Update README.md

README.md

@@ -1,7 +1,6 @@
 ---
 language:
 - en
-datasets:
 tags:
 - speech
 ---
@@ -29,13 +28,13 @@ The model is fine-tuned on the [LibriMix dataset](https://github.com/JorisCos/Li
 # Usage
 ## Speaker Diarization
 ```python
-from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification
+from transformers import Wav2Vec2FeatureExtractor, WavLMForAudioFrameClassification
 from datasets import load_dataset
 import torch
 
 dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
 feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/wavlm-base-sd')
-model = UniSpeechSatForAudioFrameClassification.from_pretrained('microsoft/wavlm-base-sd')
+model = WavLMForAudioFrameClassification.from_pretrained('microsoft/wavlm-base-sd')
 
 # audio file is decoded on the fly
 inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")