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src/daft_exprt/hparams.py | 244 ++++++ src/daft_exprt/logger.py | 157 ++++ src/daft_exprt/loss.py | 106 +++ src/daft_exprt/mfa.py | 255 ++++++ src/daft_exprt/model.py | 923 ++++++++++++++++++++ src/daft_exprt/normalize_numbers.py | 74 ++ src/daft_exprt/symbols.py | 36 + src/daft_exprt/train.py | 638 ++++++++++++++ src/daft_exprt/utils.py | 227 +++++ 47 files changed, 5776 insertions(+) create mode 100644 .dockerignore create mode 100644 .gitignore create mode 100644 LICENSE.md create mode 100644 README.md create mode 100644 environment/Dockerfile create mode 100644 environment/Makefile create mode 100644 environment/conda_requirements.txt create mode 100644 environment/pip_requirements.txt create mode 100644 scripts/benchmarks/english/sentences.txt create mode 100644 scripts/format_dataset.py create mode 100644 scripts/style_bank/english/0012_000567.wav create mode 100644 scripts/style_bank/english/0012_001611.wav create mode 100644 scripts/style_bank/english/0015_000490.wav create mode 100644 scripts/style_bank/english/0015_001566.wav create mode 100644 scripts/style_bank/english/0018_000536.wav create mode 100644 scripts/style_bank/english/0018_001645.wav create mode 100644 scripts/style_bank/english/0019_000607.wav create mode 100644 scripts/style_bank/english/0019_001536.wav create mode 100644 scripts/style_bank/english/0_audio_ref.wav create mode 100644 scripts/style_bank/english/18_audio_ref.wav create mode 100644 scripts/style_bank/english/24_audio_ref.wav create mode 100644 scripts/style_bank/english/26_audio_ref.wav create mode 100644 scripts/style_bank/english/2_audio_ref.wav create mode 100644 scripts/style_bank/english/36_audio_ref.wav create mode 100644 scripts/style_bank/english/38_audio_ref.wav create mode 100644 scripts/synthesize.py create mode 100644 scripts/training.py create mode 100644 setup.py create mode 100644 src/daft_exprt/__init__.py create mode 100644 src/daft_exprt/bin/reaper/linux/reaper create mode 100644 src/daft_exprt/cleaners.py create mode 100644 src/daft_exprt/create_sets.py create mode 100644 src/daft_exprt/data_loader.py create mode 100644 src/daft_exprt/extract_features.py create mode 100644 src/daft_exprt/features_stats.py create mode 100644 src/daft_exprt/fine_tune.py create mode 100644 src/daft_exprt/generate.py create mode 100644 src/daft_exprt/griffin_lim.py create mode 100644 src/daft_exprt/hparams.py create mode 100644 src/daft_exprt/logger.py create mode 100644 src/daft_exprt/loss.py create mode 100644 src/daft_exprt/mfa.py create mode 100644 src/daft_exprt/model.py create mode 100644 src/daft_exprt/normalize_numbers.py create mode 100644 src/daft_exprt/symbols.py create mode 100644 src/daft_exprt/train.py create mode 100644 src/daft_exprt/utils.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..18765d0 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,3 @@ +datasets/ +trainings/ +tmp/ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c384084 --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +#################### Python ##################### +**/__pycache__/ +**.egg**/ +dist/ +build/ + + +#################### Daft-Exprt ##################### +datasets/ +trainings/ +tmp/ diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..4b702cc --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2021 Ubisoft Entertainment + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..0cf1b19 --- /dev/null +++ b/README.md @@ -0,0 +1,244 @@ + +# Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis + + +### Julian Zaïdi, Hugo Seuté, Benjamin van Niekerk, Marc-André Carbonneau +In our recent [paper](https://arxiv.org/abs/2108.02271) we propose Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art on inter-speaker and inter-text prosody transfer. This improvement is achieved using FiLM conditioning layers, alongside adversarial training that encourages disentanglement between prosodic information and speaker identity. The acoustic model inherits attractive qualities from FastSpeech 2, such as fast inference and local prosody attributes prediction for finer grained control over generation. Moreover, results indicate that adversarial training effectively discards speaker identity information from the prosody representation, which