license: other
license_name: adobe-research-license
license_link: https://github.com/adobe-research/speaker-identification/blob/main/LICENSE.md
library_name: pytorch
base_model: FacebookAI/roberta-large
tags:
- speaker-identification
- quantization
- int4
- roberta
- mediasum
Joint SpeakerID INT4 (weight-only)
4-bit group-wise weight-only quantisation of the Joint Speaker Identifier from adobe-research/speaker-identification (Interspeech 2024).
This checkpoint is a quantised child of Adobe Research’s original Joint Speaker Identifier (FP32). The architecture and trained weights are Adobe’s; only the Linear weights were packed to INT4 (group size 64). No additional training.
| Metric | Value |
|---|---|
| Precision | 83.33 |
| Δ vs FP32 parent | +4.46 |
| F1 | 60.24 |
| Accuracy | 63.87 |
| Throughput | 12.09 examples/s (Apple M3 Pro, MPS, batch 2) |
| In-memory size | 294 MB (FP32 parent: 1633 MB) |
Smallest Joint checkpoint (~5.6× vs the FP32 parent). Precision rises because the model becomes more conservative; recall, F1, and accuracy drop versus the FP32/INT8 parent. Unpack-on-MPS is slower than FP16/INT8.
The INT8 sibling (hmarchant/speaker-id-joint-int8) matches the FP32 parent on precision/F1/accuracy.
Parent model
| Parent | Joint Speaker Identifier (FP32), Adobe Research |
| Original weights | logs/mediasum-joint/best-model.mdl in adobe-research/speaker-identification |
| Paper | Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models (Interspeech 2024) |
| Backbone | FacebookAI/roberta-large |
| Relation | weight-only INT4 quantisation of the Adobe Joint checkpoint |
Files
model.pt— quantised bundle (scheme=int4_weight, group size 64)config.json— metrics and load metadata
Load
from huggingface_hub import hf_hub_download
path = hf_hub_download("hmarchant/speaker-id-joint-int4", "model.pt")
License
Derived from Adobe Research Speaker Identification. The Adobe Research License allows non-commercial research use only.