import gradio as gr
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, GPT2LMHeadModel, GPT2Tokenizer, VitsProcessor, VitsForConditionalGeneration

# Load the ASR model and processor
asr_processor = Wav2Vec2Processor.from_pretrained("/path/to/canary/processor")
asr_model = Wav2Vec2ForCTC.from_pretrained("/path/to/canary/model")

# Load the text processing model and tokenizer
proc_tokenizer = GPT2Tokenizer.from_pretrained("/path/to/phi3/tokenizer")
proc_model = GPT2LMHeadModel.from_pretrained("/path/to/phi3/model")

# Load the TTS model and processor
tts_processor = VitsProcessor.from_pretrained("facebook/vits-base")
tts_model = VitsForConditionalGeneration.from_pretrained("facebook/vits-base")

def process_speech(speech):
    # Convert the speech to text
    inputs = asr_processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = asr_model(inputs.input_values, attention_mask=inputs.attention_mask).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = asr_processor.decode(predicted_ids[0])

    # 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)
    processed_text = proc_tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Convert the processed text to speech
    inputs = tts_processor(processed_text, return_tensors="pt")
    with torch.no_grad():
        logits = tts_model(inputs["input_ids"]).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    audio = tts_processor.decode(predicted_ids)

    return audio

iface = gr.Interface(fn=process_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio")

iface.launch()