ensures Daft-Exprt will consistently generate speech with the desired voice. + +Experimental results show that Daft-Exprt accurately transfers prosody, while yielding naturalness comparable to state-of-the-art expressive models. Visit our [demo page](https://ubisoft-laforge.github.io/speech/daft-exprt/) for audio samples related to the paper experiments. + +### Pre-trained model +**Full disclosure**: The model provided in this repository is not the same as in the paper evaluation. The model of the paper was trained with proprietary data which prevents us to release it publicly. +We pre-train Daft-Exprt on a combination of [LJ speech dataset](https://keithito.com/LJ-Speech-Dataset/) and the emotional speech dataset (ESD) from [Zhou et al](https://github.com/HLTSingapore/Emotional-Speech-Data). +Visit the [releases](https://github.com/ubisoft/ubisoft-laforge-daft-exprt/releases) of this repository to download the pre-trained model and to listen to prosody transfer examples using this same model. + + +## Table of Contents +- [Installation](#installation) + - [Local Environment](#local-environment) + - [Docker Image](#docker-image) +- [Quick Start Example](#quick-start-example) + - [Introduction](#introduction) + - [Dataset Formatting](#dataset-formatting) + - [Data Pre-Processing](#data-pre-processing) + - [Training](#training) + - [Fine-Tuning](#fine-tuning) + - [TTS Synthesis](#tts-synthesis) +- [Citation](#citation) +- [Contributing](#contributing) + +## Installation + +### Local Environment +Requirements: +- Ubuntu >= 20.04 +- Python >= 3.8 +- [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx?lang=en-us) >= 450.80.02 +- [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive) >= 11.1 +- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= v8.0.5 + +We recommend using conda for python environment management, for example download and install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +Create your python environment and install dependencies using the Makefile: +1. `conda create -n daft_exprt python=3.8 -y` +2. `conda activate daft_exprt` +3. `cd environment` +4. `make` + +All Linux/Conda/Python dependencies will be installed by the Makefile, and the repository will be installed as a pip package in editable mode. + +### Docker Image +Requirements: +- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) +- [NVIDIA Driver](https://www.nvidia.com/Download/index.aspx?lang=en-us) >= 450.80.02 + +Build the Docker image using the associated Dockerfile: +1. `docker build -f environment/Dockerfile -t daft_exprt .` + +## Quick Start Example + +### Introduction +This quick start guide will illustrate how to use the different scripts of this repository to: +1. Format datasets +2. Pre-process these datasets +3. Train DaftExprt on the pre-processed data +4. Generate a dataset for vocoder fine-tuning +5. Use Daft-Exprt for TTS synthesis + +All scripts are located in [scripts](scripts) directory. +Daft-Exprt source code is located in [daft_exprt](src/daft_exprt) directory. +Config parameters used in the scripts are all instanciated in [hparams.py](src/daft_exprt/hparams.py). + +As a quick start example, we consider using the 22kHz [LJ speech dataset](https://keithito.com/LJ-Speech-Dataset/) and the 16kHz emotional speech dataset (ESD) from [Zhou et al](https://github.com/HLTSingapore/Emotional-Speech-Data). +This combines a total of 11 speakers. All speaker datasets must be in the same root directory. For example: +``` +/data_dir + LJ_Speech + ESD + spk_1 + ... + spk_N +``` + +In this example, we use the docker image built in the previous section: + ``` +docker run -it --gpus all -v /path/to/data_dir:/workdir/data_dir -v path/to/repo_dir:/workdir/repo_dir IMAGE_ID +``` + + +### Dataset Formatting +The source code expects the specific tree structure for each speaker data set: +``` +/speaker_dir + metadata.csv + /wavs + wav_file_name_1.wav + ... + wav_file_name_N.wav +``` + +metadata.csv must be formatted as follows: +``` +wav_file_name_1|text_1 +... +wav_file_name_N|text_N +``` + +Given each dataset has its own nomenclature, this project does not provide a ready-made universal script. +However, the script [format_dataset.py](scripts/format_dataset.py) already proposes the code to format LJ and ESD: +``` +python