infer.py

#!/usr/bin/env python
"""
This script loads a trained model checkpoint and runs inference on a provided audio file. It uses beam search decoding to generate the final transcription.
"""

import os
import argparse
import torch
import torchaudio
from utils.config import load_config
from utils.logger import get_logger
from utils.audio_processing import load_audio, compute_melspectrogram
from model.mixture_of_experts import MixtureOfExpertsModel

def beam_search_decode(model, features, config, beam_width=5, max_decode_length=200):
    """
    A placeholder beam search decoder.
    In practice, this function should incorporate language models and proper decoding.
    """
    # For demonstration, we use greedy decoding as a simple beam search alternative.
    model.eval()
    with torch.no_grad():
        outputs = model(features.unsqueeze(0))  # [1, T, vocab_size]
        best_indices = torch.argmax(outputs, dim=-1).squeeze(0).tolist()
    # Map indices to characters/words using a dummy vocabulary
    # In production, load the appropriate vocabulary file based on language
    vocab = {i: chr(96 + i) for i in range(1, 27)}
    transcription = "".join([vocab.get(idx, "") for idx in best_indices if idx != 0])
    return transcription

def main():
    parser = argparse.ArgumentParser(description="Inference for Speech Recognition")
    parser.add_argument("--config", type=str, default="config.yaml", help="Path to configuration file")
    parser.add_argument("--audio_path", type=str, required=True, help="Path to the audio file for transcription")
    parser.add_argument("--checkpoint", type=str, default="", help="Path to the model checkpoint")
    args = parser.parse_args()

    config = load_config(args.config)
    device = torch.device(config["general"]["device"])
    logger = get_logger(config["general"]["log_dir"])
    
    # Initialize model and load checkpoint if provided
    model = MixtureOfExpertsModel(config=config["model"]).to(device)
    if args.checkpoint and os.path.isfile(args.checkpoint):
        checkpoint = torch.load(args.checkpoint, map_location=device)
        model.load_state_dict(checkpoint["model_state_dict"])
        logger.info(f"Loaded checkpoint from {args.checkpoint}")
    else:
        logger.warning("No valid checkpoint provided. Using randomly initialized model.")

    # Process the audio file
    waveform, sample_rate = load_audio(args.audio_path, target_sample_rate=config["data"]["sample_rate"])
    features = compute_melspectrogram(waveform, config["data"])
    features = features.to(device)
    
    # Perform beam search decoding
    transcription = beam_search_decode(model, features, config["inference"],
                                       beam_width=config["inference"]["beam_width"],
                                       max_decode_length=config["inference"]["max_decode_length"])
    print("Transcription:", transcription)

if __name__ == "__main__":
    main()