rakshit10

whisper-small-hi_in-fleurs-smoke

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README

License: apache-2.0

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 100
mixed_precision_training: Native AMP

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer, Cer
Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.5303	2.0	50	0.3755	0.4236	0.1563
0.2556	4.0	100	0.3560	0.3973	0.1497

Framework versions

Transformers 5.9.0
Pytorch 2.12.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

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Model Details

Model Provider

rakshit10

Model Tree

Base

openai/whisper-small

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

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README

License: apache-2.0

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 100
mixed_precision_training: Native AMP

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer, Cer
Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.5303	2.0	50	0.3755	0.4236	0.1563
0.2556	4.0	100	0.3560	0.3973	0.1497

Framework versions

Transformers 5.9.0
Pytorch 2.12.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2