# Load model directly
from transformers import AutoProcessor, HubertForSpeechClassification
processor = AutoProcessor.from_pretrained("quaja/hubert-split-data-base-amharic-speech-emotion-recognition")
model = HubertForSpeechClassification.from_pretrained("quaja/hubert-split-data-base-amharic-speech-emotion-recognition", device_map="auto")Quick Links
model_name_or_path = "quaja/hubert-base-amharic-speech-emotion-recognition" config = AutoConfig.from_pretrained(model_name_or_path) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path) sampling_rate = feature_extractor.sampling_rate model = HubertForSpeechClassification.from_pretrained(model_name_or_path)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="quaja/hubert-split-data-base-amharic-speech-emotion-recognition")