | --------- |
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| USM + turn-to-diarize baseline | 15.48 | 5.32 | 21.19 |
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| + This model | - | 4.40 | 19.76 |
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### Callhome testing set
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| System | WER (%) | WDER (%) | cpWER (%) |
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| ------- | ------- | -------- | --------- |
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| USM + turn-to-diarize baseline | 15.36 | 7.72 | 24.39 |
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| + This model | - | 12.27 | 30.80 |
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## Usage
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First, you need to install two packages:
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```
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pip install transformers diarizationlm
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```
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On a machine with GPU and CUDA, you can use the model by running the following script:
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```python
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from transformers import LlamaForCausalLM, AutoTokenizer
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from diarizationlm import utils
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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."""
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print("Loading model...")
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tokenizer = AutoTokenizer.from_pretrained("google/DiarizationLM-8b-Fisher-v1", device_map="cuda")
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model = LlamaForCausalLM.from_pretrained("google/DiarizationLM-8b-Fisher-v1", device_map="cuda")
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print("Tokenizing input...")
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inputs = tokenizer([HYPOTHESIS + " --> "], return_tensors = "pt").to("cuda")
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print("Generating completion...")
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outputs = model.generate(**inputs,
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max_new_tokens = inputs.input_ids.shape[1] * 1.2,
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use_cache = False)
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print("Decoding completion...")
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completion = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[1]:],
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skip_special_tokens = True)[0]
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print("Transferring completion to hypothesis text...")
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transferred_completion = utils.transfer_llm_completion(completion, HYPOTHESIS)
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print("========================================")
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print("Hypothesis:", HYPOTHESIS)
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print("========================================")
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print("Completion:", completion)
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print("========================================")
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print("Transferred completion:", transferred_completion)
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print("========================================")
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```
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The output will look like below:
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```
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Loading model...
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Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
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Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.32s/it]
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generation_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 172/172 [00:00<00:00, 992kB/s]
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Tokenizing input...
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Generating completion...
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Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.
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Decoding completion...
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Transferring completion to hypothesis text...
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========================================
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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.
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========================================
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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:2
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========================================
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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.
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========================================
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```
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## Citation
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Our paper is cited as:
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```
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@article{wang2024diarizationlm,
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title={{DiarizationLM: Speaker Diarization Post-Processing with Large Language Models}},
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author={Quan Wang and Yiling Huang and Guanlong Zhao and Evan Clark and Wei Xia and Hank Liao},
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journal={arXiv preprint arXiv:2401.03506},
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year={2024}
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}
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```
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