import gradio as gr import numpy as np import torch from datasets import load_dataset from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline ## Imports for MMS from transformers import 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) # For Dutch ##### speecht5 ##### # model_id = 'sanchit-gandhi/speecht5_tts_vox_nl' # processor = SpeechT5Processor.from_pretrained(model_id) # model = SpeechT5ForTextToSpeech.from_pretrained(model_id) # vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) ##### mms ##### model = VitsModel.from_pretrained("Matthijs/mms-tts-nld") tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-nld") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) # Original # def translate(audio): # outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"}) # return outputs["text"] # Dutch def translate(audio): outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "nl"}) return outputs["text"] # Original # 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() def synthesise(text): inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): outputs = model(inputs["input_ids"]) speech = outputs.audio[0] 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 English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's # [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) 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") # """ description = """ # Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Dutch. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's # [SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) 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()