StarMirror
/
MockingBird
mirror of https://github.com/babysor/MockingBird
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Init to support Chinese Dataset.

pull/7/head
babysor00 2021-08-07 11:56:00 +08:00
commit e46cd609a2
75 changed files with 6984 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitattributes vendored Normal file
View File

@ -0,0 +1 @@
*.ipynb linguist-vendored

20
.gitignore vendored Normal file
View File

@ -0,0 +1,20 @@
*.pyc
*.aux
*.log
*.out
*.synctex.gz
*.suo
*__pycache__
*.idea
*.ipynb_checkpoints
*.pickle
*.npy
*.blg
*.bbl
*.bcf
*.toc
*.wav
*.sh
encoder/saved_models/*
synthesizer/saved_models/*
vocoder/saved_models/*

24
LICENSE.txt Normal file
View File

@ -0,0 +1,24 @@
MIT License
Modified & original work Copyright (c) 2019 Corentin Jemine (https://github.com/CorentinJ)
Original work Copyright (c) 2018 Rayhane Mama (https://github.com/Rayhane-mamah)
Original work Copyright (c) 2019 fatchord (https://github.com/fatchord)
Original work Copyright (c) 2015 braindead (https://github.com/braindead)
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.

52
README-CN.md Normal file
View File

@ -0,0 +1,52 @@
## 实时语音克隆 - 中文/普通话
![WechatIMG2968](https://user-images.githubusercontent.com/7423248/128490653-f55fefa8-f944-4617-96b8-5cc94f14f8f6.png)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)
> 该库是从仅支持英语的[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) 分叉出来的。
### [English](README.md) | 中文
## 特性
🌍 **中文** 支持普通话并使用数据集进行测试：adatatang_200zh
🤩 **PyTorch** 适用于 pytorch，已在 1.9.0 版本（最新于 2021 年 8 月）中测试，GPU Tesla T4 和 GTX 2060
🌍 **Windows + Linux** 在修复 nits 后在 Windows 操作系统和 linux 操作系统中进行测试
🤩 **Easy & Awesome** 仅使用新训练的合成器（synthesizer）就有良好效果，复用预训练的编码器/声码器
## 快速开始
### 1. 安装要求
> 按照原始存储库测试您是否已准备好所有环境。
**Python 3.7 或更高版本 ** 需要运行工具箱。
* 安装 [PyTorch](https://pytorch.org/get-started/locally/)。
* 安装 [ffmpeg](https://ffmpeg.org/download.html#get-packages)。
* 运行`pip install -r requirements.txt` 来安装剩余的必要包。
### 2. 使用 aidatatang_200zh 训练合成器
* 下载 adatatang_200zh 数据集并解压：确保您可以访问 *train* 文件夹中的所有 .wav
* 使用音频和梅尔频谱图进行预处理：
`python synthesizer_preprocess_audio.py <datasets_root>`
* 预处理嵌入：
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
* 训练合成器：
`python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`
* 当您在训练文件夹 *synthesizer/saved_models/* 中看到注意线显示和损失满足您的需要时，请转到下一步。
> 仅供参考，我的注意力是在 18k 步之后出现的，并且在 50k 步之后损失变得低于 0.4。
### 3. 启动工具箱
然后您可以尝试使用工具箱：
`python demo_toolbox.py -d <datasets_root>`
＃＃ TODO
- 添加演示视频
- 添加对更多数据集的支持
- 上传预训练模型
- 🙏 欢迎补充

53
README.md Normal file
View File

@ -0,0 +1,53 @@
![WechatIMG2968](https://user-images.githubusercontent.com/7423248/128490653-f55fefa8-f944-4617-96b8-5cc94f14f8f6.png)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)
> This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English.
> English | [中文](README-CN.md)
## Features
🌍 **Chinese** supported mandarin and tested with dataset: aidatatang_200zh
🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060
🌍 **Windows + Linux** tested in both Windows OS and linux OS after fixing nits
🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder
## Quick Start
### 1. Install Requirements
> Follow the original repo to test if you got all environment ready.
**Python 3.7 or higher ** is needed to run the toolbox.
* Install [PyTorch](https://pytorch.org/get-started/locally/).
* Install [ffmpeg](https://ffmpeg.org/download.html#get-packages).
* Run `pip install -r requirements.txt` to install the remaining necessary packages.
### 2. Train synthesizer with aidatatang_200zh
* Download aidatatang_200zh dataset and unzip: make sure you can access all .wav in *train* folder
* Preprocess with the audios and the mel spectrograms:
`python synthesizer_preprocess_audio.py <datasets_root>`
* Preprocess the embeddings:
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
* Train the synthesizer:
`python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`
* Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*.
> FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
### 3. Launch the Toolbox
You can then try the toolbox:
`python demo_toolbox.py -d <datasets_root>`
or
`python demo_toolbox.py`
## TODO
- Add demo video
- Add support for more dataset
- Upload pretrained model
- 🙏 Welcome to add more

225
demo_cli.py Normal file
View File

@ -0,0 +1,225 @@
from encoder.params_model import model_embedding_size as speaker_embedding_size
from utils.argutils import print_args
from utils.modelutils import check_model_paths
from synthesizer.inference import Synthesizer
from encoder import inference as encoder
from vocoder import inference as vocoder
from pathlib import Path
import numpy as np
import soundfile as sf
import librosa
import argparse
import torch
import sys
import os
from audioread.exceptions import NoBackendError
if __name__ == '__main__':
## Info & args
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("-e", "--enc_model_fpath", type=Path,
default="encoder/saved_models/pretrained.pt",
help="Path to a saved encoder")
parser.add_argument("-s", "--syn_model_fpath", type=Path,
default="synthesizer/saved_models/pretrained/pretrained.pt",
help="Path to a saved synthesizer")
parser.add_argument("-v", "--voc_model_fpath", type=Path,
default="vocoder/saved_models/pretrained/pretrained.pt",
help="Path to a saved vocoder")
parser.add_argument("--cpu", action="store_true", help=\
"If True, processing is done on CPU, even when a GPU is available.")
parser.add_argument("--no_sound", action="store_true", help=\
"If True, audio won't be played.")
parser.add_argument("--seed", type=int, default=None, help=\
"Optional random number seed value to make toolbox deterministic.")
parser.add_argument("--no_mp3_support", action="store_true", help=\
"If True, disallows loading mp3 files to prevent audioread errors when ffmpeg is not installed.")
args = parser.parse_args()
print_args(args, parser)
if not args.no_sound:
import sounddevice as sd
if args.cpu:
# Hide GPUs from Pytorch to force CPU processing
os.environ["CUDA_VISIBLE_DEVICES"] = ""
if not args.no_mp3_support:
try:
librosa.load("samples/1320_00000.mp3")
except NoBackendError:
print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
"Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
exit(-1)
print("Running a test of your configuration...\n")
if torch.cuda.is_available():
device_id = torch.cuda.current_device()
gpu_properties = torch.cuda.get_device_properties(device_id)
## Print some environment information (for debugging purposes)
print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
"%.1fGb total memory.\n" %
(torch.cuda.device_count(),
device_id,
gpu_properties.name,
gpu_properties.major,
gpu_properties.minor,
gpu_properties.total_memory / 1e9))
else:
print("Using CPU for inference.\n")
## Remind the user to download pretrained models if needed
check_model_paths(encoder_path=args.enc_model_fpath,
synthesizer_path=args.syn_model_fpath,
vocoder_path=args.voc_model_fpath)
## Load the models one by one.
print("Preparing the encoder, the synthesizer and the vocoder...")
encoder.load_model(args.enc_model_fpath)
synthesizer = Synthesizer(args.syn_model_fpath)
vocoder.load_model(args.voc_model_fpath)
## Run a test
print("Testing your configuration with small inputs.")
# Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
# sampling rate, which may differ.
# If you're unfamiliar with digital audio, know that it is encoded as an array of floats
# (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
# The sampling rate is the number of values (samples) recorded per second, it is set to
# 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond
# to an audio of 1 second.
print("\tTesting the encoder...")
encoder.embed_utterance(np.zeros(encoder.sampling_rate))
# Create a dummy embedding. You would normally use the embedding that encoder.embed_utterance
# returns, but here we're going to make one ourselves just for the sake of showing that it's
# possible.
embed = np.random.rand(speaker_embedding_size)
# Embeddings are L2-normalized (this isn't important here, but if you want to make your own
# embeddings it will be).
embed /= np.linalg.norm(embed)
# The synthesizer can handle multiple inputs with batching. Let's create another embedding to
# illustrate that
embeds = [embed, np.zeros(speaker_embedding_size)]
texts = ["test 1", "test 2"]
print("\tTesting the synthesizer... (loading the model will output a lot of text)")
mels = synthesizer.synthesize_spectrograms(texts, embeds)
# The vocoder synthesizes one waveform at a time, but it's more efficient for long ones. We
# can concatenate the mel spectrograms to a single one.
mel = np.concatenate(mels, axis=1)
# The vocoder can take a callback function to display the generation. More on that later. For
# now we'll simply hide it like this:
no_action = lambda *args: None
print("\tTesting the vocoder...")
# For the sake of making this test short, we'll pass a short target length. The target length
# is the length of the wav segments that are processed in parallel. E.g. for audio sampled
# at 16000 Hertz, a target length of 8000 means that the target audio will be cut in chunks of
# 0.5 seconds which will all be generated together. The parameters here are absurdly short, and
# that has a detrimental effect on the quality of the audio. The default parameters are
# recommended in general.
vocoder.infer_waveform(mel, target=200, overlap=50, progress_callback=no_action)
print("All test passed! You can now synthesize speech.\n\n")
## Interactive speech generation
print("This is a GUI-less example of interface to SV2TTS. The purpose of this script is to "
"show how you can interface this project easily with your own. See the source code for "
"an explanation of what is happening.\n")
print("Interactive generation loop")
num_generated = 0
while True:
try:
# Get the reference audio filepath
message = "Reference voice: enter an audio filepath of a voice to be cloned (mp3, " \
"wav, m4a, flac, ...):\n"
in_fpath = Path(input(message).replace("\"", "").replace("\'", ""))
if in_fpath.suffix.lower() == ".mp3" and args.no_mp3_support:
print("Can't Use mp3 files please try again:")
continue
## Computing the embedding
# First, we load the wav using the function that the speaker encoder provides. This is
# important: there is preprocessing that must be applied.
# The following two methods are equivalent:
# - Directly load from the filepath:
preprocessed_wav = encoder.preprocess_wav(in_fpath)
# - If the wav is already loaded:
original_wav, sampling_rate = librosa.load(str(in_fpath))
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
print("Loaded file succesfully")
# Then we derive the embedding. There are many functions and parameters that the
# speaker encoder interfaces. These are mostly for in-depth research. You will typically
# only use this function (with its default parameters):
embed = encoder.embed_utterance(preprocessed_wav)
print("Created the embedding")
## Generating the spectrogram
text = input("Write a sentence (+-20 words) to be synthesized:\n")
# If seed is specified, reset torch seed and force synthesizer reload
if args.seed is not None:
torch.manual_seed(args.seed)
synthesizer = Synthesizer(args.syn_model_fpath)
# The synthesizer works in batch, so you need to put your data in a list or numpy array
texts = [text]
embeds = [embed]
# If you know what the attention layer alignments are, you can retrieve them here by
# passing return_alignments=True
specs = synthesizer.synthesize_spectrograms(texts, embeds)
spec = specs[0]
print("Created the mel spectrogram")
## Generating the waveform
print("Synthesizing the waveform:")
# If seed is specified, reset torch seed and reload vocoder
if args.seed is not None:
torch.manual_seed(args.seed)
vocoder.load_model(args.voc_model_fpath)
# Synthesizing the waveform is fairly straightforward. Remember that the longer the
# spectrogram, the more time-efficient the vocoder.
generated_wav = vocoder.infer_waveform(spec)
## Post-generation
# There's a bug with sounddevice that makes the audio cut one second earlier, so we
# pad it.
generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
# Trim excess silences to compensate for gaps in spectrograms (issue #53)
generated_wav = encoder.preprocess_wav(generated_wav)
# Play the audio (non-blocking)
if not args.no_sound:
try:
sd.stop()
sd.play(generated_wav, synthesizer.sample_rate)
except sd.PortAudioError as e:
print("\nCaught exception: %s" % repr(e))
print("Continuing without audio playback. Suppress this message with the \"--no_sound\" flag.\n")
except:
raise
# Save it on the disk
filename = "demo_output_%02d.wav" % num_generated
print(generated_wav.dtype)
sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate)
num_generated += 1
print("\nSaved output as %s\n\n" % filename)
except Exception as e:
print("Caught exception: %s" % repr(e))
print("Restarting\n")

43
demo_toolbox.py Normal file
View File

@ -0,0 +1,43 @@
from pathlib import Path
from toolbox import Toolbox
from utils.argutils import print_args
from utils.modelutils import check_model_paths
import argparse
import os
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Runs the toolbox",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("-d", "--datasets_root", type=Path, help= \
"Path to the directory containing your datasets. See toolbox/__init__.py for a list of "
"supported datasets.", default=None)
parser.add_argument("-e", "--enc_models_dir", type=Path, default="encoder/saved_models",
help="Directory containing saved encoder models")
parser.add_argument("-s", "--syn_models_dir", type=Path, default="synthesizer/saved_models",
help="Directory containing saved synthesizer models")
parser.add_argument("-v", "--voc_models_dir", type=Path, default="vocoder/saved_models",
help="Directory containing saved vocoder models")
parser.add_argument("--cpu", action="store_true", help=\
"If True, processing is done on CPU, even when a GPU is available.")
parser.add_argument("--seed", type=int, default=None, help=\
"Optional random number seed value to make toolbox deterministic.")
parser.add_argument("--no_mp3_support", action="store_true", help=\
"If True, no mp3 files are allowed.")
args = parser.parse_args()
print_args(args, parser)
if args.cpu:
# Hide GPUs from Pytorch to force CPU processing
os.environ["CUDA_VISIBLE_DEVICES"] = ""
del args.cpu
## Remind the user to download pretrained models if needed
check_model_paths(encoder_path=args.enc_models_dir, synthesizer_path=args.syn_models_dir,
vocoder_path=args.voc_models_dir)
# Launch the toolbox
Toolbox(**vars(args))

0
encoder/__init__.py Normal file
View File

117
encoder/audio.py Normal file
View File

@ -0,0 +1,117 @@
from scipy.ndimage.morphology import binary_dilation
from encoder.params_data import *
from pathlib import Path
from typing import Optional, Union
from warnings import warn
import numpy as np
import librosa
import struct
try:
import webrtcvad
except:
warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.")
webrtcvad=None
int16_max = (2 ** 15) - 1
def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
source_sr: Optional[int] = None,
normalize: Optional[bool] = True,
trim_silence: Optional[bool] = True):
"""
Applies the preprocessing operations used in training the Speaker Encoder to a waveform
either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
:param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not
just .wav), either the waveform as a numpy array of floats.
:param source_sr: if passing an audio waveform, the sampling rate of the waveform before
preprocessing. After preprocessing, the waveform's sampling rate will match the data
hyperparameters. If passing a filepath, the sampling rate will be automatically detected and
this argument will be ignored.
"""
# Load the wav from disk if needed
if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)
else:
wav = fpath_or_wav
# Resample the wav if needed
if source_sr is not None and source_sr != sampling_rate:
wav = librosa.resample(wav, source_sr, sampling_rate)
# Apply the preprocessing: normalize volume and shorten long silences
if normalize:
wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
if webrtcvad and trim_silence:
wav = trim_long_silences(wav)
return wav
def wav_to_mel_spectrogram(wav):
"""
Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
Note: this not a log-mel spectrogram.
"""
frames = librosa.feature.melspectrogram(
wav,
sampling_rate,
n_fft=int(sampling_rate * mel_window_length / 1000),
hop_length=int(sampling_rate * mel_window_step / 1000),
n_mels=mel_n_channels
)
return frames.astype(np.float32).T
def trim_long_silences(wav):
"""
Ensures that segments without voice in the waveform remain no longer than a
threshold determined by the VAD parameters in params.py.
:param wav: the raw waveform as a numpy array of floats
:return: the same waveform with silences trimmed away (length <= original wav length)
"""
# Compute the voice detection window size
samples_per_window = (vad_window_length * sampling_rate) // 1000
# Trim the end of the audio to have a multiple of the window size
wav = wav[:len(wav) - (len(wav) % samples_per_window)]
# Convert the float waveform to 16-bit mono PCM
pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
# Perform voice activation detection
voice_flags = []
vad = webrtcvad.Vad(mode=3)
for window_start in range(0, len(wav), samples_per_window):
window_end = window_start + samples_per_window
voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
sample_rate=sampling_rate))
voice_flags = np.array(voice_flags)
# Smooth the voice detection with a moving average
def moving_average(array, width):
array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
ret = np.cumsum(array_padded, dtype=float)
ret[width:] = ret[width:] - ret[:-width]
return ret[width - 1:] / width
audio_mask = moving_average(voice_flags, vad_moving_average_width)
audio_mask = np.round(audio_mask).astype(np.bool)
# Dilate the voiced regions
audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
audio_mask = np.repeat(audio_mask, samples_per_window)
return wav[audio_mask == True]
def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
if increase_only and decrease_only:
raise ValueError("Both increase only and decrease only are set")
dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
return wav
return wav * (10 ** (dBFS_change / 20))

45
encoder/config.py Normal file
View File

@ -0,0 +1,45 @@
librispeech_datasets = {
"train": {
"clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
"other": ["LibriSpeech/train-other-500"]
},
"test": {
"clean": ["LibriSpeech/test-clean"],
"other": ["LibriSpeech/test-other"]
},
"dev": {
"clean": ["LibriSpeech/dev-clean"],
"other": ["LibriSpeech/dev-other"]
},
}
libritts_datasets = {
"train": {
"clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
"other": ["LibriTTS/train-other-500"]
},
"test": {
"clean": ["LibriTTS/test-clean"],
"other": ["LibriTTS/test-other"]
},
"dev": {
"clean": ["LibriTTS/dev-clean"],
"other": ["LibriTTS/dev-other"]
},
}
voxceleb_datasets = {
"voxceleb1" : {
"train": ["VoxCeleb1/wav"],
"test": ["VoxCeleb1/test_wav"]
},
"voxceleb2" : {
"train": ["VoxCeleb2/dev/aac"],
"test": ["VoxCeleb2/test_wav"]
}
}
other_datasets = [
"LJSpeech-1.1",
"VCTK-Corpus/wav48",
]
anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]

2
encoder/data_objects/__init__.py Normal file
View File

@ -0,0 +1,2 @@
from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader

37
encoder/data_objects/random_cycler.py Normal file
View File

@ -0,0 +1,37 @@
import random
class RandomCycler:
"""
Creates an internal copy of a sequence and allows access to its items in a constrained random
order. For a source sequence of n items and one or several consecutive queries of a total
of m items, the following guarantees hold (one implies the other):
- Each item will be returned between m // n and ((m - 1) // n) + 1 times.
- Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
"""
def __init__(self, source):
if len(source) == 0:
raise Exception("Can't create RandomCycler from an empty collection")
self.all_items = list(source)
self.next_items = []
def sample(self, count: int):
shuffle = lambda l: random.sample(l, len(l))
out = []
while count > 0:
if count >= len(self.all_items):
out.extend(shuffle(list(self.all_items)))
count -= len(self.all_items)
continue
n = min(count, len(self.next_items))
out.extend(self.next_items[:n])
count -= n
self.next_items = self.next_items[n:]
if len(self.next_items) == 0:
self.next_items = shuffle(list(self.all_items))
return out
def __next__(self):
return self.sample(1)[0]

40
encoder/data_objects/speaker.py Normal file
View File

@ -0,0 +1,40 @@
from encoder.data_objects.random_cycler import RandomCycler
from encoder.data_objects.utterance import Utterance
from pathlib import Path
# Contains the set of utterances of a single speaker
class Speaker:
def __init__(self, root: Path):
self.root = root
self.name = root.name
self.utterances = None
self.utterance_cycler = None
def _load_utterances(self):
with self.root.joinpath("_sources.txt").open("r") as sources_file:
sources = [l.split(",") for l in sources_file]
sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
self.utterance_cycler = RandomCycler(self.utterances)
def random_partial(self, count, n_frames):
"""
Samples a batch of <count> unique partial utterances from the disk in a way that all
utterances come up at least once every two cycles and in a random order every time.
:param count: The number of partial utterances to sample from the set of utterances from
that speaker. Utterances are guaranteed not to be repeated if <count> is not larger than
the number of utterances available.
:param n_frames: The number of frames in the partial utterance.
:return: A list of tuples (utterance, frames, range) where utterance is an Utterance,
frames are the frames of the partial utterances and range is the range of the partial
utterance with regard to the complete utterance.
"""
if self.utterances is None:
self._load_utterances()
utterances = self.utterance_cycler.sample(count)
a = [(u,) + u.random_partial(n_frames) for u in utterances]
return a

12
encoder/data_objects/speaker_batch.py Normal file
View File

@ -0,0 +1,12 @@
import numpy as np
from typing import List
from encoder.data_objects.speaker import Speaker
class SpeakerBatch:
def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
self.speakers = speakers
self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
# Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
# 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])

56
encoder/data_objects/speaker_verification_dataset.py Normal file
View File

@ -0,0 +1,56 @@
from encoder.data_objects.random_cycler import RandomCycler
from encoder.data_objects.speaker_batch import SpeakerBatch
from encoder.data_objects.speaker import Speaker
from encoder.params_data import partials_n_frames
from torch.utils.data import Dataset, DataLoader
from pathlib import Path
# TODO: improve with a pool of speakers for data efficiency
class SpeakerVerificationDataset(Dataset):
def __init__(self, datasets_root: Path):
self.root = datasets_root
speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
if len(speaker_dirs) == 0:
raise Exception("No speakers found. Make sure you are pointing to the directory "
"containing all preprocessed speaker directories.")
self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
self.speaker_cycler = RandomCycler(self.speakers)
def __len__(self):
return int(1e10)
def __getitem__(self, index):
return next(self.speaker_cycler)
def get_logs(self):
log_string = ""
for log_fpath in self.root.glob("*.txt"):
with log_fpath.open("r") as log_file:
log_string += "".join(log_file.readlines())
return log_string
class SpeakerVerificationDataLoader(DataLoader):
def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None,
batch_sampler=None, num_workers=0, pin_memory=False, timeout=0,
worker_init_fn=None):
self.utterances_per_speaker = utterances_per_speaker
super().__init__(
dataset=dataset,
batch_size=speakers_per_batch,
shuffle=False,
sampler=sampler,
batch_sampler=batch_sampler,
num_workers=num_workers,
collate_fn=self.collate,
pin_memory=pin_memory,
drop_last=False,
timeout=timeout,
worker_init_fn=worker_init_fn
)
def collate(self, speakers):
return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames)

26
encoder/data_objects/utterance.py Normal file
View File

@ -0,0 +1,26 @@
import numpy as np
class Utterance:
def __init__(self, frames_fpath, wave_fpath):
self.frames_fpath = frames_fpath
self.wave_fpath = wave_fpath
def get_frames(self):
return np.load(self.frames_fpath)
def random_partial(self, n_frames):
"""
Crops the frames into a partial utterance of n_frames
:param n_frames: The number of frames of the partial utterance
:return: the partial utterance frames and a tuple indicating the start and end of the
partial utterance in the complete utterance.
"""
frames = self.get_frames()
if frames.shape[0] == n_frames:
start = 0
else:
start = np.random.randint(0, frames.shape[0] - n_frames)
end = start + n_frames
return frames[start:end], (start, end)

