---
title: DiarizationLM-Gemma-4-E4B-v1
canonical_url: "https://www.modelscope.cn/models/google/DiarizationLM-Gemma-4-E4B-v1"
md_url: "https://www.modelscope.cn/models/google/DiarizationLM-Gemma-4-E4B-v1.md"
repository: google/DiarizationLM-Gemma-4-E4B-v1
last_updated: 2026-10-05
license: apache-2.0
model_type:
  - gemma4
architectures:
  - Gemma4ForConditionalGeneration
base_model:
  - google/gemma-4-E4B
base_model_relation: quantized
parameters: 8.0B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - gguf
  - safetensors
frameworks:
  - pytorch
downloads: 137
stars: 3
tags:
  - speech
  - speaker-diarization
  - diarizationlm
  - gemma
  - gemma-4
  - gguf
  - long-context
  - multi-speaker
---

# DiarizationLM-Gemma-4-E4B-v1

> DiarizationLM-Gemma-4-E4B-v1 - google 在 ModelScope 开源的模型。DiarizationLM-Gemma-4-E4B-v1

google/DiarizationLM-Gemma-4-E4B-v1 是 ModelScope 魔搭社区上的 8.0B 参数机器学习模型，采用 apache-2.0 许可，基于 google/gemma-4-E4B 构建。

- **Repository**: google/DiarizationLM-Gemma-4-E4B-v1
- **License**: apache-2.0
- **Parameters**: 8.0B
- **Base model**: google/gemma-4-E4B
- **Tags**: speech, speaker-diarization, diarizationlm, gemma, gemma-4, gguf, long-context, multi-speaker
- **Downloads**: 137
- **Stars**: 3
- **Last updated**: 2026-10-05

Source: https://www.modelscope.cn/models/google/DiarizationLM-Gemma-4-E4B-v1

---

# DiarizationLM-Gemma-4-E4B-v1

**This is not an officially supported Google product.**

## Overview

[DiarizationLM](https://arxiv.org/abs/2401.03506) is a Large Language Model framework designed to post-process, correct, and optimize automatic speech recognition (ASR) and speaker diarization outputs.

`google/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**:
1. **Fisher English** (2-speaker conversational telephone speech)
2. **Callhome American English** (2–5 speaker informal telephone conversations)
3. **ICSI Meeting Corpus** (3–9 speaker academic research meetings)
4. **AMI Meeting Corpus** (4-speaker tabletop meetings)

* **Foundation model**: [google/gemma-4-E4B](https://huggingface.co/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](https://huggingface.co/google/DiarizationLM-8b-Fisher-v2)) or naive multi-domain SFT (which suffers from long-monologue speaker identity drift in 4+ speaker meetings), `google/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` and `Q4_0`)
* **Training Objective**: Completion-only cross-entropy loss (`<prompt> --> <completion> [eod]`)
* **Training Data**: 51,063 Fisher + 20,762 Multi-Corpus (Callhome, ICSI, AMI) locality-preserving prompt-completion pairs
* **Optimization**: 10,000 steps, global batch size 8, AdamW (beta1 = 0.9, beta2 = 0.99), peak learning rate 1.5e-4 with 500-step linear warmup and cosine decay on 8 Google Cloud TPU v5p chips
* **Prompt Segmentation Length**: 4,000 characters (maximal sequence length 2,560 tokens)

## 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`)
* `DiarizationLM-Gemma-4-E4B-v1-q4_0.gguf`: Serialized legacy 4-bit (`Q4_0`) GGUF model (`~5.15 GB`) for `llama.cpp` / Ollama / `llama-cpp-python`
* `config.json`, `generation_config.json`, `tokenizer.json`, `tokenizer_config.json`, `processor_config.json`, `chat_template.jinja`, `special_tokens_map.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):

| 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)<br>`DiarizationLM-8b-Fisher-v2` (Llama 3 8B)<br>**`DiarizationLM-Gemma-4-E4B-v1` (4B)** | 5.32 [4.93, 5.74]<br>3.28<br>**2.99 [2.65, 3.37]** | 20.88 [19.97, 21.88]<br>18.37<br>**17.62 [16.76, 18.56]** | 0.215 [0.151, 0.291]<br>---<br>**0.093 [0.047, 0.145]** | ---<br>---<br>**-2.33% [-2.51, -2.16]** (p < 0.0001) |
| **Callhome** | TEST FULL (20 calls) | 15.22 | Baseline (USM + Turn-to-Diarize)<br>`DiarizationLM-8b-Fisher-v2` (Llama 3 8B)<br>**`DiarizationLM-Gemma-4-E4B-v1` (4B)** | 7.74 [6.07, 9.65]<br>6.66<br>**4.92 [3.46, 6.75]** | 24.31 [21.27, 27.43]<br>23.57<br>**20.69 [17.98, 23.66]** | 0.050 [0.000, 0.150]<br>---<br>**0.000 [0.000, 0.000]** | ---<br>---<br>**-2.82% [-3.48, -2.14]** (p < 0.0001) |
| **ICSI** | TEST FULL (3 meetings) | 29.42 | Baseline (USM + Turn-to-Diarize)<br>**`DiarizationLM-Gemma-4-E4B-v1` (4B)** | 14.70 [11.65, 20.29]<br>**14.10 [10.77, 19.94]** | 43.90 [39.10, 51.67]<br>**43.32 [38.38, 51.20]** | 0.333 [0.000, 1.000]<br>**0.333 [0.000, 1.000]** | ---<br>**-0.60% [-0.88, -0.35]** (p < 0.0001) |
| **AMI** | TEST WORD FULL (16 meetings) | 24.33 | Baseline (USM + Turn-to-Diarize)<br>**`DiarizationLM-Gemma-4-E4B-v1` (4B)** | 15.68 [10.64, 21.11]<br>**14.89 [9.80, 20.38]** | 40.32 [32.43, 48.00]<br>**39.57 [31.57, 47.29]** | 0.500 [0.188, 0.812]<br>**0.438 [0.188, 0.750]** | ---<br>**-0.79% [-1.00, -0.60]** (p < 0.0001) |

## 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 utils
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "google/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},
}
```