Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hindi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use C10X/whisper-tiny-tr-0704 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use C10X/whisper-tiny-tr-0704 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="C10X/whisper-tiny-tr-0704")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("C10X/whisper-tiny-tr-0704") model = AutoModelForSpeechSeq2Seq.from_pretrained("C10X/whisper-tiny-tr-0704") - Notebooks
- Google Colab
- Kaggle
Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2547
- Wer: 21.6705
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1683 | 0.8857 | 1000 | 0.2547 | 21.6705 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.4.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for C10X/whisper-tiny-tr-0704
Base model
openai/whisper-smallEvaluation results
- Wer on Common Voice 11.0self-reported21.670