178
encoder/inference.py Normal file
View File

@ -0,0 +1,178 @@
from encoder.params_data import *
from encoder.model import SpeakerEncoder
from encoder.audio import preprocess_wav # We want to expose this function from here
from matplotlib import cm
from encoder import audio
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
_model = None # type: SpeakerEncoder
_device = None # type: torch.device
def load_model(weights_fpath: Path, device=None):
"""
Loads the model in memory. If this function is not explicitely called, it will be run on the
first call to embed_frames() with the default weights file.
:param weights_fpath: the path to saved model weights.
:param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The
model will be loaded and will run on this device. Outputs will however always be on the cpu.
If None, will default to your GPU if it"s available, otherwise your CPU.
"""
# TODO: I think the slow loading of the encoder might have something to do with the device it
# was saved on. Worth investigating.
global _model, _device
if device is None:
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
elif isinstance(device, str):
_device = torch.device(device)
_model = SpeakerEncoder(_device, torch.device("cpu"))
checkpoint = torch.load(weights_fpath, _device)
_model.load_state_dict(checkpoint["model_state"])
_model.eval()
print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"]))
def is_loaded():
return _model is not None
def embed_frames_batch(frames_batch):
"""
Computes embeddings for a batch of mel spectrogram.
:param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape
(batch_size, n_frames, n_channels)
:return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
"""
if _model is None:
raise Exception("Model was not loaded. Call load_model() before inference.")
frames = torch.from_numpy(frames_batch).to(_device)
embed = _model.forward(frames).detach().cpu().numpy()
return embed
def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames,
min_pad_coverage=0.75, overlap=0.5):
"""
Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain
partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel
spectrogram slices are returned, so as to make each partial utterance waveform correspond to
its spectrogram. This function assumes that the mel spectrogram parameters used are those
defined in params_data.py.
The returned ranges may be indexing further than the length of the waveform. It is
recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
:param n_samples: the number of samples in the waveform
:param partial_utterance_n_frames: the number of mel spectrogram frames in each partial
utterance
:param min_pad_coverage: when reaching the last partial utterance, it may or may not have
enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present,
then the last partial utterance will be considered, as if we padded the audio. Otherwise,
it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial
utterance, this parameter is ignored so that the function always returns at least 1 slice.
:param overlap: by how much the partial utterance should overlap. If set to 0, the partial
utterances are entirely disjoint.
:return: the waveform slices and mel spectrogram slices as lists of array slices. Index
respectively the waveform and the mel spectrogram with these slices to obtain the partial
utterances.
"""
assert 0 <= overlap < 1
assert 0 < min_pad_coverage <= 1
samples_per_frame = int((sampling_rate * mel_window_step / 1000))
n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1)
# Compute the slices
wav_slices, mel_slices = [], []
steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
for i in range(0, steps, frame_step):
mel_range = np.array([i, i + partial_utterance_n_frames])
wav_range = mel_range * samples_per_frame
mel_slices.append(slice(*mel_range))
wav_slices.append(slice(*wav_range))
# Evaluate whether extra padding is warranted or not
last_wav_range = wav_slices[-1]
coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
if coverage < min_pad_coverage and len(mel_slices) > 1:
mel_slices = mel_slices[:-1]
wav_slices = wav_slices[:-1]
return wav_slices, mel_slices
def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs):
"""
Computes an embedding for a single utterance.
# TODO: handle multiple wavs to benefit from batching on GPU
:param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
:param using_partials: if True, then the utterance is split in partial utterances of
<partial_utterance_n_frames> frames and the utterance embedding is computed from their
normalized average. If False, the utterance is instead computed from feeding the entire
spectogram to the network.
:param return_partials: if True, the partial embeddings will also be returned along with the
wav slices that correspond to the partial embeddings.
:param kwargs: additional arguments to compute_partial_splits()
:return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
<return_partials> is True, the partial utterances as a numpy array of float32 of shape
(n_partials, model_embedding_size) and the wav partials as a list of slices will also be
returned. If <using_partials> is simultaneously set to False, both these values will be None
instead.
"""
# Process the entire utterance if not using partials
if not using_partials:
frames = audio.wav_to_mel_spectrogram(wav)
embed = embed_frames_batch(frames[None, ...])[0]
if return_partials:
return embed, None, None
return embed
# Compute where to split the utterance into partials and pad if necessary
wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs)
max_wave_length = wave_slices[-1].stop
if max_wave_length >= len(wav):
wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
# Split the utterance into partials
frames = audio.wav_to_mel_spectrogram(wav)
frames_batch = np.array([frames[s] for s in mel_slices])
partial_embeds = embed_frames_batch(frames_batch)
# Compute the utterance embedding from the partial embeddings
raw_embed = np.mean(partial_embeds, axis=0)
embed = raw_embed / np.linalg.norm(raw_embed, 2)
if return_partials:
return embed, partial_embeds, wave_slices
return embed
def embed_speaker(wavs, **kwargs):
raise NotImplemented()
def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
if ax is None:
ax = plt.gca()
if shape is None:
height = int(np.sqrt(len(embed)))
shape = (height, -1)
embed = embed.reshape(shape)
cmap = cm.get_cmap()
mappable = ax.imshow(embed, cmap=cmap)
cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
sm = cm.ScalarMappable(cmap=cmap)
sm.set_clim(*color_range)
ax.set_xticks([]), ax.set_yticks([])
ax.set_title(title)

135
encoder/model.py Normal file
View File

@ -0,0 +1,135 @@
from encoder.params_model import *
from encoder.params_data import *
from scipy.interpolate import interp1d
from sklearn.metrics import roc_curve
from torch.nn.utils import clip_grad_norm_
from scipy.optimize import brentq
from torch import nn
import numpy as np
import torch
class SpeakerEncoder(nn.Module):
def __init__(self, device, loss_device):
super().__init__()
self.loss_device = loss_device
# Network defition
self.lstm = nn.LSTM(input_size=mel_n_channels,
hidden_size=model_hidden_size,
num_layers=model_num_layers,
batch_first=True).to(device)
self.linear = nn.Linear(in_features=model_hidden_size,
out_features=model_embedding_size).to(device)
self.relu = torch.nn.ReLU().to(device)
# Cosine similarity scaling (with fixed initial parameter values)
self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)
# Loss
self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
def do_gradient_ops(self):
# Gradient scale
self.similarity_weight.grad *= 0.01
self.similarity_bias.grad *= 0.01
# Gradient clipping
clip_grad_norm_(self.parameters(), 3, norm_type=2)
def forward(self, utterances, hidden_init=None):
"""
Computes the embeddings of a batch of utterance spectrograms.
:param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape
(batch_size, n_frames, n_channels)
:param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers,
batch_size, hidden_size). Will default to a tensor of zeros if None.
:return: the embeddings as a tensor of shape (batch_size, embedding_size)
"""
# Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
# and the final cell state.
out, (hidden, cell) = self.lstm(utterances, hidden_init)
# We take only the hidden state of the last layer
embeds_raw = self.relu(self.linear(hidden[-1]))
# L2-normalize it
embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5)
return embeds
def similarity_matrix(self, embeds):
"""
Computes the similarity matrix according the section 2.1 of GE2E.
:param embeds: the embeddings as a tensor of shape (speakers_per_batch,
utterances_per_speaker, embedding_size)
:return: the similarity matrix as a tensor of shape (speakers_per_batch,
utterances_per_speaker, speakers_per_batch)
"""
speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
# Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
centroids_incl = torch.mean(embeds, dim=1, keepdim=True)
centroids_incl = centroids_incl.clone() / (torch.norm(centroids_incl, dim=2, keepdim=True) + 1e-5)
# Exclusive centroids (1 per utterance)
centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds)
centroids_excl /= (utterances_per_speaker - 1)
centroids_excl = centroids_excl.clone() / (torch.norm(centroids_excl, dim=2, keepdim=True) + 1e-5)
# Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot
# product of these vectors (which is just an element-wise multiplication reduced by a sum).
# We vectorize the computation for efficiency.
sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,
speakers_per_batch).to(self.loss_device)
mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int)
for j in range(speakers_per_batch):
mask = np.where(mask_matrix[j])[0]
sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2)
sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1)
## Even more vectorized version (slower maybe because of transpose)
# sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker
# ).to(self.loss_device)
# eye = np.eye(speakers_per_batch, dtype=np.int)
# mask = np.where(1 - eye)
# sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2)
# mask = np.where(eye)
# sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2)
# sim_matrix2 = sim_matrix2.transpose(1, 2)
sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
return sim_matrix
def loss(self, embeds):
"""
Computes the softmax loss according the section 2.1 of GE2E.
:param embeds: the embeddings as a tensor of shape (speakers_per_batch,
utterances_per_speaker, embedding_size)
:return: the loss and the EER for this batch of embeddings.
"""
speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
# Loss
sim_matrix = self.similarity_matrix(embeds)
sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker,
speakers_per_batch))
ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
target = torch.from_numpy(ground_truth).long().to(self.loss_device)
loss = self.loss_fn(sim_matrix, target)
# EER (not backpropagated)
with torch.no_grad():
inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0]
labels = np.array([inv_argmax(i) for i in ground_truth])
preds = sim_matrix.detach().cpu().numpy()
# Snippet from https://yangcha.github.io/EER-ROC/
fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())
eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
return loss, eer

29
encoder/params_data.py Normal file
View File

@ -0,0 +1,29 @@
## Mel-filterbank
mel_window_length = 25 # In milliseconds
mel_window_step = 10 # In milliseconds
mel_n_channels = 40
## Audio
sampling_rate = 16000
# Number of spectrogram frames in a partial utterance
partials_n_frames = 160 # 1600 ms
# Number of spectrogram frames at inference
inference_n_frames = 80 # 800 ms
## Voice Activation Detection
# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
# This sets the granularity of the VAD. Should not need to be changed.
vad_window_length = 30 # In milliseconds
# Number of frames to average together when performing the moving average smoothing.
# The larger this value, the larger the VAD variations must be to not get smoothed out.
vad_moving_average_width = 8
# Maximum number of consecutive silent frames a segment can have.
vad_max_silence_length = 6
## Audio volume normalization
audio_norm_target_dBFS = -30

11
encoder/params_model.py Normal file
View File

@ -0,0 +1,11 @@
## Model parameters
model_hidden_size = 256
model_embedding_size = 256
model_num_layers = 3
## Training parameters
learning_rate_init = 1e-4
speakers_per_batch = 64
utterances_per_speaker = 10

175
encoder/preprocess.py Normal file
View File

@ -0,0 +1,175 @@
from multiprocess.pool import ThreadPool
from encoder.params_data import *
from encoder.config import librispeech_datasets, anglophone_nationalites
from datetime import datetime
from encoder import audio
from pathlib import Path
from tqdm import tqdm
import numpy as np
class DatasetLog:
"""
Registers metadata about the dataset in a text file.
"""
def __init__(self, root, name):
self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
self.sample_data = dict()
start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
self.write_line("Creating dataset %s on %s" % (name, start_time))
self.write_line("-----")
self._log_params()
def _log_params(self):
from encoder import params_data
self.write_line("Parameter values:")
for param_name in (p for p in dir(params_data) if not p.startswith("__")):
value = getattr(params_data, param_name)
self.write_line("\t%s: %s" % (param_name, value))
self.write_line("-----")
def write_line(self, line):
self.text_file.write("%s\n" % line)
def add_sample(self, **kwargs):
for param_name, value in kwargs.items():
if not param_name in self.sample_data:
self.sample_data[param_name] = []
self.sample_data[param_name].append(value)
def finalize(self):
self.write_line("Statistics:")
for param_name, values in self.sample_data.items():
self.write_line("\t%s:" % param_name)
self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
self.write_line("-----")
end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
self.write_line("Finished on %s" % end_time)
self.text_file.close()
def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
dataset_root = datasets_root.joinpath(dataset_name)
if not dataset_root.exists():
print("Couldn\'t find %s, skipping this dataset." % dataset_root)
return None, None
return dataset_root, DatasetLog(out_dir, dataset_name)
def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
skip_existing, logger):
print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
# Function to preprocess utterances for one speaker
def preprocess_speaker(speaker_dir: Path):
# Give a name to the speaker that includes its dataset
speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
# Create an output directory with that name, as well as a txt file containing a
# reference to each source file.
speaker_out_dir = out_dir.joinpath(speaker_name)
speaker_out_dir.mkdir(exist_ok=True)
sources_fpath = speaker_out_dir.joinpath("_sources.txt")
# There's a possibility that the preprocessing was interrupted earlier, check if
# there already is a sources file.
if sources_fpath.exists():
try:
with sources_fpath.open("r") as sources_file:
existing_fnames = {line.split(",")[0] for line in sources_file}
except:
existing_fnames = {}
else:
existing_fnames = {}
# Gather all audio files for that speaker recursively
sources_file = sources_fpath.open("a" if skip_existing else "w")
for in_fpath in speaker_dir.glob("**/*.%s" % extension):
# Check if the target output file already exists
out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
out_fname = out_fname.replace(".%s" % extension, ".npy")
if skip_existing and out_fname in existing_fnames:
continue
# Load and preprocess the waveform
wav = audio.preprocess_wav(in_fpath)
if len(wav) == 0:
continue
# Create the mel spectrogram, discard those that are too short
frames = audio.wav_to_mel_spectrogram(wav)
if len(frames) < partials_n_frames:
continue
out_fpath = speaker_out_dir.joinpath(out_fname)
np.save(out_fpath, frames)
logger.add_sample(duration=len(wav) / sampling_rate)
sources_file.write("%s,%s\n" % (out_fname, in_fpath))
sources_file.close()
# Process the utterances for each speaker
with ThreadPool(8) as pool:
list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
unit="speakers"))
logger.finalize()
print("Done preprocessing %s.\n" % dataset_name)
def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
for dataset_name in librispeech_datasets["train"]["other"]:
# Initialize the preprocessing
dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
if not dataset_root:
return
# Preprocess all speakers
speaker_dirs = list(dataset_root.glob("*"))
_preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
skip_existing, logger)
def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
# Initialize the preprocessing
dataset_name = "VoxCeleb1"
dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
if not dataset_root:
return
# Get the contents of the meta file
with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
metadata = [line.split("\t") for line in metafile][1:]
# Select the ID and the nationality, filter out non-anglophone speakers
nationalities = {line[0]: line[3] for line in metadata}
keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if
nationality.lower() in anglophone_nationalites]
print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." %
(len(keep_speaker_ids), len(nationalities)))
# Get the speaker directories for anglophone speakers only
speaker_dirs = dataset_root.joinpath("wav").glob("*")
speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
speaker_dir.name in keep_speaker_ids]
print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." %
(len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
# Preprocess all speakers
_preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
skip_existing, logger)
def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
# Initialize the preprocessing
dataset_name = "VoxCeleb2"
dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
if not dataset_root:
return
# Get the speaker directories
# Preprocess all speakers
speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
_preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
skip_existing, logger)

123
encoder/train.py Normal file
View File

@ -0,0 +1,123 @@
from encoder.visualizations import Visualizations
from encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
from encoder.params_model import *
from encoder.model import SpeakerEncoder
from utils.profiler import Profiler
from pathlib import Path
import torch
def sync(device: torch.device):
# For correct profiling (cuda operations are async)
if device.type == "cuda":
torch.cuda.synchronize(device)
def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int,
backup_every: int, vis_every: int, force_restart: bool, visdom_server: str,
no_visdom: bool):
# Create a dataset and a dataloader
dataset = SpeakerVerificationDataset(clean_data_root)
loader = SpeakerVerificationDataLoader(
dataset,
speakers_per_batch,
utterances_per_speaker,
num_workers=8,
)
# Setup the device on which to run the forward pass and the loss. These can be different,
# because the forward pass is faster on the GPU whereas the loss is often (depending on your
# hyperparameters) faster on the CPU.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# FIXME: currently, the gradient is None if loss_device is cuda
loss_device = torch.device("cpu")
# Create the model and the optimizer
model = SpeakerEncoder(device, loss_device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init)
init_step = 1
# Configure file path for the model
state_fpath = models_dir.joinpath(run_id + ".pt")
backup_dir = models_dir.joinpath(run_id + "_backups")
# Load any existing model
if not force_restart:
if state_fpath.exists():
print("Found existing model \"%s\", loading it and resuming training." % run_id)
checkpoint = torch.load(state_fpath)
init_step = checkpoint["step"]
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
optimizer.param_groups[0]["lr"] = learning_rate_init
else:
print("No model \"%s\" found, starting training from scratch." % run_id)
else:
print("Starting the training from scratch.")
model.train()
# Initialize the visualization environment
vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom)
vis.log_dataset(dataset)
vis.log_params()
device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
vis.log_implementation({"Device": device_name})
# Training loop
profiler = Profiler(summarize_every=10, disabled=False)
for step, speaker_batch in enumerate(loader, init_step):
profiler.tick("Blocking, waiting for batch (threaded)")
# Forward pass
inputs = torch.from_numpy(speaker_batch.data).to(device)
sync(device)
profiler.tick("Data to %s" % device)
embeds = model(inputs)
sync(device)
profiler.tick("Forward pass")
embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
loss, eer = model.loss(embeds_loss)
sync(loss_device)
profiler.tick("Loss")
# Backward pass
model.zero_grad()
loss.backward()
profiler.tick("Backward pass")
model.do_gradient_ops()
optimizer.step()
profiler.tick("Parameter update")
# Update visualizations
# learning_rate = optimizer.param_groups[0]["lr"]
vis.update(loss.item(), eer, step)
# Draw projections and save them to the backup folder
if umap_every != 0 and step % umap_every == 0:
print("Drawing and saving projections (step %d)" % step)
backup_dir.mkdir(exist_ok=True)
projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step))
embeds = embeds.detach().cpu().numpy()
vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
vis.save()
# Overwrite the latest version of the model
if save_every != 0 and step % save_every == 0:
print("Saving the model (step %d)" % step)
torch.save({
"step": step + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, state_fpath)
# Make a backup
if backup_every != 0 and step % backup_every == 0:
print("Making a backup (step %d)" % step)
backup_dir.mkdir(exist_ok=True)
backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step))
torch.save({
"step": step + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, backup_fpath)
profiler.tick("Extras (visualizations, saving)")

178
encoder/visualizations.py Normal file
View File

@ -0,0 +1,178 @@
from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
from datetime import datetime
from time import perf_counter as timer
import matplotlib.pyplot as plt
import numpy as np
# import webbrowser
import visdom
import umap
colormap = np.array([
[76, 255, 0],
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
], dtype=np.float) / 255
class Visualizations:
def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
# Tracking data
self.last_update_timestamp = timer()
self.update_every = update_every
self.step_times = []
self.losses = []
self.eers = []
print("Updating the visualizations every %d steps." % update_every)
# If visdom is disabled TODO: use a better paradigm for that
self.disabled = disabled
if self.disabled:
return
# Set the environment name
now = str(datetime.now().strftime("%d-%m %Hh%M"))
if env_name is None:
self.env_name = now
else:
self.env_name = "%s (%s)" % (env_name, now)
# Connect to visdom and open the corresponding window in the browser
try:
self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
except ConnectionError:
raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
"start it.")
# webbrowser.open("http://localhost:8097/env/" + self.env_name)
# Create the windows
self.loss_win = None
self.eer_win = None
# self.lr_win = None
self.implementation_win = None
self.projection_win = None
self.implementation_string = ""
def log_params(self):
if self.disabled:
return
from encoder import params_data
from encoder import params_model
param_string = "<b>Model parameters</b>:<br>"
for param_name in (p for p in dir(params_model) if not p.startswith("__")):
value = getattr(params_model, param_name)
param_string += "\t%s: %s<br>" % (param_name, value)
param_string += "<b>Data parameters</b>:<br>"
for param_name in (p for p in dir(params_data) if not p.startswith("__")):
value = getattr(params_data, param_name)
param_string += "\t%s: %s<br>" % (param_name, value)
self.vis.text(param_string, opts={"title": "Parameters"})
def log_dataset(self, dataset: SpeakerVerificationDataset):
if self.disabled:
return
dataset_string = ""
dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
dataset_string += "\n" + dataset.get_logs()
dataset_string = dataset_string.replace("\n", "<br>")
self.vis.text(dataset_string, opts={"title": "Dataset"})
def log_implementation(self, params):
if self.disabled:
return
implementation_string = ""
for param, value in params.items():
implementation_string += "<b>%s</b>: %s\n" % (param, value)
implementation_string = implementation_string.replace("\n", "<br>")
self.implementation_string = implementation_string
self.implementation_win = self.vis.text(
implementation_string,
opts={"title": "Training implementation"}
)
def update(self, loss, eer, step):
# Update the tracking data
now = timer()
self.step_times.append(1000 * (now - self.last_update_timestamp))
self.last_update_timestamp = now
self.losses.append(loss)
self.eers.append(eer)
print(".", end="")
# Update the plots every <update_every> steps
if step % self.update_every != 0:
return
time_string = "Step time: mean: %5dms std: %5dms" % \
(int(np.mean(self.step_times)), int(np.std(self.step_times)))
print("\nStep %6d Loss: %.4f EER: %.4f %s" %
(step, np.mean(self.losses), np.mean(self.eers), time_string))
if not self.disabled:
self.loss_win = self.vis.line(
[np.mean(self.losses)],
[step],
win=self.loss_win,
update="append" if self.loss_win else None,
opts=dict(
legend=["Avg. loss"],
xlabel="Step",
ylabel="Loss",
title="Loss",
)
)
self.eer_win = self.vis.line(
[np.mean(self.eers)],
[step],
win=self.eer_win,
update="append" if self.eer_win else None,
opts=dict(
legend=["Avg. EER"],
xlabel="Step",
ylabel="EER",
title="Equal error rate"
)
)
if self.implementation_win is not None:
self.vis.text(
self.implementation_string + ("<b>%s</b>" % time_string),
win=self.implementation_win,
opts={"title": "Training implementation"},
)
# Reset the tracking
self.losses.clear()
self.eers.clear()
self.step_times.clear()
def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
max_speakers=10):
max_speakers = min(max_speakers, len(colormap))
embeds = embeds[:max_speakers * utterances_per_speaker]
n_speakers = len(embeds) // utterances_per_speaker
ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
colors = [colormap[i] for i in ground_truth]
reducer = umap.UMAP()
projected = reducer.fit_transform(embeds)
plt.scatter(projected[:, 0], projected[:, 1], c=colors)
plt.gca().set_aspect("equal", "datalim")
plt.title("UMAP projection (step %d)" % step)
if not self.disabled:
self.projection_win = self.vis.matplot(plt, win=self.projection_win)
if out_fpath is not None:
plt.savefig(out_fpath)
plt.clf()
def save(self):
if not self.disabled:
self.vis.save([self.env_name])

70
encoder_preprocess.py Normal file
View File

@ -0,0 +1,70 @@
from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2
from utils.argutils import print_args
from pathlib import Path
import argparse
if __name__ == "__main__":
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
pass
parser = argparse.ArgumentParser(
description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
"writes them to the disk. This will allow you to train the encoder. The "
"datasets required are at least one of VoxCeleb1, VoxCeleb2 and LibriSpeech. "
"Ideally, you should have all three. You should extract them as they are "
"after having downloaded them and put them in a same directory, e.g.:\n"
"-[datasets_root]\n"
" -LibriSpeech\n"
" -train-other-500\n"
" -VoxCeleb1\n"
" -wav\n"
" -vox1_meta.csv\n"
" -VoxCeleb2\n"
" -dev",
formatter_class=MyFormatter
)
parser.add_argument("datasets_root", type=Path, help=\
"Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.")
parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
"Path to the output directory that will contain the mel spectrograms. If left out, "
"defaults to <datasets_root>/SV2TTS/encoder/")
parser.add_argument("-d", "--datasets", type=str,
default="librispeech_other,voxceleb1,voxceleb2", help=\
"Comma-separated list of the name of the datasets you want to preprocess. Only the train "
"set of these datasets will be used. Possible names: librispeech_other, voxceleb1, "
"voxceleb2.")
parser.add_argument("-s", "--skip_existing", action="store_true", help=\
"Whether to skip existing output files with the same name. Useful if this script was "
"interrupted.")
parser.add_argument("--no_trim", action="store_true", help=\
"Preprocess audio without trimming silences (not recommended).")
args = parser.parse_args()
# Verify webrtcvad is available
if not args.no_trim:
try:
import webrtcvad
except:
raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
"noise removal and is recommended. Please install and try again. If installation fails, "
"use --no_trim to disable this error message.")
del args.no_trim
# Process the arguments
args.datasets = args.datasets.split(",")
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder")
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
# Preprocess the datasets
print_args(args, parser)
preprocess_func = {
"librispeech_other": preprocess_librispeech,
"voxceleb1": preprocess_voxceleb1,
"voxceleb2": preprocess_voxceleb2,
}
args = vars(args)
for dataset in args.pop("datasets"):
print("Preprocessing %s" % dataset)
preprocess_func[dataset](**args)

47
encoder_train.py Normal file
View File

@ -0,0 +1,47 @@
from utils.argutils import print_args
from encoder.train import train
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the same run ID was previously "
"saved, the training will restart from there. Pass -f to overwrite saved states and "
"restart from scratch.")
parser.add_argument("clean_data_root", type=Path, help= \
"Path to the output directory of encoder_preprocess.py. If you left the default "
"output directory when preprocessing, it should be <datasets_root>/SV2TTS/encoder/.")
parser.add_argument("-m", "--models_dir", type=Path, default="encoder/saved_models/", help=\
"Path to the output directory that will contain the saved model weights, as well as "
"backups of those weights and plots generated during training.")
parser.add_argument("-v", "--vis_every", type=int, default=10, help= \
"Number of steps between updates of the loss and the plots.")
parser.add_argument("-u", "--umap_every", type=int, default=100, help= \
"Number of steps between updates of the umap projection. Set to 0 to never update the "
"projections.")
parser.add_argument("-s", "--save_every", type=int, default=500, help= \
"Number of steps between updates of the model on the disk. Set to 0 to never save the "
"model.")
parser.add_argument("-b", "--backup_every", type=int, default=7500, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model.")
parser.add_argument("--visdom_server", type=str, default="http://localhost")
parser.add_argument("--no_visdom", action="store_true", help= \
"Disable visdom.")
args = parser.parse_args()
# Process the arguments
args.models_dir.mkdir(exist_ok=True)
# Run the training
print_args(args, parser)
train(**vars(args))

17
requirements.txt Normal file
View File

@ -0,0 +1,17 @@
umap-learn
visdom
librosa>=0.8.0
matplotlib>=3.3.0
numpy==1.19.3; platform_system == "Windows"
numpy==1.19.4; platform_system != "Windows"
scipy>=1.0.0
tqdm
sounddevice
SoundFile
Unidecode
inflect
PyQt5
multiprocess
numba
webrtcvad; platform_system != "Windows"
pypinyin

BIN
samples/1320_00000.mp3 Normal file
View File

Binary file not shown.

