app.py

import gradio as gr
import wave
import numpy as np
from io import BytesIO
from huggingface_hub import hf_hub_download
from piper import PiperVoice 
from transformers import pipeline

# Load the NSFW classifier model
nsfw_detector = pipeline("text-classification", model="michellejieli/NSFW_text_classifier")

def synthesize_speech(text):
    # Check for NSFW content
    nsfw_result = nsfw_detector(text)
    if nsfw_result[0]['label'] == 'NSFW':
        yield "NSFW content detected. Cannot process."
        return

    model_path = hf_hub_download(repo_id="aigmixer/speaker_00", filename="speaker_00_model.onnx")
    config_path = hf_hub_download(repo_id="aigmixer/speaker_00", filename="speaker_00_model.onnx.json")
    voice = PiperVoice.load(model_path, config_path)

    # Synthesize speech and stream audio
    for audio_chunk in voice.synthesize(text, chunk_size=2048):
        yield audio_chunk.tobytes()

# Using Gradio Blocks
with gr.Blocks(theme=gr.themes.Base()) as blocks:
    gr.Markdown("# Text to Speech Synthesizer")
    gr.Markdown("Enter text to synthesize it into speech using PiperVoice.")
    input_text = gr.Textbox(label="Input Text")
    output_audio = gr.Audio(label="Synthesized Speech", type="numpy", streaming=True)
    submit_button = gr.Button("Synthesize")

    submit_button.click(synthesize_speech, inputs=input_text, outputs=output_audio)

# Run the app
blocks.launch()