app.py

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from logic import synthesize_voice, plot_data, plot_waveforms
import base64
import sys
import numpy as np
from io import BytesIO
from hifigan.inference_e2e import hifi_gan_inference

app = FastAPI()

@app.get("/")
def read_root():
    data = {"Voice": "Cloning", "Status": "Success"}
    return JSONResponse(content=data)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"

@app.post("/synthesize")
async def synthesize(request: Request):
    print("call successful")
    
    json = await request.json()
    print(json)
    
    font_type = json['font_select']
    input_text = json['input_text']     

    print("generating mel-spectrogram")
    # Generate mel-spectrogram using Tacotron2
    # mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned.pt")
    mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "kaggle_12000.pt")
    print("mel generation successful")
        
    # Convert mel-spectrogram to base64 for display in HTML
    mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])

    # Audio Synthesis begins
    print("Starting audio synthesis")
    buffer = BytesIO()
    np.save(buffer, mel_output_data)
    input_mel = buffer.getvalue()
    
    hifigan_checkpoint = "generator_v1"
    
    # Generate audio using Hifigan
    audio_data = hifi_gan_inference(input_mel, hifigan_checkpoint)

    print("Creating time-domain waveform")
    # Plot the waveform
    wave_base64 = plot_waveforms(audio_data)

    # Encode audio content as Base64
    audio_base64 = base64.b64encode(audio_data).decode('utf-8')

    # Customize the response based on the information you want to send to the frontend
    response_data = {
        'mel_spectrogram': mel_output_base64,
        'audio_data': audio_base64,
        'waveform': wave_base64,
    }

    return JSONResponse(content=response_data)