Update app.py

app.py

@@ -11,12 +11,13 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
 asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
-model_id = "Ellight/speecht5_finetuned_voxpopuli_nl"  # update with your model id
+model_id = "microsoft/speecht5_tts" #"Ellight/speecht5_finetuned_voxpopuli_nl"  # update with your model id
 # pipe = pipeline("automatic-speech-recognition", model=model_id)
 model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
 vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation",trust_remote_code=True)
-speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation",trust_remote_code=True))
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+# speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
 
 processor = SpeechT5Processor.from_pretrained(model_id)