format_dataset.py \ + --data_set_dir /workdir/data_dir/LJ_Speech \ + LJ + +python format_dataset.py \ + --data_set_dir /workdir/data_dir/ESD \ + ESD \ + --language english +``` + +### Data Pre-Processing +In this section, the code will: +1. Align data using MFA +2. Extract features for training +3. Create train and validation sets +4. Extract features stats on the train set for speaker standardization + +To pre-process all available formatted data (i.e. LJ and ESD in this example): +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --data_set_dir /workdir/data_dir \ + pre_process +``` + +This will pre-process data using the default hyper-parameters that are set for 22kHz audios. +All outputs related to the experiment will be stored in `/workdir/repo_dir/trainings/EXPERIMENT_NAME`. +You can also target specific speakers for data pre-processing. For example, to consider only ESD speakers: +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --speakers ESD/spk_1 ... ESD/spk_N \ + --data_set_dir /workdir/data_dir \ + pre_process +``` + +The pre-process function takes several arguments: +- `--features_dir`: absolute path where pre-processed data will be stored. Default to `/workdir/repo_dir/datasets` +- `--proportion_validation`: Proportion of examples that will be in the validation set. Default to `0.1`% per speaker. +- `--nb_jobs`: number of cores to use for python multi-processing. If set to `max`, all CPU cores are used. Default to `6`. + +Note that if it is the first time that you pre-process the data, this step will take several hours. +You can decrease computing time by increasing the `--nb_jobs` parameter. + +### Training +Once pre-processing is finished, launch training. To train on all pre-processed data: +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --data_set_dir /workdir/data_dir \ + train +``` + +Or if you targeted specific speakers during pre-processing (e.g. ESD speakers): +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --speakers ESD/spk_1 ... ESD/spk_N \ + --data_set_dir /workdir/data_dir \ + train +``` + +All outputs related to the experiment will be stored in `/workdir/repo_dir/trainings/EXPERIMENT_NAME`. + +The train function takes several arguments: +- `--checkpoint`: absolute path of a Daft-Exprt checkpoint. Default to `""` +- `--no_multiprocessing_distributed`: disable PyTorch multi-processing distributed training. Default to `False` +- `--world_size`: number of nodes for distributed training. Default to `1`. +- `--rank`: node rank for distributed training. Default to `0`. +- `--master`: url used to set up distributed training. Default to `tcp://localhost:54321`. + +These default values will launch a new training starting at iteration 0, using all available GPUs on the machine. +The code supposes that only 1 GPU is available on the machine. +Default [batch size](src/daft_exprt/hparams.py#L66) and [gradient accumulation](src/daft_exprt/hparams.py#L67) hyper-parameters are set to values to reproduce the batch size of 48 from the paper. + +The code also supports tensorboard logging. To display logging outputs: +`tensorboard --logdir_spec=EXPERIMENT_NAME:/workdir/repo_dir/trainings/EXPERIMENT_NAME/logs` + +### Fine-Tuning +Once training is finished, you can create a dataset for vocoder fine-tuning: +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --data_set_dir /workdir/data_dir \ + fine_tune \ + --checkpoint CHECKPOINT_PATH +``` + +Or if you targeted specific speakers during pre-processing and training (e.g. ESD speakers): +``` +python training.py \ + --experiment_name EXPERIMENT_NAME \ + --speakers ESD/spk_1 ... ESD/spk_N \ + --data_set_dir /workdir/data_dir \ + fine_tune \ + --checkpoint CHECKPOINT_PATH +``` + +Fine-tuning dataset will be stored in `/workdir/repo_dir/trainings/EXPERIMENT_NAME/fine_tuning_dataset`. + +### TTS Synthesis +For an example on how to use Daft-Exprt for TTS synthesis, run the script [synthesize.py](scripts/synthesize.py). +``` +python synthesize.py \ + --output_dir OUTPUT_DIR \ + --checkpoint CHECKPOINT +``` + +Default sentences and reference utterances are used in the script. + +The script also offers the possibility to: +- `--batch_size`: process batch of sentences in parallel +- `--real_time_factor`: estimate Daft-Exprt real time factor performance given