# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("halcyonzhou/whisper-base")
model = AutoModelForSpeechSeq2Seq.from_pretrained("halcyonzhou/whisper-base")Quick Links
whisper-base
This model is a fine-tuned version of openai/whisper-base on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.0027
- eval_wer: 0.3287
- eval_runtime: 19.3097
- eval_samples_per_second: 5.852
- eval_steps_per_second: 0.207
- epoch: 47.5333
- step: 380
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Framework versions
- Transformers 4.51.0
- Pytorch 2.8.0+cu129
- Datasets 3.6.0
- Tokenizers 0.21.4
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Model tree for halcyonzhou/whisper-base
Base model
openai/whisper-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="halcyonzhou/whisper-base")