BIN
samples/3575_00000.mp3 Normal file
View File

Binary file not shown.

BIN
samples/6829_00000.mp3 Normal file
View File

Binary file not shown.

BIN
samples/8230_00000.mp3 Normal file
View File

Binary file not shown.

22
samples/README.md Normal file
View File

@ -0,0 +1,22 @@
The audio files in this folder are provided for toolbox testing and
benchmarking purposes. These are the same reference utterances
used by the SV2TTS authors to generate the audio samples located at:
https://google.github.io/tacotron/publications/speaker_adaptation/index.html
The `p240_00000.mp3` and `p260_00000.mp3` files are compressed
versions of audios from the VCTK corpus available at:
https://datashare.is.ed.ac.uk/handle/10283/3443
VCTK.txt contains the copyright notices and licensing information.
The `1320_00000.mp3`, `3575_00000.mp3`, `6829_00000.mp3`
and `8230_00000.mp3` files are compressed versions of audios
from the LibriSpeech dataset available at: https://openslr.org/12
For these files, the following notice applies:
```
LibriSpeech (c) 2014 by Vassil Panayotov
LibriSpeech ASR corpus is licensed under a
Creative Commons Attribution 4.0 International License.
See <http://creativecommons.org/licenses/by/4.0/>.
```

94
samples/VCTK.txt Normal file
View File

@ -0,0 +1,94 @@
---------------------------------------------------------------------
CSTR VCTK Corpus
English Multi-speaker Corpus for CSTR Voice Cloning Toolkit
(Version 0.92)
RELEASE September 2019
The Centre for Speech Technology Research
University of Edinburgh
Copyright (c) 2019
Junichi Yamagishi
jyamagis@inf.ed.ac.uk
---------------------------------------------------------------------
Overview
This CSTR VCTK Corpus includes speech data uttered by 110 English
speakers with various accents. Each speaker reads out about 400
sentences, which were selected from a newspaper, the rainbow passage
and an elicitation paragraph used for the speech accent archive.
The newspaper texts were taken from Herald Glasgow, with permission
from Herald & Times Group. Each speaker has a different set of the
newspaper texts selected based a greedy algorithm that increases the
contextual and phonetic coverage. The details of the text selection
algorithms are described in the following paper:
C. Veaux, J. Yamagishi and S. King,
"The voice bank corpus: Design, collection and data analysis of
a large regional accent speech database,"
https://doi.org/10.1109/ICSDA.2013.6709856
The rainbow passage and elicitation paragraph are the same for all
speakers. The rainbow passage can be found at International Dialects
of English Archive:
(http://web.ku.edu/~idea/readings/rainbow.htm). The elicitation
paragraph is identical to the one used for the speech accent archive
(http://accent.gmu.edu). The details of the the speech accent archive
can be found at
http://www.ualberta.ca/~aacl2009/PDFs/WeinbergerKunath2009AACL.pdf
All speech data was recorded using an identical recording setup: an
omni-directional microphone (DPA 4035) and a small diaphragm condenser
microphone with very wide bandwidth (Sennheiser MKH 800), 96kHz
sampling frequency at 24 bits and in a hemi-anechoic chamber of
the University of Edinburgh. (However, two speakers, p280 and p315
had technical issues of the audio recordings using MKH 800).
All recordings were converted into 16 bits, were downsampled to
48 kHz, and were manually end-pointed.
This corpus was originally aimed for HMM-based text-to-speech synthesis
systems, especially for speaker-adaptive HMM-based speech synthesis
that uses average voice models trained on multiple speakers and speaker
adaptation technologies. This corpus is also suitable for DNN-based
multi-speaker text-to-speech synthesis systems and waveform modeling.
COPYING
This corpus is licensed under the Creative Commons License: Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/legalcode
VCTK VARIANTS
There are several variants of the VCTK corpus:
Speech enhancement
- Noisy speech database for training speech enhancement algorithms and TTS models where we added various types of noises to VCTK artificially: http://dx.doi.org/10.7488/ds/2117
- Reverberant speech database for training speech dereverberation algorithms and TTS models where we added various types of reverberantion to VCTK artificially http://dx.doi.org/10.7488/ds/1425
- Noisy reverberant speech database for training speech enhancement algorithms and TTS models http://dx.doi.org/10.7488/ds/2139
- Device Recorded VCTK where speech signals of the VCTK corpus were played back and re-recorded in office environments using relatively inexpensive consumer devices http://dx.doi.org/10.7488/ds/2316
- The Microsoft Scalable Noisy Speech Dataset (MS-SNSD) https://github.com/microsoft/MS-SNSD
ASV and anti-spoofing
- Spoofing and Anti-Spoofing (SAS) corpus, which is a collection of synthetic speech signals produced by nine techniques, two of which are speech synthesis, and seven are voice conversion. All of them were built using the VCTK corpus. http://dx.doi.org/10.7488/ds/252
- Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015) Database. This database consists of synthetic speech signals produced by ten techniques and this has been used in the first Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015) http://dx.doi.org/10.7488/ds/298
- ASVspoof 2019: The 3rd Automatic Speaker Verification Spoofing and Countermeasures Challenge database. This database has been used in the 3rd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2019) https://doi.org/10.7488/ds/2555
ACKNOWLEDGEMENTS
The CSTR VCTK Corpus was constructed by:
Christophe Veaux (University of Edinburgh)
Junichi Yamagishi (University of Edinburgh)
Kirsten MacDonald
The research leading to these results was partly funded from EPSRC
grants EP/I031022/1 (NST) and EP/J002526/1 (CAF), from the RSE-NSFC
grant (61111130120), and from the JST CREST (uDialogue).
Please cite this corpus as follows:
Christophe Veaux, Junichi Yamagishi, Kirsten MacDonald,
"CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit",
The Centre for Speech Technology Research (CSTR),
University of Edinburgh

BIN
samples/p240_00000.mp3 Normal file
View File

Binary file not shown.

BIN
samples/p260_00000.mp3 Normal file
View File

Binary file not shown.

24
synthesizer/LICENSE.txt Normal file
View File

@ -0,0 +1,24 @@
MIT License
Original work Copyright (c) 2018 Rayhane Mama (https://github.com/Rayhane-mamah)
Original work Copyright (c) 2019 fatchord (https://github.com/fatchord)
Modified work Copyright (c) 2019 Corentin Jemine (https://github.com/CorentinJ)
Modified work Copyright (c) 2020 blue-fish (https://github.com/blue-fish)
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.

1
synthesizer/__init__.py Normal file
View File

@ -0,0 +1 @@
#

206
synthesizer/audio.py Normal file
View File

@ -0,0 +1,206 @@
import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
import soundfile as sf
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wavfile.write(path, sr, wav.astype(np.int16))
def save_wavenet_wav(wav, path, sr):
sf.write(path, wav.astype(np.float32), sr)
def preemphasis(wav, k, preemphasize=True):
if preemphasize:
return signal.lfilter([1, -k], [1], wav)
return wav
def inv_preemphasis(wav, k, inv_preemphasize=True):
if inv_preemphasize:
return signal.lfilter([1], [1, -k], wav)
return wav
#From https://github.com/r9y9/wavenet_vocoder/blob/master/audio.py
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
def get_hop_size(hparams):
hop_size = hparams.hop_size
if hop_size is None:
assert hparams.frame_shift_ms is not None
hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
return hop_size
def linearspectrogram(wav, hparams):
D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
S = _amp_to_db(np.abs(D), hparams) - hparams.ref_level_db
if hparams.signal_normalization:
return _normalize(S, hparams)
return S
def melspectrogram(wav, hparams):
D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize), hparams)
S = _amp_to_db(_linear_to_mel(np.abs(D), hparams), hparams) - hparams.ref_level_db
if hparams.signal_normalization:
return _normalize(S, hparams)
return S
def inv_linear_spectrogram(linear_spectrogram, hparams):
"""Converts linear spectrogram to waveform using librosa"""
if hparams.signal_normalization:
D = _denormalize(linear_spectrogram, hparams)
else:
D = linear_spectrogram
S = _db_to_amp(D + hparams.ref_level_db) #Convert back to linear
if hparams.use_lws:
processor = _lws_processor(hparams)
D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
else:
return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
def inv_mel_spectrogram(mel_spectrogram, hparams):
"""Converts mel spectrogram to waveform using librosa"""
if hparams.signal_normalization:
D = _denormalize(mel_spectrogram, hparams)
else:
D = mel_spectrogram
S = _mel_to_linear(_db_to_amp(D + hparams.ref_level_db), hparams) # Convert back to linear
if hparams.use_lws:
processor = _lws_processor(hparams)
D = processor.run_lws(S.astype(np.float64).T ** hparams.power)
y = processor.istft(D).astype(np.float32)
return inv_preemphasis(y, hparams.preemphasis, hparams.preemphasize)
else:
return inv_preemphasis(_griffin_lim(S ** hparams.power, hparams), hparams.preemphasis, hparams.preemphasize)
def _lws_processor(hparams):
import lws
return lws.lws(hparams.n_fft, get_hop_size(hparams), fftsize=hparams.win_size, mode="speech")
def _griffin_lim(S, hparams):
"""librosa implementation of Griffin-Lim
Based on https://github.com/librosa/librosa/issues/434
"""
angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
S_complex = np.abs(S).astype(np.complex)
y = _istft(S_complex * angles, hparams)
for i in range(hparams.griffin_lim_iters):
angles = np.exp(1j * np.angle(_stft(y, hparams)))
y = _istft(S_complex * angles, hparams)
return y
def _stft(y, hparams):
if hparams.use_lws:
return _lws_processor(hparams).stft(y).T
else:
return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
def _istft(y, hparams):
return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams.win_size)
##########################################################
#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
def num_frames(length, fsize, fshift):
"""Compute number of time frames of spectrogram
"""
pad = (fsize - fshift)
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M
def pad_lr(x, fsize, fshift):
"""Compute left and right padding
"""
M = num_frames(len(x), fsize, fshift)
pad = (fsize - fshift)
T = len(x) + 2 * pad
r = (M - 1) * fshift + fsize - T
return pad, pad + r
##########################################################
#Librosa correct padding
def librosa_pad_lr(x, fsize, fshift):
return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
# Conversions
_mel_basis = None
_inv_mel_basis = None
def _linear_to_mel(spectogram, hparams):
global _mel_basis
if _mel_basis is None:
_mel_basis = _build_mel_basis(hparams)
return np.dot(_mel_basis, spectogram)
def _mel_to_linear(mel_spectrogram, hparams):
global _inv_mel_basis
if _inv_mel_basis is None:
_inv_mel_basis = np.linalg.pinv(_build_mel_basis(hparams))
return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))
def _build_mel_basis(hparams):
assert hparams.fmax <= hparams.sample_rate // 2
return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels,
fmin=hparams.fmin, fmax=hparams.fmax)
def _amp_to_db(x, hparams):
min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
def _db_to_amp(x):
return np.power(10.0, (x) * 0.05)
def _normalize(S, hparams):
if hparams.allow_clipping_in_normalization:
if hparams.symmetric_mels:
return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
-hparams.max_abs_value, hparams.max_abs_value)
else:
return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
if hparams.symmetric_mels:
return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
else:
return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))
def _denormalize(D, hparams):
if hparams.allow_clipping_in_normalization:
if hparams.symmetric_mels:
return (((np.clip(D, -hparams.max_abs_value,
hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
+ hparams.min_level_db)
else:
return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
if hparams.symmetric_mels:
return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
else:
return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)

92
synthesizer/hparams.py Normal file
View File

@ -0,0 +1,92 @@
import ast
import pprint
class HParams(object):
def __init__(self, **kwargs): self.__dict__.update(kwargs)
def __setitem__(self, key, value): setattr(self, key, value)
def __getitem__(self, key): return getattr(self, key)
def __repr__(self): return pprint.pformat(self.__dict__)
def parse(self, string):
# Overrides hparams from a comma-separated string of name=value pairs
if len(string) > 0:
overrides = [s.split("=") for s in string.split(",")]
keys, values = zip(*overrides)
keys = list(map(str.strip, keys))
values = list(map(str.strip, values))
for k in keys:
self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
return self
hparams = HParams(
### Signal Processing (used in both synthesizer and vocoder)
sample_rate = 16000,
n_fft = 800,
num_mels = 80,
hop_size = 200, # Tacotron uses 12.5 ms frame shift (set to sample_rate * 0.0125)
win_size = 800, # Tacotron uses 50 ms frame length (set to sample_rate * 0.050)
fmin = 55,
min_level_db = -100,
ref_level_db = 20,
max_abs_value = 4., # Gradient explodes if too big, premature convergence if too small.
preemphasis = 0.97, # Filter coefficient to use if preemphasize is True
preemphasize = True,
### Tacotron Text-to-Speech (TTS)
tts_embed_dims = 512, # Embedding dimension for the graphemes/phoneme inputs
tts_encoder_dims = 256,
tts_decoder_dims = 128,
tts_postnet_dims = 512,
tts_encoder_K = 5,
tts_lstm_dims = 1024,
tts_postnet_K = 5,
tts_num_highways = 4,
tts_dropout = 0.5,
tts_cleaner_names = ["basic_cleaners"],
tts_stop_threshold = -3.4, # Value below which audio generation ends.
# For example, for a range of [-4, 4], this
# will terminate the sequence at the first
# frame that has all values < -3.4
### Tacotron Training
tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule
(2, 5e-4, 40_000, 12), # (r, lr, step, batch_size)
(2, 2e-4, 80_000, 12), #
(2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames
(2, 3e-5, 320_000, 12), # synthesized for each decoder iteration)
(2, 1e-5, 640_000, 12)], # lr = learning rate
tts_clip_grad_norm = 1.0, # clips the gradient norm to prevent explosion - set to None if not needed
tts_eval_interval = 500, # Number of steps between model evaluation (sample generation)
# Set to -1 to generate after completing epoch, or 0 to disable
tts_eval_num_samples = 1, # Makes this number of samples
### Data Preprocessing
max_mel_frames = 900,
rescale = True,
rescaling_max = 0.9,
synthesis_batch_size = 16, # For vocoder preprocessing and inference.
### Mel Visualization and Griffin-Lim
signal_normalization = True,
power = 1.5,
griffin_lim_iters = 60,
### Audio processing options
fmax = 7600, # Should not exceed (sample_rate // 2)
allow_clipping_in_normalization = True, # Used when signal_normalization = True
clip_mels_length = True, # If true, discards samples exceeding max_mel_frames
use_lws = False, # "Fast spectrogram phase recovery using local weighted sums"
symmetric_mels = True, # Sets mel range to [-max_abs_value, max_abs_value] if True,
# and [0, max_abs_value] if False
trim_silence = True, # Use with sample_rate of 16000 for best results
### SV2TTS
speaker_embedding_size = 256, # Dimension for the speaker embedding
silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split
utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded
)
def hparams_debug_string():
return str(hparams)

171
synthesizer/inference.py Normal file
View File

@ -0,0 +1,171 @@
import torch
from synthesizer import audio
from synthesizer.hparams import hparams
from synthesizer.models.tacotron import Tacotron
from synthesizer.utils.symbols import symbols
from synthesizer.utils.text import text_to_sequence
from vocoder.display import simple_table
from pathlib import Path
from typing import Union, List
import numpy as np
import librosa
class Synthesizer:
sample_rate = hparams.sample_rate
hparams = hparams
def __init__(self, model_fpath: Path, verbose=True):
"""
The model isn't instantiated and loaded in memory until needed or until load() is called.
:param model_fpath: path to the trained model file
:param verbose: if False, prints less information when using the model
"""
self.model_fpath = model_fpath
self.verbose = verbose
# Check for GPU
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
if self.verbose:
print("Synthesizer using device:", self.device)
# Tacotron model will be instantiated later on first use.
self._model = None
def is_loaded(self):
"""
Whether the model is loaded in memory.
"""
return self._model is not None
def load(self):
"""
Instantiates and loads the model given the weights file that was passed in the constructor.
"""
self._model = Tacotron(embed_dims=hparams.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hparams.tts_encoder_dims,
decoder_dims=hparams.tts_decoder_dims,
n_mels=hparams.num_mels,
fft_bins=hparams.num_mels,
postnet_dims=hparams.tts_postnet_dims,
encoder_K=hparams.tts_encoder_K,
lstm_dims=hparams.tts_lstm_dims,
postnet_K=hparams.tts_postnet_K,
num_highways=hparams.tts_num_highways,
dropout=hparams.tts_dropout,
stop_threshold=hparams.tts_stop_threshold,
speaker_embedding_size=hparams.speaker_embedding_size).to(self.device)
self._model.load(self.model_fpath)
self._model.eval()
if self.verbose:
print("Loaded synthesizer \"%s\" trained to step %d" % (self.model_fpath.name, self._model.state_dict()["step"]))
def synthesize_spectrograms(self, texts: List[str],
embeddings: Union[np.ndarray, List[np.ndarray]],
return_alignments=False):
"""
Synthesizes mel spectrograms from texts and speaker embeddings.
:param texts: a list of N text prompts to be synthesized
:param embeddings: a numpy array or list of speaker embeddings of shape (N, 256)
:param return_alignments: if True, a matrix representing the alignments between the
characters
and each decoder output step will be returned for each spectrogram
:return: a list of N melspectrograms as numpy arrays of shape (80, Mi), where Mi is the
sequence length of spectrogram i, and possibly the alignments.
"""
# Load the model on the first request.
if not self.is_loaded():
self.load()
# Print some info about the model when it is loaded
tts_k = self._model.get_step() // 1000
simple_table([("Tacotron", str(tts_k) + "k"),
("r", self._model.r)])
# Preprocess text inputs
inputs = [text_to_sequence(text.strip(), hparams.tts_cleaner_names) for text in texts]
if not isinstance(embeddings, list):
embeddings = [embeddings]
# Batch inputs
batched_inputs = [inputs[i:i+hparams.synthesis_batch_size]
for i in range(0, len(inputs), hparams.synthesis_batch_size)]
batched_embeds = [embeddings[i:i+hparams.synthesis_batch_size]
for i in range(0, len(embeddings), hparams.synthesis_batch_size)]
specs = []
for i, batch in enumerate(batched_inputs, 1):
if self.verbose:
print(f"\n| Generating {i}/{len(batched_inputs)}")
# Pad texts so they are all the same length
text_lens = [len(text) for text in batch]
max_text_len = max(text_lens)
chars = [pad1d(text, max_text_len) for text in batch]
chars = np.stack(chars)
# Stack speaker embeddings into 2D array for batch processing
speaker_embeds = np.stack(batched_embeds[i-1])
# Convert to tensor
chars = torch.tensor(chars).long().to(self.device)
speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device)
# Inference
_, mels, alignments = self._model.generate(chars, speaker_embeddings)
mels = mels.detach().cpu().numpy()
for m in mels:
# Trim silence from end of each spectrogram
while np.max(m[:, -1]) < hparams.tts_stop_threshold:
m = m[:, :-1]
specs.append(m)
if self.verbose:
print("\n\nDone.\n")
return (specs, alignments) if return_alignments else specs
@staticmethod
def load_preprocess_wav(fpath):
"""
Loads and preprocesses an audio file under the same conditions the audio files were used to
train the synthesizer.
"""
wav = librosa.load(str(fpath), hparams.sample_rate)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
return wav
@staticmethod
def make_spectrogram(fpath_or_wav: Union[str, Path, np.ndarray]):
"""
Creates a mel spectrogram from an audio file in the same manner as the mel spectrograms that
were fed to the synthesizer when training.
"""
if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
wav = Synthesizer.load_preprocess_wav(fpath_or_wav)
else:
wav = fpath_or_wav
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
return mel_spectrogram
@staticmethod
def griffin_lim(mel):
"""
Inverts a mel spectrogram using Griffin-Lim. The mel spectrogram is expected to have been built
with the same parameters present in hparams.py.
"""
return audio.inv_mel_spectrogram(mel, hparams)
def pad1d(x, max_len, pad_value=0):
return np.pad(x, (0, max_len - len(x)), mode="constant", constant_values=pad_value)

