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.gitignore

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CODE_OF_CONDUCT.md

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+# Code of Conduct
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CONTRIBUTING.md

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+# Contributing to audiobox-aesthetics
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
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+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
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+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://bugbounty.meta.com/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to audiobox-aesthetics, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

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README.md

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+# audiobox-aesthetics
+
+Unified automatic quality assessment for speech, music, and sound.
+
+[](Paper)
+
+## Installation
+
+This repository requires Python 3.9 and Pytorch 2.2 or greater. To install, you can clone this repo and run:
+```
+pip install -e .
+```
+
+## Pre-trained Models
+
+Model | Link
+|---|---|
+All axes | [checkpoint.pt](https://dl.fbaipublicfiles.com/audiobox-aesthetics/checkpoint.pt)
+
+## Usage
+
+How to run prediction:
+
+1. Create a jsonl files with the following format
+```
+{"path":"/path/to/a.wav"}
+{"path":"/path/to/b.wav"}
+...
+{"path":"/path/to/z.wav"}
+```
+or if you only want to predict aesthetic score from certain timestamp
+```
+{"path":"/path/to/a.wav", "start_time":0, "end_time": 5}
+{"path":"/path/to/b.wav", "start_time":3, "end_time": 10}
+```
+and save it as `input.jsonl`
+
+2. Run following command
+```
+audio-aes input.jsonl --ckpt "/path/to/checkpoint.pt" > output.jsonl
+```
+
+3. Output file will contains same number of rows as `input.jsonl`. Each rows contains 4 axes prediction with JSON-formatted dictionary. Check following table for more info:
+    Axes name | Full name
+    |---|---|
+    CE | Content Enjoyment
+    CU | Content Usefulness
+    PC | Production Complexity
+    PQ | Production Quality
+    
+    Output line example:
+    ```
+    {"CE": 5.146, "CU": 5.779, "PC": 2.148, "PQ": 7.220}
+    ```
+
+
+
+4. (Extra) If you want to extract only one axis (i.e. CE), post-process the output file with following command using `jq` utility: 
+    
+    ```jq '.CE' output.jsonl > output-aes_ce.txt```
+
+
+
+## Evaluation dataset
+We released our evaluation dataset consisted of 4 axes of aesthetic annotation scores. 
+
+Here, we show an example on how to read and re-map each annotation to the actual audio file.
+```
+{
+    "data_path": "/your_path/LibriTTS/train-clean-100/1363/139304/1363_139304_000011_000000.wav", 
+    "Production_Quality": [8.0, 8.0, 8.0, 8.0, 8.0, 9.0, 8.0, 5.0, 8.0, 8.0], 
+    "Production_Complexity": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 
+    "Content_Enjoyment": [8.0, 6.0, 8.0, 5.0, 8.0, 8.0, 8.0, 6.0, 8.0, 6.0], 
+    "Content_Usefulness": [8.0, 6.0, 8.0, 7.0, 8.0, 9.0, 8.0, 6.0, 10.0, 7.0]
+}
+```
+1. Recognize the dataset name from data_path. In the example, it is LibriTTS.
+2. Replace "/your_path/" into your downloaded LibriTTS directory. 
+3. Each axes contains 10 scores annotated by 10 different human annotators.
+
+data_path | URL
+|---|---|
+LibriTTS |  https://openslr.org/60/
+cv-corpus-13.0-2023-03-09 | https://commonvoice.mozilla.org/en/datasets
+EARS | https://sp-uhh.github.io/ears_dataset/
+MUSDB18 | https://sigsep.github.io/datasets/musdb.html
+musiccaps | https://www.kaggle.com/datasets/googleai/musiccaps
+(audioset) unbalanced_train_segments | https://research.google.com/audioset/dataset/index.html 
+PAM | https://zenodo.org/records/10737388
+
+## License
+The majority of audiobox-aesthetics is licensed under CC-BY 4.0, as found in the LICENSE file.
+However, portions of the project are available under separate license terms: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm) is licensed under MIT license.
+
+## Citation
+If you found this repository useful, please use the following BibTeX entry.
+
+```
+@article{tjandra2025aes,
+    title={Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound},
+    author={Tjandra, Andros and Wu, Yi-Chiao and Guo, Baishan and Hoffman, John and Ellis, Brian and Vyas, Apoorv and Shi, Bowen and Chen, Sanyuan and Le, Matt and Zacharov, Nick and Wood, Carleigh and Lee, Ann and Hsu, Wei-ning},
+    publisher={Meta AI},
+    year={2025},
+    url={https://ai.meta.com/research/publications/meta-audiobox-aesthetics-unified-automatic-quality-assessment-for-speech-music-and-sound/}
+}
+```
+
+## Acknowledgements
+
+Part of model code are copied from [https://github.com/microsoft/unilm/tree/master/wavlm](WavLM).
+

pyproject.toml

@@ -0,0 +1,51 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "audiobox_aesthetics"
+version = "0.0.1"
+authors = [
+    {name="Andros Tjandra", email="[email protected]"},
+    {name="Yi-Chiao Wu"},
+    {name="Baishan Guo"},
+    {name="John Hoffman"},
+    {name="Brian Ellis"},
+    {name="Apoorv Vyas"},
+    {name="Bowen Shi"},
+    {name="Sanyuan Chen"},
+    {name="Matt Le"},
+    {name="Nick Zacharov"},
+    {name="Carleigh Wood"},
+    {name="Ann Lee"},
+    {name="Wei-ning Hsu"}
+]
+maintainers = [
+    {name="Andros Tjandra", email="[email protected]"}
+]
+description = "Unified automatic quality assessment for speech, music, and sound."
+requires-python = ">=3.9"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+]
+readme = "README.md"
+license = {file = "LICENSE"}
+
+dependencies = [
+  "numpy",
+  "torch>=2.2.0",
+  "torchaudio",
+  "tqdm",
+  "iopath",
+  "submitit"
+]
+
+[project.scripts]
+audio-aes = "audiobox_aesthetics.cli:app"
+
+[project.urls]
+Homepage = "https://github.com/pypa/sampleproject"
+Issues = "https://github.com/pypa/sampleproject/issues"
+
+

src/audiobox_aesthetics/cli.py

@@ -0,0 +1,113 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from functools import partial
+import itertools
+from pathlib import Path
+
+import submitit
+from .infer import load_dataset, main_predict
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("CLI for audiobox-aesthetics inference")
+    parser.add_argument("input_file", type=str)
+    parser.add_argument("--ckpt", type=str, required=True)
+    parser.add_argument("--batch-size", type=int, default=100)
+    parser.add_argument(
+        "--remote", action="store_true", default=False, help="Set true to run via SLURM"
+    )
+
+    # remote == True
+    parser.add_argument(
+        "--job-dir", default="/tmp", type=str, help="Slurm job directory"
+    )
+    parser.add_argument(
+        "--partition", default="learn", type=str, help="Slurm partition"
+    )
+    parser.add_argument("--qos", default="", type=str, help="Slurm QOS")
+    parser.add_argument("--account", default="", type=str, help="Slurm account")
+    parser.add_argument("--comment", default="", type=str, help="Slurm job comment")
+    parser.add_argument(
+        "--constraint",
+        default="",
+        type=str,
+        help="Slurm constraint eg.: ampere80gb For using A100s or volta32gb for using V100s.",
+    )
+    parser.add_argument(
+        "--exclude",
+        default="",
+        type=str,
+        help="Exclude certain nodes from the slurm job.",
+    )
+    parser.add_argument(
+        "--array", default=100, type=int, help="Slurm max array parallelism"
+    )
+    parser.add_argument(
+        "--chunk", default=1000, type=int, help="chunk size per instance"
+    )
+    return parser.parse_args()
+
+
+def app():
+    args = parse_args()
+
+    metadata = load_dataset(args.input_file, 0, 2**64)
+    fn_wrapped = partial(main_predict, batch_size=args.batch_size, ckpt=args.ckpt)
+
+    if args.remote:
+        # chunk metadata
+        chunksize = args.chunk
+        chunked = [
+            metadata[ii : ii + chunksize] for ii in range(0, len(metadata), chunksize)
+        ]
+
+        job_dir = Path(args.job_dir)
+        job_dir.mkdir(exist_ok=True)
+
+        executor = submitit.AutoExecutor(folder=f"{job_dir}/%A/")
+
+        kwargs = {}
+        if len(args.constraint):
+            kwargs["slurm_constraint"] = args.constraint
