# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("AlienKevin/whisper-tiny-jyutping-without-tones")
model = AutoModelForSpeechSeq2Seq.from_pretrained("AlienKevin/whisper-tiny-jyutping-without-tones")Quick Links
Whisper Tiny Jyutping without Tones
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 14.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2079
- Wer: 22.8645
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: 150
- training_steps: 800
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1622 | 0.62 | 800 | 0.2079 | 22.8645 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for AlienKevin/whisper-tiny-jyutping-without-tones
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AlienKevin/whisper-tiny-jyutping-without-tones")