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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- speaker-diarization |
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- speaker-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: speaker-segmentation-eng |
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results: [] |
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--- |
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# speaker-segmentation-eng |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4666 |
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- Der: 0.1827 |
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- False Alarm: 0.0590 |
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- Missed Detection: 0.0715 |
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- Confusion: 0.0522 |
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## Model description |
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This segmentation model has been trained on English data (Callhome) using diarizers. It can be loaded with two lines of code: |
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```python |
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from diarizers import SegmentationModel |
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segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn') |
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``` |
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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: |
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```python |
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from diarizers import SegmentationModel |
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from pyannote.audio import Pipeline |
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from datasets import load_dataset |
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import torch |
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") |
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# load the pre-trained pyannote pipeline |
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1") |
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pipeline.to(device) |
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model = SegmentationModel().from_pretrained("nehulagrawal/speaker-segmentation-eng") |
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model = model.to_pyannote_model() |
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pipeline._segmentation.model = model.to(device) |
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``` |
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You can now use the pipeline on audio examples: |
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```python |
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from datasets import load_dataset |
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# load dataset example |
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dataset = load_dataset("diarizers-community/callhome", "eng", split="data") |
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sample = dataset[0]["audio"] |
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# pre-process inputs |
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sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype) |
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sample["sample_rate"] = sample.pop("sampling_rate") |
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# perform inference |
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diarization = pipeline(sample) |
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# dump the diarization output to disk using RTTM format |
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with open("audio.rttm", "w") as rttm: |
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diarization.write_rttm(rttm) |
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``` |
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You can now use the pipeline on single audio examples: |
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```python |
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from diarizers import SegmentationModel |
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from pyannote.audio import Pipeline |
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from datasets import load_dataset |
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import torch |
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") |
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# load the pre-trained pyannote pipeline |
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1") |
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pipeline.to(device) |
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model = SegmentationModel().from_pretrained("nehulagrawal/speaker-segmentation-eng") |
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model = model.to_pyannote_model() |
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pipeline._segmentation.model = model.to(device) |
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diarization = pipeline("audio.wav") |
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with open("audio.rttm", "w") as rttm: |
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diarization.write_rttm(rttm) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:| |
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| 0.4224 | 1.0 | 181 | 0.4837 | 0.1939 | 0.0599 | 0.0764 | 0.0576 | |
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| 0.409 | 2.0 | 362 | 0.4692 | 0.1884 | 0.0618 | 0.0724 | 0.0543 | |
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| 0.3919 | 3.0 | 543 | 0.4700 | 0.1875 | 0.0638 | 0.0698 | 0.0540 | |
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| 0.3693 | 4.0 | 724 | 0.4718 | 0.1848 | 0.0602 | 0.0714 | 0.0533 | |
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| 0.358 | 5.0 | 905 | 0.4606 | 0.1810 | 0.0544 | 0.0754 | 0.0512 | |
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| 0.355 | 6.0 | 1086 | 0.4631 | 0.1826 | 0.0638 | 0.0677 | 0.0512 | |
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| 0.3563 | 7.0 | 1267 | 0.4646 | 0.1809 | 0.0587 | 0.0716 | 0.0505 | |
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| 0.347 | 8.0 | 1448 | 0.4682 | 0.1820 | 0.0581 | 0.0720 | 0.0519 | |
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| 0.3463 | 9.0 | 1629 | 0.4684 | 0.1827 | 0.0586 | 0.0718 | 0.0523 | |
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| 0.3299 | 10.0 | 1810 | 0.4666 | 0.1827 | 0.0590 | 0.0715 | 0.0522 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