+        if args.comment:
+            kwargs["slurm_comment"] = args.comment
+        if args.qos:
+            kwargs["slurm_qos"] = args.qos
+        if args.account:
+            kwargs["slurm_account"] = args.account
+
+        # Set the parameters for the Slurm job
+        executor.update_parameters(
+            slurm_nodes=1,
+            slurm_gpus_per_node=1,
+            slurm_tasks_per_node=1,
+            slurm_cpus_per_task=10,
+            timeout_min=60 * 20,  # max is 20 hours
+            slurm_array_parallelism=min(
+                len(chunked), args.array
+            ),  # number of tasks in the array job
+            slurm_partition=args.partition,
+            slurm_exclude=args.exclude,
+            **kwargs,
+        )
+
+        jobs = executor.map_array(fn_wrapped, chunked)
+        outputs = [job.result() for job in jobs]
+
+        outputs = itertools.chain(*outputs)
+    else:
+        outputs = fn_wrapped(metadata)
+    print("\n".join(str(x) for x in outputs))
+
+
+if __name__ == "__main__":
+    """
+    Example usage:
+    python cli.py input.jsonl --batch-size 100 --ckpt /path/to/ckpt > output.jsonl
+    """
+    app()

src/audiobox_aesthetics/infer.py

@@ -0,0 +1,214 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+import json
+import re
+import sys
+from typing import Any, Dict, List
+from tqdm import tqdm
+from iopath import PathManager
+import torch
+import torchaudio
+import torch.nn.functional as F
+
+from .model.aes_wavlm import Normalize, WavlmAudioEncoderMultiOutput
+
+Batch = Dict[str, Any]
+
+
+def read_wav(meta):
+    path = meta["path"]
+
+    if "start_time" in meta:
+        start = meta["start_time"]
+        end = meta["end_time"]
+        sr = torchaudio.info(path).sample_rate
+        wav, _ = torchaudio.load(
+            path, frame_offset=start * sr, num_frames=(end - start) * sr
+        )
+    else:
+        wav, sr = torchaudio.load(path)
+
+    if wav.shape[0] > 1:
+        wav = wav.mean(0, keepdim=True)
+
+    return wav, sr
+
+
+def make_inference_batch(
+    input_wavs: list,
+    hop_size=10,
+    window_size=10,
+    sample_rate=16000,
+    pad_zero=True,
+):
+    wavs = []
+    masks = []
+    weights = []
+    bids = []
+    offset = hop_size * sample_rate
+    winlen = window_size * sample_rate
+    for bid, wav in enumerate(input_wavs):
+        for ii in range(0, wav.shape[-1], offset):
+            wav_ii = wav[..., ii : ii + winlen]
+            wav_ii_len = wav_ii.shape[-1]
+            if wav_ii_len < winlen and pad_zero:
+                wav_ii = F.pad(wav_ii, (0, winlen - wav_ii_len))
+            mask_ii = torch.zeros_like(wav_ii, dtype=torch.bool)
+            mask_ii[:, 0:wav_ii_len] = True
+            wavs.append(wav_ii)
+            masks.append(mask_ii)
+            weights.append(wav_ii_len / winlen)
+            bids.append(bid)
+    return wavs, masks, weights, bids
+
+
+AXES_NAME = ["CE", "CU", "PC", "PQ"]
+
+
+@dataclass
+class AesWavlmPredictorMultiOutput:
+    checkpoint_pth: str
+    precision: str = "bf16"
+    batch_size: int = 1
+    data_col: str = "path"
+    sample_rate: int = 16000  # const
+
+    def setup_model(self):
+        # This method gets called before inference starts
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.path_manager = PathManager()
+        print(f"Setting up Aesthetic model on {self.device}", file=sys.stderr)
+
+        with self.path_manager.open(self.checkpoint_pth, "rb") as fin:
+            ckpt = torch.load(fin, map_location=self.device)
+            state_dict = {
+                re.sub("^model.", "", k): v for (k, v) in ckpt["state_dict"].items()
+            }
+        model = WavlmAudioEncoderMultiOutput(
+            **{
+                k: ckpt["model_cfg"][k]
+                for k in [
+                    "proj_num_layer",
+                    "proj_ln",
+                    "proj_act_fn",
+                    "proj_dropout",
+                    "nth_layer",
+                    "use_weighted_layer_sum",
+                    "precision",
+                    "normalize_embed",
+                    "output_dim",
+                ]
+            }
+        )
+        model.load_state_dict(state_dict)
+        model.to(self.device)
+        model.eval()
+
+        self.model = model
+        self.dtype = {
+            "16": torch.float16,
+            "bf16": torch.bfloat16,
+        }.get(self.precision)
+
+        self.target_transform = {
+            axis: Normalize(
+                mean=ckpt["target_transform"][axis]["mean"],
+                std=ckpt["target_transform"][axis]["std"],
+            )
+            for axis in AXES_NAME
+        }
+
+    def audio_resample_mono(self, data_list: List[Batch]) -> List:
+        wavs = []
+        for ii, item in enumerate(data_list):
+            if isinstance(item[self.data_col], str):
+                # wav, sr = torchaudio.load(item[self.data_col])
+                wav, sr = read_wav(item)
+            else:
+                wav = item[self.data_col]
+                sr = item["sample_rate"]
+
+            wav = torchaudio.functional.resample(
+                wav,
+                orig_freq=sr,
+                new_freq=self.sample_rate,
+            )
+            wav = wav.mean(dim=0, keepdim=True)
+            wavs.append(wav)
+        return wavs
+
+    def forward(self, batch):
+        with torch.inference_mode():
+            bsz = len(batch)
+            wavs = self.audio_resample_mono(batch)
+            wavs, masks, weights, bids = make_inference_batch(
+                wavs,
+                10,
+                10,
+                sample_rate=self.sample_rate,
+            )
+
+            # collate
+            wavs = torch.stack(wavs).to(self.device)
+            masks = torch.stack(masks).to(self.device)
+            weights = torch.tensor(weights).to(self.device)
+            bids = torch.tensor(bids).to(self.device)
+
+            assert wavs.shape[0] == masks.shape[0] == weights.shape[0] == bids.shape[0]
+            preds_all = self.model({"wav": wavs, "mask": masks})
+            all_result = {}
+            for axis in AXES_NAME:
+                preds = self.target_transform[axis].inverse(preds_all[axis])
+                weighted_preds = []
+                for bii in range(bsz):
+                    weights_bii = weights[bids == bii]
+                    weighted_preds.append(
+                        (
+                            (preds[bids == bii] * weights_bii).sum() / weights_bii.sum()
+                        ).item()
+                    )
+                all_result[axis] = weighted_preds
+            # re-arrenge result
+            all_rows = [
+                dict(zip(all_result.keys(), vv)) for vv in zip(*all_result.values())
+            ]
+            # convert to json str
+            all_rows = [json.dumps(x) for x in all_rows]
+            return all_rows
+
+
+def load_dataset(path, start=None, end=None) -> List[Batch]:
+    metadata = []
+    with open(path) as fr:
+        for ii, fi in enumerate(fr):
+            if start <= ii < end:
+                fi = json.loads(fi)
+                metadata.append(fi)
+    return metadata
+
+
+def main_predict(input_file, ckpt, batch_size=10):
+    predictor = AesWavlmPredictorMultiOutput(checkpoint_pth=ckpt, data_col="path")
+
+    predictor.setup_model()
+
+    # load file
+    if isinstance(input_file, str):
+        metadata = load_dataset(input_file, 0, 2**64)
+    else:
+        metadata = input_file
+
+    outputs = []
+    for ii in tqdm(range(0, len(metadata), batch_size)):
+        output = predictor.forward(metadata[ii : ii + batch_size])
+        outputs.extend(output)
+    assert len(outputs) == len(
+        metadata
+    ), f"Output {len(outputs)} != input {len(metadata)} length"
+
+    return outputs

src/audiobox_aesthetics/model/aes_wavlm.py

@@ -0,0 +1,175 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+import sys
+from torch import nn
+import torch
+
+from .utils import create_mlp_block
+from .wavlm import WavLM, WavLMConfig
+
+
+DEFAULT_AUDIO_CFG = WavLMConfig(
+    {
+        "extractor_mode": "default",
+        "encoder_layers": 12,
+        "encoder_embed_dim": 768,
+        "encoder_ffn_embed_dim": 3072,
+        "encoder_attention_heads": 12,