the chosen batch size +- `--control`: perform local prosody control + + +## Citation +``` +@article{Zaidi2018, +abstract = {}, +journal = {arXiv}, +arxivId = {2108.02271}, +author = {Za{\"{i}}di, Julian and Seut{\'{e}}, Hugo and van Niekerk, Benjamin and Carbonneau, Marc-Andr{\'{e}}}, +eprint = {2108.02271}, +title = {{Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis}}, +url = {https://arxiv.org/pdf/2108.02271.pdf}, +year = {2021} +} +``` + +## Contributing +Any contribution to this repository is more than welcome! +If you have any feedback, please send it to julian.zaidi@ubisoft.com. + + +© [2021] Ubisoft Entertainment. All Rights Reserved \ No newline at end of file diff --git a/environment/Dockerfile b/environment/Dockerfile new file mode 100644 index 0000000..81d5016 --- /dev/null +++ b/environment/Dockerfile @@ -0,0 +1,40 @@ +FROM nvidia/cuda:11.2.0-base-ubuntu20.04 + +# set environment variables +ARG DEBIAN_FRONTEND=noninteractive +ENV CONDA_AUTO_UPDATE_CONDA=false +ENV PATH=/root/miniconda3/bin:$PATH + +# install linux packages +RUN apt-get update && apt-get install -y curl libsndfile1 libopenblas-dev +RUN rm -rf /var/lib/apt/lists/* + +# install miniconda and python 3.8 +RUN curl -sLo miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \ + && chmod +x ./miniconda.sh \ + && ./miniconda.sh -b -p /root/miniconda3 \ + && rm miniconda.sh \ + && conda install -y python==3.8 \ + && conda clean -ya + +# install conda and pip packages +COPY ./environment/conda_requirements.txt /opt/daft_exprt/environment/ +COPY ./environment/pip_requirements.txt /opt/daft_exprt/environment/ +WORKDIR /opt/daft_exprt/environment +RUN conda install -c conda-forge -y --file conda_requirements.txt +RUN pip install -r pip_requirements.txt + +# install PyTorch +RUN pip install torch==1.9.0+cu111 torchaudio==0.9.0 tensorboard -f https://download.pytorch.org/whl/torch_stable.html + +# install MFA thirdparty packages +RUN mfa thirdparty download +RUN mfa thirdparty validate + +# download pre-trained MFA models for english +RUN mfa download acoustic english +RUN mfa download g2p english_g2p +RUN mfa download dictionary english + +# set working directory +WORKDIR /workdir diff --git a/environment/Makefile b/environment/Makefile new file mode 100644 index 0000000..4458bb7 --- /dev/null +++ b/environment/Makefile @@ -0,0 +1,21 @@ +.PHONY: all + +all: linux_requirements python_requirements MFA_thirdparty MFA_pretrained + +linux_requirements: + sudo apt-get update && sudo apt-get install -y libsndfile1 libopenblas-dev + +python_requirements: + conda install -c conda-forge -y --file conda_requirements.txt + pip install pip setuptools --upgrade + pip install -e ../.[pytorch] \ + --find-links https://download.pytorch.org/whl/torch_stable.html + +MFA_thirdparty: + mfa thirdparty download + mfa thirdparty validate + +MFA_pretrained: + mfa download acoustic english + mfa download g2p english_g2p + mfa download dictionary english diff --git a/environment/conda_requirements.txt b/environment/conda_requirements.txt new file mode 100644 index 0000000..2f592c4 --- /dev/null +++ b/environment/conda_requirements.txt @@ -0,0 +1,6 @@ +# MFA dependencies +baumwelch +ngram +openblas +openfst +pynini diff --git a/environment/pip_requirements.txt b/environment/pip_requirements.txt new file mode 100644 index 0000000..3f2e7e6 --- /dev/null +++ b/environment/pip_requirements.txt @@ -0,0 +1,7 @@ +inflect==5.3.0 +librosa == 0.8.1 +matplotlib == 3.4.3 +montreal-forced-aligner == 2.0.0a24 +python-dateutil == 2.8.2 +tgt == 1.4.4 +unidecode == 1.2.0 \ No newline at end of file diff --git a/scripts/benchmarks/english/sentences.txt b/scripts/benchmarks/english/sentences.txt new file mode 100644 index 0000000..a5ebf28 --- /dev/null +++ b/scripts/benchmarks/english/sentences.txt @@ -0,0 +1,19 @@ +This is the kind of quality that we get with our algorithm. +Unknown words may not be synthetized by all bingbongratata jablow. +The whole thing of doing the movie was a risk. +Wish I could be there on Sunday in person but I can't. +The blue lagoon is a nineteen eighty American romance adventure film. +The Norsemen considered the rainbow as a bridge over which the gods passed from earth to their home in the sky. +Aspects of the sublime in English poetry and painting, seventeen seventy