import gradio as gr
import os
import torch
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, VitsModel, AutoTokenizer

# Ensure the output directory exists
os.makedirs("output_audio", exist_ok=True)

# Load the models and processors
asr_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
asr_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng")
tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")

def speech_to_text(input_audio):
    # Load and preprocess the audio
    waveform, sr = sf.read(input_audio)
    input_values = asr_processor(waveform, sampling_rate=sr, return_tensors="pt").input_values
    
    # Perform speech recognition
    with torch.no_grad():
        logits = asr_model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    
    # Decode the predicted IDs to text
    transcription = asr_processor.batch_decode(predicted_ids)[0]
    return transcription

def text_to_speech(text):
    # Tokenize text and generate waveform
    inputs = tts_tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        output = tts_model(**inputs).waveform
    waveform = output.numpy()

    # Define output path and save waveform as audio file
    output_path = "output_audio/text_to_speech.wav"
    sf.write(output_path, waveform.squeeze(), 22050)

    return output_path

def speech_to_speech(input_audio, target_text):
    # Synthesize speech directly from target text without transcribing the input audio
    return text_to_speech(target_text)

iface = gr.Interface(
    fn={
        "Speech to Text": speech_to_text,
        "Text to Speech": text_to_speech,
        "Speech to Speech": speech_to_speech
    },
    inputs={
        "Speech to Text": gr.inputs.Audio(source="upload", type="file"),
        "Text to Speech": gr.inputs.Textbox(label="Text"),
        "Speech to Speech": [gr.inputs.Audio(source="upload", type="file"), gr.inputs.Textbox(label="Target Text")]
    },
    outputs={
        "Speech to Text": gr.outputs.Textbox(label="Transcription"),
        "Text to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech"),
        "Speech to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech")
    },
    title="Speech Processing Application",
    description="This app uses Facebook's Wav2Vec 2.0 for speech-to-text and VITS for text-to-speech.",
    layout="vertical"
)

if __name__ == "__main__":
    iface.launch()