Automatic Speech Recognition
Transformers
PyTorch
whisper
audio
speech
wav2vec2
Eval Results (legacy)
Instructions to use devasheeshG/whisper_medium_fp16_transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devasheeshG/whisper_medium_fp16_transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devasheeshG/whisper_medium_fp16_transformers")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devasheeshG/whisper_medium_fp16_transformers") model = AutoModelForSpeechSeq2Seq.from_pretrained("devasheeshG/whisper_medium_fp16_transformers") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8945987b3222793725cbb93a97d9fed431db54b28ada0a5def88c062b6e305c
- Size of remote file:
- 4.45 MB
- SHA256:
- 1483a4b2c200e9c0fd9c3006158665740f739c81c20da572afbbf33e3a5a3fd6
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