# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("nnaik39/whisper")
model = AutoModelForSpeechSeq2Seq.from_pretrained("nnaik39/whisper")Quick Links
whisper
This model is a fine-tuned version of openai/whisper-tiny.en on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5158
- eval_wer: 19.8611
- eval_runtime: 49.4294
- eval_samples_per_second: 10.115
- eval_steps_per_second: 1.275
- epoch: 13.8889
- step: 500
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: 128
- 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
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
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Model tree for nnaik39/whisper
Base model
openai/whisper-tiny.en
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nnaik39/whisper")