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import gradio as gr
import torch
from datasets import load_dataset
from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech
model_id = "ovieyra21/es_speecht5_tts_mabama" # 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 = [
("Ã ", "a"),
("â", "a"),
("ç", "c"),
("è", "e"),
("ë", "e"),
("î", "i"),
("ï", "i"),
("ô", "o"),
("ù", "u"),
("û", "u"),
("ü", "u"),
]
title = "Text-to-Speech"
description = """
Demo for text-to-speech translation in French. Demo uses [Sandiago21/speecht5_finetuned_facebook_voxpopuli_french](https://huggingface.co/Sandiago21/speecht5_finetuned_facebook_voxpopuli_french) checkpoint, which is based on Microsoft's
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in French 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=["Probando audio"],
title=title,
description=description,
).launch()
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