diarizers-community

DiarizationLM-Gemma-4-E4B-v1

README

License: apache-2.0

Overview

DiarizationLM is a Large Language Model framework designed to post-process, correct, and optimize automatic speech recognition (ASR) and speaker diarization outputs.

diarizers-community/DiarizationLM-Gemma-4-E4B-v1 is built on Google's Gemma 4 E4B (4B dense parameters) foundation model and fine-tuned with Locality-Preserving Oracle Supervision across all 4 canonical speaker diarization benchmark corpora:

Fisher English (2-speaker conversational telephone speech)
Callhome American English (2–5 speaker informal telephone conversations)
ICSI Meeting Corpus (3–9 speaker academic research meetings)
AMI Meeting Corpus (4-speaker tabletop meetings)

Foundation model: google/gemma-4-E4B
Open-source library & scripts: https://github.com/google/speaker-id/tree/master/DiarizationLM

Unlike earlier models trained exclusively on 2-speaker telephone data (such as google/DiarizationLM-8b-Fisher-v2) or naive multi-domain SFT (which suffers from long-monologue speaker identity drift in 4+ speaker meetings), diarizers-community/DiarizationLM-Gemma-4-E4B-v1 is trained on locality-preserving oracle targets that teach the model to correct lexical turn boundaries and backchannels (1 ≤ L ≤ 5 words) while preserving acoustic speaker anchors across long monologues (L ≥ 6 words). As a result, it achieves statistically significant (p < 0.0001) WDER and cpWER improvements simultaneously across all four benchmarks under standard out-of-the-box Transcript-Preserving Speaker Transfer (transfer_llm_completion), despite having half the parameter count (4B vs. 8B).

Training Configuration

Base Architecture: Gemma 4 E4B (42 layers, hidden size 2560, hybrid 5:1 sliding-window and global attention, 262,144 vocabulary size)
LoRA Adapter: Rank r = 256 applied to all attention (q_proj, k_proj, v_proj, o_proj), MLP (gate_proj, up_proj, down_proj), and per-layer input (per_layer_input_gate, per_layer_projection, per_layer_model_projection) linear projections, merged into 16-bit (bfloat16) base weights and serialized to 4-bit GGUF (Q4_K_M)
Training Objective: Completion-only cross-entropy loss (<prompt> --> <completion> [eod])

Model Files Included

model.safetensors: Merged 16-bit (bfloat16) Hugging Face transformers weights (~16.0 GB)
DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf: (Recommended GGUF) Serialized 4-bit K-quant Medium (Q4_K_M) GGUF model (~5.30 GB) for llama.cpp / Ollama / llama-cpp-python (uses 256-element super-blocks in Q4_K with sensitive attn_v / ffn_down and embedding matrices retained in 6-bit Q6_K)
config.json, , , , , , : Tokenizer and model configuration files

Benchmark Performance (with 95% Bootstrap Confidence Intervals)

All metrics below are micro-averaged across the full evaluation sets using the USM + turn-to-diarize baseline and scored via Hungarian-matching dynamic programming (diarizationlm.compute_metrics_on_json_dict). Ranges in brackets indicate 95% non-parametric bootstrap confidence intervals (B = 10,000 conversation-level resamples):

Table with columns: Benchmark, Test Split, WER (%), System, WDER (%) [95% CI], cpWER (%) [95% CI], SpkCntMAE [95% CI], Paired ΔWDER vs. Baseline (p-value)
Benchmark	Test Split	WER (%)	System	WDER (%) [95% CI]	cpWER (%) [95% CI]	SpkCntMAE [95% CI]	Paired ΔWDER vs. Baseline (p-value)
Fisher	TEST FULL (172 sessions)	15.37	Baseline (USM + Turn-to-Diarize)`DiarizationLM-8b-Fisher-v2` (Llama 3 8B)`DiarizationLM-Gemma-4-E4B-v1` (4B)	5.32 [4.93, 5.74]3.282.99 [2.65, 3.37]	20.88 [19.97, 21.88]18.3717.62 [16.76, 18.56]	0.215 [0.151, 0.291]---

Usage

1. Python (`transformers` + `diarizationlm`)

First, install the required packages:

bash

pip install transformers diarizationlm

Run inference on a GPU with bfloat16:

python

from diarizationlm import utilsimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_ID = "diarizers-community/DiarizationLM-Gemma-4-E4B-v1" 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(MODEL_ID, device_map="cuda")model = AutoModelForCausalLM.from_pretrained(    MODEL_ID, torch_dtype=torch.bfloat16, device_map="cuda") print("Tokenizing input...")prompt = f"<|turn>user\n{HYPOTHESIS} --> <turn|>\n<|turn>model\n"inputs = tokenizer([prompt], return_tensors="pt").to("cuda") print("Generating completion...")outputs = model.generate(    **inputs,    max_new_tokens=int(inputs.input_ids.shape[1] * 1.2),    do_sample=False,    use_cache=True,) print("Decoding completion...")completion = tokenizer.batch_decode(    outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]completion = utils.truncate_suffix_and_tailing_text(completion, " [eod]") 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("========================================")

2. GGUF (`llama.cpp`)

You can also run the quantized DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf model directly with llama.cpp:

bash

llama-cli \  -m DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf \  -p $'<|turn>user\n<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. --> <turn|>\n<|turn>model\n' \  --temp 0.0 \  -n 128

Citation

bibtex

@inproceedings{wang24h_interspeech,  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},  year      = {2024},  booktitle = {Interspeech 2024},  pages     = {3754--3758},  doi       = {10.21437/Interspeech.2024-209},}

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Model Details

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diarizers-community

Model Tree

Base

google/gemma-4-E4B

Quantized

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README

License: apache-2.0

Overview

DiarizationLM is a Large Language Model framework designed to post-process, correct, and optimize automatic speech recognition (ASR) and speaker diarization outputs.

