Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use keess/whisper-model-internal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keess/whisper-model-internal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="keess/whisper-model-internal")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("keess/whisper-model-internal") model = AutoModelForSpeechSeq2Seq.from_pretrained("keess/whisper-model-internal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 310 Bytes
3e6a512 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | from handler import EndpointHandler
from pathlib import Path
p = Path(__file__).with_name('pytorch_model.bin')
filename = p.absolute()
my_handler = EndpointHandler(path=filename)
payload = {"inputs": "contact.wav"}
transcription=my_handler(payload)
print("here is the transcription")
print(transcription)
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