DiarizationLM-Gemma-4-E4B-v1

模型介绍

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**: 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) 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 --> [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)

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。