Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Spanish
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use Cristhian2430/whisper-large-coes-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cristhian2430/whisper-large-coes-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cristhian2430/whisper-large-coes-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Cristhian2430/whisper-large-coes-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Cristhian2430/whisper-large-coes-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Large SEIN - COES SEIN - Version 3
This model is a fine-tuned version of openai/whisper-large-v3 on the SEIN COES dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 44.0630
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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0002 | 142.86 | 1000 | 0.0002 | 36.1325 |
| 0.0001 | 285.71 | 2000 | 0.0001 | 40.0109 |
| 0.0 | 428.57 | 3000 | 0.0000 | 43.4981 |
| 0.0 | 571.43 | 4000 | 0.0000 | 44.0630 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for Cristhian2430/whisper-large-coes-v3
Base model
openai/whisper-large-v3