import gradio as gr import numpy as np import torch from datasets import load_dataset from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline, VitsModel, VitsTokenizer device = "cuda:0" if torch.cuda.is_available() else "cpu" # load speech translation checkpoint asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device) # load text-to-speech checkpoint and speaker embeddings # processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") # model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device) # vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) model = VitsModel.from_pretrained('facebook/mms-tts-ron') tokenizer = VitsTokenizer.from_pretrained('facebook/mms-tts-ron') embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) def translate(audio): outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "ro"}) return outputs["text"] def synthesise(text): inputs = tokenizer(text=text, return_tensors="pt") # speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) with torch.no_grad(): tts_output = model(inputs['input_ids'].to(device)) speech = tts_output['waveform'] return speech.cpu() def speech_to_speech_translation(audio): translated_text = translate(audio) print(translated_text) synthesised_speech = synthesise(translated_text) synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) return (16000, synthesised_speech.T), translated_text title = "Cascaded STST" description = """ Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Romanian. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Facebook's [MMS-TTS-RON](https://huggingface.co/facebook/mms-tts-ron) model for text-to-speech: ![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation") """ demo = gr.Blocks() mic_translate = gr.Interface( fn=speech_to_speech_translation, inputs=gr.Audio(source="microphone", type="filepath"), outputs=[ gr.Audio(label="Generated Speech", type="numpy"),], title=title, description=description, ) file_translate = gr.Interface( fn=speech_to_speech_translation, inputs=gr.Audio(source="upload", type="filepath"), outputs=[ gr.Audio(label="Generated Speech", type="numpy"), ], examples=[["./example.wav"]], title=title, description=description, ) with demo: gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) demo.launch()