Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("eai6/whisper-tiny-exp.en")
model = AutoModelForSpeechSeq2Seq.from_pretrained("eai6/whisper-tiny-exp.en")Quick Links
Whisper tiny
This model is a fine-tuned version of openai/whisper-tiny.en on the Nyansapo AI Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.4549
- Wer: 379.5513
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: 250
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.2224 | 1.38 | 250 | 0.6100 | 153.0630 |
| 0.545 | 2.76 | 500 | 0.0995 | 662.3814 |
| 0.2906 | 4.14 | 750 | 0.0822 | 650.7334 |
| 0.2063 | 5.52 | 1000 | 0.4549 | 379.5513 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for eai6/whisper-tiny-exp.en
Base model
openai/whisper-tiny.enDataset used to train eai6/whisper-tiny-exp.en
Evaluation results
- Wer on Nyansapo AI Datasetself-reported379.551
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eai6/whisper-tiny-exp.en")