Instructions to use sgangireddy/whisper-medium-cv-fleurs-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgangireddy/whisper-medium-cv-fleurs-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sgangireddy/whisper-medium-cv-fleurs-tr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sgangireddy/whisper-medium-cv-fleurs-tr") model = AutoModelForSpeechSeq2Seq.from_pretrained("sgangireddy/whisper-medium-cv-fleurs-tr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c9f24941ea06236739fba8d8d8713877d2f74cf014f8da4fa16df0cd74f2485
- Size of remote file:
- 1.6 GB
- SHA256:
- 7eb0d1ca38e810b4db2b4257cc5420203e29a70a05d4bb989fffca1efb3be4d4
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