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import gradio as gr
import torch
from datasets import load_dataset
from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech
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)
processor = SpeechT5Processor.from_pretrained(model_id)
replacements = [
("ου", "u"),
("αυ", "af"),
("ευ", "ef"),
("ει", "i"),
("οι", "i"),
("αι", "e"),
("ού", "u"),
("εί", "i"),
("οί", "i"),
("αί", "e"),
("Ά", "A"),
("Έ", "E"),
("Ή", "H"),
("Ί", "I"),
("Ό", "O"),
("Ύ", "Y"),
("Ώ", "O"),
("ΐ", "i"),
("Α", "A"),
("Β", "B"),
("Γ", "G"),
("Δ", "L"),
("Ε", "Ε"),
("Ζ", "Z"),
("Η", "I"),
("Θ", "Th"),
("Ι", "I"),
("Κ", "K"),
("Λ", "L"),
("Μ", "M"),
("Ν", "N"),
("Ξ", "Ks"),
("Ο", "O"),
("Π", "P"),
("Ρ", "R"),
("Σ", "S"),
("Τ", "T"),
("Υ", "Y"),
("Φ", "F"),
("Χ", "X"),
("Ω", "O"),
("ά", "a"),
("έ", "e"),
("ή", "i"),
("ί", "i"),
("α", "a"),
("β", "v"),
("γ", "g"),
("δ", "d"),
("ε", "e"),
("ζ", "z"),
("η", "i"),
("θ", "th"),
("ι", "i"),
("κ", "k"),
("λ", "l"),
("μ", "m"),
("ν", "n"),
("ξ", "ks"),
("ο", "o"),
("π", "p"),
("ρ", "r"),
("ς", "s"),
("σ", "s"),
("τ", "t"),
("υ", "i"),
("φ", "f"),
("χ", "h"),
("ψ", "ps"),
("ω", "o"),
("ϊ", "i"),
("ϋ", "i"),
("ό", "o"),
("ύ", "i"),
("ώ", "o"),
("í", "i"),
("õ", "o"),
("Ε", "E"),
("Ψ", "Ps"),
]
title = "Text-to-Speech"
description = """
Demo for text-to-speech translation in Greek. Demo uses [Sandiago21/speecht5_finetuned_google_fleurs_greek](https://huggingface.co/Sandiago21/speecht5_finetuned_google_fleurs_greek) checkpoint, which is based on Microsoft's
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in Greek Audio dataset
"""
def cleanup_text(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
def synthesize_speech(text):
text = cleanup_text(text)
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
return gr.Audio.update(value=(16000, speech.cpu().numpy()))
syntesize_speech_gradio = gr.Interface(
synthesize_speech,
inputs = gr.Textbox(label="Text", placeholder="Type something here..."),
outputs=gr.Audio(),
examples=["Έλαβαν χώρα μεγάλες διαδηλώσεις στην Πολωνία όταν εκείνη η χώρα υπέγραψε την acta που οδήγησε την κυβέρνηση της πολωνίας να αποφασίσει τη μη επικύρωση της συμφωνίας προς το παρόν"],
title=title,
description=description,
).launch()
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