519
synthesizer/models/tacotron.py Normal file
View File

@ -0,0 +1,519 @@
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path
from typing import Union
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
self.W1.bias.data.fill_(0.)
def forward(self, x):
x1 = self.W1(x)
x2 = self.W2(x)
g = torch.sigmoid(x2)
y = g * F.relu(x1) + (1. - g) * x
return y
class Encoder(nn.Module):
def __init__(self, embed_dims, num_chars, encoder_dims, K, num_highways, dropout):
super().__init__()
prenet_dims = (encoder_dims, encoder_dims)
cbhg_channels = encoder_dims
self.embedding = nn.Embedding(num_chars, embed_dims)
self.pre_net = PreNet(embed_dims, fc1_dims=prenet_dims[0], fc2_dims=prenet_dims[1],
dropout=dropout)
self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels,
proj_channels=[cbhg_channels, cbhg_channels],
num_highways=num_highways)
def forward(self, x, speaker_embedding=None):
x = self.embedding(x)
x = self.pre_net(x)
x.transpose_(1, 2)
x = self.cbhg(x)
if speaker_embedding is not None:
x = self.add_speaker_embedding(x, speaker_embedding)
return x
def add_speaker_embedding(self, x, speaker_embedding):
# SV2TTS
# The input x is the encoder output and is a 3D tensor with size (batch_size, num_chars, tts_embed_dims)
# When training, speaker_embedding is also a 2D tensor with size (batch_size, speaker_embedding_size)
# (for inference, speaker_embedding is a 1D tensor with size (speaker_embedding_size))
# This concats the speaker embedding for each char in the encoder output
# Save the dimensions as human-readable names
batch_size = x.size()[0]
num_chars = x.size()[1]
if speaker_embedding.dim() == 1:
idx = 0
else:
idx = 1
# Start by making a copy of each speaker embedding to match the input text length
# The output of this has size (batch_size, num_chars * tts_embed_dims)
speaker_embedding_size = speaker_embedding.size()[idx]
e = speaker_embedding.repeat_interleave(num_chars, dim=idx)
# Reshape it and transpose
e = e.reshape(batch_size, speaker_embedding_size, num_chars)
e = e.transpose(1, 2)
# Concatenate the tiled speaker embedding with the encoder output
x = torch.cat((x, e), 2)
return x
class BatchNormConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel, relu=True):
super().__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False)
self.bnorm = nn.BatchNorm1d(out_channels)
self.relu = relu
def forward(self, x):
x = self.conv(x)
x = F.relu(x) if self.relu is True else x
return self.bnorm(x)
class CBHG(nn.Module):
def __init__(self, K, in_channels, channels, proj_channels, num_highways):
super().__init__()
# List of all rnns to call `flatten_parameters()` on
self._to_flatten = []
self.bank_kernels = [i for i in range(1, K + 1)]
self.conv1d_bank = nn.ModuleList()
for k in self.bank_kernels:
conv = BatchNormConv(in_channels, channels, k)
self.conv1d_bank.append(conv)
self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)
self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3)
self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False)
# Fix the highway input if necessary
if proj_channels[-1] != channels:
self.highway_mismatch = True
self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False)
else:
self.highway_mismatch = False
self.highways = nn.ModuleList()
for i in range(num_highways):
hn = HighwayNetwork(channels)
self.highways.append(hn)
self.rnn = nn.GRU(channels, channels // 2, batch_first=True, bidirectional=True)
self._to_flatten.append(self.rnn)
# Avoid fragmentation of RNN parameters and associated warning
self._flatten_parameters()
def forward(self, x):
# Although we `_flatten_parameters()` on init, when using DataParallel
# the model gets replicated, making it no longer guaranteed that the
# weights are contiguous in GPU memory. Hence, we must call it again
self._flatten_parameters()
# Save these for later
residual = x
seq_len = x.size(-1)
conv_bank = []
# Convolution Bank
for conv in self.conv1d_bank:
c = conv(x) # Convolution
conv_bank.append(c[:, :, :seq_len])
# Stack along the channel axis
conv_bank = torch.cat(conv_bank, dim=1)
# dump the last padding to fit residual
x = self.maxpool(conv_bank)[:, :, :seq_len]
# Conv1d projections
x = self.conv_project1(x)
x = self.conv_project2(x)
# Residual Connect
x = x + residual
# Through the highways
x = x.transpose(1, 2)
if self.highway_mismatch is True:
x = self.pre_highway(x)
for h in self.highways: x = h(x)
# And then the RNN
x, _ = self.rnn(x)
return x
def _flatten_parameters(self):
"""Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used
to improve efficiency and avoid PyTorch yelling at us."""
[m.flatten_parameters() for m in self._to_flatten]
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
self.fc2 = nn.Linear(fc1_dims, fc2_dims)
self.p = dropout
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = F.dropout(x, self.p, training=True)
x = self.fc2(x)
x = F.relu(x)
x = F.dropout(x, self.p, training=True)
return x
class Attention(nn.Module):
def __init__(self, attn_dims):
super().__init__()
self.W = nn.Linear(attn_dims, attn_dims, bias=False)
self.v = nn.Linear(attn_dims, 1, bias=False)
def forward(self, encoder_seq_proj, query, t):
# print(encoder_seq_proj.shape)
# Transform the query vector
query_proj = self.W(query).unsqueeze(1)
# Compute the scores
u = self.v(torch.tanh(encoder_seq_proj + query_proj))
scores = F.softmax(u, dim=1)
return scores.transpose(1, 2)
class LSA(nn.Module):
def __init__(self, attn_dim, kernel_size=31, filters=32):
super().__init__()
self.conv = nn.Conv1d(1, filters, padding=(kernel_size - 1) // 2, kernel_size=kernel_size, bias=True)
self.L = nn.Linear(filters, attn_dim, bias=False)
self.W = nn.Linear(attn_dim, attn_dim, bias=True) # Include the attention bias in this term
self.v = nn.Linear(attn_dim, 1, bias=False)
self.cumulative = None
self.attention = None
def init_attention(self, encoder_seq_proj):
device = next(self.parameters()).device # use same device as parameters
b, t, c = encoder_seq_proj.size()
self.cumulative = torch.zeros(b, t, device=device)
self.attention = torch.zeros(b, t, device=device)
def forward(self, encoder_seq_proj, query, t, chars):
if t == 0: self.init_attention(encoder_seq_proj)
processed_query = self.W(query).unsqueeze(1)
location = self.cumulative.unsqueeze(1)
processed_loc = self.L(self.conv(location).transpose(1, 2))
u = self.v(torch.tanh(processed_query + encoder_seq_proj + processed_loc))
u = u.squeeze(-1)
# Mask zero padding chars
u = u * (chars != 0).float()
# Smooth Attention
# scores = torch.sigmoid(u) / torch.sigmoid(u).sum(dim=1, keepdim=True)
scores = F.softmax(u, dim=1)
self.attention = scores
self.cumulative = self.cumulative + self.attention
return scores.unsqueeze(-1).transpose(1, 2)
class Decoder(nn.Module):
# Class variable because its value doesn't change between classes
# yet ought to be scoped by class because its a property of a Decoder
max_r = 20
def __init__(self, n_mels, encoder_dims, decoder_dims, lstm_dims,
dropout, speaker_embedding_size):
super().__init__()
self.register_buffer("r", torch.tensor(1, dtype=torch.int))
self.n_mels = n_mels
prenet_dims = (decoder_dims * 2, decoder_dims * 2)
self.prenet = PreNet(n_mels, fc1_dims=prenet_dims[0], fc2_dims=prenet_dims[1],
dropout=dropout)
self.attn_net = LSA(decoder_dims)
self.attn_rnn = nn.GRUCell(encoder_dims + prenet_dims[1] + speaker_embedding_size, decoder_dims)
self.rnn_input = nn.Linear(encoder_dims + decoder_dims + speaker_embedding_size, lstm_dims)
self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims)
self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims)
self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False)
self.stop_proj = nn.Linear(encoder_dims + speaker_embedding_size + lstm_dims, 1)
def zoneout(self, prev, current, p=0.1):
device = next(self.parameters()).device # Use same device as parameters
mask = torch.zeros(prev.size(), device=device).bernoulli_(p)
return prev * mask + current * (1 - mask)
def forward(self, encoder_seq, encoder_seq_proj, prenet_in,
hidden_states, cell_states, context_vec, t, chars):
# Need this for reshaping mels
batch_size = encoder_seq.size(0)
# Unpack the hidden and cell states
attn_hidden, rnn1_hidden, rnn2_hidden = hidden_states
rnn1_cell, rnn2_cell = cell_states
# PreNet for the Attention RNN
prenet_out = self.prenet(prenet_in)
# Compute the Attention RNN hidden state
attn_rnn_in = torch.cat([context_vec, prenet_out], dim=-1)
attn_hidden = self.attn_rnn(attn_rnn_in.squeeze(1), attn_hidden)
# Compute the attention scores
scores = self.attn_net(encoder_seq_proj, attn_hidden, t, chars)
# Dot product to create the context vector
context_vec = scores @ encoder_seq
context_vec = context_vec.squeeze(1)
# Concat Attention RNN output w. Context Vector & project
x = torch.cat([context_vec, attn_hidden], dim=1)
x = self.rnn_input(x)
# Compute first Residual RNN
rnn1_hidden_next, rnn1_cell = self.res_rnn1(x, (rnn1_hidden, rnn1_cell))
if self.training:
rnn1_hidden = self.zoneout(rnn1_hidden, rnn1_hidden_next)
else:
rnn1_hidden = rnn1_hidden_next
x = x + rnn1_hidden
# Compute second Residual RNN
rnn2_hidden_next, rnn2_cell = self.res_rnn2(x, (rnn2_hidden, rnn2_cell))
if self.training:
rnn2_hidden = self.zoneout(rnn2_hidden, rnn2_hidden_next)
else:
rnn2_hidden = rnn2_hidden_next
x = x + rnn2_hidden
# Project Mels
mels = self.mel_proj(x)
mels = mels.view(batch_size, self.n_mels, self.max_r)[:, :, :self.r]
hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden)
cell_states = (rnn1_cell, rnn2_cell)
# Stop token prediction
s = torch.cat((x, context_vec), dim=1)
s = self.stop_proj(s)
stop_tokens = torch.sigmoid(s)
return mels, scores, hidden_states, cell_states, context_vec, stop_tokens
class Tacotron(nn.Module):
def __init__(self, embed_dims, num_chars, encoder_dims, decoder_dims, n_mels,
fft_bins, postnet_dims, encoder_K, lstm_dims, postnet_K, num_highways,
dropout, stop_threshold, speaker_embedding_size):
super().__init__()
self.n_mels = n_mels
self.lstm_dims = lstm_dims
self.encoder_dims = encoder_dims
self.decoder_dims = decoder_dims
self.speaker_embedding_size = speaker_embedding_size
self.encoder = Encoder(embed_dims, num_chars, encoder_dims,
encoder_K, num_highways, dropout)
self.encoder_proj = nn.Linear(encoder_dims + speaker_embedding_size, decoder_dims, bias=False)
self.decoder = Decoder(n_mels, encoder_dims, decoder_dims, lstm_dims,
dropout, speaker_embedding_size)
self.postnet = CBHG(postnet_K, n_mels, postnet_dims,
[postnet_dims, fft_bins], num_highways)
self.post_proj = nn.Linear(postnet_dims, fft_bins, bias=False)
self.init_model()
self.num_params()
self.register_buffer("step", torch.zeros(1, dtype=torch.long))
self.register_buffer("stop_threshold", torch.tensor(stop_threshold, dtype=torch.float32))
@property
def r(self):
return self.decoder.r.item()
@r.setter
def r(self, value):
self.decoder.r = self.decoder.r.new_tensor(value, requires_grad=False)
def forward(self, x, m, speaker_embedding):
device = next(self.parameters()).device # use same device as parameters
self.step += 1
batch_size, _, steps = m.size()
# Initialise all hidden states and pack into tuple
attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device)
rnn1_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
rnn2_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden)
# Initialise all lstm cell states and pack into tuple
rnn1_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
rnn2_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
cell_states = (rnn1_cell, rnn2_cell)
# <GO> Frame for start of decoder loop
go_frame = torch.zeros(batch_size, self.n_mels, device=device)
# Need an initial context vector
context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device)
# SV2TTS: Run the encoder with the speaker embedding
# The projection avoids unnecessary matmuls in the decoder loop
encoder_seq = self.encoder(x, speaker_embedding)
encoder_seq_proj = self.encoder_proj(encoder_seq)
# Need a couple of lists for outputs
mel_outputs, attn_scores, stop_outputs = [], [], []
# Run the decoder loop
for t in range(0, steps, self.r):
prenet_in = m[:, :, t - 1] if t > 0 else go_frame
mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \
self.decoder(encoder_seq, encoder_seq_proj, prenet_in,
hidden_states, cell_states, context_vec, t, x)
mel_outputs.append(mel_frames)
attn_scores.append(scores)
stop_outputs.extend([stop_tokens] * self.r)
# Concat the mel outputs into sequence
mel_outputs = torch.cat(mel_outputs, dim=2)
# Post-Process for Linear Spectrograms
postnet_out = self.postnet(mel_outputs)
linear = self.post_proj(postnet_out)
linear = linear.transpose(1, 2)
# For easy visualisation
attn_scores = torch.cat(attn_scores, 1)
# attn_scores = attn_scores.cpu().data.numpy()
stop_outputs = torch.cat(stop_outputs, 1)
return mel_outputs, linear, attn_scores, stop_outputs
def generate(self, x, speaker_embedding=None, steps=2000):
self.eval()
device = next(self.parameters()).device # use same device as parameters
batch_size, _ = x.size()
# Need to initialise all hidden states and pack into tuple for tidyness
attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device)
rnn1_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
rnn2_hidden = torch.zeros(batch_size, self.lstm_dims, device=device)
hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden)
# Need to initialise all lstm cell states and pack into tuple for tidyness
rnn1_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
rnn2_cell = torch.zeros(batch_size, self.lstm_dims, device=device)
cell_states = (rnn1_cell, rnn2_cell)
# Need a <GO> Frame for start of decoder loop
go_frame = torch.zeros(batch_size, self.n_mels, device=device)
# Need an initial context vector
context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device)
# SV2TTS: Run the encoder with the speaker embedding
# The projection avoids unnecessary matmuls in the decoder loop
encoder_seq = self.encoder(x, speaker_embedding)
encoder_seq_proj = self.encoder_proj(encoder_seq)
# Need a couple of lists for outputs
mel_outputs, attn_scores, stop_outputs = [], [], []
# Run the decoder loop
for t in range(0, steps, self.r):
prenet_in = mel_outputs[-1][:, :, -1] if t > 0 else go_frame
mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \
self.decoder(encoder_seq, encoder_seq_proj, prenet_in,
hidden_states, cell_states, context_vec, t, x)
mel_outputs.append(mel_frames)
attn_scores.append(scores)
stop_outputs.extend([stop_tokens] * self.r)
# Stop the loop when all stop tokens in batch exceed threshold
if (stop_tokens > 0.5).all() and t > 10: break
# Concat the mel outputs into sequence
mel_outputs = torch.cat(mel_outputs, dim=2)
# Post-Process for Linear Spectrograms
postnet_out = self.postnet(mel_outputs)
linear = self.post_proj(postnet_out)
linear = linear.transpose(1, 2)
# For easy visualisation
attn_scores = torch.cat(attn_scores, 1)
stop_outputs = torch.cat(stop_outputs, 1)
self.train()
return mel_outputs, linear, attn_scores
def init_model(self):
for p in self.parameters():
if p.dim() > 1: nn.init.xavier_uniform_(p)
def get_step(self):
return self.step.data.item()
def reset_step(self):
# assignment to parameters or buffers is overloaded, updates internal dict entry
self.step = self.step.data.new_tensor(1)
def log(self, path, msg):
with open(path, "a") as f:
print(msg, file=f)
def load(self, path, optimizer=None):
# Use device of model params as location for loaded state
device = next(self.parameters()).device
checkpoint = torch.load(str(path), map_location=device)
self.load_state_dict(checkpoint["model_state"])
if "optimizer_state" in checkpoint and optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer_state"])
def save(self, path, optimizer=None):
if optimizer is not None:
torch.save({
"model_state": self.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, str(path))
else:
torch.save({
"model_state": self.state_dict(),
}, str(path))
def num_params(self, print_out=True):
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
if print_out:
print("Trainable Parameters: %.3fM" % parameters)
return parameters

358
synthesizer/preprocess.py Normal file
View File

@ -0,0 +1,358 @@
from multiprocessing.pool import Pool
from synthesizer import audio
from functools import partial
from itertools import chain
from encoder import inference as encoder
from pathlib import Path
from utils import logmmse
from tqdm import tqdm
import numpy as np
import librosa
import platform
from pypinyin import Style
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
from pypinyin.converter import DefaultConverter
from pypinyin.core import Pinyin
class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
pass
pinyin = Pinyin(PinyinConverter()).pinyin
def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
skip_existing: bool, hparams, no_alignments: bool,
datasets_name: str, subfolders: str):
# Gather the input directories
dataset_root = datasets_root.joinpath(datasets_name)
input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in subfolders.split(",")]
print("\n ".join(map(str, ["Using data from:"] + input_dirs)))
assert all(input_dir.exists() for input_dir in input_dirs)
# Create the output directories for each output file type
out_dir.joinpath("mels").mkdir(exist_ok=True)
out_dir.joinpath("audio").mkdir(exist_ok=True)
# Create a metadata file
metadata_fpath = out_dir.joinpath("train.txt")
metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8")
# Preprocess the dataset
speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
func = partial(preprocess_speaker, out_dir=out_dir, skip_existing=skip_existing,
hparams=hparams, no_alignments=no_alignments)
job = Pool(n_processes).imap(func, speaker_dirs)
for speaker_metadata in tqdm(job, datasets_name, len(speaker_dirs), unit="speakers"):
for metadatum in speaker_metadata:
metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
metadata_file.close()
# Verify the contents of the metadata file
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
mel_frames = sum([int(m[4]) for m in metadata])
timesteps = sum([int(m[3]) for m in metadata])
sample_rate = hparams.sample_rate
hours = (timesteps / sample_rate) / 3600
print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." %
(len(metadata), mel_frames, timesteps, hours))
print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
def preprocess_speaker(speaker_dir, out_dir: Path, skip_existing: bool, hparams, no_alignments: bool):
metadata = []
for book_dir in speaker_dir.glob("*"):
if no_alignments:
# Gather the utterance audios and texts
# LibriTTS uses .wav but we will include extensions for compatibility with other datasets
extensions = ["*.wav", "*.flac", "*.mp3"]
for extension in extensions:
wav_fpaths = book_dir.glob(extension)
for wav_fpath in wav_fpaths:
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Get the corresponding text
# Check for .txt (for compatibility with other datasets)
text_fpath = wav_fpath.with_suffix(".txt")
if not text_fpath.exists():
# Check for .normalized.txt (LibriTTS)
text_fpath = wav_fpath.with_suffix(".normalized.txt")
assert text_fpath.exists()
with text_fpath.open("r") as text_file:
text = "".join([line for line in text_file])
text = text.replace("\"", "")
text = text.strip()
# Process the utterance
metadata.append(process_utterance(wav, text, out_dir, str(wav_fpath.with_suffix("").name),
skip_existing, hparams))
else:
# Process alignment file (LibriSpeech support)
# Gather the utterance audios and texts
try:
alignments_fpath = next(book_dir.glob("*.alignment.txt"))
with alignments_fpath.open("r") as alignments_file:
alignments = [line.rstrip().split(" ") for line in alignments_file]
except StopIteration:
# A few alignment files will be missing
continue
# Iterate over each entry in the alignments file
for wav_fname, words, end_times in alignments:
wav_fpath = book_dir.joinpath(wav_fname + ".flac")
assert wav_fpath.exists()
words = words.replace("\"", "").split(",")
end_times = list(map(float, end_times.replace("\"", "").split(",")))
# Process each sub-utterance
wavs, texts = split_on_silences(wav_fpath, words, end_times, hparams)
for i, (wav, text) in enumerate(zip(wavs, texts)):
sub_basename = "%s_%02d" % (wav_fname, i)
metadata.append(process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def split_on_silences(wav_fpath, words, end_times, hparams):
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
words = np.array(words)
start_times = np.array([0.0] + end_times[:-1])
end_times = np.array(end_times)
assert len(words) == len(end_times) == len(start_times)
assert words[0] == "" and words[-1] == ""
# Find pauses that are too long
mask = (words == "") & (end_times - start_times >= hparams.silence_min_duration_split)
mask[0] = mask[-1] = True
breaks = np.where(mask)[0]
# Profile the noise from the silences and perform noise reduction on the waveform
silence_times = [[start_times[i], end_times[i]] for i in breaks]
silence_times = (np.array(silence_times) * hparams.sample_rate).astype(np.int)
noisy_wav = np.concatenate([wav[stime[0]:stime[1]] for stime in silence_times])
if len(noisy_wav) > hparams.sample_rate * 0.02:
profile = logmmse.profile_noise(noisy_wav, hparams.sample_rate)
wav = logmmse.denoise(wav, profile, eta=0)
# Re-attach segments that are too short
segments = list(zip(breaks[:-1], breaks[1:]))
segment_durations = [start_times[end] - end_times[start] for start, end in segments]
i = 0
while i < len(segments) and len(segments) > 1:
if segment_durations[i] < hparams.utterance_min_duration:
# See if the segment can be re-attached with the right or the left segment
left_duration = float("inf") if i == 0 else segment_durations[i - 1]
right_duration = float("inf") if i == len(segments) - 1 else segment_durations[i + 1]
joined_duration = segment_durations[i] + min(left_duration, right_duration)
# Do not re-attach if it causes the joined utterance to be too long
if joined_duration > hparams.hop_size * hparams.max_mel_frames / hparams.sample_rate:
i += 1
continue
# Re-attach the segment with the neighbour of shortest duration
j = i - 1 if left_duration <= right_duration else i
segments[j] = (segments[j][0], segments[j + 1][1])
segment_durations[j] = joined_duration
del segments[j + 1], segment_durations[j + 1]
else:
i += 1
# Split the utterance
segment_times = [[end_times[start], start_times[end]] for start, end in segments]
segment_times = (np.array(segment_times) * hparams.sample_rate).astype(np.int)
wavs = [wav[segment_time[0]:segment_time[1]] for segment_time in segment_times]
texts = [" ".join(words[start + 1:end]).replace(" ", " ") for start, end in segments]
# # DEBUG: play the audio segments (run with -n=1)
# import sounddevice as sd
# if len(wavs) > 1:
# print("This sentence was split in %d segments:" % len(wavs))
# else:
# print("There are no silences long enough for this sentence to be split:")
# for wav, text in zip(wavs, texts):
# # Pad the waveform with 1 second of silence because sounddevice tends to cut them early
# # when playing them. You shouldn't need to do that in your parsers.
# wav = np.concatenate((wav, [0] * 16000))
# print("\t%s" % text)
# sd.play(wav, 16000, blocking=True)
# print("")
return wavs, texts
def process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
skip_existing: bool, hparams):
## FOR REFERENCE:
# For you not to lose your head if you ever wish to change things here or implement your own
# synthesizer.
# - Both the audios and the mel spectrograms are saved as numpy arrays
# - There is no processing done to the audios that will be saved to disk beyond volume
# normalization (in split_on_silences)
# - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
# is why we re-apply it on the audio on the side of the vocoder.
# - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
# without extra padding. This means that you won't have an exact relation between the length
# of the wav and of the mel spectrogram. See the vocoder data loader.
# Skip existing utterances if needed
mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
if skip_existing and mel_fpath.exists() and wav_fpath.exists():
return None
# Trim silence
if hparams.trim_silence:
wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
# Skip utterances that are too short
if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
return None
# Compute the mel spectrogram
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
# Skip utterances that are too long
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
# Write the spectrogram, embed and audio to disk
np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
np.save(wav_fpath, wav, allow_pickle=False)
# Return a tuple describing this training example
return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
# Compute the speaker embedding of the utterance
wav_fpath, embed_fpath = fpaths
wav = np.load(wav_fpath)
wav = encoder.preprocess_wav(wav)
embed = encoder.embed_utterance(wav)
np.save(embed_fpath, embed, allow_pickle=False)
def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int):
wav_dir = synthesizer_root.joinpath("audio")
metadata_fpath = synthesizer_root.joinpath("train.txt")
assert wav_dir.exists() and metadata_fpath.exists()
embed_dir = synthesizer_root.joinpath("embeds")
embed_dir.mkdir(exist_ok=True)
# Gather the input wave filepath and the target output embed filepath
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
# TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
# Embed the utterances in separate threads
func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
job = Pool(n_processes).imap(func, fpaths)
list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
# aidatatang_200zh
def preprocess_aidatatang_200zh(datasets_root: Path, out_dir: Path, n_processes: int,
skip_existing: bool, hparams, no_alignments: bool, datasets_name=None, subfolders=None):
# Gather the input directories
dataset_root = datasets_root.joinpath("aidatatang_200zh")
dict_info = {}
transcript_dirs = dataset_root.joinpath("transcript/aidatatang_200_zh_transcript.txt")
with open(transcript_dirs,"rb") as fp:
dict_transcript = [v.decode() for v in fp]
for v in dict_transcript:
if not v:
continue
v = v.strip().replace("\n","").split(" ")
dict_info[v[0]] = " ".join(v[1:])
input_dirs = [dataset_root.joinpath("corpus/train")]
print("\n ".join(map(str, ["Using data from:"] + input_dirs)))
assert all(input_dir.exists() for input_dir in input_dirs)
# Create the output directories for each output file type
out_dir.joinpath("mels").mkdir(exist_ok=True)
out_dir.joinpath("audio").mkdir(exist_ok=True)
# Create a metadata file
metadata_fpath = out_dir.joinpath("train.txt")
metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8")
# Preprocess the dataset
speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
func = partial(preprocess_speaker_aidatatang_200zh, out_dir=out_dir, skip_existing=skip_existing,
hparams=hparams, dict_info=dict_info, no_alignments=no_alignments)
job = Pool(n_processes).imap(func, speaker_dirs)
for speaker_metadata in tqdm(job, "aidatatang_200zh", len(speaker_dirs), unit="speakers"):
for metadatum in speaker_metadata:
metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
metadata_file.close()
# Verify the contents of the metadata file
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
mel_frames = sum([int(m[4]) for m in metadata])
timesteps = sum([int(m[3]) for m in metadata])
sample_rate = hparams.sample_rate
hours = (timesteps / sample_rate) / 3600
print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." %
(len(metadata), mel_frames, timesteps, hours))
print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
def preprocess_speaker_aidatatang_200zh(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool):
metadata = []
if platform.system() == "Windows":
split = "\\"
else:
split = "/"
# for book_dir in speaker_dir.glob("*"):
# Gather the utterance audios and texts
for wav_fpath in speaker_dir.glob("*.wav"):
# D:\dataset\data_aishell\wav\train\S0002\BAC009S0002W0122.wav
# Process each sub-utterance
name = str(wav_fpath).split(split)[-1]
key = name.split(".")[0]
words = dict_info.get(key)
if not words:
continue
sub_basename = "%s_%02d" % (name, 0)
wav, text = split_on_silences_aidatatang_200zh(wav_fpath, words, hparams)
metadata.append(process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def split_on_silences_aidatatang_200zh(wav_fpath, words, hparams):
# Load the audio waveform
wav, _ = librosa.load(wav_fpath, hparams.sample_rate)
wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=512)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
resp = pinyin(words, style=Style.TONE3)
res = [v[0] for v in resp if v[0].strip()]
res = " ".join(res)
return wav, res