+        "activation_fn": "gelu",
+        "dropout": 0.1,
+        "attention_dropout": 0.1,
+        "activation_dropout": 0.0,
+        "encoder_layerdrop": 0.05,
+        "dropout_input": 0.1,
+        "dropout_features": 0.1,
+        "layer_norm_first": False,
+        "conv_feature_layers": "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2",
+        "conv_bias": False,
+        "feature_grad_mult": 0.1,
+        "mask_length": 10,
+        "mask_prob": 0.8,
+        "mask_selection": "static",
+        "mask_other": 0.0,
+        "no_mask_overlap": False,
+        "mask_min_space": 1,
+        "mask_channel_length": 10,
+        "mask_channel_prob": 0.0,
+        "mask_channel_selection": "static",
+        "mask_channel_other": 0.0,
+        "no_mask_channel_overlap": False,
+        "mask_channel_min_space": 1,
+        "conv_pos": 128,
+        "conv_pos_groups": 16,
+        "relative_position_embedding": True,
+        "num_buckets": 320,
+        "max_distance": 800,
+        "gru_rel_pos": True,
+        "normalize": False,
+    }
+)
+
+
+@dataclass(eq=False)
+class Normalize:
+    mean: float
+    std: float
+
+    def transform(self, x):
+        return (x - self.mean) / self.std
+
+    def inverse(self, x):
+        return x * self.std + self.mean
+
+
+AXES_NAME = ["CE", "CU", "PC", "PQ"]
+
+
+@dataclass(eq=False)
+class WavlmAudioEncoderMultiOutput(nn.Module):
+    proj_num_layer: int = 1
+    proj_ln: bool = False
+    proj_act_fn: str = "gelu"
+    proj_dropout: float = 0
+    nth_layer: int = 13
+    use_weighted_layer_sum: bool = True
+    precision: str = "32"
+    normalize_embed: bool = True
+    output_dim: int = 1
+
+    def __post_init__(self):
+        super().__init__()
+        amodel_cfg = DEFAULT_AUDIO_CFG
+        self.wavlm_model = WavLM(amodel_cfg)
+        wavlm_out_dim = self.wavlm_model.cfg.encoder_embed_dim
+
+        self.axes_name = AXES_NAME
+        self.proj_layer = nn.ModuleDict(
+            {
+                x: nn.Sequential(
+                    *create_mlp_block(
+                        wavlm_out_dim,
+                        self.output_dim,
+                        self.proj_num_layer,
+                        self.proj_act_fn,
+                        self.proj_ln,
+                        dropout=self.proj_dropout,
+                    )
+                )
+                for x in self.axes_name
+            }
+        )
+        if self.use_weighted_layer_sum:
+            self.layer_weights = nn.ParameterDict(
+                {
+                    x: torch.nn.Parameter(torch.ones(self.nth_layer) / (self.nth_layer))
+                    for x in self.axes_name
+                }
+            )
+
+        precision_map = {
+            "64": torch.float64,
+            "32": torch.float32,
+            "16": torch.half,
+            "bf16": torch.bfloat16,
+        }
+        self.precision = precision_map[str(self.precision)]
+        self.enable_autocast = str(self.precision) in {"16", "bf16"}
+        print(
+            f"precision: {self.precision}, enable autocast: {self.enable_autocast}",
+            file=sys.stderr,
+        )
+
+    def forward(self, batch):
+        assert batch["wav"].ndim == 3
+
+        # frames: [B, C, T]
+        wav = batch["wav"].squeeze(1)
+
+        if "mask" in batch:
+            padding_mask = ~batch["mask"].squeeze(1)
+        else:
+            padding_mask = torch.zeros_like(wav, dtype=torch.bool)
+
+        with (
+            torch.amp.autocast(
+                device_type=wav.device.type,
+                dtype=self.precision,
+                enabled=self.enable_autocast,
+            ),
+            torch.no_grad(),
+        ):
+            if self.wavlm_model.cfg.normalize:
+                wav = torch.nn.functional.layer_norm(wav, wav.shape)
+            (_, all_outputs), embed_padding_mask = self.wavlm_model.extract_features(
+                source=wav,
+                padding_mask=padding_mask,
+                output_layer=self.nth_layer,
+                ret_layer_results=True,
+            )
+            all_outputs = torch.stack([gg[0] for gg in all_outputs], dim=-1)  # T B C L
+            preds = {}
+            for name in self.axes_name:
+                if self.use_weighted_layer_sum:
+                    norm_weights = torch.nn.functional.softmax(
+                        self.layer_weights[name], dim=-1
+                    )  # L
+                    audio_embed = torch.einsum("tbcl,l->btc", all_outputs, norm_weights)
+                else:
+                    audio_embed = all_outputs[-1][0].transpose(1, 0)
+
+                embed_mask = (
+                    (~embed_padding_mask).unsqueeze(dim=-1).type_as(audio_embed)
+                )
+                audio_embed = (audio_embed * embed_mask).sum(dim=1) / embed_mask.sum(
+                    dim=1
+                ).clamp(min=1)
+                if self.normalize_embed:
+                    audio_embed = torch.nn.functional.normalize(audio_embed, dim=-1)
+
+                preds[name] = self.proj_layer[name](audio_embed).squeeze(-1)
+        return preds

src/audiobox_aesthetics/model/utils.py

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch import nn
+
+
+def create_mlp_block(input_dim, output_dim, num_layer, act_fn, layer_norm, dropout=0):
+    proj_layer = []
+    for ii in range(num_layer):
+        if ii == num_layer - 1:
+            proj_layer.append(nn.Linear(input_dim, output_dim))
+        else:
+            proj_layer.append(nn.Linear(input_dim, input_dim))
+            if layer_norm:
+                proj_layer.append(nn.LayerNorm(normalized_shape=(input_dim)))
+            if act_fn == "gelu":
+                proj_layer.append(nn.GELU())
+            else:
+                raise ValueError()
+            if dropout != 0:
+                proj_layer.append(nn.Dropout(p=dropout))
+    return proj_layer

src/audiobox_aesthetics/model/wavlm.py

@@ -0,0 +1,1597 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# Based on WavLM code
+# --------------------------------------------------------
+# WavLM: Large-Scale Self-Supervised  Pre-training  for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
+# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Based on fairseq code bases
+# https://github.com/pytorch/fairseq
+# --------------------------------------------------------
+
+import logging
+import math
+
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+
+import warnings
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch.nn import LayerNorm, Parameter
+
+
+logger = logging.getLogger(__name__)
+
+
+class TransposeLast(nn.Module):
+    def __init__(self, deconstruct_idx=None):
+        super().__init__()
+        self.deconstruct_idx = deconstruct_idx
+
+    def forward(self, x):
+        if self.deconstruct_idx is not None:
+            x = x[self.deconstruct_idx]
+        return x.transpose(-2, -1)
+
+
+class Fp32LayerNorm(nn.LayerNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.layer_norm(
+            input.float(),
+            self.normalized_shape,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class Fp32GroupNorm(nn.GroupNorm):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, input):
+        output = F.group_norm(
+            input.float(),
+            self.num_groups,
+            self.weight.float() if self.weight is not None else None,
+            self.bias.float() if self.bias is not None else None,
+            self.eps,
+        )
+        return output.type_as(input)
+
+
+class GradMultiply(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale):
+        ctx.scale = scale
+        res = x.new(x)
+        return res
+
+    @staticmethod
+    def backward(ctx, grad):
+        return grad * ctx.scale, None
+
+
+class SamePad(nn.Module):
+    def __init__(self, kernel_size, causal=False):
+        super().__init__()
+        if causal:
+            self.remove = kernel_size - 1
+        else:
+            self.remove = 1 if kernel_size % 2 == 0 else 0
+
+    def forward(self, x):
+        if self.remove > 0:
+            x = x[:, :, : -self.remove]
+        return x
+
+
+class Swish(nn.Module):
+    """Swish function"""
+
+    def __init__(self):
+        """Construct an MultiHeadedAttention object."""
