
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)
{"language": ["am"], "tags": ["speech", "audio", "audio-classification", "hubert"], "pipeline_tag": "audio-classification"}
transformers pytorch hubert speech audio audio-classification am endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #hubert #speech #audio #audio-classification #am #endpoints_compatible #has_space #region-us 


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)
TAGS
#transformers #pytorch #hubert #speech #audio #audio-classification #am #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #hubert #speech #audio #audio-classification #am #endpoints_compatible #has_space #region-us 
