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import gradio as gr |
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import torch |
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from datasets import load_dataset |
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from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech |
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model_id = "Sandiago21/speecht5_finetuned_voxpopuli_it" |
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model = SpeechT5ForTextToSpeech.from_pretrained(model_id) |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0) |
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checkpoint = "microsoft/speecht5_tts" |
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processor = SpeechT5Processor.from_pretrained(checkpoint) |
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replacements = [ |
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("á", "a"), |
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("ç", "c"), |
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("è", "e"), |
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("ì", "i"), |
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("í", "i"), |
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("ò", "o"), |
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("ó", "o"), |
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("ù", "u"), |
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("ú", "u"), |
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("š", "s"), |
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("ï", "i"), |
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] |
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title = "Text-to-Speech" |
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description = """ |
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Demo for text-to-speech translation in Italian. Demo uses [Sandiago21/speecht5_finetuned_voxpopuli_it](https://huggingface.co/Sandiago21/speecht5_finetuned_voxpopuli_it) checkpoint, which is based on Microsoft's |
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[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in Italian Audio dataset |
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""" |
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def cleanup_text(text): |
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for src, dst in replacements: |
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text = text.replace(src, dst) |
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return text |
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def synthesize_speech(text): |
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text = cleanup_text(text) |
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inputs = processor(text=text, return_tensors="pt") |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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return gr.Audio.update(value=(16000, speech.cpu().numpy())) |
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syntesize_speech_gradio = gr.Interface( |
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synthesize_speech, |
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inputs = gr.Textbox(label="Text", placeholder="Type something here..."), |
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outputs=gr.Audio(), |
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examples=["Grandi manifestazioni in polonia al momento della firma dell'accordo che hanno portato il governo polacco a decidere di non ratificare l'accordo per il momento"], |
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title=title, |
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description=description, |
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).launch() |
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