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whisper-diarization-0.2 – AI Model by anakib1 | AlphaNeural AI
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whisper-diarization-0.2
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transformers
tensorboard
safetensors
whisper
generated_from_trainer
openai/whisper-tiny
finetune
apache-2.0
endpoints_compatible
us
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whisper-diarization-0.2
This model is a fine-tuned version of
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
