Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Malayalam
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use simpragma/breeze-dsw-base-ml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simpragma/breeze-dsw-base-ml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="simpragma/breeze-dsw-base-ml")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("simpragma/breeze-dsw-base-ml") model = AutoModelForSpeechSeq2Seq.from_pretrained("simpragma/breeze-dsw-base-ml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 324 Bytes
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"epoch": 29.02,
"eval_loss": 0.6259765625,
"eval_runtime": 1588.1017,
"eval_samples_per_second": 0.417,
"eval_steps_per_second": 0.026,
"eval_wer": 42.72474513438369,
"train_loss": 0.0,
"train_runtime": 35.9854,
"train_samples_per_second": 1778.5,
"train_steps_per_second": 55.578
} |