import torch from transformers import pipeline, VitsModel, VitsTokenizer import numpy as np import gradio as gr device = "cuda:0" if torch.cuda.is_available() else "cpu" # Load Whisper-small pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=device ) # Load the model checkpoint and tokenizer #model = VitsModel.from_pretrained("Matthijs/mms-tts-fra") #tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra") model = VitsModel.from_pretrained("facebook/mms-tts-fra") tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra") # Define a function to translate an audio, in english here def translate(audio): outputs = pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "eng"}) return outputs["text"] # Define function to generate the waveform output def synthesise(text): inputs = tokenizer(text, return_tensors="pt") input_ids = inputs["input_ids"] with torch.no_grad(): outputs = model(input_ids) return outputs.audio[0] # Define the pipeline 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 def predict(transType, language, audio, audio_mic = None): if not audio and audio_mic: audio = audio_mic if transType == "Text": return translate(audio) if transType == "Audio": return speech_to_speech_translation(audio) # Define the title etc title = "Swedish STSOT (Speech To Speech Or Text)" description="Use Whisper pretrained model to convert swedish audio to english (text or audio)" supportLangs = ["Swedish", "French (in training)"] transTypes = ["Text", "Audio"] # examples = [ # ["Text", "Swedish", "ex1.mp3", None], # ["Audio", "Swedish", "ex2.mp3", None] # ] examples = [] demo = gr.Interface( fn=predict, inputs=[ gr.Radio(label="Choose your output format", choices=transTypes), gr.Radio(label="Choose a source language", choices=supportLangs, value="Swedish"), gr.Audio(label="Import an audio", sources="upload", type="numpy"), gr.Audio(label="Record an audio", sources="microphone", type="numpy"), ], outputs=[ gr.Text(label="Translation"), ], title=title, description=description, article="", examples=examples, ) demo.launch()