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:
- 2d6786d75b9349f301f625c5e6332e40360e8a2e6d394924f549ff8f223eeab3
- Size of remote file:
- 3.06 GB
- SHA256:
- e0c89c27ba41f7e03ff694983917ce9f6d79cf09d0137f2350ee6ca4ce837faa
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