funasr_detach/frontends/s3prl.py

import copy
import logging
import os
from argparse import Namespace
from typing import Optional
from typing import Tuple
from typing import Union

import humanfriendly
import torch
import torch.nn as nn

from funasr_detach.frontends.utils.frontend import Frontend
from funasr_detach.models.transformer.utils.nets_utils import pad_list


def base_s3prl_setup(args):
    args.upstream_feature_selection = getattr(args, "upstream_feature_selection", None)
    args.upstream_model_config = getattr(args, "upstream_model_config", None)
    args.upstream_refresh = getattr(args, "upstream_refresh", False)
    args.upstream_ckpt = getattr(args, "upstream_ckpt", None)
    args.init_ckpt = getattr(args, "init_ckpt", None)
    args.verbose = getattr(args, "verbose", False)
    args.tile_factor = getattr(args, "tile_factor", 1)
    return args


class S3prlFrontend(nn.Module):
    """Speech Pretrained Representation frontend structure for ASR."""

    def __init__(
        self,
        fs: Union[int, str] = 16000,
        frontend_conf: Optional[dict] = None,
        download_dir: str = None,
        multilayer_feature: bool = False,
    ):
        super().__init__()
        if isinstance(fs, str):
            fs = humanfriendly.parse_size(fs)

        if download_dir is not None:
            torch.hub.set_dir(download_dir)

        self.multilayer_feature = multilayer_feature
        self.upstream, self.featurizer = self._get_upstream(frontend_conf)
        self.pretrained_params = copy.deepcopy(self.upstream.state_dict())
        self.output_dim = self.featurizer.output_dim
        self.frontend_type = "s3prl"
        self.hop_length = self.upstream.get_downsample_rates("key")

    def _get_upstream(self, frontend_conf):
        """Get S3PRL upstream model."""
        s3prl_args = base_s3prl_setup(
            Namespace(**frontend_conf, device="cpu"),
        )
        self.args = s3prl_args

        s3prl_path = None
        python_path_list = os.environ.get("PYTHONPATH", "(None)").split(":")
        for p in python_path_list:
            if p.endswith("s3prl"):
                s3prl_path = p
                break
        assert s3prl_path is not None

        s3prl_upstream = torch.hub.load(
            s3prl_path,
            s3prl_args.upstream,
            ckpt=s3prl_args.upstream_ckpt,
            model_config=s3prl_args.upstream_model_config,
            refresh=s3prl_args.upstream_refresh,
            source="local",
        ).to("cpu")

        if getattr(
            s3prl_upstream, "model", None
        ) is not None and s3prl_upstream.model.__class__.__name__ in [
            "Wav2Vec2Model",
            "HubertModel",
        ]:
            s3prl_upstream.model.encoder.layerdrop = 0.0

        from s3prl.upstream.interfaces import Featurizer

        if self.multilayer_feature is None:
            feature_selection = "last_hidden_state"
        else:
            feature_selection = "hidden_states"
        s3prl_featurizer = Featurizer(
            upstream=s3prl_upstream,
            feature_selection=feature_selection,
            upstream_device="cpu",
        )

        return s3prl_upstream, s3prl_featurizer

    def _tile_representations(self, feature):
        """Tile up the representations by `tile_factor`.
        Input - sequence of representations
                shape: (batch_size, seq_len, feature_dim)
        Output - sequence of tiled representations
                 shape: (batch_size, seq_len * factor, feature_dim)
        """
        assert (
            len(feature.shape) == 3
        ), "Input argument `feature` has invalid shape: {}".format(feature.shape)
        tiled_feature = feature.repeat(1, 1, self.args.tile_factor)
        tiled_feature = tiled_feature.reshape(
            feature.size(0), feature.size(1) * self.args.tile_factor, feature.size(2)
        )
        return tiled_feature

    def output_size(self) -> int:
        return self.output_dim

    def forward(
        self, input: torch.Tensor, input_lengths: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        wavs = [wav[: input_lengths[i]] for i, wav in enumerate(input)]
        self.upstream.eval()
        with torch.no_grad():
            feats = self.upstream(wavs)
        feats = self.featurizer(wavs, feats)

        if self.args.tile_factor != 1:
            feats = self._tile_representations(feats)

        input_feats = pad_list(feats, 0.0)
        feats_lens = torch.tensor([f.shape[0] for f in feats], dtype=torch.long)

        # Saving CUDA Memory
        del feats

        return input_feats, feats_lens

    def reload_pretrained_parameters(self):
        self.upstream.load_state_dict(self.pretrained_params)
        logging.info("Pretrained S3PRL frontend model parameters reloaded!")