97
synthesizer/synthesize.py Normal file
View File

@ -0,0 +1,97 @@
import torch
from torch.utils.data import DataLoader
from synthesizer.hparams import hparams_debug_string
from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
from synthesizer.models.tacotron import Tacotron
from synthesizer.utils.text import text_to_sequence
from synthesizer.utils.symbols import symbols
import numpy as np
from pathlib import Path
from tqdm import tqdm
def run_synthesis(in_dir, out_dir, model_dir, hparams):
# This generates ground truth-aligned mels for vocoder training
synth_dir = Path(out_dir).joinpath("mels_gta")
synth_dir.mkdir(exist_ok=True)
print(hparams_debug_string(hparams))
# Check for GPU
if torch.cuda.is_available():
device = torch.device("cuda")
if hparams.synthesis_batch_size % torch.cuda.device_count() != 0:
raise ValueError("`hparams.synthesis_batch_size` must be evenly divisible by n_gpus!")
else:
device = torch.device("cpu")
print("Synthesizer using device:", device)
# Instantiate Tacotron model
model = Tacotron(embed_dims=hparams.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hparams.tts_encoder_dims,
decoder_dims=hparams.tts_decoder_dims,
n_mels=hparams.num_mels,
fft_bins=hparams.num_mels,
postnet_dims=hparams.tts_postnet_dims,
encoder_K=hparams.tts_encoder_K,
lstm_dims=hparams.tts_lstm_dims,
postnet_K=hparams.tts_postnet_K,
num_highways=hparams.tts_num_highways,
dropout=0., # Use zero dropout for gta mels
stop_threshold=hparams.tts_stop_threshold,
speaker_embedding_size=hparams.speaker_embedding_size).to(device)
# Load the weights
model_dir = Path(model_dir)
model_fpath = model_dir.joinpath(model_dir.stem).with_suffix(".pt")
print("\nLoading weights at %s" % model_fpath)
model.load(model_fpath)
print("Tacotron weights loaded from step %d" % model.step)
# Synthesize using same reduction factor as the model is currently trained
r = np.int32(model.r)
# Set model to eval mode (disable gradient and zoneout)
model.eval()
# Initialize the dataset
in_dir = Path(in_dir)
metadata_fpath = in_dir.joinpath("train.txt")
mel_dir = in_dir.joinpath("mels")
embed_dir = in_dir.joinpath("embeds")
dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
data_loader = DataLoader(dataset,
collate_fn=lambda batch: collate_synthesizer(batch, r),
batch_size=hparams.synthesis_batch_size,
num_workers=2,
shuffle=False,
pin_memory=True)
# Generate GTA mels
meta_out_fpath = Path(out_dir).joinpath("synthesized.txt")
with open(meta_out_fpath, "w") as file:
for i, (texts, mels, embeds, idx) in tqdm(enumerate(data_loader), total=len(data_loader)):
texts = texts.to(device)
mels = mels.to(device)
embeds = embeds.to(device)
# Parallelize model onto GPUS using workaround due to python bug
if device.type == "cuda" and torch.cuda.device_count() > 1:
_, mels_out, _ = data_parallel_workaround(model, texts, mels, embeds)
else:
_, mels_out, _ = model(texts, mels, embeds)
for j, k in enumerate(idx):
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = Path(synth_dir).joinpath(dataset.metadata[k][1])
mel_out = mels_out[j].detach().cpu().numpy().T
# Use the length of the ground truth mel to remove padding from the generated mels
mel_out = mel_out[:int(dataset.metadata[k][4])]
# Write the spectrogram to disk
np.save(mel_filename, mel_out, allow_pickle=False)
# Write metadata into the synthesized file
file.write("|".join(dataset.metadata[k]))

92
synthesizer/synthesizer_dataset.py Normal file
View File

@ -0,0 +1,92 @@
import torch
from torch.utils.data import Dataset
import numpy as np
from pathlib import Path
from synthesizer.utils.text import text_to_sequence
class SynthesizerDataset(Dataset):
def __init__(self, metadata_fpath: Path, mel_dir: Path, embed_dir: Path, hparams):
print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, embed_dir))
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
mel_fnames = [x[1] for x in metadata if int(x[4])]
mel_fpaths = [mel_dir.joinpath(fname) for fname in mel_fnames]
embed_fnames = [x[2] for x in metadata if int(x[4])]
embed_fpaths = [embed_dir.joinpath(fname) for fname in embed_fnames]
self.samples_fpaths = list(zip(mel_fpaths, embed_fpaths))
self.samples_texts = [x[5].strip() for x in metadata if int(x[4])]
self.metadata = metadata
self.hparams = hparams
print("Found %d samples" % len(self.samples_fpaths))
def __getitem__(self, index):
# Sometimes index may be a list of 2 (not sure why this happens)
# If that is the case, return a single item corresponding to first element in index
if index is list:
index = index[0]
mel_path, embed_path = self.samples_fpaths[index]
mel = np.load(mel_path).T.astype(np.float32)
# Load the embed
embed = np.load(embed_path)
# Get the text and clean it
text = text_to_sequence(self.samples_texts[index], self.hparams.tts_cleaner_names)
# Convert the list returned by text_to_sequence to a numpy array
text = np.asarray(text).astype(np.int32)
return text, mel.astype(np.float32), embed.astype(np.float32), index
def __len__(self):
return len(self.samples_fpaths)
def collate_synthesizer(batch):
# Text
x_lens = [len(x[0]) for x in batch]
max_x_len = max(x_lens)
chars = [pad1d(x[0], max_x_len) for x in batch]
chars = np.stack(chars)
# Mel spectrogram
spec_lens = [x[1].shape[-1] for x in batch]
max_spec_len = max(spec_lens) + 1
if max_spec_len % 2 != 0: # FIXIT: Hardcoded due to incompatibility with Windows (no lambda)
max_spec_len += 2 - max_spec_len % 2
# WaveRNN mel spectrograms are normalized to [0, 1] so zero padding adds silence
# By default, SV2TTS uses symmetric mels, where -1*max_abs_value is silence.
# if hparams.symmetric_mels:
# mel_pad_value = -1 * hparams.max_abs_value
# else:
# mel_pad_value = 0
mel_pad_value = -4 # FIXIT: Hardcoded due to incompatibility with Windows (no lambda)
mel = [pad2d(x[1], max_spec_len, pad_value=mel_pad_value) for x in batch]
mel = np.stack(mel)
# Speaker embedding (SV2TTS)
embeds = [x[2] for x in batch]
# Index (for vocoder preprocessing)
indices = [x[3] for x in batch]
# Convert all to tensor
chars = torch.tensor(chars).long()
mel = torch.tensor(mel)
embeds = torch.tensor(embeds)
return chars, mel, embeds, indices
def pad1d(x, max_len, pad_value=0):
return np.pad(x, (0, max_len - len(x)), mode="constant", constant_values=pad_value)
def pad2d(x, max_len, pad_value=0):
return np.pad(x, ((0, 0), (0, max_len - x.shape[-1])), mode="constant", constant_values=pad_value)

268
synthesizer/train.py Normal file
View File

@ -0,0 +1,268 @@
import torch
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from synthesizer import audio
from synthesizer.models.tacotron import Tacotron
from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
from synthesizer.utils import ValueWindow, data_parallel_workaround
from synthesizer.utils.plot import plot_spectrogram
from synthesizer.utils.symbols import symbols
from synthesizer.utils.text import sequence_to_text
from vocoder.display import *
from datetime import datetime
import numpy as np
from pathlib import Path
import sys
import time
def np_now(x: torch.Tensor): return x.detach().cpu().numpy()
def time_string():
return datetime.now().strftime("%Y-%m-%d %H:%M")
def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
backup_every: int, force_restart:bool, hparams):
syn_dir = Path(syn_dir)
models_dir = Path(models_dir)
models_dir.mkdir(exist_ok=True)
model_dir = models_dir.joinpath(run_id)
plot_dir = model_dir.joinpath("plots")
wav_dir = model_dir.joinpath("wavs")
mel_output_dir = model_dir.joinpath("mel-spectrograms")
meta_folder = model_dir.joinpath("metas")
model_dir.mkdir(exist_ok=True)
plot_dir.mkdir(exist_ok=True)
wav_dir.mkdir(exist_ok=True)
mel_output_dir.mkdir(exist_ok=True)
meta_folder.mkdir(exist_ok=True)
weights_fpath = model_dir.joinpath(run_id).with_suffix(".pt")
metadata_fpath = syn_dir.joinpath("train.txt")
print("Checkpoint path: {}".format(weights_fpath))
print("Loading training data from: {}".format(metadata_fpath))
print("Using model: Tacotron")
# Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
# From WaveRNN/train_tacotron.py
if torch.cuda.is_available():
device = torch.device("cuda")
for session in hparams.tts_schedule:
_, _, _, batch_size = session
if batch_size % torch.cuda.device_count() != 0:
raise ValueError("`batch_size` must be evenly divisible by n_gpus!")
else:
device = torch.device("cpu")
print("Using device:", device)
# Instantiate Tacotron Model
print("\nInitialising Tacotron Model...\n")
model = Tacotron(embed_dims=hparams.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hparams.tts_encoder_dims,
decoder_dims=hparams.tts_decoder_dims,
n_mels=hparams.num_mels,
fft_bins=hparams.num_mels,
postnet_dims=hparams.tts_postnet_dims,
encoder_K=hparams.tts_encoder_K,
lstm_dims=hparams.tts_lstm_dims,
postnet_K=hparams.tts_postnet_K,
num_highways=hparams.tts_num_highways,
dropout=hparams.tts_dropout,
stop_threshold=hparams.tts_stop_threshold,
speaker_embedding_size=hparams.speaker_embedding_size).to(device)
# Initialize the optimizer
optimizer = optim.Adam(model.parameters())
# Load the weights
if force_restart or not weights_fpath.exists():
print("\nStarting the training of Tacotron from scratch\n")
model.save(weights_fpath)
# Embeddings metadata
char_embedding_fpath = meta_folder.joinpath("CharacterEmbeddings.tsv")
with open(char_embedding_fpath, "w", encoding="utf-8") as f:
for symbol in symbols:
if symbol == " ":
symbol = "\\s" # For visual purposes, swap space with \s
f.write("{}\n".format(symbol))
else:
print("\nLoading weights at %s" % weights_fpath)
model.load(weights_fpath, optimizer)
print("Tacotron weights loaded from step %d" % model.step)
# Initialize the dataset
metadata_fpath = syn_dir.joinpath("train.txt")
mel_dir = syn_dir.joinpath("mels")
embed_dir = syn_dir.joinpath("embeds")
dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=True,
pin_memory=True)
for i, session in enumerate(hparams.tts_schedule):
current_step = model.get_step()
r, lr, max_step, batch_size = session
training_steps = max_step - current_step
# Do we need to change to the next session?
if current_step >= max_step:
# Are there no further sessions than the current one?
if i == len(hparams.tts_schedule) - 1:
# We have completed training. Save the model and exit
model.save(weights_fpath, optimizer)
break
else:
# There is a following session, go to it
continue
model.r = r
# Begin the training
simple_table([(f"Steps with r={r}", str(training_steps // 1000) + "k Steps"),
("Batch Size", batch_size),
("Learning Rate", lr),
("Outputs/Step (r)", model.r)])
for p in optimizer.param_groups:
p["lr"] = lr
data_loader = DataLoader(dataset,
collate_fn=collate_synthesizer,
batch_size=batch_size, #change if you got graphic card OOM
num_workers=2,
shuffle=True,
pin_memory=True)
total_iters = len(dataset)
steps_per_epoch = np.ceil(total_iters / batch_size).astype(np.int32)
epochs = np.ceil(training_steps / steps_per_epoch).astype(np.int32)
for epoch in range(1, epochs+1):
for i, (texts, mels, embeds, idx) in enumerate(data_loader, 1):
start_time = time.time()
# Generate stop tokens for training
stop = torch.ones(mels.shape[0], mels.shape[2])
for j, k in enumerate(idx):
stop[j, :int(dataset.metadata[k][4])-1] = 0
texts = texts.to(device)
mels = mels.to(device)
embeds = embeds.to(device)
stop = stop.to(device)
# Forward pass
# Parallelize model onto GPUS using workaround due to python bug
if device.type == "cuda" and torch.cuda.device_count() > 1:
m1_hat, m2_hat, attention, stop_pred = data_parallel_workaround(model, texts,
mels, embeds)
else:
m1_hat, m2_hat, attention, stop_pred = model(texts, mels, embeds)
# Backward pass
m1_loss = F.mse_loss(m1_hat, mels) + F.l1_loss(m1_hat, mels)
m2_loss = F.mse_loss(m2_hat, mels)
stop_loss = F.binary_cross_entropy(stop_pred, stop)
loss = m1_loss + m2_loss + stop_loss
optimizer.zero_grad()
loss.backward()
if hparams.tts_clip_grad_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams.tts_clip_grad_norm)
if np.isnan(grad_norm.cpu()):
print("grad_norm was NaN!")
optimizer.step()
time_window.append(time.time() - start_time)
loss_window.append(loss.item())
step = model.get_step()
k = step // 1000
msg = f"| Epoch: {epoch}/{epochs} ({i}/{steps_per_epoch}) | Loss: {loss_window.average:#.4} | {1./time_window.average:#.2} steps/s | Step: {k}k | "
stream(msg)
# Backup or save model as appropriate
if backup_every != 0 and step % backup_every == 0 :
backup_fpath = Path("{}/{}_{}k.pt".format(str(weights_fpath.parent), run_id, k))
model.save(backup_fpath, optimizer)
if save_every != 0 and step % save_every == 0 :
# Must save latest optimizer state to ensure that resuming training
# doesn't produce artifacts
model.save(weights_fpath, optimizer)
# Evaluate model to generate samples
epoch_eval = hparams.tts_eval_interval == -1 and i == steps_per_epoch # If epoch is done
step_eval = hparams.tts_eval_interval > 0 and step % hparams.tts_eval_interval == 0 # Every N steps
if epoch_eval or step_eval:
for sample_idx in range(hparams.tts_eval_num_samples):
# At most, generate samples equal to number in the batch
if sample_idx + 1 <= len(texts):
# Remove padding from mels using frame length in metadata
mel_length = int(dataset.metadata[idx[sample_idx]][4])
mel_prediction = np_now(m2_hat[sample_idx]).T[:mel_length]
target_spectrogram = np_now(mels[sample_idx]).T[:mel_length]
attention_len = mel_length // model.r
eval_model(attention=np_now(attention[sample_idx][:, :attention_len]),
mel_prediction=mel_prediction,
target_spectrogram=target_spectrogram,
input_seq=np_now(texts[sample_idx]),
step=step,
plot_dir=plot_dir,
mel_output_dir=mel_output_dir,
wav_dir=wav_dir,
sample_num=sample_idx + 1,
loss=loss,
hparams=hparams)
# Break out of loop to update training schedule
if step >= max_step:
break
# Add line break after every epoch
print("")
def eval_model(attention, mel_prediction, target_spectrogram, input_seq, step,
plot_dir, mel_output_dir, wav_dir, sample_num, loss, hparams):
# Save some results for evaluation
attention_path = str(plot_dir.joinpath("attention_step_{}_sample_{}".format(step, sample_num)))
save_attention(attention, attention_path)
# save predicted mel spectrogram to disk (debug)
mel_output_fpath = mel_output_dir.joinpath("mel-prediction-step-{}_sample_{}.npy".format(step, sample_num))
np.save(str(mel_output_fpath), mel_prediction, allow_pickle=False)
# save griffin lim inverted wav for debug (mel -> wav)
wav = audio.inv_mel_spectrogram(mel_prediction.T, hparams)
wav_fpath = wav_dir.joinpath("step-{}-wave-from-mel_sample_{}.wav".format(step, sample_num))
audio.save_wav(wav, str(wav_fpath), sr=hparams.sample_rate)
# save real and predicted mel-spectrogram plot to disk (control purposes)
spec_fpath = plot_dir.joinpath("step-{}-mel-spectrogram_sample_{}.png".format(step, sample_num))
title_str = "{}, {}, step={}, loss={:.5f}".format("Tacotron", time_string(), step, loss)
plot_spectrogram(mel_prediction, str(spec_fpath), title=title_str,
target_spectrogram=target_spectrogram,
max_len=target_spectrogram.size // hparams.num_mels)
print("Input at step {}: {}".format(step, sequence_to_text(input_seq)))

45
synthesizer/utils/__init__.py Normal file
View File

@ -0,0 +1,45 @@
import torch
_output_ref = None
_replicas_ref = None
def data_parallel_workaround(model, *input):
global _output_ref
global _replicas_ref
device_ids = list(range(torch.cuda.device_count()))
output_device = device_ids[0]
replicas = torch.nn.parallel.replicate(model, device_ids)
# input.shape = (num_args, batch, ...)
inputs = torch.nn.parallel.scatter(input, device_ids)
# inputs.shape = (num_gpus, num_args, batch/num_gpus, ...)
replicas = replicas[:len(inputs)]
outputs = torch.nn.parallel.parallel_apply(replicas, inputs)
y_hat = torch.nn.parallel.gather(outputs, output_device)
_output_ref = outputs
_replicas_ref = replicas
return y_hat
class ValueWindow():
def __init__(self, window_size=100):
self._window_size = window_size
self._values = []
def append(self, x):
self._values = self._values[-(self._window_size - 1):] + [x]
@property
def sum(self):
return sum(self._values)
@property
def count(self):
return len(self._values)
@property
def average(self):
return self.sum / max(1, self.count)
def reset(self):
self._values = []

62
synthesizer/utils/_cmudict.py Normal file
View File

@ -0,0 +1,62 @@
import re
valid_symbols = [
"AA", "AA0", "AA1", "AA2", "AE", "AE0", "AE1", "AE2", "AH", "AH0", "AH1", "AH2",
"AO", "AO0", "AO1", "AO2", "AW", "AW0", "AW1", "AW2", "AY", "AY0", "AY1", "AY2",
"B", "CH", "D", "DH", "EH", "EH0", "EH1", "EH2", "ER", "ER0", "ER1", "ER2", "EY",
"EY0", "EY1", "EY2", "F", "G", "HH", "IH", "IH0", "IH1", "IH2", "IY", "IY0", "IY1",
"IY2", "JH", "K", "L", "M", "N", "NG", "OW", "OW0", "OW1", "OW2", "OY", "OY0",
"OY1", "OY2", "P", "R", "S", "SH", "T", "TH", "UH", "UH0", "UH1", "UH2", "UW",
"UW0", "UW1", "UW2", "V", "W", "Y", "Z", "ZH"
]
_valid_symbol_set = set(valid_symbols)
class CMUDict:
"""Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict"""
def __init__(self, file_or_path, keep_ambiguous=True):
if isinstance(file_or_path, str):
with open(file_or_path, encoding="latin-1") as f:
entries = _parse_cmudict(f)
else:
entries = _parse_cmudict(file_or_path)
if not keep_ambiguous:
entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
self._entries = entries
def __len__(self):
return len(self._entries)
def lookup(self, word):
"""Returns list of ARPAbet pronunciations of the given word."""
return self._entries.get(word.upper())
_alt_re = re.compile(r"\([0-9]+\)")
def _parse_cmudict(file):
cmudict = {}
for line in file:
if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
parts = line.split(" ")
word = re.sub(_alt_re, "", parts[0])
pronunciation = _get_pronunciation(parts[1])
if pronunciation:
if word in cmudict:
cmudict[word].append(pronunciation)
else:
cmudict[word] = [pronunciation]
return cmudict
def _get_pronunciation(s):
parts = s.strip().split(" ")
for part in parts:
if part not in _valid_symbol_set:
return None
return " ".join(parts)

88
synthesizer/utils/cleaners.py Normal file
View File

@ -0,0 +1,88 @@
"""
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You"ll typically want to use:
1. "english_cleaners" for English text
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
the symbols in symbols.py to match your data).
"""
import re
from unidecode import unidecode
from .numbers import normalize_numbers
# Regular expression matching whitespace:
_whitespace_re = re.compile(r"\s+")
# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) for x in [
("mrs", "misess"),
("mr", "mister"),
("dr", "doctor"),
("st", "saint"),
("co", "company"),
("jr", "junior"),
("maj", "major"),
("gen", "general"),
("drs", "doctors"),
("rev", "reverend"),
("lt", "lieutenant"),
("hon", "honorable"),
("sgt", "sergeant"),
("capt", "captain"),
("esq", "esquire"),
("ltd", "limited"),
("col", "colonel"),
("ft", "fort"),
]]
def expand_abbreviations(text):
for regex, replacement in _abbreviations:
text = re.sub(regex, replacement, text)
return text
def expand_numbers(text):
return normalize_numbers(text)
def lowercase(text):
"""lowercase input tokens."""
return text.lower()
def collapse_whitespace(text):
return re.sub(_whitespace_re, " ", text)
def convert_to_ascii(text):
return unidecode(text)
def basic_cleaners(text):
"""Basic pipeline that lowercases and collapses whitespace without transliteration."""
text = lowercase(text)
text = collapse_whitespace(text)
return text
def transliteration_cleaners(text):
"""Pipeline for non-English text that transliterates to ASCII."""
text = convert_to_ascii(text)
text = lowercase(text)
text = collapse_whitespace(text)
return text
def english_cleaners(text):
"""Pipeline for English text, including number and abbreviation expansion."""
text = convert_to_ascii(text)
text = lowercase(text)
text = expand_numbers(text)
text = expand_abbreviations(text)
text = collapse_whitespace(text)
return text

68
synthesizer/utils/numbers.py Normal file
View File

@ -0,0 +1,68 @@
import re
import inflect
_inflect = inflect.engine()
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
_number_re = re.compile(r"[0-9]+")
def _remove_commas(m):
return m.group(1).replace(",", "")
def _expand_decimal_point(m):
return m.group(1).replace(".", " point ")
def _expand_dollars(m):
match = m.group(1)
parts = match.split(".")
if len(parts) > 2:
return match + " dollars" # Unexpected format
dollars = int(parts[0]) if parts[0] else 0
cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
if dollars and cents:
dollar_unit = "dollar" if dollars == 1 else "dollars"
cent_unit = "cent" if cents == 1 else "cents"
return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
elif dollars:
dollar_unit = "dollar" if dollars == 1 else "dollars"
return "%s %s" % (dollars, dollar_unit)
elif cents:
cent_unit = "cent" if cents == 1 else "cents"
return "%s %s" % (cents, cent_unit)
else:
return "zero dollars"
def _expand_ordinal(m):
return _inflect.number_to_words(m.group(0))
def _expand_number(m):
num = int(m.group(0))
if num > 1000 and num < 3000:
if num == 2000:
return "two thousand"
elif num > 2000 and num < 2010:
return "two thousand " + _inflect.number_to_words(num % 100)
elif num % 100 == 0:
return _inflect.number_to_words(num // 100) + " hundred"
else:
return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
else:
return _inflect.number_to_words(num, andword="")
def normalize_numbers(text):
text = re.sub(_comma_number_re, _remove_commas, text)
text = re.sub(_pounds_re, r"\1 pounds", text)
text = re.sub(_dollars_re, _expand_dollars, text)
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
text = re.sub(_ordinal_re, _expand_ordinal, text)
text = re.sub(_number_re, _expand_number, text)
return text

76
synthesizer/utils/plot.py Normal file
View File

@ -0,0 +1,76 @@
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
def split_title_line(title_text, max_words=5):
"""
A function that splits any string based on specific character
(returning it with the string), with maximum number of words on it
"""
seq = title_text.split()
return "\n".join([" ".join(seq[i:i + max_words]) for i in range(0, len(seq), max_words)])
def plot_alignment(alignment, path, title=None, split_title=False, max_len=None):
if max_len is not None:
alignment = alignment[:, :max_len]
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
im = ax.imshow(
alignment,
aspect="auto",
origin="lower",
interpolation="none")
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if split_title:
title = split_title_line(title)
plt.xlabel(xlabel)
plt.title(title)
plt.ylabel("Encoder timestep")
plt.tight_layout()
plt.savefig(path, format="png")
plt.close()
def plot_spectrogram(pred_spectrogram, path, title=None, split_title=False, target_spectrogram=None, max_len=None, auto_aspect=False):
if max_len is not None:
target_spectrogram = target_spectrogram[:max_len]
pred_spectrogram = pred_spectrogram[:max_len]
if split_title:
title = split_title_line(title)
fig = plt.figure(figsize=(10, 8))
# Set common labels
fig.text(0.5, 0.18, title, horizontalalignment="center", fontsize=16)
#target spectrogram subplot
if target_spectrogram is not None:
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312)
if auto_aspect:
im = ax1.imshow(np.rot90(target_spectrogram), aspect="auto", interpolation="none")
else:
im = ax1.imshow(np.rot90(target_spectrogram), interpolation="none")
ax1.set_title("Target Mel-Spectrogram")
fig.colorbar(mappable=im, shrink=0.65, orientation="horizontal", ax=ax1)
ax2.set_title("Predicted Mel-Spectrogram")
else:
ax2 = fig.add_subplot(211)
if auto_aspect:
im = ax2.imshow(np.rot90(pred_spectrogram), aspect="auto", interpolation="none")
else:
im = ax2.imshow(np.rot90(pred_spectrogram), interpolation="none")
fig.colorbar(mappable=im, shrink=0.65, orientation="horizontal", ax=ax2)
plt.tight_layout()
plt.savefig(path, format="png")
plt.close()

16
synthesizer/utils/symbols.py Normal file
View File

@ -0,0 +1,16 @@
"""
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run
through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details.
"""
# from . import cmudict
_pad = "_"
_eos = "~"
_characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz12340!\'(),-.:;? '
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
#_arpabet = ["@' + s for s in cmudict.valid_symbols]
# Export all symbols:
symbols = [_pad, _eos] + list(_characters) #+ _arpabet

