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
import typing

model_path = hf_hub_download(repo_id="larcanio/piper-voices", filename="es_AR-daniela-high.onnx")
config_path = hf_hub_download(repo_id="larcanio/piper-voices", filename="es_AR-daniela-high.json")
voice = PiperVoice.load(model_path, config_path)


def synthesize_speech(text):


    # 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
        # eztext = preprocess_text(text)
        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

BANNER_TEXT = """
# Demo en español argentino con Piper

[***Piper***](https://huggingface.co/rhasspy/piper-voices/) es un modelo de abierto de Texto a Voz (TTS)
que permite entrenarse con voz propia, destaca por no requerir conectarse a Internet y ofrecer resultados
sin exigir GPU.  Inicialmente diseñado para Raspberri Pi.

Este demo solo muestra español, puedes probar [voces en otros idiomas](https://rhasspy.github.io/piper-samples/).
"""

# Using Gradio Blocks
with gr.Blocks(theme=gr.themes.Base()) as blocks:
    gr.Markdown(BANNER_TEXT)
    input_text = gr.Textbox(label=" ", placeholder="Introduce el texto a leer aquí")
    output_audio = gr.Audio(label="Audio generado", type="numpy")
    output_text = gr.Textbox(label="Tokens generados", visible=False)
    submit_button = gr.Button("Genera audio")

    submit_button.click(synthesize_speech, inputs=input_text, outputs=[output_audio, output_text])
# Run the app
blocks.launch()