Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use geninhu/whisper-medium-gl-fleurs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use geninhu/whisper-medium-gl-fleurs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="geninhu/whisper-medium-gl-fleurs")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("geninhu/whisper-medium-gl-fleurs") model = AutoModelForSpeechSeq2Seq.from_pretrained("geninhu/whisper-medium-gl-fleurs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from geninhu/whisper-medium-gl-fleurs: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/geninhu/whisper-medium-gl-fleurs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://geninhu/whisper-medium-gl-fleurs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/geninhu/whisper-medium-gl-fleurs/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 60a0110fded46d0d109555af4629713d909d7bd6d45037489cd4037a49530ef3
- Size of remote file:
- 3.06 GB
- SHA256:
- 313d85b72ea0649686af2c3cfb0dadac2d541829057de58a7f06a67fc465a966
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