+        super(Swish, self).__init__()
+        self.act = torch.nn.Sigmoid()
+
+    def forward(self, x):
+        return x * self.act(x)
+
+
+class GLU_Linear(nn.Module):
+    def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True):
+        super(GLU_Linear, self).__init__()
+
+        self.glu_type = glu_type
+        self.output_dim = output_dim
+
+        if glu_type == "sigmoid":
+            self.glu_act = torch.nn.Sigmoid()
+        elif glu_type == "swish":
+            self.glu_act = Swish()
+        elif glu_type == "relu":
+            self.glu_act = torch.nn.ReLU()
+        elif glu_type == "gelu":
+            self.glu_act = torch.nn.GELU()
+
+        if bias_in_glu:
+            self.linear = nn.Linear(input_dim, output_dim * 2, True)
+        else:
+            self.linear = nn.Linear(input_dim, output_dim * 2, False)
+
+    def forward(self, x):
+        # to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case
+        x = self.linear(x)
+
+        if self.glu_type == "bilinear":
+            x = (
+                x[:, :, 0 : self.output_dim]
+                * x[:, :, self.output_dim : self.output_dim * 2]
+            )
+        else:
+            x = x[:, :, 0 : self.output_dim] * self.glu_act(
+                x[:, :, self.output_dim : self.output_dim * 2]
+            )
+
+        return x
+
+
+def gelu_accurate(x):
+    if not hasattr(gelu_accurate, "_a"):
+        gelu_accurate._a = math.sqrt(2 / math.pi)
+    return (
+        0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
+    )
+
+
+def gelu(x: torch.Tensor) -> torch.Tensor:
+    return torch.nn.functional.gelu(x.float()).type_as(x)
+
+
+def get_activation_fn(activation: str):
+    """Returns the activation function corresponding to `activation`"""
+
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return gelu
+    elif activation == "gelu_fast":
+        warnings.warn("--activation-fn=gelu_fast has been renamed to gelu_accurate")
+        return gelu_accurate
+    elif activation == "gelu_accurate":
+        return gelu_accurate
+    elif activation == "tanh":
+        return torch.tanh
+    elif activation == "linear":
+        return lambda x: x
+    elif activation == "glu":
+        return lambda x: x
+    else:
+        raise RuntimeError("--activation-fn {} not supported".format(activation))
+
+
+def init_bert_params(module):
+    """
+    Initialize the weights specific to the BERT Model.
+    This overrides the default initializations depending on the specified arguments.
+        1. If normal_init_linear_weights is set then weights of linear
+           layer will be initialized using the normal distribution and
+           bais will be set to the specified value.
+        2. If normal_init_embed_weights is set then weights of embedding
+           layer will be initialized using the normal distribution.
+        3. If normal_init_proj_weights is set then weights of
+           in_project_weight for MultiHeadAttention initialized using
+           the normal distribution (to be validated).
+    """
+
+    def normal_(data):
+        # with FSDP, module params will be on CUDA, so we cast them back to CPU
+        # so that the RNG is consistent with and without FSDP
+        data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
+
+    if isinstance(module, nn.Linear):
+        normal_(module.weight.data)
+        if module.bias is not None:
+            module.bias.data.zero_()
+    if isinstance(module, nn.Embedding):
+        normal_(module.weight.data)
+        if module.padding_idx is not None:
+            module.weight.data[module.padding_idx].zero_()
+    if isinstance(module, MultiheadAttention):
+        normal_(module.q_proj.weight.data)
+        normal_(module.k_proj.weight.data)
+        normal_(module.v_proj.weight.data)
+
+
+def quant_noise(module, p, block_size):
+    """
+    Wraps modules and applies quantization noise to the weights for
+    subsequent quantization with Iterative Product Quantization as
+    described in "Training with Quantization Noise for Extreme Model Compression"
+
+    Args:
+        - module: nn.Module
+        - p: amount of Quantization Noise
+        - block_size: size of the blocks for subsequent quantization with iPQ
+
+    Remarks:
+        - Module weights must have the right sizes wrt the block size
+        - Only Linear, Embedding and Conv2d modules are supported for the moment
+        - For more detail on how to quantize by blocks with convolutional weights,
+          see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
+        - We implement the simplest form of noise here as stated in the paper
+          which consists in randomly dropping blocks
+    """
+
+    # if no quantization noise, don't register hook
+    if p <= 0:
+        return module
+
+    # supported modules
+    assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
+
+    # test whether module.weight has the right sizes wrt block_size
+    is_conv = module.weight.ndim == 4
+
+    # 2D matrix
+    if not is_conv:
+        assert (
+            module.weight.size(1) % block_size == 0
+        ), "Input features must be a multiple of block sizes"
+
+    # 4D matrix
+    else:
+        # 1x1 convolutions
+        if module.kernel_size == (1, 1):
+            assert (
+                module.in_channels % block_size == 0
+            ), "Input channels must be a multiple of block sizes"
+        # regular convolutions
+        else:
+            k = module.kernel_size[0] * module.kernel_size[1]
+            assert k % block_size == 0, "Kernel size must be a multiple of block size"
+
+    def _forward_pre_hook(mod, input):
+        # no noise for evaluation
+        if mod.training:
+            if not is_conv:
+                # gather weight and sizes
+                weight = mod.weight
+                in_features = weight.size(1)
+                out_features = weight.size(0)
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                mask = torch.zeros(
+                    in_features // block_size * out_features, device=weight.device
+                )
+                mask.bernoulli_(p)
+                mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
+
+            else:
+                # gather weight and sizes
+                weight = mod.weight
+                in_channels = mod.in_channels
+                out_channels = mod.out_channels
+
+                # split weight matrix into blocks and randomly drop selected blocks
+                if mod.kernel_size == (1, 1):
+                    mask = torch.zeros(
+                        int(in_channels // block_size * out_channels),
+                        device=weight.device,
+                    )
+                    mask.bernoulli_(p)
+                    mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
+                else:
+                    mask = torch.zeros(
+                        weight.size(0), weight.size(1), device=weight.device
+                    )
+                    mask.bernoulli_(p)
+                    mask = (
+                        mask.unsqueeze(2)
+                        .unsqueeze(3)
+                        .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
+                    )
+
+            # scale weights and apply mask
+            mask = mask.to(
+                torch.bool
+            )  # x.bool() is not currently supported in TorchScript
+            s = 1 / (1 - p)
+            mod.weight.data = s * weight.masked_fill(mask, 0)
+
+    module.register_forward_pre_hook(_forward_pre_hook)
+    return module
+
+
+class MultiheadAttention(nn.Module):
+    """Multi-headed attention.
+
+    See "Attention Is All You Need" for more details.
