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Model: google/DiarizationLM-8b-Fisher-v1 Source: Original Platform
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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Note: This model is outdated.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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config.json
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{
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"_name_or_path": "unsloth/llama-3-8b-bnb-4bit",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.41.2",
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"unsloth_version": "2024.6",
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"use_cache": true,
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"vocab_size": 128256
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": 128001,
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"max_length": 4096,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.41.2"
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}
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||||
}
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||||
3
q4_k_m.gguf
Normal file
3
q4_k_m.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:2a5772f400cddedd9ccf56ec3beb793903367ba8eada647a22fd8900f487b11f
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size 4920733568
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3
q8_0.gguf
Normal file
3
q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:7943a1c2d7ebb699b8cb3a61eb3b46969214c52137ed925f3600101665fb02fe
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size 8540770176
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
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"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
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||||
"pad_token": {
|
||||
"content": "<|reserved_special_token_250|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e134af98b985517b4f068e3755ae90d4e9cd2d45d328325dc503f1c6b2d06cc7
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||||
size 9085698
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2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user