import gradio as gr import torch from transformers import AutoTokenizer, AutoModelForCausalLM, VitsModel from nemo.collections.asr.models import EncDecMultiTaskModel # load speech to text model canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b') canary_model.eval() canary_model.to('cpu') # update decode params canary_model.change_decoding_strategy(None) decode_cfg = canary_model.cfg.decoding decode_cfg.beam.beam_size = 1 canary_model.change_decoding_strategy(decode_cfg) # Load the text processing model and tokenizer proc_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") proc_model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True, ) proc_model.eval() proc_model.to('cpu') # Load the TTS model tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng") tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") tts_model.eval() tts_model.to('cpu') def process_speech(speech): # Convert the speech to text transcription = canary_model.transcribe( speech, logprobs=False, ) # Process the text inputs = proc_tokenizer.encode(transcription + proc_tokenizer.eos_token, return_tensors='pt') outputs = proc_model.generate(inputs, max_length=100, temperature=0.7, pad_token_id=proc_tokenizer.eos_token_id) text = proc_tokenizer.decode(outputs[0], skip_special_tokens=True) processed_text = tts_tokenizer(text, return_tensors="pt") # Convert the processed text to speech with torch.no_grad(): audio = tts_model(**inputs).waveform return audio iface = gr.Interface(fn=process_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio") iface.launch()