Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use fsicoli/whisper-medium-pt-cv19-fleurs2-lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fsicoli/whisper-medium-pt-cv19-fleurs2-lr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fsicoli/whisper-medium-pt-cv19-fleurs2-lr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fsicoli/whisper-medium-pt-cv19-fleurs2-lr") model = AutoModelForSpeechSeq2Seq.from_pretrained("fsicoli/whisper-medium-pt-cv19-fleurs2-lr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 11.441647597254004, | |
| "eval_loss": 0.1832965463399887, | |
| "eval_runtime": 5415.1082, | |
| "eval_samples": 9522, | |
| "eval_samples_per_second": 1.758, | |
| "eval_steps_per_second": 0.22, | |
| "eval_wer": 0.10381071222883471 | |
| } |