import gradio as gr import numpy as np import torch from datasets import load_dataset from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline device = "cuda:0" if torch.cuda.is_available() else "cpu" # load speech translation checkpoint asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device) # load text-to-speech checkpoint and speaker embeddings model_id = "Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german" # update with your model id # pipe = pipeline("automatic-speech-recognition", model=model_id) model = SpeechT5ForTextToSpeech.from_pretrained(model_id) processor = SpeechT5Processor.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) 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"), ] def cleanup_text(text): for src, dst in replacements: text = text.replace(src, dst) return text def transcribe_to_german(audio): outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "german"}) return outputs["text"] def synthesise_from_german(text): text = cleanup_text(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() def speech_to_speech_translation(audio): translated_text = transcribe_to_german(audio) synthesised_speech = synthesise_from_german(translated_text) synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) return ((16000, synthesised_speech), 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 German. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co./openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german](https://huggingface.co./Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german) checkpoint for text-to-speech, which is based on Microsoft's [SpeechT5 TTS](https://huggingface.co./microsoft/speecht5_tts) model for text-to-speech, fine-tuned in German Audio dataset: ![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"), gr.outputs.Textbox()], 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"), gr.outputs.Textbox()], examples=[["./example.wav"]], title=title, description=description, ) with demo: gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) demo.launch()