+    """
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        kdim=None,
+        vdim=None,
+        dropout=0.0,
+        bias=True,
+        add_bias_kv=False,
+        add_zero_attn=False,
+        self_attention=False,
+        encoder_decoder_attention=False,
+        q_noise=0.0,
+        qn_block_size=8,
+        has_relative_attention_bias=False,
+        num_buckets=32,
+        max_distance=128,
+        gru_rel_pos=False,
+        rescale_init=False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout_module = nn.Dropout(dropout)
+
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
+
+        self.head_dim = embed_dim // num_heads
+        self.q_head_dim = self.head_dim
+        self.k_head_dim = self.head_dim
+        assert (
+            self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim**-0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, (
+            "Self-attention requires query, key and " "value to be of the same size"
+        )
+
+        k_bias = True
+        if rescale_init:
+            k_bias = False
+
+        k_embed_dim = embed_dim
+        q_embed_dim = embed_dim
+
+        self.k_proj = quant_noise(
+            nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size
+        )
+        self.v_proj = quant_noise(
+            nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
+        )
+        self.q_proj = quant_noise(
+            nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size
+        )
+
+        self.out_proj = quant_noise(
+            nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
+        )
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.gru_rel_pos = gru_rel_pos
+        if self.gru_rel_pos:
+            self.grep_linear = nn.Linear(self.q_head_dim, 8)
+            self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            # Empirically observed the convergence to be much better with
+            # the scaled initialization
+            nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
+            nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
+        else:
+            nn.init.xavier_uniform_(self.k_proj.weight)
+            nn.init.xavier_uniform_(self.v_proj.weight)
+            nn.init.xavier_uniform_(self.q_proj.weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.out_proj.bias is not None:
+            nn.init.constant_(self.out_proj.bias, 0.0)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+        if self.has_relative_attention_bias:
+            nn.init.xavier_normal_(self.relative_attention_bias.weight)
+
+    def _relative_positions_bucket(self, relative_positions, bidirectional=True):
+        num_buckets = self.num_buckets
+        max_distance = self.max_distance
+        relative_buckets = 0
+
+        if bidirectional:
+            num_buckets = num_buckets // 2
+            relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
+            relative_positions = torch.abs(relative_positions)
+        else:
+            relative_positions = -torch.min(
+                relative_positions, torch.zeros_like(relative_positions)
+            )
+
+        max_exact = num_buckets // 2
+        is_small = relative_positions < max_exact
+
+        relative_postion_if_large = max_exact + (
+            torch.log(relative_positions.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_postion_if_large = torch.min(
+            relative_postion_if_large,
+            torch.full_like(relative_postion_if_large, num_buckets - 1),
+        )
+
+        relative_buckets += torch.where(
+            is_small, relative_positions, relative_postion_if_large
+        )
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length):
+        context_position = torch.arange(query_length, dtype=torch.long)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position
+        relative_position_bucket = self._relative_positions_bucket(
+            relative_position, bidirectional=True
+        )
+        relative_position_bucket = relative_position_bucket.to(
+            self.relative_attention_bias.weight.device
+        )
+        values = self.relative_attention_bias(relative_position_bucket)
+        values = values.permute([2, 0, 1])
+        return values
+
+    def forward(
+        self,
+        query,
+        key: Optional[Tensor],
+        value: Optional[Tensor],
+        key_padding_mask: Optional[Tensor] = None,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
+        need_weights: bool = True,
+        static_kv: bool = False,
+        attn_mask: Optional[Tensor] = None,
+        before_softmax: bool = False,
+        need_head_weights: bool = False,
+        position_bias: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        is_tpu = query.device.type == "xla"
+
+        tgt_len, bsz, embed_dim = query.size()
+        src_len = tgt_len
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+        if key is not None:
+            src_len, key_bsz, _ = key.size()
+            if not torch.jit.is_scripting():
+                assert key_bsz == bsz
+                assert value is not None
+                assert src_len, bsz == value.shape[:2]
+
+        if self.has_relative_attention_bias and position_bias is None:
+            position_bias = self.compute_bias(tgt_len, src_len)
+            position_bias = (
+                position_bias.unsqueeze(0)
+                .repeat(bsz, 1, 1, 1)
+                .view(bsz * self.num_heads, tgt_len, src_len)
+            )
+
+        if (
+            not is_tpu  # don't use PyTorch version on TPUs
+            and incremental_state is None
+            and not static_kv
+            # A workaround for quantization to work. Otherwise JIT compilation
+            # treats bias in linear module as method.
+            and not torch.jit.is_scripting()
+            and self.q_head_dim == self.head_dim
+        ):
+            assert key is not None and value is not None
+            assert attn_mask is None
+
+            attn_mask_rel_pos = None
+            if position_bias is not None:
+                attn_mask_rel_pos = position_bias
+                if self.gru_rel_pos:
+                    query_layer = query.transpose(0, 1)
+                    new_x_shape = query_layer.size()[:-1] + (self.num_heads, -1)
+                    query_layer = query_layer.view(*new_x_shape)
+                    query_layer = query_layer.permute(0, 2, 1, 3)
+                    _B, _H, _L, __ = query_layer.size()
+
+                    gate_a, gate_b = torch.sigmoid(
+                        self.grep_linear(query_layer)
+                        .view(_B, _H, _L, 2, 4)
+                        .sum(-1, keepdim=False)
+                    ).chunk(2, dim=-1)
+                    gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                    attn_mask_rel_pos = (
+                        gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+                    )
+
+                attn_mask_rel_pos = attn_mask_rel_pos.view((-1, tgt_len, tgt_len))
+            k_proj_bias = self.k_proj.bias
+            if k_proj_bias is None:
+                k_proj_bias = torch.zeros_like(self.q_proj.bias)
+
+            x, attn = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                torch.empty([0]),
+                torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout_module.p,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                self.training,
+                # self.training or self.dropout_module.apply_during_inference,
+                key_padding_mask,
+                need_weights,
+                attn_mask_rel_pos,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj.weight,
+                k_proj_weight=self.k_proj.weight,
+                v_proj_weight=self.v_proj.weight,
+            )
+            return x, attn, position_bias
+
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if saved_state is not None and "prev_key" in saved_state:
+                # previous time steps are cached - no need to recompute
+                # key and value if they are static
+                if static_kv:
+                    assert self.encoder_decoder_attention and not self.self_attention
+                    key = value = None
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            q = self.q_proj(query)
+            k = self.k_proj(query)
+            v = self.v_proj(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.q_proj(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.k_proj(key)
+                v = self.v_proj(key)
+
+        else:
+            assert key is not None and value is not None
+            q = self.q_proj(query)
+            k = self.k_proj(key)
+            v = self.v_proj(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat(
+                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
+                )
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
+                    ],
+                    dim=1,
+                )
+
+        q = (
+            q.contiguous()
+            .view(tgt_len, bsz * self.num_heads, self.q_head_dim)
+            .transpose(0, 1)
+        )
+        if k is not None:
+            k = (
+                k.contiguous()
+                .view(-1, bsz * self.num_heads, self.k_head_dim)
+                .transpose(0, 1)
+            )
+        if v is not None:
+            v = (
+                v.contiguous()
+                .view(-1, bsz * self.num_heads, self.head_dim)
+                .transpose(0, 1)
+            )
+
+        if saved_state is not None:
+            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
+            if "prev_key" in saved_state:
+                _prev_key = saved_state["prev_key"]
+                assert _prev_key is not None
+                prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    k = prev_key
+                else:
+                    assert k is not None
+                    k = torch.cat([prev_key, k], dim=1)
+                src_len = k.size(1)
+            if "prev_value" in saved_state:
+                _prev_value = saved_state["prev_value"]
+                assert _prev_value is not None
+                prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    v = prev_value
+                else:
+                    assert v is not None
+                    v = torch.cat([prev_value, v], dim=1)
+            prev_key_padding_mask: Optional[Tensor] = None
