import torch from transformers import pipeline device = "cuda:0" if torch.cuda.is_available() else "cpu" pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-base", device=device ) from datasets import load_dataset # dataset = load_dataset("facebook/voxpopuli", "en", split="validation", streaming=True, trust_remote_code=True) # sample = next(iter(dataset)) def translate(audio): outputs = pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "fr"}) # "language": "fr" return outputs["text"] from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = SpeechT5ForTextToSpeech.from_pretrained("ccourc23/fine_tuned_SpeechT5") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) def synthesise(text): inputs = processor(text=text, return_tensors="pt") speech = model.generate_speech( inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder ) return speech.cpu() import numpy as np target_dtype = np.int16 max_range = np.iinfo(target_dtype).max def speech_to_speech_translation(audio): translated_text = translate(audio) synthesised_speech = synthesise(translated_text) synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16) return 16000, synthesised_speech import gradio as gr file_translate = gr.Interface( fn=speech_to_speech_translation, inputs=gr.Audio(sources="upload", type="filepath"), outputs=gr.Audio(label="Generated Speech"), ) file_translate.launch(share=True)