Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Catalan
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("JulioCastro/whisper-medium-ca")
model = AutoModelForSpeechSeq2Seq.from_pretrained("JulioCastro/whisper-medium-ca")Quick Links
Whisper Medium Ca
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0, the Fleurs, the SLR69, the tb3_parla and the parlament_parla datasets. It achieves the following results on the evaluation set:
- eval_loss: 0.1905
- eval_wer: 10.0031
- eval_runtime: 10456.4485
- eval_samples_per_second: 1.563
- eval_steps_per_second: 0.195
- epoch: 0.2
- step: 2000
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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Datasets used to train JulioCastro/whisper-medium-ca
Evaluation results
- Wer on Common Voice 11.0test set self-reported10.003
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JulioCastro/whisper-medium-ca")