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Distay
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whisper-tiny

Automatic Speech Recognition
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whisper
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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="Distay/whisper-tiny")
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("Distay/whisper-tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Distay/whisper-tiny")
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MnG

This model is a fine-tuned version of openai/whisper-tiny on the Custom dataset.

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 1000
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.29.0.dev0
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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