import gradio as gr import torch from datasets import load_dataset from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech model_id = "Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german" # update with your 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 = [ ("Ä", "E"), ("Æ", "E"), ("Ç", "C"), ("É", "E"), ("Í", "I"), ("Ó", "O"), ("Ö", "E"), ("Ü", "Y"), ("ß", "S"), ("à", "a"), ("á", "a"), ("ã", "a"), ("ä", "e"), ("å", "a"), ("ë", "e"), ("í", "i"), ("ï", "i"), ("ð", "o"), ("ñ", "n"), ("ò", "o"), ("ó", "o"), ("ô", "o"), ("ö", "u"), ("ú", "u"), ("ü", "y"), ("ý", "y"), ("Ā", "A"), ("ā", "a"), ("ă", "a"), ("ą", "a"), ("ć", "c"), ("Č", "C"), ("č", "c"), ("ď", "d"), ("Đ", "D"), ("ę", "e"), ("ě", "e"), ("ğ", "g"), ("İ", "I"), ("О", "O"), ("Ł", "L"), ("ń", "n"), ("ň", "n"), ("Ō", "O"), ("ō", "o"), ("ő", "o"), ("ř", "r"), ("Ś", "S"), ("ś", "s"), ("Ş", "S"), ("ş", "s"), ("Š", "S"), ("š", "s"), ("ū", "u"), ("ź", "z"), ("Ż", "Z"), ("Ž", "Z"), ("ǐ", "i"), ("ǐ", "i"), ("ș", "s"), ("ț", "t"), ] title = "Text-to-Speech" description = """ Demo for text-to-speech translation in German. Demo uses [Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german](https://huggingface.co./Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german) checkpoint, which is based on Microsoft's [SpeechT5 TTS](https://huggingface.co./microsoft/speecht5_tts) model and is fine-tuned in German Audio dataset ![Text-to-Speech (TTS)"](https://geekflare.com/wp-content/uploads/2021/07/texttospeech-1200x385.png "Diagram of Text-to-Speech (TTS)") """ 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=["Daher wird die Reform der Europäischen Sozialfondsverordnung, die wir morgen beschließen, auch umgehend in Kraft treten."], title=title, description=description, ).launch()