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Cataloged from google/DiarizationLM-Gemma-4-E4B-v1
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DiarizationLM 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:
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), 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).
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)<prompt> --> <completion> [eod])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-pythonconfig.json, generation_config.json, tokenizer.json, tokenizer_config.json, processor_config.json, chat_template.jinja, special_tokens_map.json: Tokenizer and model configuration filesAll 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):
transformers + diarizationlm)First, install the required packages:
Run inference on a GPU with bfloat16:
llama.cpp)You can also run the quantized DiarizationLM-Gemma-4-E4B-v1-q4_k_m.gguf model directly with llama.cpp:
pip install transformers diarizationlmfrom diarizationlm import utilsimport torchfrom 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("========================================")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@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},}