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mn720
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inctraining3

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swahili
whisper
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
 Metrics Community
How to use from the
Use from the
Transformers library
# Gated model: Login with a HF token with gated access permission
hf auth login
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="mn720/inctraining3")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("mn720/inctraining3")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mn720/inctraining3")
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Incremental Swahili Luganda

This model is a fine-tuned version of openai/whisper-small on the Mix data dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3430
  • Wer: 31.7183

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2355 0.0894 500 0.3831 35.9432
0.2275 0.1789 1000 0.3818 35.3379
0.245 0.2683 1500 0.3727 34.4346
0.2321 0.3577 2000 0.3637 33.5439
0.2396 0.4472 2500 0.3569 32.9164
0.2231 0.5366 3000 0.3512 33.0780
0.2039 0.6261 3500 0.3468 32.3184
0.2283 0.7155 4000 0.3430 31.7183

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.2.2+cu118
  • Datasets 2.19.0
  • Tokenizers 0.19.1
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