Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use jarguello76/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jarguello76/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jarguello76/whisper-tiny-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jarguello76/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("jarguello76/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d2e446cbbfccc51d42dd56ed076b2fb5e19928769a2cb5c9264b600f2f393ea
- Size of remote file:
- 4.09 kB
- SHA256:
- 8b83026cba0dee174c92df86e21d0d9798058d0bc41447c99934756b13512e59
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