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
- Xet hash:
- d50f3c781c2864dcee4f9403f6d3c3e6042f1174aceea67c36ab5e248e563022
- Size of remote file:
- 14.3 kB
- SHA256:
- 11a6bd70b2ab2dcd9f9b8f379c1b92ddb6f6d211c145bb4ac5ad716b196ce1f4
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