profiling_tools.rs

use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
use std::time::Instant;
use tch::{nn, Device, Tensor};

// Struct to store profiling information
struct ProfilingInfo {
    name: String,
    duration: std::time::Duration,
}

// Function to generate audio samples using the pre-trained model
fn generate_audio_samples(model: &tch::CModule, num_samples: usize) -> Vec<f32> {
    // Generate random noise as input to the model
    let noise = Tensor::rand(&[1, 100], kind::FLOAT_CPU);

    // Use the loaded model to generate audio samples
    let output = model
        .forward_ts(&[noise])
        .expect("Model forward pass failed") // Possible Error: Forward pass might fail
        .squeeze()
        .to(Device::Cpu)
        .data()
        .as_slice::<f32>()
        .expect("Failed to convert tensor data to slice") // Possible Error: Conversion might fail
        .to_vec();

    // Truncate or pad the output to the desired number of samples
    if output.len() >= num_samples {
        output[..num_samples].to_vec()
    } else {
        let mut samples = output;
        samples.resize(num_samples, 0.0); // Pad with zeros if not enough samples
        samples
    }
}

// Python binding function to generate audio samples with profiling
#[pyfunction]
fn generate_audio_with_profiling(
    model_path: &str,
    num_samples: usize,
) -> PyResult<(Vec<f32>, Vec<ProfilingInfo>)> {
    let mut profiling_info = Vec::new();

    // Load the pre-trained model
    let start_time = Instant::now();
    let model = tch::CModule::load(model_path).map_err(|e| {
        PyErr::new::<pyo3::exceptions::PyIOError, _>(format!("Failed to load model: {}", e))
        // Possible Error: Model loading might fail due to incorrect path or file format
        // Solution: Ensure the model path is correct and the file is a valid PyTorch model
    })?;
    profiling_info.push(ProfilingInfo {
        name: "Model Loading".to_string(),
        duration: start_time.elapsed(),
    });

    // Generate audio samples
    let start_time = Instant::now();
    let samples = generate_audio_samples(&model, num_samples);
    profiling_info.push(ProfilingInfo {
        name: "Audio Generation".to_string(),
        duration: start_time.elapsed(),
    });

    Ok((samples, profiling_info))
}

// Python module to expose the audio generation function with profiling
#[pymodule]
fn profiling_tools(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(generate_audio_with_profiling, m)?)?;
    Ok(())
}