import gradio as gr
import torch
from datasets import load_dataset
from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech

model_id = "Sandiago21/speecht5_finetuned_facebook_voxpopuli_french"  # 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(model_id)

replacements = [
    ("à", "a"),
    ("â", "a"),
    ("ç", "c"),
    ("è", "e"),
    ("ë", "e"),
    ("î", "i"),
    ("ï", "i"),
    ("ô", "o"),
    ("ù", "u"),
    ("û", "u"),
    ("ü", "u"),
]


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=["Je n'entrerai pas dans les détails, mais je profiterai des secondes qui me restent pour exposer la position ALDE sur le marquage CE, un des points cruciaux de ce rapport."],
).launch()