Instructions to use helenai/MIT-ast-finetuned-speech-commands-v2-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/MIT-ast-finetuned-speech-commands-v2-ov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="helenai/MIT-ast-finetuned-speech-commands-v2-ov")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("helenai/MIT-ast-finetuned-speech-commands-v2-ov") model = AutoModelForAudioClassification.from_pretrained("helenai/MIT-ast-finetuned-speech-commands-v2-ov") - Notebooks
- Google Colab
- Kaggle
MIT/ast-finetuned-speech-commands-v2
This is the MIT/ast-finetuned-speech-commands-v2 model converted to OpenVINO, for accellerated inference.
An example of how to do inference on this model:
from optimum.intel.openvino import OVModelForAudioClassification
from transformers import AutoFeatureExtractor, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/MIT-ast-finetuned-speech-commands-v2-ov"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = OVModelForAudioClassification.from_pretrained(model_id)
pipe = pipeline("audio-classification", model=model, feature_extractor=feature_extractor)
result = pipe("https://datasets-server.huggingface.co/assets/speech_commands/--/v0.01/test/38/audio/audio.mp3")
print(result)
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