Update app.py

app.py

@@ -12,10 +12,10 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
 asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
-processor = SpeechT5Processor.from_pretrained("facebook/tts_transformer-fr-cv7_css10")
+processor = SpeechT5Processor.from_pretrained("Sandiago21/speecht5_finetuned_facebook_voxpopuli_french")
 
-model = SpeechT5ForTextToSpeech.from_pretrained("facebook/tts_transformer-fr-cv7_css10").to(device)
-vocoder = SpeechT5HifiGan.from_pretrained("facebook/tts_transformer-fr-cv7_css10").to(device)
+model = SpeechT5ForTextToSpeech.from_pretrained("Sandiago21/speecht5_finetuned_facebook_voxpopuli_french").to(device)
+vocoder = SpeechT5HifiGan.from_pretrained("Sandiago21/speecht5_finetuned_facebook_voxpopuli_french").to(device)
 
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
 speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)