---
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](https://github.com/adobe-research/speaker-identification)
([Interspeech 2024](https://arxiv.org/abs/2407.12094)).

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](https://huggingface.co/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](https://github.com/adobe-research/speaker-identification) |
| Paper | [Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models](https://arxiv.org/abs/2407.12094) (Interspeech 2024) |
| Backbone | [FacebookAI/roberta-large](https://huggingface.co/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

```python
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](https://github.com/adobe-research/speaker-identification/blob/main/LICENSE.md)
allows **non-commercial research use only**.