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classla
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wav2vecbert2-prosodicUnit

Transformers
Safetensors
Slovenian
wav2vec2-bert
audio-frame-classification
prosody
segmentation
audio
speech
Model card Files
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5roop commited on
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Update README.md

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README.md CHANGED
@@ -91,7 +91,7 @@ if torch.cuda.is_available():
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  else:
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  device = torch.device("cpu")
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- model_name = "5roop/Wav2Vec2BertProsodicUnitsFrameClassifier"
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  feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
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  model = Wav2Vec2BertForAudioFrameClassification.from_pretrained(model_name).to(device)
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  f = "data/Rog-Art-N-G6007-P600702_181.070_211.070.wav"
@@ -173,7 +173,7 @@ if torch.cuda.is_available():
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  else:
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  device = torch.device("cpu")
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- model_name = "5roop/Wav2Vec2BertProsodicUnitsFrameClassifier"
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  feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
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  model = Wav2Vec2BertForAudioFrameClassification.from_pretrained(model_name).to(device)
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  f = "ROG/ROG-Art/WAV/Rog-Art-N-G5025-P600022.wav"
 
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  else:
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  device = torch.device("cpu")
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+ model_name = "classla/wav2vecbert2-prosodicUnit"
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  feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
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  model = Wav2Vec2BertForAudioFrameClassification.from_pretrained(model_name).to(device)
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  f = "data/Rog-Art-N-G6007-P600702_181.070_211.070.wav"
 
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  else:
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  device = torch.device("cpu")
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+ model_name = "classla/wav2vecbert2-prosodicUnit"
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  feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
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  model = Wav2Vec2BertForAudioFrameClassification.from_pretrained(model_name).to(device)
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  f = "ROG/ROG-Art/WAV/Rog-Art-N-G5025-P600022.wav"