app definition

app.py

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+
+import gradio as gr
+from inference import text_to_speech
+
+
+def greet(text=""):
+    return "this is a f test"
+
+with gr.Blocks() as demo:
+    gr.Markdown("Text-to-Speech Gustavo Petro")
+   
+    with gr.Row():
+        text_input = gr.Textbox(lines=2, placeholder="Hola soy Gustavo Petro y esta es mi voz de prueba")
+    
+    text_button = gr.Button("Generate")
+
+    text_button.click(text_to_speech, inputs=text_input, outputs=gr.Audio(label="Model Response"))
+
+demo.launch()

inference.py

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+from TTS.api import TTS
+from scipy.io import wavfile
+import noisereduce as nr
+import gdown
+import os
+
+cur_path=os.getcwd()
+
+## download weights and config file
+file_id = '1-uVNiBjN9d6GBZy3jubvgq7slyEgObyr'
+url = f'https://drive.google.com/uc?id='
+output = f'{cur_path}/best_model.pth'
+gdown.download(f"{url}{file_id}", output, quiet=False)
+
+file_id = '1-Jot_x7bkWUE5SyAcKaaAAC-_T8DSEYe'
+url = f'https://drive.google.com/uc?id='
+output = f'{cur_path}/config.json'
+gdown.download(f"{url}{file_id}", output, quiet=False)
+
+
+##load model
+fake_voice=TTS(model_path=f"{cur_path}/best_model.pth",
+               config_path=f"{cur_path}/config.json")
+
+def text_to_speech(text="Hola soy gustavo petro y esto es una prueba",output_name="test_audio.wav",model=fake_voice):
+
+    model.tts_to_file(text,file_path=output_name)
+    rate, data = wavfile.read(output_name)
+    reduced_noise = nr.reduce_noise(y=data, sr=rate)
+    wavfile.write(output_name, rate, reduced_noise)
+    return output_name
+
+
+
+    
+

requirements.txt

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+TTS==0.13.3
+git+https://github.com/openai/whisper.git
+pydub
+noisereduce
+gdown
+numpy
+gdown