+            if "prev_key_padding_mask" in saved_state:
+                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
+            assert k is not None and v is not None
+            key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
+                key_padding_mask=key_padding_mask,
+                prev_key_padding_mask=prev_key_padding_mask,
+                batch_size=bsz,
+                src_len=k.size(1),
+                static_kv=static_kv,
+            )
+
+            saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state["prev_key_padding_mask"] = key_padding_mask
+            # In this branch incremental_state is never None
+            assert incremental_state is not None
+            incremental_state = self._set_input_buffer(incremental_state, saved_state)
+        assert k is not None
+        assert k.size(1) == src_len
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.dim() == 0:
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            assert v is not None
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat(
+                    [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
+                )
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [
+                        key_padding_mask,
+                        torch.zeros(key_padding_mask.size(0), 1).type_as(
+                            key_padding_mask
+                        ),
+                    ],
+                    dim=1,
+                )
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.unsqueeze(0)
+            attn_weights += attn_mask
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            if not is_tpu:
+                attn_weights = attn_weights.masked_fill(
+                    key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
+                    float("-inf"),
+                )
+            else:
+                attn_weights = attn_weights.transpose(0, 2)
+                attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
+                attn_weights = attn_weights.transpose(0, 2)
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v, position_bias
+
+        if position_bias is not None:
+            if self.gru_rel_pos == 1:
+                query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim)
+                _B, _H, _L, __ = query_layer.size()
+                gate_a, gate_b = torch.sigmoid(
+                    self.grep_linear(query_layer)
+                    .view(_B, _H, _L, 2, 4)
+                    .sum(-1, keepdim=False)
+                ).chunk(2, dim=-1)
+                gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
+                position_bias = (
+                    gate_a_1.view(bsz * self.num_heads, -1, 1) * position_bias
+                )
+
+            position_bias = position_bias.view(attn_weights.size())
+
+            attn_weights = attn_weights + position_bias
+
+        attn_weights_float = F.softmax(attn_weights, dim=-1)
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = self.dropout_module(attn_weights)
+
+        assert v is not None
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+        attn_weights: Optional[Tensor] = None
+        if need_weights:
+            attn_weights = attn_weights_float.view(
+                bsz, self.num_heads, tgt_len, src_len
+            ).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+
+        return attn, attn_weights, position_bias
+
+    @staticmethod
+    def _append_prev_key_padding_mask(
+        key_padding_mask: Optional[Tensor],
+        prev_key_padding_mask: Optional[Tensor],
+        batch_size: int,
+        src_len: int,
+        static_kv: bool,
+    ) -> Optional[Tensor]:
+        # saved key padding masks have shape (bsz, seq_len)
+        if prev_key_padding_mask is not None and static_kv:
+            new_key_padding_mask = prev_key_padding_mask
+        elif prev_key_padding_mask is not None and key_padding_mask is not None:
+            new_key_padding_mask = torch.cat(
+                [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
+            )
+        # During incremental decoding, as the padding token enters and
+        # leaves the frame, there will be a time when prev or current
+        # is None
+        elif prev_key_padding_mask is not None:
+            if src_len > prev_key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - prev_key_padding_mask.size(1)),
+                    device=prev_key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat(
+                    [prev_key_padding_mask.float(), filler.float()], dim=1
+                )
+            else:
+                new_key_padding_mask = prev_key_padding_mask.float()
+        elif key_padding_mask is not None:
+            if src_len > key_padding_mask.size(1):
+                filler = torch.zeros(
+                    (batch_size, src_len - key_padding_mask.size(1)),
+                    device=key_padding_mask.device,
+                )
+                new_key_padding_mask = torch.cat(
+                    [filler.float(), key_padding_mask.float()], dim=1
+                )
+            else:
+                new_key_padding_mask = key_padding_mask.float()
+        else:
+            new_key_padding_mask = prev_key_padding_mask
+        return new_key_padding_mask
+
+    def _get_input_buffer(
+        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
+    ) -> Dict[str, Optional[Tensor]]:
+        result = self.get_incremental_state(incremental_state, "attn_state")
+        if result is not None:
+            return result
+        else:
+            empty_result: Dict[str, Optional[Tensor]] = {}
+            return empty_result
+
+    def _set_input_buffer(
+        self,
+        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
+        buffer: Dict[str, Optional[Tensor]],
+    ):
+        return self.set_incremental_state(incremental_state, "attn_state", buffer)
+
+    def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
+        return attn_weights
+
+
+def compute_mask_indices(
+    shape: Tuple[int, int],
+    padding_mask: Optional[torch.Tensor],
+    mask_prob: float,
+    mask_length: int,
+    mask_type: str = "static",
+    mask_other: float = 0.0,
+    min_masks: int = 0,
+    no_overlap: bool = False,
+    min_space: int = 0,
+) -> np.ndarray:
+    """
+    Computes random mask spans for a given shape
+
+    Args:
+        shape: the the shape for which to compute masks.
+            should be of size 2 where first element is batch size and 2nd is timesteps
+        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
+        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
+        mask_type: how to compute mask lengths
+            static = fixed size
+            uniform = sample from uniform distribution [mask_other, mask_length*2]
+            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
+            poisson = sample from possion distribution with lambda = mask length
+        min_masks: minimum number of masked spans
+        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
+        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
+    """
+
+    bsz, all_sz = shape
+    mask = np.full((bsz, all_sz), False)
+
+    all_num_mask = int(
+        # add a random number for probabilistic rounding
+        mask_prob * all_sz / float(mask_length) + np.random.rand()
+    )
+
+    all_num_mask = max(min_masks, all_num_mask)
+
+    mask_idcs = []
+    for i in range(bsz):
+        if padding_mask is not None:
+            sz = all_sz - padding_mask[i].long().sum().item()
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                mask_prob * sz / float(mask_length) + np.random.rand()
+            )
+            num_mask = max(min_masks, num_mask)
+        else:
+            sz = all_sz
+            num_mask = all_num_mask
+
+        if mask_type == "static":
+            lengths = np.full(num_mask, mask_length)
+        elif mask_type == "uniform":
+            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
+        elif mask_type == "normal":
+            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
+            lengths = [max(1, int(round(x))) for x in lengths]
+        elif mask_type == "poisson":
+            lengths = np.random.poisson(mask_length, size=num_mask)
+            lengths = [int(round(x)) for x in lengths]
+        else:
+            raise Exception("unknown mask selection " + mask_type)
+
+        if sum(lengths) == 0:
+            lengths[0] = min(mask_length, sz - 1)
+
+        if no_overlap:
+            mask_idc = []
+
+            def arrange(s, e, length, keep_length):
+                span_start = np.random.randint(s, e - length)
+                mask_idc.extend(span_start + i for i in range(length))
+
+                new_parts = []
+                if span_start - s - min_space >= keep_length:
+                    new_parts.append((s, span_start - min_space + 1))
+                if e - span_start - keep_length - min_space > keep_length:
+                    new_parts.append((span_start + length + min_space, e))
+                return new_parts
+
+            parts = [(0, sz)]
+            min_length = min(lengths)
+            for length in sorted(lengths, reverse=True):
+                lens = np.fromiter(
+                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
+                    np.int,
+                )
+                l_sum = np.sum(lens)
+                if l_sum == 0:
+                    break
+                probs = lens / np.sum(lens)
+                c = np.random.choice(len(parts), p=probs)
+                s, e = parts.pop(c)
+                parts.extend(arrange(s, e, length, min_length))
+            mask_idc = np.asarray(mask_idc)
+        else:
+            min_len = min(lengths)
+            if sz - min_len <= num_mask:
+                min_len = sz - num_mask - 1
+
+            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
+
+            mask_idc = np.asarray(
+                [
+                    mask_idc[j] + offset
+                    for j in range(len(mask_idc))
+                    for offset in range(lengths[j])
+                ]
+            )
+
+        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
+
+    min_len = min([len(m) for m in mask_idcs])
+    for i, mask_idc in enumerate(mask_idcs):
+        if len(mask_idc) > min_len:
