Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Ouwen/whisper-large-v3-grumble-english-finetuned")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Ouwen/whisper-large-v3-grumble-english-finetuned", device_map="auto")Quick Links
Whisper Large V3 Grumble English
This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 11.0 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
- training_steps: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 9.1 | 10 | 0.3390 | 0.0 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Ouwen/whisper-large-v3-grumble-english-finetuned
Base model
openai/whisper-large-v3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ouwen/whisper-large-v3-grumble-english-finetuned")