diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..db5324f --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 interspeech2023blind + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..67d53f2 --- /dev/null +++ b/README.md @@ -0,0 +1,135 @@ +# Voice Conversion With Just Nearest Neighbors (kNN-VC) + +The official code repo! This repo contains training and inference code for kNN-VC -- an any-to-any voice conversion model from our paper, "Voice Conversion With Just k-Nearest Neighbors". The trained checkpoints are available under the 'Releases' tab and through `torch.hub`. + + + +![kNN-VC method](./knn-vc.png) + +Figure: kNN-VC setup, as explained in the paper. + +**Authors**: HIDDEN FOR BLIND SUBMISSION + +## Quickstart + +We use `torch.hub` to make loading the model easy -- no cloning of the repo needed. The steps to perform inference are simple: + +1. **Install dependancies**: we have 3 inference dependencies only `torch`, `torchaudio`, and `numpy`. Python must be at version 3.10 or greater. +2. **Load models**: load the WavLM encoder and HiFiGAN vocoder: + +```python +import torch, torchaudio + +knn_vc = torch.hub.load('REMOVED_FOR_BLIND_SUBMISSION', 'knn_vc', prematched=True, trust_repo=True, pretrained=True) +# Or, if you would like the vocoder trained not using prematched data, set prematched=False. +``` +3. **Compute features** for input and reference audio: + +```python +src_wav_path = '.wav' +ref_wav_paths = ['.wav', '.wav', ...] + +query_seq = knn_vc.get_features(src_wav_path) +matching_set = knn_vc.get_matching_set(ref_wav_paths) +``` + +4. **Perform the kNN matching and vocoding**: + +```python +out_wav = knn_vc.match(query_seq, matching_set, topk=4) +# out_wav is (T,) tensor converted output wav using k=4 for kNN. +``` + +That's it! These default settings provide pretty good results, but feel free to modify the kNN `topk` or use the non-prematched vocoder. +Note: the target speaker from `ref_wav_paths` _can be anything_, but should be clean speech from the desired speaker. The longer the cumulative duration of all reference waveforms, the better the quality will be (but the slower it will take to run). The improvement in quality diminishes beyond 5 minutes of reference speech. + +## Checkpoints + +Under the releases tab of this repo we provide three checkpoints: + +- The frozen WavLM encoder taken from the [original WavLM authors](https://github.com/microsoft/unilm/tree/master/wavlm), which we host here for convenience and torch hub integration. +- The HiFiGAN vocoder trained on layer 6 of WavLM features. +- The HiFiGAN vocoder trained on **prematched** layer 6 of WavLM features (the best model in the paper). + +The performance on the LibriSpeech dev-clean set is summarized: + +| checkpoint | WER (%) | CER (%) | EER (%) | +| ----------- | :-----------: | :----: | :--: | +| [kNN-VC with prematched HiFiGAN](https://TODO) | 6.29 | 2.34 | 35.73 | +| [kNN-VC with regular HiFiGAN](https://TODO) | 6.39 | 2.41 | 32.55 | + + +## Training + +We follow the typical encoder-converter-vocoder setup for voice conversion. The encoder is WavLM, the converter is k-nearest neighbors regression, and vocoder is HiFiGAN. The only component that requires training is the vocoder: + +1. **WavLM encoder**: we simply use the pretrained WavLM-Large model and do not train it for any part of our work. We suggest checking out the original [WavLM repo](https://github.com/microsoft/unilm) to train your own SSL encoder. +2. **kNN conversion model**: kNN is non-parametric and does not require any training :) +3. **HiFiGAN vocoder**: we adapt the original [HiFiGAN author's repo](https://github.com/jik876/hifi-gan) for vocoding WavLM features. This is the only part which requires training. + +### HiFiGAN training + +For training we require the same dependencies as the original HiFiGAN training [here](https://github.com/jik876/hifi-gan/blob/master/requirements.txt) -- namely `librosa`, `tensorboard`, `matplotlib`, `fastprogress`, `scipy`. +Then, to train the HiFiGAN: + +1. **Precompute WavLM features of the vocoder dataset**: we provide a utility for this for the LibriSpeech dataset in `prematch_dataset.py`: + + ```bash + usage: prematch_dataset.py [-h] --librispeech_path LIBRISPEECH_PATH + [--seed SEED] --out_path OUT_PATH [--device DEVICE] + [--topk TOPK] [--matching_layer MATCHING_LAYER] + [--synthesis_layer SYNTHESIS_LAYER] [--prematch] + [--resume] + ``` + + where you can specify `--prematch` or not to determine whether to use prematching when generating features or not. For example, to generate the dataset used to train the prematched HiFiGAN from the paper: + `python prematch_dataset.py --librispeech_path /path/to/librispeech/root --out_path /path/where/you/want/outputs/to/go --topk 4 --matching_layer 6 --synthesis_layer 6 --prematch` + +2. **Train HiFiGAN**: we adapt the training script from the [original HiFiGAN repo](https://github.com/jik876/hifi-gan) to work for WavLM features in `hifigan/train.py`. To train a hifigan model on the features you produced above: + + ```bash + python -m hifigan.train --audio_root_path /path/to/librispeech/root/ --feature_root_path /path/to/the/output/of/previous/step/ --input_training_file data_splits/wavlm-hifigan-train.csv --input_validation_file data_splits/wavlm-hifigan-valid.csv --checkpoint_path /path/where/you/want/to/save/checkpoint --fp16 False --config hifigan/config_v1_wavlm.json --stdout_interval 25 --training_epochs 1800 --fine_tuning + ``` + + That's it! Once it is run up till 2.5M updates (or it starts to sound worse) you can stop training and use the pretrained checkpoint. + + +## Repository structure + +``` +├── data_splits # csv train/validation splits for librispeech train-clean-100 +│   ├── wavlm-hifigan-train.csv +│   └── wavlm-hifigan-valid.csv +├── hifigan # adapted hifigan code to vocode wavlm features +│   ├── config_v1_wavlm.json # hifigan config for use with wavlm features +│   ├── meldataset.py # mel-spectrogram transform used during hifigan training +│   ├── models.py # hifigan model definition +│   ├── train.py # hifigan training script +│   └── utils.py # utilities used for hifigan inference/training +├── hubconf.py # torchhub integration +├── matcher.py # kNN matching logic and model wrapper +├── prematch_dataset.py # script to precompute wavlm features for librispeech +├── README.md +└── wavlm + ├── modules.py # wavlm helper functions (from original WavLM repo) + └── WavLM.py # wavlm modules (from original WavLM repo) +``` + + +## Acknowledgements + +Parts of code for this project are adapted from the following repositories -- please make sure to check them out! Thank you to the authors of: + +- HiFiGAN: https://github.com/jik876/hifi-gan +- WavLM: https://github.com/microsoft/unilm/tree/master/wavlm + + +## HIDDEN FOR BLIND SUBMISSION + + + diff --git a/data_splits/wavlm-hifigan-train.csv b/data_splits/wavlm-hifigan-train.csv new file mode 100644 index 0000000..1c9d7b1 --- /dev/null +++ b/data_splits/wavlm-hifigan-train.csv @@ -0,0 +1,28540 @@ +audio_path,feat_path +train-clean-100/103/1240/103-1240-0000.flac,train-clean-100/103/1240/103-1240-0000.pt +train-clean-100/103/1240/103-1240-0001.flac,train-clean-100/103/1240/103-1240-0001.pt +train-clean-100/103/1240/103-1240-0002.flac,train-clean-100/103/1240/103-1240-0002.pt +train-clean-100/103/1240/103-1240-0003.flac,train-clean-100/103/1240/103-1240-0003.pt +train-clean-100/103/1240/103-1240-0004.flac,train-clean-100/103/1240/103-1240-0004.pt +train-clean-100/103/1240/103-1240-0005.flac,train-clean-100/103/1240/103-1240-0005.pt 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1024, + "hifi_dim": 512, + + "segment_size": 7040, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 320, + "win_size": 1024, + + "sampling_rate": 16000, + + "fmin": 0, + "fmax": 8000, + "fmax_for_loss": null, + + "num_workers": 4, + + "dist_config": { + "dist_backend": "nccl", + "dist_url": "tcp://localhost:54321", + "world_size": 1 + } +} diff --git a/hifigan/meldataset.py b/hifigan/meldataset.py new file mode 100644 index 0000000..fa2b5eb --- /dev/null +++ b/hifigan/meldataset.py @@ -0,0 +1,208 @@ +import math +import os +import random +from pathlib import Path + +import librosa +import numpy as np +import pandas as pd +import torch +import torch.nn.functional as F +import torch.utils.data +import torchaudio +from librosa.filters import mel as librosa_mel_fn +from librosa.util import normalize +from scipy.io.wavfile import read + + +def load_wav(full_path): + #sampling_rate, data = read(full_path) + #return data, sampling_rate + data, sampling_rate = librosa.load(full_path, sr=None) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + +class LogMelSpectrogram(torch.nn.Module): + def __init__(self, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + super().__init__() + self.melspctrogram = torchaudio.transforms.MelSpectrogram( + sample_rate=sampling_rate, + n_fft=n_fft, + win_length=win_size, + hop_length=hop_size, + center=center, + power=1.0, + norm="slaney", + onesided=True, + n_mels=num_mels, + mel_scale="slaney", + f_min=fmin, + f_max=fmax + ) + self.n_fft = n_fft + self.hop_size = hop_size + + def forward(self, wav): + wav = F.pad(wav, ((self.n_fft - self.hop_size) // 2, (self.n_fft - self.hop_size) // 2), "reflect") + mel = self.melspctrogram(wav) + logmel = torch.log(torch.clamp(mel, min=1e-5)) + return logmel + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + global mel_basis, hann_window + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + # print("Padding by", int((n_fft - hop_size)/2), y.shape) + # pre-padding + n_pad = hop_size - ( y.shape[1] % hop_size ) + y = F.pad(y.unsqueeze(1), (0, n_pad), mode='reflect').squeeze(1) + # print("intermediate:", y.shape) + + y = F.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], + center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) + spec = spec.abs().clamp_(3e-5) + # print("Post: ", y.shape, spec.shape) + + spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + train_df = pd.read_csv(a.input_training_file) + valid_df = pd.read_csv(a.input_validation_file) + return train_df, valid_df + + +class MelDataset(torch.utils.data.Dataset): + def __init__(self, training_files, segment_size, n_fft, num_mels, + hop_size, win_size, sampling_rate, fmin, fmax, split=True, shuffle=True, n_cache_reuse=1, + device=None, fmax_loss=None, fine_tuning=False, audio_root_path=None, feat_root_path=None, use_alt_melcalc=False): + self.audio_files = training_files + if shuffle: + self.audio_files = self.audio_files.sample(frac=1, random_state=1234) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.audio_root_path = Path(audio_root_path) + self.feat_root_path = Path(feat_root_path) + self.alt_melspec = LogMelSpectrogram(n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax) + self.use_alt_melcalc = use_alt_melcalc + + def __getitem__(self, index): + row = self.audio_files.iloc[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_wav(self.audio_root_path/row.audio_path) + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError("{} SR doesn't match target {} SR".format( + sampling_rate, self.sampling_rate)) + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.tensor(audio, dtype=torch.float32) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start:audio_start+self.segment_size] + else: + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') + + if self.use_alt_melcalc: + mel = self.alt_melspec(audio) + else: + mel1 = mel_spectrogram(audio, self.n_fft, self.num_mels, + self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax, + center=False) + + mel = mel.permute(0, 2, 1) # (1, dim, seq_len) --> (1, seq_len, dim) + else: + mel = torch.load(self.feat_root_path/row.feat_path, map_location='cpu').float() + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) # (1, seq_len, dim) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(1) - frames_per_seg - 1) + mel = mel[:, mel_start:mel_start + frames_per_seg, :] + audio = audio[:, mel_start * self.hop_size:(mel_start + frames_per_seg) * self.hop_size] + else: + mel = torch.nn.functional.pad(mel, (0, 0, 0, frames_per_seg - mel.size(2)), 'constant') + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), 'constant') + + + if self.use_alt_melcalc: + mel_loss = self.alt_melspec(audio) + else: + mel_loss = mel_spectrogram(audio, self.n_fft, self.num_mels, + self.sampling_rate, self.hop_size, self.win_size, self.fmin, self.fmax_loss, + center=False) + return (mel.squeeze(), audio.squeeze(0), str(row.audio_path), mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) diff --git a/hifigan/models.py b/hifigan/models.py new file mode 100644 index 0000000..bdd1954 --- /dev/null +++ b/hifigan/models.py @@ -0,0 +1,289 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from .utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.lin_pre = nn.Linear(h.hubert_dim, h.hifi_dim) + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm(Conv1d(h.hifi_dim, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == '1' else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + + self.ups.append(weight_norm( + ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)), + k, u, padding=(k-u)//2))) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel//(2**(i+1)) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + """ `x` as (bs, seq_len, dim), regular hifi assumes input of shape (bs, n_mels, seq_len) """ + x = self.lin_pre(x) + x = x.permute(0, 2, 1) # (bs, seq_len, dim) --> (bs, dim, seq_len) + + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i*self.num_kernels+j](x) + else: + xs += self.resblocks[i*self.num_kernels+j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ]) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList([ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ]) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList([ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ]) + self.meanpools = nn.ModuleList([ + AvgPool1d(4, 2, padding=2), + AvgPool1d(4, 2, padding=2) + ]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i-1](y) + y_hat = self.meanpools[i-1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss*2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1-dr)**2) + g_loss = torch.mean(dg**2) + loss += (r_loss + g_loss) + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1-dg)**2) + gen_losses.append(l) + loss += l + + return loss, gen_losses + diff --git a/hifigan/train.py b/hifigan/train.py new file mode 100644 index 0000000..ee03944 --- /dev/null +++ b/hifigan/train.py @@ -0,0 +1,333 @@ +import argparse +import itertools +import json +import os +import time + +import torch +import torch.multiprocessing as mp +import torch.nn.functional as F +from fastprogress import master_bar, progress_bar +from torch.cuda.amp.grad_scaler import GradScaler +from torch.distributed import init_process_group +from torch.nn.parallel import DistributedDataParallel +from torch.utils.data import DataLoader, DistributedSampler +from torch.utils.tensorboard import SummaryWriter + +from .meldataset import (LogMelSpectrogram, MelDataset, get_dataset_filelist, + mel_spectrogram) +from .models import (Generator, MultiPeriodDiscriminator, + MultiScaleDiscriminator, discriminator_loss, feature_loss, + generator_loss) +from .utils import (AttrDict, build_env, load_checkpoint, plot_spectrogram, + save_checkpoint, scan_checkpoint) + +torch.backends.cudnn.benchmark = True +USE_ALT_MELCALC = True + + +def train(rank, a, h): + if h.num_gpus > 1: + init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'], + world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank) + + torch.cuda.manual_seed(h.seed) + device = torch.device('cuda:{:d}'.format(rank)) + + generator = Generator(h).to(device) + mpd = MultiPeriodDiscriminator().to(device) + msd = MultiScaleDiscriminator().to(device) + + if rank == 0: + print(generator) + os.makedirs(a.checkpoint_path, exist_ok=True) + print("checkpoints directory : ", a.checkpoint_path) + + if os.path.isdir(a.checkpoint_path): + cp_g = scan_checkpoint(a.checkpoint_path, 'g_') + cp_do = scan_checkpoint(a.checkpoint_path, 'do_') + + steps = 0 + if cp_g is None or cp_do is None: + state_dict_do = None + last_epoch = -1 + else: + state_dict_g = load_checkpoint(cp_g, device) + state_dict_do = load_checkpoint(cp_do, device) + generator.load_state_dict(state_dict_g['generator']) + mpd.load_state_dict(state_dict_do['mpd']) + msd.load_state_dict(state_dict_do['msd']) + steps = state_dict_do['steps'] + 1 + last_epoch = state_dict_do['epoch'] + print(f"Restored checkpoint from {cp_g} and {cp_do}") + + if h.num_gpus > 1: + print("Multi-gpu detected") + generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) + mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) + msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) + + optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) + optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()), + h.learning_rate, betas=[h.adam_b1, h.adam_b2]) + + if state_dict_do is not None: + optim_g.load_state_dict(state_dict_do['optim_g']) + optim_d.load_state_dict(state_dict_do['optim_d']) + + scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch) + scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch) + if a.fp16: + scaler_g = GradScaler() + scaler_d = GradScaler() + + train_df, valid_df = get_dataset_filelist(a) + + trainset = MelDataset(train_df, h.segment_size, h.n_fft, h.num_mels, + h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0, + shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device, + fine_tuning=a.fine_tuning, + audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, + use_alt_melcalc=USE_ALT_MELCALC) + + train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None + + train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False, + sampler=train_sampler, + batch_size=h.batch_size, + pin_memory=True, + drop_last=True) + + alt_melspec = LogMelSpectrogram(h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin).to(device) + + if rank == 0: + validset = MelDataset(valid_df, h.segment_size, h.n_fft, h.num_mels, + h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0, + fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, + audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, + use_alt_melcalc=USE_ALT_MELCALC) + validation_loader = DataLoader(validset, num_workers=1, shuffle=False, + sampler=None, + batch_size=1, + pin_memory=True, + drop_last=True) + + sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) + + generator.train() + mpd.train() + msd.train() + + if rank == 0: mb = master_bar(range(max(0, last_epoch), a.training_epochs)) + else: mb = range(max(0, last_epoch), a.training_epochs) + + for epoch in mb: + if rank == 0: + start = time.time() + mb.write("Epoch: {}".format(epoch+1)) + + if h.num_gpus > 1: + train_sampler.set_epoch(epoch) + + if rank == 0: pb = progress_bar(enumerate(train_loader), total=len(train_loader), parent=mb) + else: pb = enumerate(train_loader) + + + for i, batch in pb: + if rank == 0: + start_b = time.time() + x, y, _, y_mel = batch + x = x.to(device, non_blocking=True) + y = y.to(device, non_blocking=True) + y_mel = y_mel.to(device, non_blocking=True) + y = y.unsqueeze(1) + + with torch.cuda.amp.autocast(enabled=a.fp16): + y_g_hat = generator(x) + if USE_ALT_MELCALC: + y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) + else: + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, + h.fmin, h.fmax_for_loss) + # print(x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape) + optim_d.zero_grad() + + with torch.cuda.amp.autocast(enabled=a.fp16): + # MPD + y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) + loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g) + + # MSD + y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) + loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g) + + loss_disc_all = loss_disc_s + loss_disc_f + + if a.fp16: + scaler_d.scale(loss_disc_all).backward() + scaler_d.step(optim_d) + scaler_d.update() + else: + loss_disc_all.backward() + optim_d.step() + + # Generator + optim_g.zero_grad() + + with torch.cuda.amp.autocast(enabled=a.fp16): + # L1 Mel-Spectrogram Loss + loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 + + y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) + y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) + loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) + loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) + loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) + loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) + loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel + + if a.fp16: + scaler_g.scale(loss_gen_all).backward() + scaler_g.step(optim_g) + scaler_g.update() + else: + loss_gen_all.backward() + optim_g.step() + + if rank == 0: + # STDOUT logging + if steps % a.stdout_interval == 0: + with torch.no_grad(): + mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() + + mb.write('Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, sec/batch : {:4.3f}, peak mem: {:5.2f}GB'. \ + format(steps, loss_gen_all, mel_error, time.time() - start_b, torch.cuda.max_memory_allocated()/1e9)) + mb.child.comment = "Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}". \ + format(steps, loss_gen_all, mel_error) + + + # checkpointing + if steps % a.checkpoint_interval == 0 and steps != 0: + checkpoint_path = "{}/g_{:08d}.pt".