transformers tensorboard safetensors whisper generated_from_trainer base_model:openai/whisper-tiny license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
model-index:
- name: whisper-diarization-0.2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# whisper-diarization-0.2

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5895
- eval_speech_scored: 619.9204
- eval_speech_miss: 375.8010
- eval_speech_falarm: 214.8806
- eval_speaker_miss: 891.5274
- eval_speaker_falarm: 215.0796
- eval_speaker_error: 136.2537
- eval_speaker_correct: 1040.2952
- eval_diarization_error: 1242.8607
- eval_frames: 1500.0
- eval_speaker_wide_frames: 1511.7811
- eval_speech_scored_ratio: 0.4133
- eval_speech_miss_ratio: 0.2505
- eval_speech_falarm_ratio: 0.1433
- eval_speaker_correct_ratio: 0.6935
- eval_speaker_miss_ratio: 0.5219
- eval_speaker_falarm_ratio: 0.5440
- eval_speaker_error_ratio: 0.0796
- eval_diarization_error_ratio: 1.1455
- eval_runtime: 2.2469
- eval_samples_per_second: 89.456
- eval_steps_per_second: 4.006
- epoch: 3.0
- step: 36

## Model description

More information needed

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
