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app.py

@@ -3,15 +3,15 @@ import torch
 from datasets import load_dataset
 from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech
 
-model_id = "Sandiago21/speecht5_finetuned_voxpopuli_it"  # update with your model id
+model_id = "Sandiago21/speecht5_finetuned_google_fleurs_greek"  # 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")
 speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
 
-checkpoint = "microsoft/speecht5_tts"
-processor = SpeechT5Processor.from_pretrained(checkpoint)
+# checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(model_id)
 
 replacements = [
     ("ου", "u"),
@@ -89,8 +89,13 @@ replacements = [
     ("ό", "o"),
     ("ύ", "i"),
     ("ώ", "o"),
+    ("í", "i"),
+    ("õ", "o"),
+    ("Ε", "E"),
+    ("Ψ", "Ps"),
 ]
 
+
 def cleanup_text(text):
     for src, dst in replacements:
         text = text.replace(src, dst)