Automatic Speech Recognition
Transformers
PyTorch
whisper
audio
speech
wav2vec2
Eval Results (legacy)
Instructions to use devasheeshG/whisper_large_v2_fp16_transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devasheeshG/whisper_large_v2_fp16_transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devasheeshG/whisper_large_v2_fp16_transformers")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devasheeshG/whisper_large_v2_fp16_transformers") model = AutoModelForSpeechSeq2Seq.from_pretrained("devasheeshG/whisper_large_v2_fp16_transformers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f9bdce542f8bf107627e2a926af4a6dd9c512bb8dda9146279272ddb0dde73a
- Size of remote file:
- 3.09 GB
- SHA256:
- dbd3cf722ca90b62a601687a666af7dd37a114d944390092112a53fad80a7214
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