production_ready_pipeline.rs

use anyhow::{Error, Result}; // Import error handling libraries
use pyo3::prelude::*; // Import PyO3 for Python interoperability
use pyo3::types::IntoPyDict; // Import PyO3 utility
use std::fs::File; // Import File handling
use std::io::prelude::*; // Import prelude for I/O operations
use std::path::Path; // Import Path for handling file paths
use tch::{nn, Device, Kind, Tensor}; // Import Torch library for tensor operations

// Define the audio generation model
struct AudioGenerationModel {
    model: nn::Sequential, // Model field to hold the neural network
}

impl AudioGenerationModel {
    // Constructor to create a new model instance
    fn new(model_path: &str) -> Result<Self> {
        // Load the pre-trained model
        let model = tch::CModule::load(model_path)
            .map_err(|e| Error::msg(format!("Failed to load model: {}", e)))?;
        Ok(AudioGenerationModel {
            model: model.sequential(),
        })
    }

    // Forward pass to generate audio
    fn forward(&self, input: &Tensor) -> Tensor {
        self.model.forward(input)
    }
}

// Function to preprocess the input data
fn preprocess_data(data: Vec<f32>, sequence_length: i64) -> Tensor {
    // Convert the input data to a tensor
    let input_tensor = Tensor::of_slice(&data)
        .view([1, sequence_length])
        .to_kind(Kind::Float);

    // Normalize the input data
    let mean = input_tensor.mean(Kind::Float);
    let std = input_tensor.std(Kind::Float);
    let normalized_tensor = (&input_tensor - mean) / std;

    normalized_tensor
}

// Function to postprocess the generated audio
fn postprocess_audio(audio_tensor: Tensor, sample_rate: i64) -> Vec<f32> {
    // Convert the audio tensor to a vector
    let audio_vec: Vec<f32> = audio_tensor.squeeze().into();

    // Possible error: Division by zero if max_val == min_val
    let max_val = audio_vec.iter().cloned().fold(f32::MIN, f32::max);
    let min_val = audio_vec.iter().cloned().fold(f32::MAX, f32::min);

    if max_val == min_val {
        panic!("Max and min values of the audio vector are the same, causing division by zero.");
    }

    let scale_factor = 2.0 / (max_val - min_val);
    let scaled_audio: Vec<f32> = audio_vec
        .iter()
        .map(|&x| (x - min_val) * scale_factor - 1.0)
        .collect();

    scaled_audio
}

// Function to generate audio using the model
fn generate_audio(model: &AudioGenerationModel, input_data: Vec<f32>, sequence_length: i64, sample_rate: i64) -> Vec<f32> {
    // Preprocess the input data
    let preprocessed_data = preprocess_data(input_data, sequence_length);

    // Perform inference using the model
    let output_tensor = model.forward(&preprocessed_data);

    // Postprocess the generated audio
    let audio_data = postprocess_audio(output_tensor, sample_rate);

    audio_data
}

// Function to save the generated audio to a file
fn save_audio_to_file(audio_data: &[f32], file_path: &str, sample_rate: i64) -> Result<()> {
    // Create the output file
    let mut file = File::create(file_path)
        .map_err(|e| Error::msg(format!("Failed to create file: {}", e)))?;

    // Write the WAV header
    let num_samples = audio_data.len() as u32;
    let bytes_per_sample = 2;
    let byte_rate = sample_rate as u32 * bytes_per_sample;
    let block_align = bytes_per_sample;
    let bits_per_sample = 16;
    let subchunk2_size = num_samples * bytes_per_sample;
    let chunk_size = 36 + subchunk2_size;

    // Possible error: Failure to write to file
    file.write_all(b"RIFF")?;
    file.write_all(&chunk_size.to_le_bytes())?;
    file.write_all(b"WAVE")?;
    file.write_all(b"fmt ")?;
    file.write_all(&[16, 0, 0, 0])?;
    file.write_all(&[1, 0])?;
    file.write_all(&[1, 0])?;
    file.write_all(&sample_rate.to_le_bytes())?;
    file.write_all(&byte_rate.to_le_bytes())?;
    file.write_all(&block_align.to_le_bytes())?;
    file.write_all(&bits_per_sample.to_le_bytes())?;
    file.write_all(b"data")?;
    file.write_all(&subchunk2_size.to_le_bytes())?;

    // Write the audio samples
    for sample in audio_data {
        let sample_i16 = (sample * i16::MAX as f32) as i16;
        file.write_all(&sample_i16.to_le_bytes())?;
    }

    Ok(())
}

// Function to load the model and generate audio
fn load_and_generate(model_path: &str, input_data: Vec<f32>, sequence_length: i64, sample_rate: i64, output_path: &str) -> Result<()> {
    // Load the pre-trained model
    let model = AudioGenerationModel::new(model_path)?;

    // Generate audio using the model
    let audio_data = generate_audio(&model, input_data, sequence_length, sample_rate);

    // Save the generated audio to a file
    save_audio_to_file(&audio_data, output_path, sample_rate)?;

    Ok(())
}

// Define the Python module
#[pymodule]
fn production_ready_pipeline(_py: Python, m: &PyModule) -> PyResult<()> {
    // Define the Python function for audio generation
    #[pyfn(m)]
    #[pyo3(name = "generate_audio")]
    fn generate_audio_py(
        model_path: &str,
        input_data: Vec<f32>,
        sequence_length: i64,
        sample_rate: i64,
        output_path: &str,
    ) -> PyResult<String> {
        // Call the Rust function to load the model and generate audio
        match load_and_generate(model_path, input_data, sequence_length, sample_rate, output_path) {
            Ok(_) => Ok(output_path.to_string()),
            Err(e) => Err(pyo3::exceptions::PyRuntimeError::new_err(format!(
                "Failed to generate audio: {}",
                e
            ))),
        }
    }

    Ok(())
}