model/vocos/pretrained.py

from __future__ import annotations

from typing import Any, Dict, Tuple, Union, Optional

import torch
import yaml
from huggingface_hub import hf_hub_download
from torch import nn
from model.vocos.feature_extractors import FeatureExtractor, EncodecFeatures
from model.vocos.heads import FourierHead
from model.vocos.models import Backbone


def instantiate_class(args: Union[Any, Tuple[Any, ...]], init: Dict[str, Any]) -> Any:
    """Instantiates a class with the given args and init.

    Args:
        args: Positional arguments required for instantiation.
        init: Dict of the form {"class_path":...,"init_args":...}.

    Returns:
        The instantiated class object.
    """
    kwargs = init.get("init_args", {})
    if not isinstance(args, tuple):
        args = (args,)
    class_module, class_name = init["class_path"].rsplit(".", 1)
    module = __import__(class_module, fromlist=[class_name])
    args_class = getattr(module, class_name)
    return args_class(*args, **kwargs)


class Vocos(nn.Module):
    """
    The Vocos class represents a Fourier-based neural vocoder for audio synthesis.
    This class is primarily designed for inference, with support for loading from pretrained
    model checkpoints. It consists of three main components: a feature extractor,
    a backbone, and a head.
    """

    def __init__(
        self, feature_extractor: nn.Module, backbone: Backbone, head: FourierHead,
    ):
        super().__init__()
        self.feature_extractor = feature_extractor
        self.backbone = backbone
        self.head = head

    @classmethod
    def from_hparams(cls, config_path: str) -> "Vocos":
        """
        Class method to create a new Vocos model instance from hyperparameters stored in a yaml configuration file.
        """
        with open(config_path, "r") as f:
            config = yaml.safe_load(f)
        feature_extractor = instantiate_class(args=(), init=config["feature_extractor"])
        backbone = instantiate_class(args=(), init=config["backbone"])
        head = instantiate_class(args=(), init=config["head"])
        model = cls(feature_extractor=feature_extractor, backbone=backbone, head=head)
        return model

    @classmethod
    def from_pretrained(self, config_path: str, model_path: str, model: nn.Module=None) -> "Vocos":
        """
        Class method to create a new Vocos model instance from a pre-trained model stored in the Hugging Face model hub.
        """
        if model is None:
            model = self.from_hparams(config_path)
        state_dict = torch.load(model_path, map_location="cpu")
        prefixes = ("backbone", "feature_extractor", "head")
        state_dict = {
            key: value
            for key, value in state_dict.items()
            if any(key.startswith(prefix) for prefix in prefixes)
        }
        if isinstance(model.feature_extractor, EncodecFeatures):
            encodec_parameters = {
                "feature_extractor.encodec." + key: value
                for key, value in model.feature_extractor.encodec.state_dict().items()
            }
            state_dict.update(encodec_parameters)
        model.load_state_dict(state_dict)
        model.eval()
        return model

    @torch.inference_mode()
    def forward(self, features_input: torch.Tensor, X_norm, **kwargs: Any) -> torch.Tensor:
        """
        Method to run a copy-synthesis from audio waveform. The feature extractor first processes the audio input,
        which is then passed through the backbone and the head to reconstruct the audio output.

        Args:
            audio_input (Tensor): The input tensor representing the audio waveform of shape (B, T),
                                        where B is the batch size and L is the waveform length.


        Returns:
            Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T).
        """
        audio_output = self.decode(features_input, **kwargs)
        return audio_output / X_norm

    @torch.inference_mode()
    def decode(self, features_input: torch.Tensor, **kwargs: Any) -> torch.Tensor:
        """
        Method to decode audio waveform from already calculated features. The features input is passed through
        the backbone and the head to reconstruct the audio output.

        Args:
            features_input (Tensor): The input tensor of features of shape (B, C, L), where B is the batch size,
                                     C denotes the feature dimension, and L is the sequence length.

        Returns:
            Tensor: The output tensor representing the reconstructed audio waveform of shape (B, T).
        """
        x = self.backbone(features_input, **kwargs)
        audio_output = self.head(x)
        return audio_output

    @torch.inference_mode()
    def codes_to_features(self, codes: torch.Tensor) -> torch.Tensor:
        """
        Transforms an input sequence of discrete tokens (codes) into feature embeddings using the feature extractor's
        codebook weights.

        Args:
            codes (Tensor): The input tensor. Expected shape is (K, L) or (K, B, L),
                            where K is the number of codebooks, B is the batch size and L is the sequence length.

        Returns:
            Tensor: Features of shape (B, C, L), where B is the batch size, C denotes the feature dimension,
                    and L is the sequence length.
        """
        assert isinstance(
            self.feature_extractor, EncodecFeatures
        ), "Feature extractor should be an instance of EncodecFeatures"

        if codes.dim() == 2:
            codes = codes.unsqueeze(1)

        n_bins = self.feature_extractor.encodec.quantizer.bins
        offsets = torch.arange(0, n_bins * len(codes), n_bins, device=codes.device)
        embeddings_idxs = codes + offsets.view(-1, 1, 1)
        features = torch.nn.functional.embedding(embeddings_idxs, self.feature_extractor.codebook_weights).sum(dim=0)
        features = features.transpose(1, 2)

        return features


if __name__ == "__main__":
    model = Vocos.from_pretrained(
        "/nvmework3/shaonian/MelSpatialNet/MelSpatialNet/models/vocos/pretrained/pretrained_rec_normed.yaml", 
        "/nvmework3/shaonian/MelSpatialNet/MelSpatialNet/models/vocos/pretrained/vocos_hop128_clip1e-5_rts.ckpt").to("meta")
    x = torch.randn(1, 80, 501)  
    x = x.to('meta')
    from torch.utils.flop_counter import FlopCounterMode # requires torch>=2.1.0
    with FlopCounterMode(model, display=False) as fcm:
        y = model.decode(x)
        flops_forward_eval = fcm.get_total_flops()

    params_eval = sum(param.numel() for param in model.parameters())
    print(f"flops_forward={flops_forward_eval/4e9:.2f}G, params={params_eval/1e6:.2f} M")