Update README.md

README.md

@@ -32,7 +32,7 @@ from transformers import pipeline
 dataset = load_dataset("ashraq/esc50")
 audio = dataset["train"]["audio"][-1]["array"]
 
-audio_classifier = pipeline(task="zero-shot-audio-classification", model="ylacombe/larger_clap_general")
+audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/larger_clap_general")
 output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
 print(output)
 >>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
@@ -51,8 +51,8 @@ from transformers import ClapModel, ClapProcessor
 librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 audio_sample = librispeech_dummy[0]
 
-model = ClapModel.from_pretrained("ylacombe/larger_clap_general")
-processor = ClapProcessor.from_pretrained("ylacombe/larger_clap_general")
+model = ClapModel.from_pretrained("laion/larger_clap_general")
+processor = ClapProcessor.from_pretrained("laion/larger_clap_general")
 
 inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
 audio_embed = model.get_audio_features(**inputs)
@@ -67,8 +67,8 @@ from transformers import ClapModel, ClapProcessor
 librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 audio_sample = librispeech_dummy[0]
 
-model = ClapModel.from_pretrained("ylacombe/larger_clap_general").to(0)
-processor = ClapProcessor.from_pretrained("ylacombe/larger_clap_general")
+model = ClapModel.from_pretrained("laion/larger_clap_general").to(0)
+processor = ClapProcessor.from_pretrained("laion/larger_clap_general")
 
 inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
 audio_embed = model.get_audio_features(**inputs)