74
synthesizer/utils/text.py Normal file
View File

@ -0,0 +1,74 @@
from .symbols import symbols
from . import cleaners
import re
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
def text_to_sequence(text, cleaner_names):
"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string to convert to a sequence
cleaner_names: names of the cleaner functions to run the text through
Returns:
List of integers corresponding to the symbols in the text
"""
sequence = []
# Check for curly braces and treat their contents as ARPAbet:
while len(text):
m = _curly_re.match(text)
if not m:
sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
break
sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
sequence += _arpabet_to_sequence(m.group(2))
text = m.group(3)
# Append EOS token
sequence.append(_symbol_to_id["~"])
return sequence
def sequence_to_text(sequence):
"""Converts a sequence of IDs back to a string"""
result = ""
for symbol_id in sequence:
if symbol_id in _id_to_symbol:
s = _id_to_symbol[symbol_id]
# Enclose ARPAbet back in curly braces:
if len(s) > 1 and s[0] == "@":
s = "{%s}" % s[1:]
result += s
return result.replace("}{", " ")
def _clean_text(text, cleaner_names):
for name in cleaner_names:
cleaner = getattr(cleaners, name)
if not cleaner:
raise Exception("Unknown cleaner: %s" % name)
text = cleaner(text)
return text
def _symbols_to_sequence(symbols):
return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
def _arpabet_to_sequence(text):
return _symbols_to_sequence(["@" + s for s in text.split()])
def _should_keep_symbol(s):
return s in _symbol_to_id and s not in ("_", "~")

60
synthesizer_preprocess_audio.py Normal file
View File

@ -0,0 +1,60 @@
from synthesizer.preprocess import preprocess_aidatatang_200zh
from synthesizer.hparams import hparams
from utils.argutils import print_args
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
"and writes them to the disk. Audio files are also saved, to be used by the "
"vocoder for training.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("datasets_root", type=Path, help=\
"Path to the directory containing your LibriSpeech/TTS datasets.")
parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
"Path to the output directory that will contain the mel spectrograms, the audios and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
parser.add_argument("-n", "--n_processes", type=int, default=None, help=\
"Number of processes in parallel.")
parser.add_argument("-s", "--skip_existing", action="store_true", help=\
"Whether to overwrite existing files with the same name. Useful if the preprocessing was "
"interrupted.")
parser.add_argument("--hparams", type=str, default="", help=\
"Hyperparameter overrides as a comma-separated list of name-value pairs")
parser.add_argument("--no_trim", action="store_true", help=\
"Preprocess audio without trimming silences (not recommended).")
parser.add_argument("--no_alignments", action="store_true", help=\
"Use this option when dataset does not include alignments\
(these are used to split long audio files into sub-utterances.)")
parser.add_argument("--datasets_name", type=str, default="LibriSpeech", help=\
"Name of the dataset directory to process.")
parser.add_argument("--subfolders", type=str, default="train-clean-100, train-clean-360", help=\
"Comma-separated list of subfolders to process inside your dataset directory")
args = parser.parse_args()
# Process the arguments
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
# Create directories
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
# Verify webrtcvad is available
if not args.no_trim:
try:
import webrtcvad
except:
raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
"noise removal and is recommended. Please install and try again. If installation fails, "
"use --no_trim to disable this error message.")
del args.no_trim
# Preprocess the dataset
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
# preprocess_dataset(**vars(args))
preprocess_aidatatang_200zh(**vars(args))

25
synthesizer_preprocess_embeds.py Normal file
View File

@ -0,0 +1,25 @@
from synthesizer.preprocess import create_embeddings
from utils.argutils import print_args
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Creates embeddings for the synthesizer from the LibriSpeech utterances.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("synthesizer_root", type=Path, help=\
"Path to the synthesizer training data that contains the audios and the train.txt file. "
"If you let everything as default, it should be <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("-e", "--encoder_model_fpath", type=Path,
default="encoder/saved_models/pretrained.pt", help=\
"Path your trained encoder model.")
parser.add_argument("-n", "--n_processes", type=int, default=4, help= \
"Number of parallel processes. An encoder is created for each, so you may need to lower "
"this value on GPUs with low memory. Set it to 1 if CUDA is unhappy.")
args = parser.parse_args()
# Preprocess the dataset
print_args(args, parser)
create_embeddings(**vars(args))

35
synthesizer_train.py Normal file
View File

@ -0,0 +1,35 @@
from synthesizer.hparams import hparams
from synthesizer.train import train
from utils.argutils import print_args
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the same run ID was previously "
"saved, the training will restart from there. Pass -f to overwrite saved states and "
"restart from scratch.")
parser.add_argument("syn_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
"the wavs and the embeds.")
parser.add_argument("-m", "--models_dir", type=str, default="synthesizer/saved_models/", help=\
"Path to the output directory that will contain the saved model weights and the logs.")
parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
"Number of steps between updates of the model on the disk. Set to 0 to never save the "
"model.")
parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model and restart from scratch.")
parser.add_argument("--hparams", default="",
help="Hyperparameter overrides as a comma-separated list of name=value "
"pairs")
args = parser.parse_args()
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
# Run the training
train(**vars(args))

359
toolbox/__init__.py Normal file
View File

@ -0,0 +1,359 @@
from toolbox.ui import UI
from encoder import inference as encoder
from synthesizer.inference import Synthesizer
from vocoder import inference as vocoder
from pathlib import Path
from time import perf_counter as timer
from toolbox.utterance import Utterance
import numpy as np
import traceback
import sys
import torch
import librosa
from audioread.exceptions import NoBackendError
# Use this directory structure for your datasets, or modify it to fit your needs
recognized_datasets = [
"LibriSpeech/dev-clean",
"LibriSpeech/dev-other",
"LibriSpeech/test-clean",
"LibriSpeech/test-other",
"LibriSpeech/train-clean-100",
"LibriSpeech/train-clean-360",
"LibriSpeech/train-other-500",
"LibriTTS/dev-clean",
"LibriTTS/dev-other",
"LibriTTS/test-clean",
"LibriTTS/test-other",
"LibriTTS/train-clean-100",
"LibriTTS/train-clean-360",
"LibriTTS/train-other-500",
"LJSpeech-1.1",
"VoxCeleb1/wav",
"VoxCeleb1/test_wav",
"VoxCeleb2/dev/aac",
"VoxCeleb2/test/aac",
"VCTK-Corpus/wav48",
"aidatatang_200zh/corpus/dev",
"aidatatang_200zh/corpus/test",
]
#Maximum of generated wavs to keep on memory
MAX_WAVES = 15
class Toolbox:
def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, seed, no_mp3_support):
if not no_mp3_support:
try:
librosa.load("samples/6829_00000.mp3")
except NoBackendError:
print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
"Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
exit(-1)
self.no_mp3_support = no_mp3_support
sys.excepthook = self.excepthook
self.datasets_root = datasets_root
self.utterances = set()
self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
self.current_wav = None
self.waves_list = []
self.waves_count = 0
self.waves_namelist = []
# Check for webrtcvad (enables removal of silences in vocoder output)
try:
import webrtcvad
self.trim_silences = True
except:
self.trim_silences = False
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, seed)
self.setup_events()
self.ui.start()
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
recognized_datasets,
level)
self.ui.random_dataset_button.clicked.connect(random_func(0))
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
#Audio
self.ui.setup_audio_devices(Synthesizer.sample_rate)
#Wav playback & save
func = lambda: self.replay_last_wav()
self.ui.replay_wav_button.clicked.connect(func)
func = lambda: self.export_current_wave()
self.ui.export_wav_button.clicked.connect(func)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def set_current_wav(self, index):
self.current_wav = self.waves_list[index]
def export_current_wave(self):
self.ui.save_audio_file(self.current_wav, Synthesizer.sample_rate)
def replay_last_wav(self):
self.ui.play(self.current_wav, Synthesizer.sample_rate)
def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir, seed):
self.ui.populate_browser(self.datasets_root, recognized_datasets, 0, True)
self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir)
self.ui.populate_gen_options(seed, self.trim_silences)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root,
self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
name = str(fpath.relative_to(self.datasets_root))
speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
if fpath.suffix.lower() == ".mp3" and self.no_mp3_support:
self.ui.log("Error: No mp3 file argument was passed but an mp3 file was used")
return
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_%05d" % np.random.randint(100000)
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Update the synthesizer random seed
if self.ui.random_seed_checkbox.isChecked():
seed = int(self.ui.seed_textbox.text())
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = None
if seed is not None:
torch.manual_seed(seed)
# Synthesize the spectrogram
if self.synthesizer is None or seed is not None:
self.init_synthesizer()
texts = self.ui.text_prompt.toPlainText().split("\n")
embed = self.ui.selected_utterance.embed
embeds = [embed] * len(texts)
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
self.ui.draw_spec(spec, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Initialize the vocoder model and make it determinstic, if user provides a seed
if self.ui.random_seed_checkbox.isChecked():
seed = int(self.ui.seed_textbox.text())
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = None
if seed is not None:
torch.manual_seed(seed)
# Synthesize the waveform
if not vocoder.is_loaded() or seed is not None:
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Trim excessive silences
if self.ui.trim_silences_checkbox.isChecked():
wav = encoder.preprocess_wav(wav)
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
# Name it (history displayed in combobox)
# TODO better naming for the combobox items?
wav_name = str(self.waves_count + 1)
#Update waves combobox
self.waves_count += 1
if self.waves_count > MAX_WAVES:
self.waves_list.pop()
self.waves_namelist.pop()
self.waves_list.insert(0, wav)
self.waves_namelist.insert(0, wav_name)
self.ui.waves_cb.disconnect()
self.ui.waves_cb_model.setStringList(self.waves_namelist)
self.ui.waves_cb.setCurrentIndex(0)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Update current wav
self.set_current_wav(0)
#Enable replay and save buttons:
self.ui.replay_wav_button.setDisabled(False)
self.ui.export_wav_button.setDisabled(False)
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % np.random.randint(100000)
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_synthesizer(self):
model_fpath = self.ui.current_synthesizer_fpath
self.ui.log("Loading the synthesizer %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
self.synthesizer = Synthesizer(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
vocoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def update_seed_textbox(self):
self.ui.update_seed_textbox()

611
toolbox/ui.py Normal file
View File

@ -0,0 +1,611 @@
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from PyQt5.QtCore import Qt, QStringListModel
from PyQt5.QtWidgets import *
from encoder.inference import plot_embedding_as_heatmap
from toolbox.utterance import Utterance
from pathlib import Path
from typing import List, Set
import sounddevice as sd
import soundfile as sf
import numpy as np
# from sklearn.manifold import TSNE # You can try with TSNE if you like, I prefer UMAP
from time import sleep
import umap
import sys
from warnings import filterwarnings, warn
filterwarnings("ignore")
colormap = np.array([
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[97, 142, 151],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
[76, 255, 0],
], dtype=np.float) / 255
default_text = \
"Welcome to the toolbox! To begin, load an utterance from your datasets or record one " \
"yourself.\nOnce its embedding has been created, you can synthesize any text written here.\n" \
"The synthesizer expects to generate " \
"outputs that are somewhere between 5 and 12 seconds.\nTo mark breaks, write a new line. " \
"Each line will be treated separately.\nThen, they are joined together to make the final " \
"spectrogram. Use the vocoder to generate audio.\nThe vocoder generates almost in constant " \
"time, so it will be more time efficient for longer inputs like this one.\nOn the left you " \
"have the embedding projections. Load or record more utterances to see them.\nIf you have " \
"at least 2 or 3 utterances from a same speaker, a cluster should form.\nSynthesized " \
"utterances are of the same color as the speaker whose voice was used, but they're " \
"represented with a cross."
class UI(QDialog):
min_umap_points = 4
max_log_lines = 5
max_saved_utterances = 20
def draw_utterance(self, utterance: Utterance, which):
self.draw_spec(utterance.spec, which)
self.draw_embed(utterance.embed, utterance.name, which)
def draw_embed(self, embed, name, which):
embed_ax, _ = self.current_ax if which == "current" else self.gen_ax
embed_ax.figure.suptitle("" if embed is None else name)
## Embedding
# Clear the plot
if len(embed_ax.images) > 0:
embed_ax.images[0].colorbar.remove()
embed_ax.clear()
# Draw the embed
if embed is not None:
plot_embedding_as_heatmap(embed, embed_ax)
embed_ax.set_title("embedding")
embed_ax.set_aspect("equal", "datalim")
embed_ax.set_xticks([])
embed_ax.set_yticks([])
embed_ax.figure.canvas.draw()
def draw_spec(self, spec, which):
_, spec_ax = self.current_ax if which == "current" else self.gen_ax
## Spectrogram
# Draw the spectrogram
spec_ax.clear()
if spec is not None:
im = spec_ax.imshow(spec, aspect="auto", interpolation="none")
# spec_ax.figure.colorbar(mappable=im, shrink=0.65, orientation="horizontal",
# spec_ax=spec_ax)
spec_ax.set_title("mel spectrogram")
spec_ax.set_xticks([])
spec_ax.set_yticks([])
spec_ax.figure.canvas.draw()
if which != "current":
self.vocode_button.setDisabled(spec is None)
def draw_umap_projections(self, utterances: Set[Utterance]):
self.umap_ax.clear()
speakers = np.unique([u.speaker_name for u in utterances])
colors = {speaker_name: colormap[i] for i, speaker_name in enumerate(speakers)}
embeds = [u.embed for u in utterances]
# Display a message if there aren't enough points
if len(utterances) < self.min_umap_points:
self.umap_ax.text(.5, .5, "Add %d more points to\ngenerate the projections" %
(self.min_umap_points - len(utterances)),
horizontalalignment='center', fontsize=15)
self.umap_ax.set_title("")
# Compute the projections
else:
if not self.umap_hot:
self.log(
"Drawing UMAP projections for the first time, this will take a few seconds.")
self.umap_hot = True
reducer = umap.UMAP(int(np.ceil(np.sqrt(len(embeds)))), metric="cosine")
# reducer = TSNE()
projections = reducer.fit_transform(embeds)
speakers_done = set()
for projection, utterance in zip(projections, utterances):
color = colors[utterance.speaker_name]
mark = "x" if "_gen_" in utterance.name else "o"
label = None if utterance.speaker_name in speakers_done else utterance.speaker_name
speakers_done.add(utterance.speaker_name)
self.umap_ax.scatter(projection[0], projection[1], c=[color], marker=mark,
label=label)
# self.umap_ax.set_title("UMAP projections")
self.umap_ax.legend(prop={'size': 10})
# Draw the plot
self.umap_ax.set_aspect("equal", "datalim")
self.umap_ax.set_xticks([])
self.umap_ax.set_yticks([])
self.umap_ax.figure.canvas.draw()
def save_audio_file(self, wav, sample_rate):
dialog = QFileDialog()
dialog.setDefaultSuffix(".wav")
fpath, _ = dialog.getSaveFileName(
parent=self,
caption="Select a path to save the audio file",
filter="Audio Files (*.flac *.wav)"
)
if fpath:
#Default format is wav
if Path(fpath).suffix == "":
fpath += ".wav"
sf.write(fpath, wav, sample_rate)
def setup_audio_devices(self, sample_rate):
input_devices = []
output_devices = []
for device in sd.query_devices():
# Check if valid input
try:
sd.check_input_settings(device=device["name"], samplerate=sample_rate)
input_devices.append(device["name"])
except:
pass
# Check if valid output
try:
sd.check_output_settings(device=device["name"], samplerate=sample_rate)
output_devices.append(device["name"])
except Exception as e:
# Log a warning only if the device is not an input
if not device["name"] in input_devices:
warn("Unsupported output device %s for the sample rate: %d \nError: %s" % (device["name"], sample_rate, str(e)))
if len(input_devices) == 0:
self.log("No audio input device detected. Recording may not work.")
self.audio_in_device = None
else:
self.audio_in_device = input_devices[0]
if len(output_devices) == 0:
self.log("No supported output audio devices were found! Audio output may not work.")
self.audio_out_devices_cb.addItems(["None"])
self.audio_out_devices_cb.setDisabled(True)
else:
self.audio_out_devices_cb.clear()
self.audio_out_devices_cb.addItems(output_devices)
self.audio_out_devices_cb.currentTextChanged.connect(self.set_audio_device)
self.set_audio_device()
def set_audio_device(self):
output_device = self.audio_out_devices_cb.currentText()
if output_device == "None":
output_device = None
# If None, sounddevice queries portaudio
sd.default.device = (self.audio_in_device, output_device)
def play(self, wav, sample_rate):
try:
sd.stop()
sd.play(wav, sample_rate)
except Exception as e:
print(e)
self.log("Error in audio playback. Try selecting a different audio output device.")
self.log("Your device must be connected before you start the toolbox.")
def stop(self):
sd.stop()
def record_one(self, sample_rate, duration):
self.record_button.setText("Recording...")
self.record_button.setDisabled(True)
self.log("Recording %d seconds of audio" % duration)
sd.stop()
try:
wav = sd.rec(duration * sample_rate, sample_rate, 1)
except Exception as e:
print(e)
self.log("Could not record anything. Is your recording device enabled?")
self.log("Your device must be connected before you start the toolbox.")
return None
for i in np.arange(0, duration, 0.1):
self.set_loading(i, duration)
sleep(0.1)
self.set_loading(duration, duration)
sd.wait()
self.log("Done recording.")
self.record_button.setText("Record")
self.record_button.setDisabled(False)
return wav.squeeze()
@property
def current_dataset_name(self):
return self.dataset_box.currentText()
@property
def current_speaker_name(self):
return self.speaker_box.currentText()
@property
def current_utterance_name(self):
return self.utterance_box.currentText()
def browse_file(self):
fpath = QFileDialog().getOpenFileName(
parent=self,
caption="Select an audio file",
filter="Audio Files (*.mp3 *.flac *.wav *.m4a)"
)
return Path(fpath[0]) if fpath[0] != "" else ""
@staticmethod
def repopulate_box(box, items, random=False):
"""
Resets a box and adds a list of items. Pass a list of (item, data) pairs instead to join
data to the items
"""
box.blockSignals(True)
box.clear()
for item in items:
item = list(item) if isinstance(item, tuple) else [item]
box.addItem(str(item[0]), *item[1:])
if len(items) > 0:
box.setCurrentIndex(np.random.randint(len(items)) if random else 0)
box.setDisabled(len(items) == 0)
box.blockSignals(False)
def populate_browser(self, datasets_root: Path, recognized_datasets: List, level: int,
random=True):
# Select a random dataset
if level <= 0:
if datasets_root is not None:
datasets = [datasets_root.joinpath(d) for d in recognized_datasets]
datasets = [d.relative_to(datasets_root) for d in datasets if d.exists()]
self.browser_load_button.setDisabled(len(datasets) == 0)
if datasets_root is None or len(datasets) == 0:
msg = "Warning: you d" + ("id not pass a root directory for datasets as argument" \
if datasets_root is None else "o not have any of the recognized datasets" \
" in %s" % datasets_root)
self.log(msg)
msg += ".\nThe recognized datasets are:\n\t%s\nFeel free to add your own. You " \
"can still use the toolbox by recording samples yourself." % \
("\n\t".join(recognized_datasets))
print(msg, file=sys.stderr)
self.random_utterance_button.setDisabled(True)
self.random_speaker_button.setDisabled(True)
self.random_dataset_button.setDisabled(True)
self.utterance_box.setDisabled(True)
self.speaker_box.setDisabled(True)
self.dataset_box.setDisabled(True)
self.browser_load_button.setDisabled(True)
self.auto_next_checkbox.setDisabled(True)
return
self.repopulate_box(self.dataset_box, datasets, random)
# Select a random speaker
if level <= 1:
speakers_root = datasets_root.joinpath(self.current_dataset_name)
speaker_names = [d.stem for d in speakers_root.glob("*") if d.is_dir()]
self.repopulate_box(self.speaker_box, speaker_names, random)
# Select a random utterance
if level <= 2:
utterances_root = datasets_root.joinpath(
self.current_dataset_name,
self.current_speaker_name
)
utterances = []
for extension in ['mp3', 'flac', 'wav', 'm4a']:
utterances.extend(Path(utterances_root).glob("**/*.%s" % extension))
utterances = [fpath.relative_to(utterances_root) for fpath in utterances]
self.repopulate_box(self.utterance_box, utterances, random)
def browser_select_next(self):
index = (self.utterance_box.currentIndex() + 1) % len(self.utterance_box)
self.utterance_box.setCurrentIndex(index)
@property
def current_encoder_fpath(self):
return self.encoder_box.itemData(self.encoder_box.currentIndex())
@property
def current_synthesizer_fpath(self):
return self.synthesizer_box.itemData(self.synthesizer_box.currentIndex())
@property
def current_vocoder_fpath(self):
return self.vocoder_box.itemData(self.vocoder_box.currentIndex())
def populate_models(self, encoder_models_dir: Path, synthesizer_models_dir: Path,
vocoder_models_dir: Path):
# Encoder
encoder_fpaths = list(encoder_models_dir.glob("*.pt"))
if len(encoder_fpaths) == 0:
raise Exception("No encoder models found in %s" % encoder_models_dir)
self.repopulate_box(self.encoder_box, [(f.stem, f) for f in encoder_fpaths])
# Synthesizer
synthesizer_fpaths = list(synthesizer_models_dir.glob("**/*.pt"))
if len(synthesizer_fpaths) == 0:
raise Exception("No synthesizer models found in %s" % synthesizer_models_dir)
self.repopulate_box(self.synthesizer_box, [(f.stem, f) for f in synthesizer_fpaths])
# Vocoder
vocoder_fpaths = list(vocoder_models_dir.glob("**/*.pt"))
vocoder_items = [(f.stem, f) for f in vocoder_fpaths] + [("Griffin-Lim", None)]
self.repopulate_box(self.vocoder_box, vocoder_items)
@property
def selected_utterance(self):
return self.utterance_history.itemData(self.utterance_history.currentIndex())
def register_utterance(self, utterance: Utterance):
self.utterance_history.blockSignals(True)
self.utterance_history.insertItem(0, utterance.name, utterance)
self.utterance_history.setCurrentIndex(0)
self.utterance_history.blockSignals(False)
if len(self.utterance_history) > self.max_saved_utterances:
self.utterance_history.removeItem(self.max_saved_utterances)
self.play_button.setDisabled(False)
self.generate_button.setDisabled(False)
self.synthesize_button.setDisabled(False)
def log(self, line, mode="newline"):
if mode == "newline":
self.logs.append(line)
if len(self.logs) > self.max_log_lines:
del self.logs[0]
elif mode == "append":
self.logs[-1] += line
elif mode == "overwrite":
self.logs[-1] = line
log_text = '\n'.join(self.logs)
self.log_window.setText(log_text)
self.app.processEvents()
def set_loading(self, value, maximum=1):
self.loading_bar.setValue(value * 100)
self.loading_bar.setMaximum(maximum * 100)
self.loading_bar.setTextVisible(value != 0)
self.app.processEvents()
def populate_gen_options(self, seed, trim_silences):
if seed is not None:
self.random_seed_checkbox.setChecked(True)
self.seed_textbox.setText(str(seed))
self.seed_textbox.setEnabled(True)
else:
self.random_seed_checkbox.setChecked(False)
self.seed_textbox.setText(str(0))
self.seed_textbox.setEnabled(False)
if not trim_silences:
self.trim_silences_checkbox.setChecked(False)
self.trim_silences_checkbox.setDisabled(True)
def update_seed_textbox(self):
if self.random_seed_checkbox.isChecked():
self.seed_textbox.setEnabled(True)
else:
self.seed_textbox.setEnabled(False)
def reset_interface(self):
self.draw_embed(None, None, "current")
self.draw_embed(None, None, "generated")
self.draw_spec(None, "current")
self.draw_spec(None, "generated")
self.draw_umap_projections(set())
self.set_loading(0)
self.play_button.setDisabled(True)
self.generate_button.setDisabled(True)
self.synthesize_button.setDisabled(True)
self.vocode_button.setDisabled(True)
self.replay_wav_button.setDisabled(True)
self.export_wav_button.setDisabled(True)
[self.log("") for _ in range(self.max_log_lines)]
def __init__(self):
## Initialize the application
self.app = QApplication(sys.argv)
super().__init__(None)
self.setWindowTitle("SV2TTS toolbox")
## Main layouts
# Root
root_layout = QGridLayout()
self.setLayout(root_layout)
# Browser
browser_layout = QGridLayout()
root_layout.addLayout(browser_layout, 0, 0, 1, 2)
# Generation
gen_layout = QVBoxLayout()
root_layout.addLayout(gen_layout, 0, 2, 1, 2)
# Projections
self.projections_layout = QVBoxLayout()
root_layout.addLayout(self.projections_layout, 1, 0, 1, 1)
# Visualizations
vis_layout = QVBoxLayout()
root_layout.addLayout(vis_layout, 1, 1, 1, 3)
## Projections
# UMap
fig, self.umap_ax = plt.subplots(figsize=(3, 3), facecolor="#F0F0F0")
fig.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.98)
self.projections_layout.addWidget(FigureCanvas(fig))
self.umap_hot = False
self.clear_button = QPushButton("Clear")
self.projections_layout.addWidget(self.clear_button)
## Browser
# Dataset, speaker and utterance selection
i = 0
self.dataset_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Dataset</b>"), i, 0)
browser_layout.addWidget(self.dataset_box, i + 1, 0)
self.speaker_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Speaker</b>"), i, 1)
browser_layout.addWidget(self.speaker_box, i + 1, 1)
self.utterance_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Utterance</b>"), i, 2)
browser_layout.addWidget(self.utterance_box, i + 1, 2)
self.browser_load_button = QPushButton("Load")
browser_layout.addWidget(self.browser_load_button, i + 1, 3)
i += 2
# Random buttons
self.random_dataset_button = QPushButton("Random")
browser_layout.addWidget(self.random_dataset_button, i, 0)
self.random_speaker_button = QPushButton("Random")
browser_layout.addWidget(self.random_speaker_button, i, 1)
self.random_utterance_button = QPushButton("Random")
browser_layout.addWidget(self.random_utterance_button, i, 2)
self.auto_next_checkbox = QCheckBox("Auto select next")
self.auto_next_checkbox.setChecked(True)
browser_layout.addWidget(self.auto_next_checkbox, i, 3)
i += 1
# Utterance box
browser_layout.addWidget(QLabel("<b>Use embedding from:</b>"), i, 0)
self.utterance_history = QComboBox()
browser_layout.addWidget(self.utterance_history, i, 1, 1, 3)
i += 1
# Random & next utterance buttons
self.browser_browse_button = QPushButton("Browse")
browser_layout.addWidget(self.browser_browse_button, i, 0)
self.record_button = QPushButton("Record")
browser_layout.addWidget(self.record_button, i, 1)
self.play_button = QPushButton("Play")
browser_layout.addWidget(self.play_button, i, 2)
self.stop_button = QPushButton("Stop")
browser_layout.addWidget(self.stop_button, i, 3)
i += 1
# Model and audio output selection
self.encoder_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Encoder</b>"), i, 0)
browser_layout.addWidget(self.encoder_box, i + 1, 0)
self.synthesizer_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Synthesizer</b>"), i, 1)
browser_layout.addWidget(self.synthesizer_box, i + 1, 1)
self.vocoder_box = QComboBox()
browser_layout.addWidget(QLabel("<b>Vocoder</b>"), i, 2)
browser_layout.addWidget(self.vocoder_box, i + 1, 2)
self.audio_out_devices_cb=QComboBox()
browser_layout.addWidget(QLabel("<b>Audio Output</b>"), i, 3)
browser_layout.addWidget(self.audio_out_devices_cb, i + 1, 3)
i += 2
#Replay & Save Audio
browser_layout.addWidget(QLabel("<b>Toolbox Output:</b>"), i, 0)
self.waves_cb = QComboBox()
self.waves_cb_model = QStringListModel()
self.waves_cb.setModel(self.waves_cb_model)
self.waves_cb.setToolTip("Select one of the last generated waves in this section for replaying or exporting")
browser_layout.addWidget(self.waves_cb, i, 1)
self.replay_wav_button = QPushButton("Replay")
self.replay_wav_button.setToolTip("Replay last generated vocoder")
browser_layout.addWidget(self.replay_wav_button, i, 2)
self.export_wav_button = QPushButton("Export")
self.export_wav_button.setToolTip("Save last generated vocoder audio in filesystem as a wav file")
browser_layout.addWidget(self.export_wav_button, i, 3)
i += 1
## Embed & spectrograms
vis_layout.addStretch()
gridspec_kw = {"width_ratios": [1, 4]}
fig, self.current_ax = plt.subplots(1, 2, figsize=(10, 2.25), facecolor="#F0F0F0",
gridspec_kw=gridspec_kw)
fig.subplots_adjust(left=0, bottom=0.1, right=1, top=0.8)
vis_layout.addWidget(FigureCanvas(fig))
fig, self.gen_ax = plt.subplots(1, 2, figsize=(10, 2.25), facecolor="#F0F0F0",
gridspec_kw=gridspec_kw)
fig.subplots_adjust(left=0, bottom=0.1, right=1, top=0.8)
vis_layout.addWidget(FigureCanvas(fig))
for ax in self.current_ax.tolist() + self.gen_ax.tolist():
ax.set_facecolor("#F0F0F0")
for side in ["top", "right", "bottom", "left"]:
ax.spines[side].set_visible(False)
## Generation
self.text_prompt = QPlainTextEdit(default_text)
gen_layout.addWidget(self.text_prompt, stretch=1)
self.generate_button = QPushButton("Synthesize and vocode")
gen_layout.addWidget(self.generate_button)
layout = QHBoxLayout()
self.synthesize_button = QPushButton("Synthesize only")
layout.addWidget(self.synthesize_button)
self.vocode_button = QPushButton("Vocode only")
layout.addWidget(self.vocode_button)
gen_layout.addLayout(layout)
layout_seed = QGridLayout()
self.random_seed_checkbox = QCheckBox("Random seed:")
self.random_seed_checkbox.setToolTip("When checked, makes the synthesizer and vocoder deterministic.")
layout_seed.addWidget(self.random_seed_checkbox, 0, 0)
self.seed_textbox = QLineEdit()
self.seed_textbox.setMaximumWidth(80)
layout_seed.addWidget(self.seed_textbox, 0, 1)
self.trim_silences_checkbox = QCheckBox("Enhance vocoder output")
self.trim_silences_checkbox.setToolTip("When checked, trims excess silence in vocoder output."
" This feature requires `webrtcvad` to be installed.")
layout_seed.addWidget(self.trim_silences_checkbox, 0, 2, 1, 2)
gen_layout.addLayout(layout_seed)
self.loading_bar = QProgressBar()
gen_layout.addWidget(self.loading_bar)
self.log_window = QLabel()
self.log_window.setAlignment(Qt.AlignBottom | Qt.AlignLeft)
gen_layout.addWidget(self.log_window)
self.logs = []
gen_layout.addStretch()
## Set the size of the window and of the elements
max_size = QDesktopWidget().availableGeometry(self).size() * 0.8
self.resize(max_size)
## Finalize the display
self.reset_interface()
self.show()
def start(self):
self.app.exec_()

