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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':
return "NSFW content detected. Cannot process.", None
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)
# Create an in-memory buffer for the WAV file
buffer = BytesIO()
with wave.open(buffer, 'wb') as wav_file:
wav_file.setframerate(voice.config.sample_rate)
wav_file.setsampwidth(2) # 16-bit
wav_file.setnchannels(1) # mono
# Synthesize speech
voice.synthesize(text, wav_file)
# Convert buffer to NumPy array for Gradio output
buffer.seek(0)
audio_data = np.frombuffer(buffer.read(), dtype=np.int16)
return audio_data.tobytes(), None
# 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")
output_text = gr.Textbox(label="Output Text", visible=False) # This is the new text output component
submit_button = gr.Button("Synthesize")
submit_button.click(synthesize_speech, inputs=input_text, outputs=[output_audio, output_text])
# Run the app
blocks.launch() |