to eighteen fifty. +How many pickled peppers did Peter Piper pick? +Sally sells sea shells by the sea shore. +He thought it was time to present the present. +We are not out of the wood yet, but we still do not think of the witch. +To my readers. +But just now, my loving tirans, won't allow me. +And as one of my small friends, Hadly states. +It isn't a real Oz story without her. +Then he dreamed about it, and waking or dreaming he found the tale hard to believe. +Do you think this sentence will have the good pitch? +Do you rest? +Is it a good choice? \ No newline at end of file diff --git a/scripts/format_dataset.py b/scripts/format_dataset.py new file mode 100644 index 0000000..ce7c8b8 --- /dev/null +++ b/scripts/format_dataset.py @@ -0,0 +1,117 @@ +import argparse +import logging +import os + +from shutil import copyfile + + +_logger = logging.getLogger(__name__) + + +''' + This script modifies speakers data sets to match the required format + Each speaker data set must be of the following format: + + /speaker_name + metadata.csv + /wavs + wav_file_name_1.wav + wav_file_name_2.wav + ... + + metadata.csv must be formatted as follows (pipe "|" separator): + wav_file_name_1|text_1 + wav_file_name_2|text_2 + ... +''' + + +def format_LJ_speech(lj_args): + ''' Format LJ data set + Only metadata.csv needs to be modified + ''' + # read metadata lines + _logger.info('Formatting LJ Speech') + metadata = os.path.join(lj_args.data_set_dir, 'metadata.csv') + assert(os.path.isfile(metadata)), _logger.error(f'There is no such file {metadata}') + with open(metadata, 'r', encoding='utf-8') as f: + metadata_lines = f.readlines() + # create new metadata.csv + metadata_lines = [line.strip().split(sep='|') for line in metadata_lines] + metadata_lines = [f'{line[0]}|{line[2]}\n' for line in metadata_lines] + with open(metadata, 'w', encoding='utf-8') as f: + f.writelines(metadata_lines) + _logger.info('Done!') + + +def format_ESD(esd_args): + ''' Format ESD data set + ''' + # extract speaker dirs depending on the language + _logger.info(f'Formatting ESD -- Language = {esd_args.language}') + speakers = [x for x in os.listdir(esd_args.data_set_dir) if + os.path.isdir(os.path.join(esd_args.data_set_dir, x))] + speakers.sort() + if esd_args.language == 'english': + for speaker in speakers[10:]: + _logger.info(f'Speaker -- {speaker}') + speaker_dir = os.path.join(esd_args.data_set_dir, speaker) + spk_out_dir = os.path.join(esd_args.data_set_dir, esd_args.language, speaker) + os.makedirs(spk_out_dir, exist_ok=True) + # read metadata lines + if speaker == speakers[10]: + metadata = os.path.join(speaker_dir,f'{speaker}.txt') + assert(os.path.isfile(metadata)), _logger.error(f'There is no such file {metadata}') + with open(metadata, 'r', encoding='utf-8') as f: + metadata_lines = f.readlines() + metadata_lines = [line.strip().split(sep='\t') for line in metadata_lines] + # create new metadata.csv + spk_metadata_lines = [f'{speaker}_{line[0].strip().split(sep="_")[1]}|{line[1]}\n' + for line in metadata_lines] + with open(os.path.join(spk_out_dir, 'metadata.csv'), 'w', encoding='utf-8') as f: + f.writelines(spk_metadata_lines) + # copy all audio files to /wavs directory + wavs_dir = os.path.join(spk_out_dir, 'wavs') + os.makedirs(wavs_dir, exist_ok=True) + for root, _, files in os.walk(speaker_dir): + wav_files = [x for x in files if x.endswith('.wav')] + for wav_file in wav_files: + src = os.path.join(root, wav_file) + dst = os.path.join(wavs_dir, wav_file) + copyfile(src, dst) + elif esd_args.language == 'mandarin': + _logger.error(f'"mandarin" not implemented') + else: + _logger.error(f'"language" must be either "english" or "mandarin", not "{esd_args.language}"') + _logger.info('Done!') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='script to format speakers data sets') + subparsers = parser.add_subparsers(help='commands for targeting a specific data set') + + parser.add_argument('-dd', '--data_set_dir', type=str, + help='path to the directory containing speakers data sets to format') + + parser_LJ = subparsers.add_parser('LJ', help='format LJ data set') + parser_LJ.set_defaults(func=format_LJ_speech) + + parser_ESD = subparsers.add_parser('ESD', help='format emotional speech dataset 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