Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Chinese
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("jethrowang/whisper-tiny-chinese")
model = AutoModelForSpeechSeq2Seq.from_pretrained("jethrowang/whisper-tiny-chinese")Quick Links
Whisper Tiny Chinese - Jethro Wang
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4227
- Cer: 38.7242
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: 125
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.4799 | 0.3546 | 250 | 0.4715 | 82.0728 |
| 0.4727 | 0.7092 | 500 | 0.4438 | 44.1150 |
| 0.3326 | 1.0638 | 750 | 0.4278 | 39.4984 |
| 0.3154 | 1.4184 | 1000 | 0.4227 | 38.7242 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for jethrowang/whisper-tiny-chinese
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jethrowang/whisper-tiny-chinese")