5
toolbox/utterance.py Normal file
View File

@ -0,0 +1,5 @@
from collections import namedtuple
Utterance = namedtuple("Utterance", "name speaker_name wav spec embed partial_embeds synth")
Utterance.__eq__ = lambda x, y: x.name == y.name
Utterance.__hash__ = lambda x: hash(x.name)

0
utils/__init__.py Normal file
View File

40
utils/argutils.py Normal file
View File

@ -0,0 +1,40 @@
from pathlib import Path
import numpy as np
import argparse
_type_priorities = [ # In decreasing order
Path,
str,
int,
float,
bool,
]
def _priority(o):
p = next((i for i, t in enumerate(_type_priorities) if type(o) is t), None)
if p is not None:
return p
p = next((i for i, t in enumerate(_type_priorities) if isinstance(o, t)), None)
if p is not None:
return p
return len(_type_priorities)
def print_args(args: argparse.Namespace, parser=None):
args = vars(args)
if parser is None:
priorities = list(map(_priority, args.values()))
else:
all_params = [a.dest for g in parser._action_groups for a in g._group_actions ]
priority = lambda p: all_params.index(p) if p in all_params else len(all_params)
priorities = list(map(priority, args.keys()))
pad = max(map(len, args.keys())) + 3
indices = np.lexsort((list(args.keys()), priorities))
items = list(args.items())
print("Arguments:")
for i in indices:
param, value = items[i]
print(" {0}:{1}{2}".format(param, ' ' * (pad - len(param)), value))
print("")

247
utils/logmmse.py Normal file
View File

@ -0,0 +1,247 @@
# The MIT License (MIT)
#
# Copyright (c) 2015 braindead
#
# 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.
#
#
# This code was extracted from the logmmse package (https://pypi.org/project/logmmse/) and I
# simply modified the interface to meet my needs.
import numpy as np
import math
from scipy.special import expn
from collections import namedtuple
NoiseProfile = namedtuple("NoiseProfile", "sampling_rate window_size len1 len2 win n_fft noise_mu2")
def profile_noise(noise, sampling_rate, window_size=0):
"""
Creates a profile of the noise in a given waveform.
:param noise: a waveform containing noise ONLY, as a numpy array of floats or ints.
:param sampling_rate: the sampling rate of the audio
:param window_size: the size of the window the logmmse algorithm operates on. A default value
will be picked if left as 0.
:return: a NoiseProfile object
"""
noise, dtype = to_float(noise)
noise += np.finfo(np.float64).eps
if window_size == 0:
window_size = int(math.floor(0.02 * sampling_rate))
if window_size % 2 == 1:
window_size = window_size + 1
perc = 50
len1 = int(math.floor(window_size * perc / 100))
len2 = int(window_size - len1)
win = np.hanning(window_size)
win = win * len2 / np.sum(win)
n_fft = 2 * window_size
noise_mean = np.zeros(n_fft)
n_frames = len(noise) // window_size
for j in range(0, window_size * n_frames, window_size):
noise_mean += np.absolute(np.fft.fft(win * noise[j:j + window_size], n_fft, axis=0))
noise_mu2 = (noise_mean / n_frames) ** 2
return NoiseProfile(sampling_rate, window_size, len1, len2, win, n_fft, noise_mu2)
def denoise(wav, noise_profile: NoiseProfile, eta=0.15):
"""
Cleans the noise from a speech waveform given a noise profile. The waveform must have the
same sampling rate as the one used to create the noise profile.
:param wav: a speech waveform as a numpy array of floats or ints.
:param noise_profile: a NoiseProfile object that was created from a similar (or a segment of
the same) waveform.
:param eta: voice threshold for noise update. While the voice activation detection value is
below this threshold, the noise profile will be continuously updated throughout the audio.
Set to 0 to disable updating the noise profile.
:return: the clean wav as a numpy array of floats or ints of the same length.
"""
wav, dtype = to_float(wav)
wav += np.finfo(np.float64).eps
p = noise_profile
nframes = int(math.floor(len(wav) / p.len2) - math.floor(p.window_size / p.len2))
x_final = np.zeros(nframes * p.len2)
aa = 0.98
mu = 0.98
ksi_min = 10 ** (-25 / 10)
x_old = np.zeros(p.len1)
xk_prev = np.zeros(p.len1)
noise_mu2 = p.noise_mu2
for k in range(0, nframes * p.len2, p.len2):
insign = p.win * wav[k:k + p.window_size]
spec = np.fft.fft(insign, p.n_fft, axis=0)
sig = np.absolute(spec)
sig2 = sig ** 2
gammak = np.minimum(sig2 / noise_mu2, 40)
if xk_prev.all() == 0:
ksi = aa + (1 - aa) * np.maximum(gammak - 1, 0)
else:
ksi = aa * xk_prev / noise_mu2 + (1 - aa) * np.maximum(gammak - 1, 0)
ksi = np.maximum(ksi_min, ksi)
log_sigma_k = gammak * ksi/(1 + ksi) - np.log(1 + ksi)
vad_decision = np.sum(log_sigma_k) / p.window_size
if vad_decision < eta:
noise_mu2 = mu * noise_mu2 + (1 - mu) * sig2
a = ksi / (1 + ksi)
vk = a * gammak
ei_vk = 0.5 * expn(1, np.maximum(vk, 1e-8))
hw = a * np.exp(ei_vk)
sig = sig * hw
xk_prev = sig ** 2
xi_w = np.fft.ifft(hw * spec, p.n_fft, axis=0)
xi_w = np.real(xi_w)
x_final[k:k + p.len2] = x_old + xi_w[0:p.len1]
x_old = xi_w[p.len1:p.window_size]
output = from_float(x_final, dtype)
output = np.pad(output, (0, len(wav) - len(output)), mode="constant")
return output
## Alternative VAD algorithm to webrctvad. It has the advantage of not requiring to install that
## darn package and it also works for any sampling rate. Maybe I'll eventually use it instead of
## webrctvad
# def vad(wav, sampling_rate, eta=0.15, window_size=0):
# """
# TODO: fix doc
# Creates a profile of the noise in a given waveform.
#
# :param wav: a waveform containing noise ONLY, as a numpy array of floats or ints.
# :param sampling_rate: the sampling rate of the audio
# :param window_size: the size of the window the logmmse algorithm operates on. A default value
# will be picked if left as 0.
# :param eta: voice threshold for noise update. While the voice activation detection value is
# below this threshold, the noise profile will be continuously updated throughout the audio.
# Set to 0 to disable updating the noise profile.
# """
# wav, dtype = to_float(wav)
# wav += np.finfo(np.float64).eps
#
# if window_size == 0:
# window_size = int(math.floor(0.02 * sampling_rate))
#
# if window_size % 2 == 1:
# window_size = window_size + 1
#
# perc = 50
# len1 = int(math.floor(window_size * perc / 100))
# len2 = int(window_size - len1)
#
# win = np.hanning(window_size)
# win = win * len2 / np.sum(win)
# n_fft = 2 * window_size
#
# wav_mean = np.zeros(n_fft)
# n_frames = len(wav) // window_size
# for j in range(0, window_size * n_frames, window_size):
# wav_mean += np.absolute(np.fft.fft(win * wav[j:j + window_size], n_fft, axis=0))
# noise_mu2 = (wav_mean / n_frames) ** 2
#
# wav, dtype = to_float(wav)
# wav += np.finfo(np.float64).eps
#
# nframes = int(math.floor(len(wav) / len2) - math.floor(window_size / len2))
# vad = np.zeros(nframes * len2, dtype=np.bool)
#
# aa = 0.98
# mu = 0.98
# ksi_min = 10 ** (-25 / 10)
#
# xk_prev = np.zeros(len1)
# noise_mu2 = noise_mu2
# for k in range(0, nframes * len2, len2):
# insign = win * wav[k:k + window_size]
#
# spec = np.fft.fft(insign, n_fft, axis=0)
# sig = np.absolute(spec)
# sig2 = sig ** 2
#
# gammak = np.minimum(sig2 / noise_mu2, 40)
#
# if xk_prev.all() == 0:
# ksi = aa + (1 - aa) * np.maximum(gammak - 1, 0)
# else:
# ksi = aa * xk_prev / noise_mu2 + (1 - aa) * np.maximum(gammak - 1, 0)
# ksi = np.maximum(ksi_min, ksi)
#
# log_sigma_k = gammak * ksi / (1 + ksi) - np.log(1 + ksi)
# vad_decision = np.sum(log_sigma_k) / window_size
# if vad_decision < eta:
# noise_mu2 = mu * noise_mu2 + (1 - mu) * sig2
# print(vad_decision)
#
# a = ksi / (1 + ksi)
# vk = a * gammak
# ei_vk = 0.5 * expn(1, np.maximum(vk, 1e-8))
# hw = a * np.exp(ei_vk)
# sig = sig * hw
# xk_prev = sig ** 2
#
# vad[k:k + len2] = vad_decision >= eta
#
# vad = np.pad(vad, (0, len(wav) - len(vad)), mode="constant")
# return vad
def to_float(_input):
if _input.dtype == np.float64:
return _input, _input.dtype
elif _input.dtype == np.float32:
return _input.astype(np.float64), _input.dtype
elif _input.dtype == np.uint8:
return (_input - 128) / 128., _input.dtype
elif _input.dtype == np.int16:
return _input / 32768., _input.dtype
elif _input.dtype == np.int32:
return _input / 2147483648., _input.dtype
raise ValueError('Unsupported wave file format')
def from_float(_input, dtype):
if dtype == np.float64:
return _input, np.float64
elif dtype == np.float32:
return _input.astype(np.float32)
elif dtype == np.uint8:
return ((_input * 128) + 128).astype(np.uint8)
elif dtype == np.int16:
return (_input * 32768).astype(np.int16)
elif dtype == np.int32:
print(_input)
return (_input * 2147483648).astype(np.int32)
raise ValueError('Unsupported wave file format')

17
utils/modelutils.py Normal file
View File

@ -0,0 +1,17 @@
from pathlib import Path
def check_model_paths(encoder_path: Path, synthesizer_path: Path, vocoder_path: Path):
# This function tests the model paths and makes sure at least one is valid.
if encoder_path.is_file() or encoder_path.is_dir():
return
if synthesizer_path.is_file() or synthesizer_path.is_dir():
return
if vocoder_path.is_file() or vocoder_path.is_dir():
return
# If none of the paths exist, remind the user to download models if needed
print("********************************************************************************")
print("Error: Model files not found. Follow these instructions to get and install the models:")
print("https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models")
print("********************************************************************************\n")
quit(-1)

45
utils/profiler.py Normal file
View File

@ -0,0 +1,45 @@
from time import perf_counter as timer
from collections import OrderedDict
import numpy as np
class Profiler:
def __init__(self, summarize_every=5, disabled=False):
self.last_tick = timer()
self.logs = OrderedDict()
self.summarize_every = summarize_every
self.disabled = disabled
def tick(self, name):
if self.disabled:
return
# Log the time needed to execute that function
if not name in self.logs:
self.logs[name] = []
if len(self.logs[name]) >= self.summarize_every:
self.summarize()
self.purge_logs()
self.logs[name].append(timer() - self.last_tick)
self.reset_timer()
def purge_logs(self):
for name in self.logs:
self.logs[name].clear()
def reset_timer(self):
self.last_tick = timer()
def summarize(self):
n = max(map(len, self.logs.values()))
assert n == self.summarize_every
print("\nAverage execution time over %d steps:" % n)
name_msgs = ["%s (%d/%d):" % (name, len(deltas), n) for name, deltas in self.logs.items()]
pad = max(map(len, name_msgs))
for name_msg, deltas in zip(name_msgs, self.logs.values()):
print(" %s mean: %4.0fms std: %4.0fms" %
(name_msg.ljust(pad), np.mean(deltas) * 1000, np.std(deltas) * 1000))
print("", flush=True)

22
vocoder/LICENSE.txt Normal file
View File

@ -0,0 +1,22 @@
MIT License
Original work Copyright (c) 2019 fatchord (https://github.com/fatchord)
Modified work Copyright (c) 2019 Corentin Jemine (https://github.com/CorentinJ)
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.

108
vocoder/audio.py Normal file
View File

@ -0,0 +1,108 @@
import math
import numpy as np
import librosa
import vocoder.hparams as hp
from scipy.signal import lfilter
import soundfile as sf
def label_2_float(x, bits) :
return 2 * x / (2**bits - 1.) - 1.
def float_2_label(x, bits) :
assert abs(x).max() <= 1.0
x = (x + 1.) * (2**bits - 1) / 2
return x.clip(0, 2**bits - 1)
def load_wav(path) :
return librosa.load(str(path), sr=hp.sample_rate)[0]
def save_wav(x, path) :
sf.write(path, x.astype(np.float32), hp.sample_rate)
def split_signal(x) :
unsigned = x + 2**15
coarse = unsigned // 256
fine = unsigned % 256
return coarse, fine
def combine_signal(coarse, fine) :
return coarse * 256 + fine - 2**15
def encode_16bits(x) :
return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)
mel_basis = None
def linear_to_mel(spectrogram):
global mel_basis
if mel_basis is None:
mel_basis = build_mel_basis()
return np.dot(mel_basis, spectrogram)
def build_mel_basis():
return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)
def normalize(S):
return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)
def denormalize(S):
return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db
def amp_to_db(x):
return 20 * np.log10(np.maximum(1e-5, x))
def db_to_amp(x):
return np.power(10.0, x * 0.05)
def spectrogram(y):
D = stft(y)
S = amp_to_db(np.abs(D)) - hp.ref_level_db
return normalize(S)
def melspectrogram(y):
D = stft(y)
S = amp_to_db(linear_to_mel(np.abs(D)))
return normalize(S)
def stft(y):
return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)
def pre_emphasis(x):
return lfilter([1, -hp.preemphasis], [1], x)
def de_emphasis(x):
return lfilter([1], [1, -hp.preemphasis], x)
def encode_mu_law(x, mu) :
mu = mu - 1
fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
return np.floor((fx + 1) / 2 * mu + 0.5)
def decode_mu_law(y, mu, from_labels=True) :
if from_labels:
y = label_2_float(y, math.log2(mu))
mu = mu - 1
x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
return x

120
vocoder/display.py Normal file
View File

@ -0,0 +1,120 @@
import matplotlib.pyplot as plt
import time
import numpy as np
import sys
def progbar(i, n, size=16):
done = (i * size) // n
bar = ''
for i in range(size):
bar += '█' if i <= done else '░'
return bar
def stream(message) :
try:
sys.stdout.write("\r{%s}" % message)
except:
#Remove non-ASCII characters from message
message = ''.join(i for i in message if ord(i)<128)
sys.stdout.write("\r{%s}" % message)
def simple_table(item_tuples) :
border_pattern = '+---------------------------------------'
whitespace = ' '
headings, cells, = [], []
for item in item_tuples :
heading, cell = str(item[0]), str(item[1])
pad_head = True if len(heading) < len(cell) else False
pad = abs(len(heading) - len(cell))
pad = whitespace[:pad]
pad_left = pad[:len(pad)//2]
pad_right = pad[len(pad)//2:]
if pad_head :
heading = pad_left + heading + pad_right
else :
cell = pad_left + cell + pad_right
headings += [heading]
cells += [cell]
border, head, body = '', '', ''
for i in range(len(item_tuples)) :
temp_head = f'| {headings[i]} '
temp_body = f'| {cells[i]} '
border += border_pattern[:len(temp_head)]
head += temp_head
body += temp_body
if i == len(item_tuples) - 1 :
head += '|'
body += '|'
border += '+'
print(border)
print(head)
print(border)
print(body)
print(border)
print(' ')
def time_since(started) :
elapsed = time.time() - started
m = int(elapsed // 60)
s = int(elapsed % 60)
if m >= 60 :
h = int(m // 60)
m = m % 60
return f'{h}h {m}m {s}s'
else :
return f'{m}m {s}s'
def save_attention(attn, path) :
fig = plt.figure(figsize=(12, 6))
plt.imshow(attn.T, interpolation='nearest', aspect='auto')
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def save_spectrogram(M, path, length=None) :
M = np.flip(M, axis=0)
if length : M = M[:, :length]
fig = plt.figure(figsize=(12, 6))
plt.imshow(M, interpolation='nearest', aspect='auto')
fig.savefig(f'{path}.png', bbox_inches='tight')
plt.close(fig)
def plot(array) :
fig = plt.figure(figsize=(30, 5))
ax = fig.add_subplot(111)
ax.xaxis.label.set_color('grey')
ax.yaxis.label.set_color('grey')
ax.xaxis.label.set_fontsize(23)
ax.yaxis.label.set_fontsize(23)
ax.tick_params(axis='x', colors='grey', labelsize=23)
ax.tick_params(axis='y', colors='grey', labelsize=23)
plt.plot(array)
def plot_spec(M) :
M = np.flip(M, axis=0)
plt.figure(figsize=(18,4))
plt.imshow(M, interpolation='nearest', aspect='auto')
plt.show()