+            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
+        mask[i, mask_idc] = True
+
+    return mask
+
+
+class WavLMConfig:
+    def __init__(self, cfg=None):
+        self.extractor_mode: str = "default"  # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
+        self.encoder_layers: int = 12  # num encoder layers in the transformer
+
+        self.encoder_embed_dim: int = 768  # encoder embedding dimension
+        self.encoder_ffn_embed_dim: int = 3072  # encoder embedding dimension for FFN
+        self.encoder_attention_heads: int = 12  # num encoder attention heads
+        self.activation_fn: str = "gelu"  # activation function to use
+
+        self.layer_norm_first: bool = False  # apply layernorm first in the transformer
+        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"  # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
+        self.conv_bias: bool = False  # include bias in conv encoder
+        self.feature_grad_mult: float = (
+            1.0  # multiply feature extractor var grads by this
+        )
+
+        self.normalize: bool = (
+            False  # normalize input to have 0 mean and unit variance during training
+        )
+
+        # dropouts
+        self.dropout: float = 0.1  # dropout probability for the transformer
+        self.attention_dropout: float = 0.1  # dropout probability for attention weights
+        self.activation_dropout: float = (
+            0.0  # dropout probability after activation in FFN
+        )
+        self.encoder_layerdrop: float = (
+            0.0  # probability of dropping a tarnsformer layer
+        )
+        self.dropout_input: float = (
+            0.0  # dropout to apply to the input (after feat extr)
+        )
+        self.dropout_features: float = (
+            0.0  # dropout to apply to the features (after feat extr)
+        )
+
+        # masking
+        self.mask_length: int = 10  # mask length
+        self.mask_prob: float = 0.65  # probability of replacing a token with mask
+        self.mask_selection: str = "static"  # how to choose mask length
+        self.mask_other: float = 0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
+        self.no_mask_overlap: bool = False  # whether to allow masks to overlap
+        self.mask_min_space: int = (
+            1  # min space between spans (if no overlap is enabled)
+        )
+
+        # channel masking
+        self.mask_channel_length: int = 10  # length of the mask for features (channels)
+        self.mask_channel_prob: float = 0.0  # probability of replacing a feature with 0
+        self.mask_channel_selection: str = (
+            "static"  # how to choose mask length for channel masking
+        )
+        self.mask_channel_other: float = 0  # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
+        self.no_mask_channel_overlap: bool = (
+            False  # whether to allow channel masks to overlap
+        )
+        self.mask_channel_min_space: int = (
+            1  # min space between spans (if no overlap is enabled)
+        )
+
+        # positional embeddings
+        self.conv_pos: int = (
+            128  # number of filters for convolutional positional embeddings
+        )
+        self.conv_pos_groups: int = (
+            16  # number of groups for convolutional positional embedding
+        )
+
+        # relative position embedding
+        self.relative_position_embedding: bool = (
+            False  # apply relative position embedding
+        )
+        self.num_buckets: int = 320  # number of buckets for relative position embedding
+        self.max_distance: int = (
+            1280  # maximum distance for relative position embedding
+        )
+        self.gru_rel_pos: bool = False  # apply gated relative position embedding
+
+        if cfg is not None:
+            self.update(cfg)
+
+    def update(self, cfg: dict):
+        self.__dict__.update(cfg)
+
+
+class WavLM(nn.Module):
+    def __init__(
+        self,
+        cfg: WavLMConfig,
+    ) -> None:
+        super().__init__()
+        logger.info(f"WavLM Config: {cfg.__dict__}")
+
+        self.cfg = cfg
+        feature_enc_layers = eval(cfg.conv_feature_layers)
+        self.embed = feature_enc_layers[-1][0]
+
+        self.feature_extractor = ConvFeatureExtractionModel(
+            conv_layers=feature_enc_layers,
+            dropout=0.0,
+            mode=cfg.extractor_mode,
+            conv_bias=cfg.conv_bias,
+        )
+
+        self.post_extract_proj = (
+            nn.Linear(self.embed, cfg.encoder_embed_dim)
+            if self.embed != cfg.encoder_embed_dim
+            else None
+        )
+
+        self.mask_prob = cfg.mask_prob
+        self.mask_selection = cfg.mask_selection
+        self.mask_other = cfg.mask_other
+        self.mask_length = cfg.mask_length
+        self.no_mask_overlap = cfg.no_mask_overlap
+        self.mask_min_space = cfg.mask_min_space
+
+        self.mask_channel_prob = cfg.mask_channel_prob
+        self.mask_channel_selection = cfg.mask_channel_selection
+        self.mask_channel_other = cfg.mask_channel_other
+        self.mask_channel_length = cfg.mask_channel_length
+        self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
+        self.mask_channel_min_space = cfg.mask_channel_min_space
+
+        self.dropout_input = nn.Dropout(cfg.dropout_input)
+        self.dropout_features = nn.Dropout(cfg.dropout_features)
+
+        self.feature_grad_mult = cfg.feature_grad_mult
+
+        self.mask_emb = nn.Parameter(
+            torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
+        )
+
+        self.encoder = TransformerEncoder(cfg)
+        self.layer_norm = LayerNorm(self.embed)
+
+    def apply_mask(self, x, padding_mask):
+        B, T, C = x.shape
+        if self.mask_prob > 0:
+            mask_indices = compute_mask_indices(
+                (B, T),
+                padding_mask,
+                self.mask_prob,
+                self.mask_length,
+                self.mask_selection,
+                self.mask_other,
+                min_masks=2,
+                no_overlap=self.no_mask_overlap,
+                min_space=self.mask_min_space,
+            )
+            mask_indices = torch.from_numpy(mask_indices).to(x.device)
+            x[mask_indices] = self.mask_emb
+        else:
+            mask_indices = None
+
+        if self.mask_channel_prob > 0:
+            mask_channel_indices = compute_mask_indices(
+                (B, C),
+                None,
+                self.mask_channel_prob,
+                self.mask_channel_length,
+                self.mask_channel_selection,
+                self.mask_channel_other,
+                no_overlap=self.no_mask_channel_overlap,
+                min_space=self.mask_channel_min_space,
+            )
+            mask_channel_indices = (
+                torch.from_numpy(mask_channel_indices)
+                .to(x.device)
+                .unsqueeze(1)
+                .expand(-1, T, -1)
+            )
+            x[mask_channel_indices] = 0
+
+        return x, mask_indices
+
+    def forward_padding_mask(
+        self,
+        features: torch.Tensor,
+        padding_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        extra = padding_mask.size(1) % features.size(1)
+        if extra > 0:
+            padding_mask = padding_mask[:, :-extra]
+        padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1)
+        padding_mask = padding_mask.all(-1)
+        return padding_mask
+
+    def extract_features(
+        self,
+        source: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        mask: bool = False,
+        ret_conv: bool = False,
+        output_layer: Optional[int] = None,
+        ret_layer_results: bool = False,
+    ):
+        if self.feature_grad_mult > 0:
+            features = self.feature_extractor(source)
+            if self.feature_grad_mult != 1.0:
+                features = GradMultiply.apply(features, self.feature_grad_mult)
+        else:
+            with torch.no_grad():
+                features = self.feature_extractor(source)
+
+        features = features.transpose(1, 2)
+        features = self.layer_norm(features)
+
+        if padding_mask is not None:
+            padding_mask = self.forward_padding_mask(features, padding_mask)
+
+        if self.post_extract_proj is not None:
+            features = self.post_extract_proj(features)
+
+        features = self.dropout_input(features)
+
+        if mask:
+            x, mask_indices = self.apply_mask(features, padding_mask)
+        else:
+            x = features
+
+        # feature: (B, T, D), float
+        # target: (B, T), long
+        # x: (B, T, D), float
+        # padding_mask: (B, T), bool
+        # mask_indices: (B, T), bool
+        x, layer_results = self.encoder(
+            x,
+            padding_mask=padding_mask,
+            layer=None if output_layer is None else output_layer - 1,
+        )
+
+        res = {
+            "x": x,
+            "padding_mask": padding_mask,
+            "features": features,
+            "layer_results": layer_results,
+        }
+
+        feature = res["features"] if ret_conv else res["x"]
+        if ret_layer_results:
+            feature = (feature, res["layer_results"])
+        return feature, res["padding_mask"]
+
+
+class ConvFeatureExtractionModel(nn.Module):
+    def __init__(
+        self,
+        conv_layers: List[Tuple[int, int, int]],
+        dropout: float = 0.0,
+        mode: str = "default",
+        conv_bias: bool = False,
+        conv_type: str = "default",
+    ):
+        super().__init__()
+
+        assert mode in {"default", "layer_norm"}
+
+        def block(
+            n_in,
+            n_out,
+            k,
+            stride,
+            is_layer_norm=False,
+            is_group_norm=False,
+            conv_bias=False,
+        ):
+            def make_conv():
+                conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
+                nn.init.kaiming_normal_(conv.weight)
+                return conv
+
+            assert (
+                is_layer_norm and is_group_norm
+            ) is False, "layer norm and group norm are exclusive"
+
+            if is_layer_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    nn.Sequential(
+                        TransposeLast(),
+                        Fp32LayerNorm(dim, elementwise_affine=True),
+                        TransposeLast(),
+                    ),
