app.py

# Install necessary libraries (if not installed)
# !pip install gradio transformers soundfile torch

import gradio as gr
import torch
import soundfile as sf
from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan

# Load the pre-trained model, vocoder, and processor
model = SpeechT5ForTextToSpeech.from_pretrained("krishna195/speecht5_krishna_finatuned")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")

# Speaker embeddings for speech generation (replace this with actual embeddings if needed)
speaker_embeddings = torch.randn(1, 512)  # Example speaker embedding size (dummy embeddings)

# Function to generate speech from input text
def text_to_speech(input_text):
    # Process the input text
    inputs = processor(text=input_text, return_tensors="pt")

    # Generate speech using the model and vocoder
    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)

    # Save the audio to a file (temporary storage)
    output_file = "generated_speech.wav"
    sf.write(output_file, speech.numpy(), 16000)

    # Return the path to the audio file for Gradio to play it
    return output_file

# Create Gradio UI
iface = gr.Interface(
    fn=text_to_speech,
    inputs="text",
    outputs="audio",
    title="Text to Speech Generator",
    description="Enter the text you want to convert to speech, and the model will generate the corresponding speech.",
    examples=[
        ["Hello, how are you doing today?"],
        ["The CUDA programming model allows parallel computing on GPUs."],
        ["TensorFlow and PyTorch are popular machine learning frameworks."]
    ]
)

# Launch the Gradio interface
iface.launch()