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 # Adjust import as per your project structure

#file_path = hf_hub_download("rhasspy/piper-voices", "en_GB-alan-medium.onnx")

def synthesize_speech(text):
 # Load the PiperVoice model and configuration
 # model_path = "en_GB-alan-medium.onnx" # this is for loading local model
 # config_path = "en_GB-alan-medium.onnx.json" # for loading local json
 model_path = hf_hub_download(repo_id="rhasspy/piper-voices", filename="en/en_GB/alan/medium/en_GB-alan-medium.onnx")
 config_path = hf_hub_download(repo_id="rhasspy/piper-voices", filename="en/en_GB/alan/medium/en_GB-alan-medium.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()

# Create a Gradio interface with labels
iface = gr.Interface(
 fn=synthesize_speech,
 inputs=gr.Textbox(label="Input Text"),
 outputs=[gr.Audio(label="Synthesized Speech")],
 title="Text to Speech Synthesizer",
 description="Enter text to synthesize it into speech using PiperVoice.",
 allow_flagging="never"
 
)

# Run the app
iface.launch()