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, + {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()}) + checkpoint_path = "{}/do_{:08d}.pt".format(a.checkpoint_path, steps) + save_checkpoint(checkpoint_path, + {'mpd': (mpd.module if h.num_gpus > 1 + else mpd).state_dict(), + 'msd': (msd.module if h.num_gpus > 1 + else msd).state_dict(), + 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps, + 'epoch': epoch}) + + # Tensorboard summary logging + if steps % a.summary_interval == 0: + sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) + sw.add_scalar("training/mel_spec_error", mel_error, steps) + sw.add_scalar("training/disc_loss_total", loss_disc_all, steps) + + # Validation + if steps % a.validation_interval == 0: # and steps != 0: + generator.eval() + torch.cuda.empty_cache() + val_err_tot = 0 + with torch.no_grad(): + for j, batch in progress_bar(enumerate(validation_loader), total=len(validation_loader), parent=mb): + x, y, _, y_mel = batch + y_g_hat = generator(x.to(device)) + y_mel = y_mel.to(device, non_blocking=True) + if USE_ALT_MELCALC: + y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) + if y_g_hat_mel.shape[-1] != y_mel.shape[-1]: + # pad it + n_pad = h.hop_size + y_g_hat = F.pad(y_g_hat, (n_pad//2, n_pad - n_pad//2)) + y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1)) + else: + y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, + h.hop_size, h.win_size, + h.fmin, h.fmax_for_loss) + #print('valid', x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape) + val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() + + if j <= 4: + if steps == 0: + sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate) + sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps) + + sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate) + if USE_ALT_MELCALC: + y_hat_spec = alt_melspec(y_g_hat.squeeze(1)) + else: + y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, + h.hop_size, h.win_size, + h.fmin, h.fmax_for_loss) + + sw.add_figure('generated/y_hat_spec_{}'.format(j), + plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps) + + val_err = val_err_tot / (j+1) + sw.add_scalar("validation/mel_spec_error", val_err, steps) + mb.write(f"validation run complete at {steps:,d} steps. validation mel spec error: {val_err:5.4f}") + + generator.train() + sw.add_scalar("memory/max_allocated_gb", torch.cuda.max_memory_allocated()/1e9, steps) + sw.add_scalar("memory/max_reserved_gb", torch.cuda.max_memory_reserved()/1e9, steps) + torch.cuda.reset_peak_memory_stats() + torch.cuda.reset_accumulated_memory_stats() + + steps += 1 + + scheduler_g.step() + scheduler_d.step() + + if rank == 0: + print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start))) + + +def main(): + print('Initializing Training Process..') + + parser = argparse.ArgumentParser() + + parser.add_argument('--group_name', default=None) + parser.add_argument('--audio_root_path', required=True) + parser.add_argument('--feature_root_path', required=True) + parser.add_argument('--input_training_file', default='LJSpeech-1.1/training.txt') + parser.add_argument('--input_validation_file', default='LJSpeech-1.1/validation.txt') + parser.add_argument('--checkpoint_path', default='cp_hifigan') + parser.add_argument('--config', default='') + parser.add_argument('--training_epochs', default=1500, type=int) + parser.add_argument('--stdout_interval', default=5, type=int) + parser.add_argument('--checkpoint_interval', default=5000, type=int) + parser.add_argument('--summary_interval', default=25, type=int) + parser.add_argument('--validation_interval', default=1000, type=int) + parser.add_argument('--fp16', default=False, type=bool) + parser.add_argument('--fine_tuning', action='store_true') + + a = parser.parse_args() + print(a) + with open(a.config) as f: + data = f.read() + + json_config = json.loads(data) + h = AttrDict(json_config) + build_env(a.config, 'config.json', a.checkpoint_path) + + torch.manual_seed(h.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(h.seed) + h.num_gpus = torch.cuda.device_count() + h.batch_size = int(h.batch_size / h.num_gpus) + print('Batch size per GPU :', h.batch_size) + else: + pass + + if h.num_gpus > 1: + mp.spawn(train, nprocs=h.num_gpus, args=(a, h,)) + else: + train(0, a, h) + + +if __name__ == '__main__': + main() diff --git a/hifigan/utils.py b/hifigan/utils.py new file mode 100644 index 0000000..3b1aa63 --- /dev/null +++ b/hifigan/utils.py @@ -0,0 +1,73 @@ +import glob +import os +import shutil + +import torch +from torch.nn.utils import weight_norm +import json + + +def plot_spectrogram(spectrogram): + import matplotlib.pylab as plt + import matplotlib + matplotlib.use("Agg") + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", + interpolation='none') + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + '*') + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/hubconf.py b/hubconf.py new file mode 100644 index 0000000..9674b9e --- /dev/null +++ b/hubconf.py @@ -0,0 +1,75 @@ +dependencies = ['torch', 'torchaudio', 'numpy'] + +import