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--- |
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license: mit |
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base_model: pyannote/segmentation-3.0 |
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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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datasets: |
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- diarizers-community/callhome |
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model-index: |
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- name: speaker-segmentation-fine-tuned-callhome-deu |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# speaker-segmentation-fine-tuned-callhome-deu |
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This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome deu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3780 |
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- Der: 0.1415 |
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- False Alarm: 0.0724 |
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- Missed Detection: 0.0490 |
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- Confusion: 0.0201 |
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## Model description |
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This segmentation model has been trained on German data (Callhome) using [diarizers](https://github.com/huggingface/diarizers/tree/main). |
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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-deu') |
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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 pyannote.audio import Pipeline |
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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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# replace the segmentation model with your fine-tuned one |
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model = segmentation_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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# load dataset example |
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dataset = load_dataset("diarizers-community/callhome", "deu", 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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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 32 |
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- eval_batch_size: 32 |
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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: 5.0 |
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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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| 0.4622 | 1.0 | 330 | 0.3844 | 0.1439 | 0.0653 | 0.0562 | 0.0223 | |
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| 0.4306 | 2.0 | 660 | 0.4004 | 0.1519 | 0.0763 | 0.0515 | 0.0241 | |
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| 0.4069 | 3.0 | 990 | 0.3775 | 0.1407 | 0.0707 | 0.0496 | 0.0204 | |
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| 0.3949 | 4.0 | 1320 | 0.3771 | 0.1408 | 0.0710 | 0.0498 | 0.0200 | |
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| 0.3879 | 5.0 | 1650 | 0.3780 | 0.1415 | 0.0724 | 0.0490 | 0.0201 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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