+                    nn.GELU(),
+                )
+            elif is_group_norm:
+                return nn.Sequential(
+                    make_conv(),
+                    nn.Dropout(p=dropout),
+                    Fp32GroupNorm(dim, dim, affine=True),
+                    nn.GELU(),
+                )
+            else:
+                return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
+
+        self.conv_type = conv_type
+        if self.conv_type == "default":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3, "invalid conv definition: " + str(cl)
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(
+                    block(
+                        in_d,
+                        dim,
+                        k,
+                        stride,
+                        is_layer_norm=mode == "layer_norm",
+                        is_group_norm=mode == "default" and i == 0,
+                        conv_bias=conv_bias,
+                    )
+                )
+                in_d = dim
+        elif self.conv_type == "conv2d":
+            in_d = 1
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+
+                self.conv_layers.append(torch.nn.Conv2d(in_d, dim, k, stride))
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+        elif self.conv_type == "custom":
+            in_d = 1
+            idim = 80
+            self.conv_layers = nn.ModuleList()
+            for i, cl in enumerate(conv_layers):
+                assert len(cl) == 3
+                (dim, k, stride) = cl
+                self.conv_layers.append(
+                    torch.nn.Conv2d(in_d, dim, k, stride, padding=1)
+                )
+                self.conv_layers.append(torch.nn.LayerNorm([dim, idim]))
+                self.conv_layers.append(torch.nn.ReLU())
+                in_d = dim
+                if (i + 1) % 2 == 0:
+                    self.conv_layers.append(
+                        torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
+                    )
+                    idim = int(math.ceil(idim / 2))
+        else:
+            pass
+
+    def forward(self, x, mask=None):
+        # BxT -> BxCxT
+        x = x.unsqueeze(1)
+        if self.conv_type == "custom":
+            for conv in self.conv_layers:
+                if isinstance(conv, nn.LayerNorm):
+                    x = x.transpose(1, 2)
+                    x = conv(x).transpose(1, 2)
+                else:
+                    x = conv(x)
+            x = x.transpose(2, 3).contiguous()
+            x = x.view(x.size(0), -1, x.size(-1))
+        else:
+            for conv in self.conv_layers:
+                x = conv(x)
+            if self.conv_type == "conv2d":
+                b, c, t, f = x.size()
+                x = x.transpose(2, 3).contiguous().view(b, c * f, t)
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+
+        self.dropout = args.dropout
+        self.embedding_dim = args.encoder_embed_dim
+
+        self.pos_conv = nn.Conv1d(
+            self.embedding_dim,
+            self.embedding_dim,
+            kernel_size=args.conv_pos,
+            padding=args.conv_pos // 2,
+            groups=args.conv_pos_groups,
+        )
+        dropout = 0
+        std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
+        nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
+        nn.init.constant_(self.pos_conv.bias, 0)
+
+        self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
+        self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
+
+        if hasattr(args, "relative_position_embedding"):
+            self.relative_position_embedding = args.relative_position_embedding
+            self.num_buckets = args.num_buckets
+            self.max_distance = args.max_distance
+        else:
+            self.relative_position_embedding = False
+            self.num_buckets = 0
+            self.max_distance = 0
+
+        self.layers = nn.ModuleList(
+            [
+                TransformerSentenceEncoderLayer(
+                    embedding_dim=self.embedding_dim,
+                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
+                    num_attention_heads=args.encoder_attention_heads,
+                    dropout=self.dropout,
+                    attention_dropout=args.attention_dropout,
+                    activation_dropout=args.activation_dropout,
+                    activation_fn=args.activation_fn,
+                    layer_norm_first=args.layer_norm_first,
+                    has_relative_attention_bias=(
+                        self.relative_position_embedding and i == 0
+                    ),
+                    num_buckets=self.num_buckets,
+                    max_distance=self.max_distance,
+                    gru_rel_pos=args.gru_rel_pos,
+                )
+                for i in range(args.encoder_layers)
+            ]
+        )
+
+        self.layer_norm_first = args.layer_norm_first
+        self.layer_norm = LayerNorm(self.embedding_dim)
+        self.layerdrop = args.encoder_layerdrop
+
+        self.apply(init_bert_params)
+
+    def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
+        x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)
+
+        if self.layer_norm_first and layer is None:
+            x = self.layer_norm(x)
+
+        return x, layer_results
+
+    def extract_features(
+        self, x, padding_mask=None, streaming_mask=None, tgt_layer=None
+    ):
+        if padding_mask is not None:
+            x[padding_mask] = 0
+
+        x_conv = self.pos_conv(x.transpose(1, 2))
+        x_conv = x_conv.transpose(1, 2)
+        x = x + x_conv
+
+        if not self.layer_norm_first:
+            x = self.layer_norm(x)
+
+        x = F.dropout(x, p=self.dropout, training=self.training)
+
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1)
+
+        layer_results = []
+        z = None
+        if tgt_layer is not None:
+            layer_results.append((x, z))
+        r = None
+        pos_bias = None
+        for i, layer in enumerate(self.layers):
+            dropout_probability = np.random.random()
+            if not self.training or (dropout_probability > self.layerdrop):
+                x, z, pos_bias = layer(
+                    x,
+                    self_attn_padding_mask=padding_mask,
+                    need_weights=False,
+                    self_attn_mask=streaming_mask,
+                    pos_bias=pos_bias,
+                )
+            if tgt_layer is not None:
+                layer_results.append((x, z))
+            if i == tgt_layer:
+                r = x
+                break
+
+        if r is not None:
+            x = r
+
+        # T x B x C -> B x T x C
+        x = x.transpose(0, 1)
+
+        return x, layer_results
+
+
+class TransformerSentenceEncoderLayer(nn.Module):
+    """
+    Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
+    models.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: float = 768,
+        ffn_embedding_dim: float = 3072,
+        num_attention_heads: float = 8,
+        dropout: float = 0.1,
+        attention_dropout: float = 0.1,
+        activation_dropout: float = 0.1,
+        activation_fn: str = "relu",
+        layer_norm_first: bool = False,
+        has_relative_attention_bias: bool = False,
+        num_buckets: int = 0,
+        max_distance: int = 0,
+        rescale_init: bool = False,
+        gru_rel_pos: bool = False,
+    ) -> None:
+        super().__init__()
+        # Initialize parameters
+        self.embedding_dim = embedding_dim
+        self.dropout = dropout
+        self.activation_dropout = activation_dropout
+
+        # Initialize blocks
+        self.activation_name = activation_fn
+        self.activation_fn = get_activation_fn(activation_fn)
+        self.self_attn = MultiheadAttention(
+            self.embedding_dim,
+            num_attention_heads,
+            dropout=attention_dropout,
+            self_attention=True,
+            has_relative_attention_bias=has_relative_attention_bias,
+            num_buckets=num_buckets,
+            max_distance=max_distance,
+            rescale_init=rescale_init,
+            gru_rel_pos=gru_rel_pos,
+        )
+
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(self.activation_dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.layer_norm_first = layer_norm_first
+
+        # layer norm associated with the self attention layer
+        self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
+
+        if self.activation_name == "glu":
+            self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
+        else:
+            self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
+        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
+
+        # layer norm associated with the position wise feed-forward NN
+        self.final_layer_norm = LayerNorm(self.embedding_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        self_attn_mask: torch.Tensor = None,
+        self_attn_padding_mask: torch.Tensor = None,
+        need_weights: bool = False,
+        pos_bias=None,
+    ):
+        """
+        LayerNorm is applied either before or after the self-attention/ffn
+        modules similar to the original Transformer imlementation.
+        """
+        residual = x
+
+        if self.layer_norm_first:
+            x = self.self_attn_layer_norm(x)
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=False,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias,
+            )
+            x = self.dropout1(x)
+            x = residual + x
+
+            residual = x
+            x = self.final_layer_norm(x)
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+        else:
+            x, attn, pos_bias = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=self_attn_padding_mask,
+                need_weights=need_weights,
+                attn_mask=self_attn_mask,
+                position_bias=pos_bias,
+            )
+
+            x = self.dropout1(x)
+            x = residual + x
+
+            x = self.self_attn_layer_norm(x)
+
+            residual = x
+            if self.activation_name == "glu":
+                x = self.fc1(x)
+            else:
+                x = self.activation_fn(self.fc1(x))
+            x = self.dropout2(x)
+            x = self.fc2(x)
+            x = self.dropout3(x)
+            x = residual + x
+            x = self.final_layer_norm(x)
+
+        return x, attn, pos_bias