torch +from torch import Tensor +import torch.nn as nn +import torch.nn.functional as F +import logging +import json +from pathlib import Path + + +from wavlm.WavLM import WavLM, WavLMConfig +from hifigan.models import Generator as HiFiGAN +from hifigan.utils import AttrDict +from matcher import KNN_VC + + +def knn_vc(pretrained=True, progress=True, prematched=True, device='cuda') -> KNN_VC: + """ Load kNN-VC (WavLM encoder and HiFiGAN decoder). Optionally use vocoder trained on `prematched` data. """ + hifigan, hifigan_cfg = hifigan_wavlm(pretrained, progress, prematched, device) + wavlm = wavlm_large(pretrained, progress, device) + knnvc = KNN_VC(wavlm, hifigan, hifigan_cfg, device) + return knnvc + + +def hifigan_wavlm(pretrained=True, progress=True, prematched=True, device='cuda') -> HiFiGAN: + """ Load pretrained hifigan trained to vocode wavlm features. Optionally use weights trained on `prematched` data. """ + cp = Path(__file__).parent.absolute() + + with open(cp/'hifigan'/'config_v1_wavlm.json') as f: + data = f.read() + json_config = json.loads(data) + h = AttrDict(json_config) + device = torch.device(device) + + generator = HiFiGAN(h).to(device) + + if pretrained: + if prematched: + url = "https://github.com/interspeech2023blind/knn-vc/releases/download/v0.1/prematch_g_02500000.pt" + else: + url = "https://github.com/interspeech2023blind/knn-vc/releases/download/v0.1/g_02500000.pt" + state_dict_g = torch.hub.load_state_dict_from_url( + url, + map_location=device, + progress=progress + ) + generator.load_state_dict(state_dict_g['generator']) + generator.eval() + generator.remove_weight_norm() + print(f"[HiFiGAN] Generator loaded with {sum([p.numel() for p in generator.parameters()]):,d} parameters.") + return generator, h + + +def wavlm_large(pretrained=True, progress=True, device='cuda') -> WavLM: + """Load the WavLM large checkpoint from the original paper. See https://github.com/microsoft/unilm/tree/master/wavlm for details. """ + if torch.cuda.is_available() == False: + if str(device) != 'cpu': + logging.warning(f"Overriding device {device} to cpu since no GPU is available.") + device = 'cpu' + checkpoint = torch.hub.load_state_dict_from_url( + "https://github.com/interspeech2023blind/knn-vc/releases/download/v0.1/WavLM-Large.pt", + map_location=device, + progress=progress + ) + + cfg = WavLMConfig(checkpoint['cfg']) + device = torch.device(device) + model = WavLM(cfg) + if pretrained: + model.load_state_dict(checkpoint['model']) + model = model.to(device) + model.eval() + print(f"WavLM-Large loaded with {sum([p.numel() for p in model.parameters()]):,d} parameters.") + return model diff --git a/knn-vc.png b/knn-vc.png new file mode 100644 index 0000000..ab5baf7 Binary files /dev/null and b/knn-vc.png differ diff --git a/matcher.py b/matcher.py new file mode 100644 index 0000000..6aa9787 --- /dev/null +++ b/matcher.py @@ -0,0 +1,145 @@ + +from pathlib import Path + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchaudio +import torchaudio.transforms as T +from hifigan.models import Generator as HiFiGAN +from hifigan.utils import AttrDict +from torch import Tensor +from torchaudio.sox_effects import apply_effects_tensor +from wavlm.WavLM import WavLM + + +SPEAKER_INFORMATION_WEIGHTS = [ + 0, 0, 0, 0, 0, 0, # layer 0-5 + 1.0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, # layer 15 + 0, 0, 0, 0, 0, 0, # layer 16-21 + 0, # layer 22 + 0, 0 # layer 23-24 +] + + +def fast_cosine_dist(source_feats, matching_pool): + """ Like torch.cdist, but fixed dim=-1 and for cosine distance.""" + source_norms = torch.norm(source_feats, p=2, dim=-1).cpu() + matching_norms = torch.norm(matching_pool, p=2, dim=-1) + dotprod = -torch.cdist(source_feats[None].cpu(), matching_pool[None], p=2)[0]**2 + source_norms[:, None]**2 + matching_norms[None]**2 + dotprod /= 2 + + dists = 1 - ( dotprod / (source_norms[:, None].cpu() * matching_norms[None]) ) + return dists + + +class KNN_VC(nn.Module): + + def __init__(self, + wavlm: WavLM, + hifigan: HiFiGAN, + hifigan_cfg: AttrDict, + device='cuda' + ) -> None: + """ kNN-VC matcher. + Arguments: + - `wavlm` : trained WavLM model + - `hifigan`: trained hifigan model + - `hifigan_cfg`: hifigan config to use for vocoding. + """ + super().__init__() + # set which features to extract from wavlm + self.weighting = torch.tensor(SPEAKER_INFORMATION_WEIGHTS, device=device)[:, None] + # load hifigan + self.hifigan = hifigan.eval() + self.h = hifigan_cfg + # store wavlm + self.wavlm = wavlm.eval() + self.device = torch.device(device) + self.sr = self.h.sampling_rate + + def get_matching_set(self, wavs: list[Path] | list[Tensor], weights=None) -> Tensor: + """ Get concatenated wavlm features for the matching set using all waveforms in `wavs`, + specified as either a list of paths or list of loaded waveform tensors of + shape (channels, T), assumed to be of 16kHz sample rate. + Optionally specify custom WavLM feature weighting with `weights`. + """ + feats = [] + for p in wavs: + feats.append(self.get_features(p, weights=self.weighting if weights is None else weights)) + + feats = torch.concat(feats, dim=0).cpu() + return feats + + + @torch.inference_mode() + def vocode(self, c: Tensor) -> Tensor: + """ Vocode features with hifigan. `c` is of shape (bs, seq_len, c_dim) """ + y_g_hat = self.hifigan(c) + y_g_hat = y_g_hat.squeeze(1) + return y_g_hat + + + @torch.inference_mode() + def get_features(self, path, weights=None, vad_trigger_level=7): + """Returns features of `path` waveform as a tensor of shape (seq_len, dim)""" + # load audio + if weights == None: weights = self.weighting + if type(path) in [str, path]: + x, sr = torchaudio.load(path, normalize=True) + else: + x: Tensor = path + sr = self.sr + if x.dim() == 1: x = x[None] + assert sr == self.sr, "input audio sample rate must be 16kHz." + + # trim silence from front and back + transform = T.Vad(sample_rate=sr, trigger_level=vad_trigger_level) + x_front_trim = transform(x) + waveform_reversed, sr = apply_effects_tensor(x_front_trim, sr, [["reverse"]]) + waveform_reversed_front_trim = transform(waveform_reversed) + waveform_end_trim, sr = apply_effects_tensor( + waveform_reversed_front_trim, sr, [["reverse"]] + ) + x = waveform_end_trim + # extract the representation of each layer + wav_input_16khz = x.to(self.device) + rep, layer_results = self.wavlm.extract_features(wav_input_16khz, output_layer=self.wavlm.cfg.encoder_layers, ret_layer_results=True)[0] + features = torch.cat([x.transpose(0, 1) for x, _ in layer_results], dim=0) # (n_layers, seq_len, dim) + + # save full sequence + features = ( features*weights[:, None] ).sum(dim=0) # (seq_len, dim) + return features + + + @torch.inference_mode() + def match(self, query_seq: Tensor, matching_set: Tensor, synth_set: Tensor = None, topk: int = 4, tgt_loudness_db: float | None = -16) -> Tensor: + """ Given `query_seq`, `matching_set`, and `synth_set` tensors of shape (N, dim), perform kNN regression matching + with k=`topk`. Inputs: + - `query_seq`: Tensor (N1, dim) of the input/source query features. + - `matching_set`: Tensor (N2, dim) of the matching set used as the 'training set' for the kNN algorithm. + - `synth_set`: optional Tensor (N2, dim) corresponding to the matching set. We use the matching set to assign each query + vector to a vector in the matching set, and then use the corresponding vector from the synth set during HiFiGAN synthesis. + By default, and for best performance, this should be identical to the matching set. + - `topk`: k in the kNN -- the number of nearest neighbors to average over. + - `tgt_loudness_db`: float db used to normalize the output volume. Set to None to disable. + Returns: + - converted waveform of shape (T,) + """ + if synth_set is None: synth_set = matching_set + dists = fast_cosine_dist(query_seq.cpu(), matching_set.cpu()) + best = dists.topk(k=topk, largest=False, dim=-1) + out_feats = synth_set[best.indices].mean(dim=1).cpu() + + prediction = self.vocode(out_feats[None].cuda()).cpu().squeeze() + + # normalization + if tgt_loudness_db is not None: + src_loudness = torchaudio.functional.loudness(prediction[None], self.h.sampling_rate) + tgt_loudness = tgt_loudness_db + pred_wav = torchaudio.functional.gain(prediction, tgt_loudness - src_loudness) + else: pred_wav = prediction + return pred_wav + + diff --git a/prematch_dataset.py b/prematch_dataset.py new file mode 100644 index 0000000..3caebac --- /dev/null +++ b/prematch_dataset.py @@ -0,0 +1,173 @@ +import argparse +import gc +import os +import sys +import time +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchaudio +from fastprogress.fastprogress import master_bar, progress_bar +from torch import Tensor + +from hubconf import wavlm_large + +DOWNSAMPLE_FACTOR = 320 + +global feature_cache +feature_cache = {} +global synthesis_cache +synthesis_cache = {} + +def make_librispeech_df(root_path: Path) -> pd.DataFrame: + all_files = [] + folders = ['train-clean-100', 'dev-clean'] + print(f"[LIBRISPEECH] Computing folders {folders}") + for f in folders: + all_files.extend(list((root_path/f).rglob('**/*.flac'))) + speakers = ['ls-' + f.stem.split('-')[0] for f in all_files] + df = pd.DataFrame({'path': all_files, 'speaker': speakers}) + return df + + +def main(args): + device = torch.device(args.device) + SYNTH_WEIGHTINGS = F.one_hot(torch.tensor(args.synthesis_layer), num_classes=25).float().to(device)[:, None] + MATCH_WEIGHTINGS = F.one_hot(torch.tensor(args.matching_layer), num_classes=25).float().to(device)[:, None] + + print(f"Matching weightings: {MATCH_WEIGHTINGS.squeeze()}\nSynthesis weightings: {SYNTH_WEIGHTINGS.squeeze()}") + ls_df = make_librispeech_df(Path(args.librispeech_path)) + + print(f"Loading wavlm.") + wavlm = wavlm_large(pretrained=True, progress=True, device=args.device) + + np.random.seed(args.seed) + torch.manual_seed(args.seed) + extract(ls_df, wavlm, args.device, Path(args.librispeech_path), Path(args.out_path), SYNTH_WEIGHTINGS, MATCH_WEIGHTINGS) + print("All done!", flush=True) + + +def path2pools(path: Path, wavlm: nn.Module(), match_weights: Tensor, synth_weights: Tensor, device): + """Given a waveform `path`, compute the matching pool""" + + uttrs_from_same_spk = sorted(list(path.parent.rglob('**/*.flac'))) + uttrs_from_same_spk.remove(path) + matching_pool = [] + synth_pool = [] + for pth in uttrs_from_same_spk: + if pth in feature_cache and pth in synthesis_cache: + matching_feats = feature_cache[pth].float() # (seq_len, dim) + synth_feats = synthesis_cache[pth].float() # (seq_len, dim) + else: + feats = get_full_features(pth, wavlm, device) + matching_feats = ( feats*match_weights[:, None] ).sum(dim=0) # (seq_len, dim) + synth_feats = ( feats*synth_weights[:, None] ).sum(dim=0) # (seq_len, dim) + feature_cache[pth] = matching_feats.half().cpu() + synthesis_cache[pth] = synth_feats.half().cpu() + + matching_pool.append(matching_feats.cpu()) + synth_pool.append(synth_feats.cpu()) + matching_pool = torch.concat(matching_pool, dim=0) + synth_pool = torch.concat(synth_pool, dim=0) + return matching_pool, synth_pool # (N, dim) + + +@torch.inference_mode() +def get_full_features(path, wavlm, device): + + x, sr = torchaudio.load(path) + assert sr == 16000 + # This does not work i.t.o the hifigan training. + # x = F.pad(x, (DOWNSAMPLE_FACTOR//2, DOWNSAMPLE_FACTOR - DOWNSAMPLE_FACTOR//2), value=0) + # This does. + n_pad = DOWNSAMPLE_FACTOR - (x.shape[-1] % DOWNSAMPLE_FACTOR) + x = F.pad(x, (0, n_pad), value=0) + + # extract the representation of each layer + wav_input_16khz = x.to(device) + rep, layer_results = wavlm.extract_features(wav_input_16khz, output_layer=wavlm.cfg.encoder_layers, ret_layer_results=True)[0] + features = torch.cat([x.transpose(0, 1) for x, _ in layer_results], dim=0) # (n_layers, seq_len, dim) + + return features + + +def fast_cosine_dist(source_feats, matching_pool): + source_norms = torch.norm(source_feats, p=2, dim=-1) + matching_norms = torch.norm(matching_pool, p=2, dim=-1) + dotprod = -torch.cdist(source_feats[None], matching_pool[None], p=2)[0]**2 + source_norms[:, None]**2 + matching_norms[None]**2 + dotprod /= 2 + + dists = 1 - ( dotprod / (source_norms[:, None] * matching_norms[None]) ) + return dists + + +@torch.inference_mode() +def extract(df: pd.DataFrame, wavlm: nn.Module, device, ls_path: Path, out_path: Path, synth_weights: Tensor, match_weights: Tensor): + + if args.fast_l2: pb = progress_bar(df.iterrows(), total=len(df)) + else: pb = master_bar(df.iterrows(), total=len(df)) + + for i, row in pb: + rel_path = Path(row.path).relative_to(ls_path) + targ_path = (out_path/rel_path).with_suffix('.pt') + if args.resume: + if targ_path.is_file(): continue + # if targ_path.is_file(): continue + os.makedirs(targ_path.parent, exist_ok=True) + + if Path(row.path) in feature_cache: + source_feats = feature_cache[Path(row.path)].float() + else: + source_feats = get_full_features(row.path, wavlm, device) + source_feats = ( source_feats*match_weights[:, None] ).sum(dim=0) # (seq_len, dim) + + matching_pool, synth_pool = path2pools(row.path, wavlm, match_weights, synth_weights, device) + + if args.prematch: + out_feats = source_feats.cpu() + else: + dists = fast_cosine_dist(source_feats.cpu(), matching_pool.cpu()).cpu() + best = dists.topk(k=args.topk, dim=-1, largest=False) # (src_len, 4) + out_feats = synth_pool[best.indices].mean(dim=1) # (N, dim) + + # save matched sequence + if i < 3: print("Feature has shape: ", out_feats.shape, flush=True) + # 3. save + torch.save(out_feats.cpu().half(), str(targ_path)) + if hasattr(pb, 'child'): + pb.child.comment = str(rel_path) + pb.child.wait_for = min(pb.child.wait_for, 10) + pb.main_bar.comment = str(rel_path) + else: + pb.wait_for = min(pb.wait_for, 10) + pb.comment = str(rel_path) + + + if i % 1000 == 0: + print(f"Done {i:,d}/{len(df):,d}", flush=True) + feature_cache.clear() + synthesis_cache.clear() + gc.collect() + time.sleep(4) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Compute matched wavlm features for a librispeech dataset") + + parser.add_argument('--librispeech_path', required=True, type=str) + parser.add_argument('--seed', default=123, type=int) + parser.add_argument('--out_path', required=True, type=str) + parser.add_argument('--device', default='cuda', type=str) + parser.add_argument('--topk', type=int, default=4) + parser.add_argument('--matching_layer', type=int, default=6) + parser.add_argument('--synthesis_layer', type=int, default=6) + parser.add_argument('--prematch', action='store_true', help='prematch') + parser.add_argument('--resume', action='store_true') + + args = parser.parse_args() + main(args) + diff --git a/wavlm/WavLM.py b/wavlm/WavLM.py new file mode 100644 index 0000000..644ab89 --- /dev/null +++ b/wavlm/WavLM.py @@ -0,0 +1,743 @@ +# -------------------------------------------------------- +# 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 math +import logging +from typing import List, Optional, Tuple + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import LayerNorm +from .modules import ( + Fp32GroupNorm, + Fp32LayerNorm, + GradMultiply, + MultiheadAttention, + SamePad, + init_bert_params, + get_activation_fn, + TransposeLast, + GLU_Linear, +) + +logger = logging.getLogger(__name__) + + +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 + ) == 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_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 + diff --git a/wavlm/modules.py b/wavlm/modules.py new file mode 100644 index 0000000..1dcfc6f --- /dev/null +++ b/wavlm/modules.py @@ -0,0 +1,827 @@ +# -------------------------------------------------------- +# 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 math +import warnings +from typing import Dict, Optional, Tuple +import torch +from torch import Tensor, nn +from torch.nn import Parameter +import torch.nn.functional as F + + +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