Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use AkshitSaxena1/whisper-tiny_to_chinese_accent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AkshitSaxena1/whisper-tiny_to_chinese_accent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AkshitSaxena1/whisper-tiny_to_chinese_accent")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("AkshitSaxena1/whisper-tiny_to_chinese_accent") model = AutoModelForSpeechSeq2Seq.from_pretrained("AkshitSaxena1/whisper-tiny_to_chinese_accent") - Notebooks
- Google Colab
- Kaggle
Whisper tiny Chinese
This model is a fine-tuned version of openai/whisper-tiny on the Chinese English dataset. It achieves the following results on the evaluation set:
- Loss: 0.3068
- Wer: 12.7562
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: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1223 | 1.2837 | 1000 | 0.3156 | 13.3916 |
| 0.0589 | 2.5674 | 2000 | 0.3068 | 12.7562 |
Framework versions
- Transformers 5.5.0.dev0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for AkshitSaxena1/whisper-tiny_to_chinese_accent
Base model
openai/whisper-tinyEvaluation results
- Wer on Chinese Englishself-reported12.756