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_facebook_voxpopuli_spanish" # update with your model id # pipe = pipeline("automatic-speech-recognition", model=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 = [ ("á", "a"), ("ç", "c"), ("è", "e"), ("ì", "i"), ("í", "i"), ("ò", "o"), ("ó", "o"), ("ù", "u"), ("ú", "u"), ("š", "s"), ("ï", "i"), ("ñ", "n"), ("ü", "u"), ] 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"].to(device), speaker_embeddings.to(device), vocoder=vocoder) return gr.Audio.update(value=(16000, speech.cpu().numpy())) def translate(audio): outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "spanish"}) return outputs["text"] def synthesise(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 = translate(audio) synthesised_speech = synthesise(translated_text) synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) return 16000, synthesised_speech title = "Cascaded STST" description = """ Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Spanish. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_facebook_voxpopuli_spanish](https://huggingface.co/Sandiago21/speecht5_finetuned_facebook_voxpopuli_spanish) 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 Spanish 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"), 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()