import gradio as gr import torch from datasets import load_dataset from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech model_id = "ovieyra21/mabama-tt5" # update with your model id pipe = pipeline("text-to-speech", model="ovieyra21/mabama-tt5") 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"), ] title = "Text-to-Speech" description = """ Demo for text-to-speech translation in French. Demo uses [gitgato/trtts](https://huggingface.co) checkpoint, which is based on Microsoft's [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in French 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) # Crear el objeto gr.Audio directamente con los parámetros necesarios return gr.Audio((16000, speech.cpu().numpy())) # Definir la interfaz de Gradio syntesize_speech_gradio = gr.Interface( synthesize_speech, inputs=gr.Textbox(label="Text", placeholder="Type something here..."), outputs=gr.Audio(label="Speech"), 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."], title=title, description=description, ) # Lanzar la interfaz syntesize_speech_gradio.launch()