Fisher English (2-speaker conversational telephone speech)
Callhome American English (2–5 speaker informal telephone conversations)
ICSI Meeting Corpus (3–9 speaker academic research meetings)
AMI Meeting Corpus (4-speaker tabletop meetings)

Foundation model: google/gemma-4-E4B
Open-source library & scripts: https://github.com/google/speaker-id/tree/master/DiarizationLM

Training Configuration

Base Architecture: Gemma 4 E4B (42 layers, hidden size 2560, hybrid 5:1 sliding-window and global attention, 262,144 vocabulary size)
LoRA Adapter: Rank r = 256 applied to all attention (q_proj, k_proj, v_proj, o_proj), MLP (gate_proj, up_proj, down_proj), and per-layer input (per_layer_input_gate, per_layer_projection, per_layer_model_projection) linear projections, merged into 16-bit (bfloat16) base weights and serialized to 4-bit GGUF (Q4_K_M)
Training Objective: Completion-only cross-entropy loss (<prompt> --> <completion> [eod])

Model Files Included

model.safetensors: Merged 16-bit (bfloat16) Hugging Face transformers weights (~16.0 GB)
DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf: (Recommended GGUF) Serialized 4-bit K-quant Medium (Q4_K_M) GGUF model (~5.30 GB) for llama.cpp / Ollama / llama-cpp-python (uses 256-element super-blocks in Q4_K with sensitive attn_v / ffn_down and embedding matrices retained in 6-bit Q6_K)
config.json, , , , , , : Tokenizer and model configuration files

Benchmark Performance (with 95% Bootstrap Confidence Intervals)

Table with columns: Benchmark, Test Split, WER (%), System, WDER (%) [95% CI], cpWER (%) [95% CI], SpkCntMAE [95% CI], Paired ΔWDER vs. Baseline (p-value)
Benchmark	Test Split	WER (%)	System	WDER (%) [95% CI]	cpWER (%) [95% CI]	SpkCntMAE [95% CI]	Paired ΔWDER vs. Baseline (p-value)
Fisher	TEST FULL (172 sessions)	15.37	Baseline (USM + Turn-to-Diarize)`DiarizationLM-8b-Fisher-v2` (Llama 3 8B)`DiarizationLM-Gemma-4-E4B-v1` (4B)	5.32 [4.93, 5.74]3.282.99 [2.65, 3.37]	20.88 [19.97, 21.88]18.3717.62 [16.76, 18.56]	0.215 [0.151, 0.291]---

Usage

1. Python (`transformers` + `diarizationlm`)

First, install the required packages:

bash

pip install transformers diarizationlm

Run inference on a GPU with bfloat16:

python

from diarizationlm import utilsimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_ID = "diarizers-community/DiarizationLM-Gemma-4-E4B-v1" 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(MODEL_ID, device_map="cuda")model = AutoModelForCausalLM.from_pretrained(    MODEL_ID, torch_dtype=torch.bfloat16, device_map="cuda") print("Tokenizing input...")prompt = f"<|turn>user\n{HYPOTHESIS} --> <turn|>\n<|turn>model\n"inputs = tokenizer([prompt], return_tensors="pt").to("cuda") print("Generating completion...")outputs = model.generate(    **inputs,    max_new_tokens=int(inputs.input_ids.shape[1] * 1.2),    do_sample=False,    use_cache=True,) print("Decoding completion...")completion = tokenizer.batch_decode(    outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]completion = utils.truncate_suffix_and_tailing_text(completion, " [eod]") 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("========================================")

2. GGUF (`llama.cpp`)

You can also run the quantized DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf model directly with llama.cpp:

bash

llama-cli \  -m DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf \  -p $'<|turn>user\n<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. --> <turn|>\n<|turn>model\n' \  --temp 0.0 \  -n 128

Citation

bibtex

@inproceedings{wang24h_interspeech,  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},  year      = {2024},  booktitle = {Interspeech 2024},  pages     = {3754--3758},  doi       = {10.21437/Interspeech.2024-209},}

DiarizationLM-Gemma-4-E4B-v1

README

Overview

Training Configuration

Model Files Included

Benchmark Performance (with 95% Bootstrap Confidence Intervals)

Usage

1. Python (transformers + diarizationlm)

2. GGUF (llama.cpp)

Citation

Explore FriendliAI today

README

Overview

Training Configuration

Model Files Included

Benchmark Performance (with 95% Bootstrap Confidence Intervals)

Usage

1. Python (transformers + diarizationlm)

2. GGUF (llama.cpp)

Citation

1. Python (`transformers` + `diarizationlm`)

2. GGUF (`llama.cpp`)

1. Python (`transformers` + `diarizationlm`)

2. GGUF (`llama.cpp`)