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speaker-segmentation-eng
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---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
model-index:
- name: speaker-segmentation-eng
  results: []
---


# speaker-segmentation-eng

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4666
- Der: 0.1827
- False Alarm: 0.0590
- Missed Detection: 0.0715
- Confusion: 0.0522

## Model description

This segmentation model has been trained on English data (Callhome) using diarizers. It can be loaded with two lines of code:
```python
from diarizers import SegmentationModel

segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn')
```

To use it within a pyannote speaker diarization pipeline, load the [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline, and convert the model to a pyannote compatible format: 

```python

from diarizers import SegmentationModel
from pyannote.audio import Pipeline
from datasets import load_dataset
import torch

device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

# load the pre-trained pyannote pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
pipeline.to(device)

model = SegmentationModel().from_pretrained("nehulagrawal/speaker-segmentation-eng")
model = model.to_pyannote_model()
pipeline._segmentation.model = model.to(device)
```

You can now use the pipeline on audio examples: 

```python
from datasets import load_dataset
# load dataset example
dataset = load_dataset("diarizers-community/callhome", "eng", split="data")
sample = dataset[0]["audio"]

# pre-process inputs
sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype)
sample["sample_rate"] = sample.pop("sampling_rate")

# perform inference
diarization = pipeline(sample)

# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
    diarization.write_rttm(rttm)
```

You can now use the pipeline on single audio examples:

```python

from diarizers import SegmentationModel
from pyannote.audio import Pipeline
from datasets import load_dataset
import torch

device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

# load the pre-trained pyannote pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
pipeline.to(device)

model = SegmentationModel().from_pretrained("nehulagrawal/speaker-segmentation-eng")
model = model.to_pyannote_model()
pipeline._segmentation.model = model.to(device)

diarization = pipeline("audio.wav")
with open("audio.rttm", "w") as rttm:
    diarization.write_rttm(rttm)

```
## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4224        | 1.0   | 181  | 0.4837          | 0.1939 | 0.0599      | 0.0764           | 0.0576    |
| 0.409         | 2.0   | 362  | 0.4692          | 0.1884 | 0.0618      | 0.0724           | 0.0543    |
| 0.3919        | 3.0   | 543  | 0.4700          | 0.1875 | 0.0638      | 0.0698           | 0.0540    |
| 0.3693        | 4.0   | 724  | 0.4718          | 0.1848 | 0.0602      | 0.0714           | 0.0533    |
| 0.358         | 5.0   | 905  | 0.4606          | 0.1810 | 0.0544      | 0.0754           | 0.0512    |
| 0.355         | 6.0   | 1086 | 0.4631          | 0.1826 | 0.0638      | 0.0677           | 0.0512    |
| 0.3563        | 7.0   | 1267 | 0.4646          | 0.1809 | 0.0587      | 0.0716           | 0.0505    |
| 0.347         | 8.0   | 1448 | 0.4682          | 0.1820 | 0.0581      | 0.0720           | 0.0519    |
| 0.3463        | 9.0   | 1629 | 0.4684          | 0.1827 | 0.0586      | 0.0718           | 0.0523    |
| 0.3299        | 10.0  | 1810 | 0.4666          | 0.1827 | 0.0590      | 0.0715           | 0.0522    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1