132
vocoder/distribution.py Normal file
View File

@ -0,0 +1,132 @@
import numpy as np
import torch
import torch.nn.functional as F
def log_sum_exp(x):
""" numerically stable log_sum_exp implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
# It is adapted from https://github.com/r9y9/wavenet_vocoder/blob/master/wavenet_vocoder/mixture.py
def discretized_mix_logistic_loss(y_hat, y, num_classes=65536,
log_scale_min=None, reduce=True):
if log_scale_min is None:
log_scale_min = float(np.log(1e-14))
y_hat = y_hat.permute(0,2,1)
assert y_hat.dim() == 3
assert y_hat.size(1) % 3 == 0
nr_mix = y_hat.size(1) // 3
# (B x T x C)
y_hat = y_hat.transpose(1, 2)
# unpack parameters. (B, T, num_mixtures) x 3
logit_probs = y_hat[:, :, :nr_mix]
means = y_hat[:, :, nr_mix:2 * nr_mix]
log_scales = torch.clamp(y_hat[:, :, 2 * nr_mix:3 * nr_mix], min=log_scale_min)
# B x T x 1 -> B x T x num_mixtures
y = y.expand_as(means)
centered_y = y - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_y + 1. / (num_classes - 1))
cdf_plus = torch.sigmoid(plus_in)
min_in = inv_stdv * (centered_y - 1. / (num_classes - 1))
cdf_min = torch.sigmoid(min_in)
# log probability for edge case of 0 (before scaling)
# equivalent: torch.log(F.sigmoid(plus_in))
log_cdf_plus = plus_in - F.softplus(plus_in)
# log probability for edge case of 255 (before scaling)
# equivalent: (1 - F.sigmoid(min_in)).log()
log_one_minus_cdf_min = -F.softplus(min_in)
# probability for all other cases
cdf_delta = cdf_plus - cdf_min
mid_in = inv_stdv * centered_y
# log probability in the center of the bin, to be used in extreme cases
# (not actually used in our code)
log_pdf_mid = mid_in - log_scales - 2. * F.softplus(mid_in)
# tf equivalent
"""
log_probs = tf.where(x < -0.999, log_cdf_plus,
tf.where(x > 0.999, log_one_minus_cdf_min,
tf.where(cdf_delta > 1e-5,
tf.log(tf.maximum(cdf_delta, 1e-12)),
log_pdf_mid - np.log(127.5))))
"""
# TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value
# for num_classes=65536 case? 1e-7? not sure..
inner_inner_cond = (cdf_delta > 1e-5).float()
inner_inner_out = inner_inner_cond * \
torch.log(torch.clamp(cdf_delta, min=1e-12)) + \
(1. - inner_inner_cond) * (log_pdf_mid - np.log((num_classes - 1) / 2))
inner_cond = (y > 0.999).float()
inner_out = inner_cond * log_one_minus_cdf_min + (1. - inner_cond) * inner_inner_out
cond = (y < -0.999).float()
log_probs = cond * log_cdf_plus + (1. - cond) * inner_out
log_probs = log_probs + F.log_softmax(logit_probs, -1)
if reduce:
return -torch.mean(log_sum_exp(log_probs))
else:
return -log_sum_exp(log_probs).unsqueeze(-1)
def sample_from_discretized_mix_logistic(y, log_scale_min=None):
"""
Sample from discretized mixture of logistic distributions
Args:
y (Tensor): B x C x T
log_scale_min (float): Log scale minimum value
Returns:
Tensor: sample in range of [-1, 1].
"""
if log_scale_min is None:
log_scale_min = float(np.log(1e-14))
assert y.size(1) % 3 == 0
nr_mix = y.size(1) // 3
# B x T x C
y = y.transpose(1, 2)
logit_probs = y[:, :, :nr_mix]
# sample mixture indicator from softmax
temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5)
temp = logit_probs.data - torch.log(- torch.log(temp))
_, argmax = temp.max(dim=-1)
# (B, T) -> (B, T, nr_mix)
one_hot = to_one_hot(argmax, nr_mix)
# select logistic parameters
means = torch.sum(y[:, :, nr_mix:2 * nr_mix] * one_hot, dim=-1)
log_scales = torch.clamp(torch.sum(
y[:, :, 2 * nr_mix:3 * nr_mix] * one_hot, dim=-1), min=log_scale_min)
# sample from logistic & clip to interval
# we don't actually round to the nearest 8bit value when sampling
u = means.data.new(means.size()).uniform_(1e-5, 1.0 - 1e-5)
x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1. - u))
x = torch.clamp(torch.clamp(x, min=-1.), max=1.)
return x
def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
if tensor.is_cuda:
one_hot = one_hot.cuda()
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
return one_hot

31
vocoder/gen_wavernn.py Normal file
View File

@ -0,0 +1,31 @@
from vocoder.models.fatchord_version import WaveRNN
from vocoder.audio import *
def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, save_path):
k = model.get_step() // 1000
for i, (m, x) in enumerate(test_set, 1):
if i > samples:
break
print('\n| Generating: %i/%i' % (i, samples))
x = x[0].numpy()
bits = 16 if hp.voc_mode == 'MOL' else hp.bits
if hp.mu_law and hp.voc_mode != 'MOL' :
x = decode_mu_law(x, 2**bits, from_labels=True)
else :
x = label_2_float(x, bits)
save_wav(x, save_path.joinpath("%dk_steps_%d_target.wav" % (k, i)))
batch_str = "gen_batched_target%d_overlap%d" % (target, overlap) if batched else \
"gen_not_batched"
save_str = save_path.joinpath("%dk_steps_%d_%s.wav" % (k, i, batch_str))
wav = model.generate(m, batched, target, overlap, hp.mu_law)
save_wav(wav, save_str)

44
vocoder/hparams.py Normal file
View File

@ -0,0 +1,44 @@
from synthesizer.hparams import hparams as _syn_hp
# Audio settings------------------------------------------------------------------------
# Match the values of the synthesizer
sample_rate = _syn_hp.sample_rate
n_fft = _syn_hp.n_fft
num_mels = _syn_hp.num_mels
hop_length = _syn_hp.hop_size
win_length = _syn_hp.win_size
fmin = _syn_hp.fmin
min_level_db = _syn_hp.min_level_db
ref_level_db = _syn_hp.ref_level_db
mel_max_abs_value = _syn_hp.max_abs_value
preemphasis = _syn_hp.preemphasis
apply_preemphasis = _syn_hp.preemphasize
bits = 9 # bit depth of signal
mu_law = True # Recommended to suppress noise if using raw bits in hp.voc_mode
# below
# WAVERNN / VOCODER --------------------------------------------------------------------------------
voc_mode = 'RAW' # either 'RAW' (softmax on raw bits) or 'MOL' (sample from
# mixture of logistics)
voc_upsample_factors = (5, 5, 8) # NB - this needs to correctly factorise hop_length
voc_rnn_dims = 512
voc_fc_dims = 512
voc_compute_dims = 128
voc_res_out_dims = 128
voc_res_blocks = 10
# Training
voc_batch_size = 100
voc_lr = 1e-4
voc_gen_at_checkpoint = 5 # number of samples to generate at each checkpoint
voc_pad = 2 # this will pad the input so that the resnet can 'see' wider
# than input length
voc_seq_len = hop_length * 5 # must be a multiple of hop_length
# Generating / Synthesizing
voc_gen_batched = True # very fast (realtime+) single utterance batched generation
voc_target = 8000 # target number of samples to be generated in each batch entry
voc_overlap = 400 # number of samples for crossfading between batches

64
vocoder/inference.py Normal file
View File

@ -0,0 +1,64 @@
from vocoder.models.fatchord_version import WaveRNN
from vocoder import hparams as hp
import torch
_model = None # type: WaveRNN
def load_model(weights_fpath, verbose=True):
global _model, _device
if verbose:
print("Building Wave-RNN")
_model = WaveRNN(
rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode
)
if torch.cuda.is_available():
_model = _model.cuda()
_device = torch.device('cuda')
else:
_device = torch.device('cpu')
if verbose:
print("Loading model weights at %s" % weights_fpath)
checkpoint = torch.load(weights_fpath, _device)
_model.load_state_dict(checkpoint['model_state'])
_model.eval()
def is_loaded():
return _model is not None
def infer_waveform(mel, normalize=True, batched=True, target=8000, overlap=800,
progress_callback=None):
"""
Infers the waveform of a mel spectrogram output by the synthesizer (the format must match
that of the synthesizer!)
:param normalize:
:param batched:
:param target:
:param overlap:
:return:
"""
if _model is None:
raise Exception("Please load Wave-RNN in memory before using it")
if normalize:
mel = mel / hp.mel_max_abs_value
mel = torch.from_numpy(mel[None, ...])
wav = _model.generate(mel, batched, target, overlap, hp.mu_law, progress_callback)
return wav

170
vocoder/models/deepmind_version.py Normal file
View File

@ -0,0 +1,170 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.display import *
from utils.dsp import *
class WaveRNN(nn.Module) :
def __init__(self, hidden_size=896, quantisation=256) :
super(WaveRNN, self).__init__()
self.hidden_size = hidden_size
self.split_size = hidden_size // 2
# The main matmul
self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
# Output fc layers
self.O1 = nn.Linear(self.split_size, self.split_size)
self.O2 = nn.Linear(self.split_size, quantisation)
self.O3 = nn.Linear(self.split_size, self.split_size)
self.O4 = nn.Linear(self.split_size, quantisation)
# Input fc layers
self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)
# biases for the gates
self.bias_u = nn.Parameter(torch.zeros(self.hidden_size))
self.bias_r = nn.Parameter(torch.zeros(self.hidden_size))
self.bias_e = nn.Parameter(torch.zeros(self.hidden_size))
# display num params
self.num_params()
def forward(self, prev_y, prev_hidden, current_coarse) :
# Main matmul - the projection is split 3 ways
R_hidden = self.R(prev_hidden)
R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)
# Project the prev input
coarse_input_proj = self.I_coarse(prev_y)
I_coarse_u, I_coarse_r, I_coarse_e = \
torch.split(coarse_input_proj, self.split_size, dim=1)
# Project the prev input and current coarse sample
fine_input = torch.cat([prev_y, current_coarse], dim=1)
fine_input_proj = self.I_fine(fine_input)
I_fine_u, I_fine_r, I_fine_e = \
torch.split(fine_input_proj, self.split_size, dim=1)
# concatenate for the gates
I_u = torch.cat([I_coarse_u, I_fine_u], dim=1)
I_r = torch.cat([I_coarse_r, I_fine_r], dim=1)
I_e = torch.cat([I_coarse_e, I_fine_e], dim=1)
# Compute all gates for coarse and fine
u = F.sigmoid(R_u + I_u + self.bias_u)
r = F.sigmoid(R_r + I_r + self.bias_r)
e = F.tanh(r * R_e + I_e + self.bias_e)
hidden = u * prev_hidden + (1. - u) * e
# Split the hidden state
hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
# Compute outputs
out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
out_fine = self.O4(F.relu(self.O3(hidden_fine)))
return out_coarse, out_fine, hidden
def generate(self, seq_len):
with torch.no_grad():
# First split up the biases for the gates
b_coarse_u, b_fine_u = torch.split(self.bias_u, self.split_size)
b_coarse_r, b_fine_r = torch.split(self.bias_r, self.split_size)
b_coarse_e, b_fine_e = torch.split(self.bias_e, self.split_size)
# Lists for the two output seqs
c_outputs, f_outputs = [], []
# Some initial inputs
out_coarse = torch.LongTensor([0]).cuda()
out_fine = torch.LongTensor([0]).cuda()
# We'll meed a hidden state
hidden = self.init_hidden()
# Need a clock for display
start = time.time()
# Loop for generation
for i in range(seq_len) :
# Split into two hidden states
hidden_coarse, hidden_fine = \
torch.split(hidden, self.split_size, dim=1)
# Scale and concat previous predictions
out_coarse = out_coarse.unsqueeze(0).float() / 127.5 - 1.
out_fine = out_fine.unsqueeze(0).float() / 127.5 - 1.
prev_outputs = torch.cat([out_coarse, out_fine], dim=1)
# Project input
coarse_input_proj = self.I_coarse(prev_outputs)
I_coarse_u, I_coarse_r, I_coarse_e = \
torch.split(coarse_input_proj, self.split_size, dim=1)
# Project hidden state and split 6 ways
R_hidden = self.R(hidden)
R_coarse_u , R_fine_u, \
R_coarse_r, R_fine_r, \
R_coarse_e, R_fine_e = torch.split(R_hidden, self.split_size, dim=1)
# Compute the coarse gates
u = F.sigmoid(R_coarse_u + I_coarse_u + b_coarse_u)
r = F.sigmoid(R_coarse_r + I_coarse_r + b_coarse_r)
e = F.tanh(r * R_coarse_e + I_coarse_e + b_coarse_e)
hidden_coarse = u * hidden_coarse + (1. - u) * e
# Compute the coarse output
out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
posterior = F.softmax(out_coarse, dim=1)
distrib = torch.distributions.Categorical(posterior)
out_coarse = distrib.sample()
c_outputs.append(out_coarse)
# Project the [prev outputs and predicted coarse sample]
coarse_pred = out_coarse.float() / 127.5 - 1.
fine_input = torch.cat([prev_outputs, coarse_pred.unsqueeze(0)], dim=1)
fine_input_proj = self.I_fine(fine_input)
I_fine_u, I_fine_r, I_fine_e = \
torch.split(fine_input_proj, self.split_size, dim=1)
# Compute the fine gates
u = F.sigmoid(R_fine_u + I_fine_u + b_fine_u)
r = F.sigmoid(R_fine_r + I_fine_r + b_fine_r)
e = F.tanh(r * R_fine_e + I_fine_e + b_fine_e)
hidden_fine = u * hidden_fine + (1. - u) * e
# Compute the fine output
out_fine = self.O4(F.relu(self.O3(hidden_fine)))
posterior = F.softmax(out_fine, dim=1)
distrib = torch.distributions.Categorical(posterior)
out_fine = distrib.sample()
f_outputs.append(out_fine)
# Put the hidden state back together
hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
# Display progress
speed = (i + 1) / (time.time() - start)
stream('Gen: %i/%i -- Speed: %i', (i + 1, seq_len, speed))
coarse = torch.stack(c_outputs).squeeze(1).cpu().data.numpy()
fine = torch.stack(f_outputs).squeeze(1).cpu().data.numpy()
output = combine_signal(coarse, fine)
return output, coarse, fine
def init_hidden(self, batch_size=1) :
return torch.zeros(batch_size, self.hidden_size).cuda()
def num_params(self) :
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
print('Trainable Parameters: %.3f million' % parameters)

434
vocoder/models/fatchord_version.py Normal file
View File

@ -0,0 +1,434 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from vocoder.distribution import sample_from_discretized_mix_logistic
from vocoder.display import *
from vocoder.audio import *
class ResBlock(nn.Module):
def __init__(self, dims):
super().__init__()
self.conv1 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
self.conv2 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
self.batch_norm1 = nn.BatchNorm1d(dims)
self.batch_norm2 = nn.BatchNorm1d(dims)
def forward(self, x):
residual = x
x = self.conv1(x)
x = self.batch_norm1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.batch_norm2(x)
return x + residual
class MelResNet(nn.Module):
def __init__(self, res_blocks, in_dims, compute_dims, res_out_dims, pad):
super().__init__()
k_size = pad * 2 + 1
self.conv_in = nn.Conv1d(in_dims, compute_dims, kernel_size=k_size, bias=False)
self.batch_norm = nn.BatchNorm1d(compute_dims)
self.layers = nn.ModuleList()
for i in range(res_blocks):
self.layers.append(ResBlock(compute_dims))
self.conv_out = nn.Conv1d(compute_dims, res_out_dims, kernel_size=1)
def forward(self, x):
x = self.conv_in(x)
x = self.batch_norm(x)
x = F.relu(x)
for f in self.layers: x = f(x)
x = self.conv_out(x)
return x
class Stretch2d(nn.Module):
def __init__(self, x_scale, y_scale):
super().__init__()
self.x_scale = x_scale
self.y_scale = y_scale
def forward(self, x):
b, c, h, w = x.size()
x = x.unsqueeze(-1).unsqueeze(3)
x = x.repeat(1, 1, 1, self.y_scale, 1, self.x_scale)
return x.view(b, c, h * self.y_scale, w * self.x_scale)
class UpsampleNetwork(nn.Module):
def __init__(self, feat_dims, upsample_scales, compute_dims,
res_blocks, res_out_dims, pad):
super().__init__()
total_scale = np.cumproduct(upsample_scales)[-1]
self.indent = pad * total_scale
self.resnet = MelResNet(res_blocks, feat_dims, compute_dims, res_out_dims, pad)
self.resnet_stretch = Stretch2d(total_scale, 1)
self.up_layers = nn.ModuleList()
for scale in upsample_scales:
k_size = (1, scale * 2 + 1)
padding = (0, scale)
stretch = Stretch2d(scale, 1)
conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False)
conv.weight.data.fill_(1. / k_size[1])
self.up_layers.append(stretch)
self.up_layers.append(conv)
def forward(self, m):
aux = self.resnet(m).unsqueeze(1)
aux = self.resnet_stretch(aux)
aux = aux.squeeze(1)
m = m.unsqueeze(1)
for f in self.up_layers: m = f(m)
m = m.squeeze(1)[:, :, self.indent:-self.indent]
return m.transpose(1, 2), aux.transpose(1, 2)
class WaveRNN(nn.Module):
def __init__(self, rnn_dims, fc_dims, bits, pad, upsample_factors,
feat_dims, compute_dims, res_out_dims, res_blocks,
hop_length, sample_rate, mode='RAW'):
super().__init__()
self.mode = mode
self.pad = pad
if self.mode == 'RAW' :
self.n_classes = 2 ** bits
elif self.mode == 'MOL' :
self.n_classes = 30
else :
RuntimeError("Unknown model mode value - ", self.mode)
self.rnn_dims = rnn_dims
self.aux_dims = res_out_dims // 4
self.hop_length = hop_length
self.sample_rate = sample_rate
self.upsample = UpsampleNetwork(feat_dims, upsample_factors, compute_dims, res_blocks, res_out_dims, pad)
self.I = nn.Linear(feat_dims + self.aux_dims + 1, rnn_dims)
self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
self.rnn2 = nn.GRU(rnn_dims + self.aux_dims, rnn_dims, batch_first=True)
self.fc1 = nn.Linear(rnn_dims + self.aux_dims, fc_dims)
self.fc2 = nn.Linear(fc_dims + self.aux_dims, fc_dims)
self.fc3 = nn.Linear(fc_dims, self.n_classes)
self.step = nn.Parameter(torch.zeros(1).long(), requires_grad=False)
self.num_params()
def forward(self, x, mels):
self.step += 1
bsize = x.size(0)
if torch.cuda.is_available():
h1 = torch.zeros(1, bsize, self.rnn_dims).cuda()
h2 = torch.zeros(1, bsize, self.rnn_dims).cuda()
else:
h1 = torch.zeros(1, bsize, self.rnn_dims).cpu()
h2 = torch.zeros(1, bsize, self.rnn_dims).cpu()
mels, aux = self.upsample(mels)
aux_idx = [self.aux_dims * i for i in range(5)]
a1 = aux[:, :, aux_idx[0]:aux_idx[1]]
a2 = aux[:, :, aux_idx[1]:aux_idx[2]]
a3 = aux[:, :, aux_idx[2]:aux_idx[3]]
a4 = aux[:, :, aux_idx[3]:aux_idx[4]]
x = torch.cat([x.unsqueeze(-1), mels, a1], dim=2)
x = self.I(x)
res = x
x, _ = self.rnn1(x, h1)
x = x + res
res = x
x = torch.cat([x, a2], dim=2)
x, _ = self.rnn2(x, h2)
x = x + res
x = torch.cat([x, a3], dim=2)
x = F.relu(self.fc1(x))
x = torch.cat([x, a4], dim=2)
x = F.relu(self.fc2(x))
return self.fc3(x)
def generate(self, mels, batched, target, overlap, mu_law, progress_callback=None):
mu_law = mu_law if self.mode == 'RAW' else False
progress_callback = progress_callback or self.gen_display
self.eval()
output = []
start = time.time()
rnn1 = self.get_gru_cell(self.rnn1)
rnn2 = self.get_gru_cell(self.rnn2)
with torch.no_grad():
if torch.cuda.is_available():
mels = mels.cuda()
else:
mels = mels.cpu()
wave_len = (mels.size(-1) - 1) * self.hop_length
mels = self.pad_tensor(mels.transpose(1, 2), pad=self.pad, side='both')
mels, aux = self.upsample(mels.transpose(1, 2))
if batched:
mels = self.fold_with_overlap(mels, target, overlap)
aux = self.fold_with_overlap(aux, target, overlap)
b_size, seq_len, _ = mels.size()
if torch.cuda.is_available():
h1 = torch.zeros(b_size, self.rnn_dims).cuda()
h2 = torch.zeros(b_size, self.rnn_dims).cuda()
x = torch.zeros(b_size, 1).cuda()
else:
h1 = torch.zeros(b_size, self.rnn_dims).cpu()
h2 = torch.zeros(b_size, self.rnn_dims).cpu()
x = torch.zeros(b_size, 1).cpu()
d = self.aux_dims
aux_split = [aux[:, :, d * i:d * (i + 1)] for i in range(4)]
for i in range(seq_len):
m_t = mels[:, i, :]
a1_t, a2_t, a3_t, a4_t = (a[:, i, :] for a in aux_split)
x = torch.cat([x, m_t, a1_t], dim=1)
x = self.I(x)
h1 = rnn1(x, h1)
x = x + h1
inp = torch.cat([x, a2_t], dim=1)
h2 = rnn2(inp, h2)
x = x + h2
x = torch.cat([x, a3_t], dim=1)
x = F.relu(self.fc1(x))
x = torch.cat([x, a4_t], dim=1)
x = F.relu(self.fc2(x))
logits = self.fc3(x)
if self.mode == 'MOL':
sample = sample_from_discretized_mix_logistic(logits.unsqueeze(0).transpose(1, 2))
output.append(sample.view(-1))
if torch.cuda.is_available():
# x = torch.FloatTensor([[sample]]).cuda()
x = sample.transpose(0, 1).cuda()
else:
x = sample.transpose(0, 1)
elif self.mode == 'RAW' :
posterior = F.softmax(logits, dim=1)
distrib = torch.distributions.Categorical(posterior)
sample = 2 * distrib.sample().float() / (self.n_classes - 1.) - 1.
output.append(sample)
x = sample.unsqueeze(-1)
else:
raise RuntimeError("Unknown model mode value - ", self.mode)
if i % 100 == 0:
gen_rate = (i + 1) / (time.time() - start) * b_size / 1000
progress_callback(i, seq_len, b_size, gen_rate)
output = torch.stack(output).transpose(0, 1)
output = output.cpu().numpy()
output = output.astype(np.float64)
if batched:
output = self.xfade_and_unfold(output, target, overlap)
else:
output = output[0]
if mu_law:
output = decode_mu_law(output, self.n_classes, False)
if hp.apply_preemphasis:
output = de_emphasis(output)
# Fade-out at the end to avoid signal cutting out suddenly
fade_out = np.linspace(1, 0, 20 * self.hop_length)
output = output[:wave_len]
output[-20 * self.hop_length:] *= fade_out
self.train()
return output
def gen_display(self, i, seq_len, b_size, gen_rate):
pbar = progbar(i, seq_len)
msg = f'| {pbar} {i*b_size}/{seq_len*b_size} | Batch Size: {b_size} | Gen Rate: {gen_rate:.1f}kHz | '
stream(msg)
def get_gru_cell(self, gru):
gru_cell = nn.GRUCell(gru.input_size, gru.hidden_size)
gru_cell.weight_hh.data = gru.weight_hh_l0.data
gru_cell.weight_ih.data = gru.weight_ih_l0.data
gru_cell.bias_hh.data = gru.bias_hh_l0.data
gru_cell.bias_ih.data = gru.bias_ih_l0.data
return gru_cell
def pad_tensor(self, x, pad, side='both'):
# NB - this is just a quick method i need right now
# i.e., it won't generalise to other shapes/dims
b, t, c = x.size()
total = t + 2 * pad if side == 'both' else t + pad
if torch.cuda.is_available():
padded = torch.zeros(b, total, c).cuda()
else:
padded = torch.zeros(b, total, c).cpu()
if side == 'before' or side == 'both':
padded[:, pad:pad + t, :] = x
elif side == 'after':
padded[:, :t, :] = x
return padded
def fold_with_overlap(self, x, target, overlap):
''' Fold the tensor with overlap for quick batched inference.
Overlap will be used for crossfading in xfade_and_unfold()
Args:
x (tensor) : Upsampled conditioning features.
shape=(1, timesteps, features)
target (int) : Target timesteps for each index of batch
overlap (int) : Timesteps for both xfade and rnn warmup
Return:
(tensor) : shape=(num_folds, target + 2 * overlap, features)
Details:
x = [[h1, h2, ... hn]]
Where each h is a vector of conditioning features
Eg: target=2, overlap=1 with x.size(1)=10
folded = [[h1, h2, h3, h4],
[h4, h5, h6, h7],
[h7, h8, h9, h10]]
'''
_, total_len, features = x.size()
# Calculate variables needed
num_folds = (total_len - overlap) // (target + overlap)
extended_len = num_folds * (overlap + target) + overlap
remaining = total_len - extended_len
# Pad if some time steps poking out
if remaining != 0:
num_folds += 1
padding = target + 2 * overlap - remaining
x = self.pad_tensor(x, padding, side='after')
if torch.cuda.is_available():
folded = torch.zeros(num_folds, target + 2 * overlap, features).cuda()
else:
folded = torch.zeros(num_folds, target + 2 * overlap, features).cpu()
# Get the values for the folded tensor
for i in range(num_folds):
start = i * (target + overlap)
end = start + target + 2 * overlap
folded[i] = x[:, start:end, :]
return folded
def xfade_and_unfold(self, y, target, overlap):
''' Applies a crossfade and unfolds into a 1d array.
Args:
y (ndarry) : Batched sequences of audio samples
shape=(num_folds, target + 2 * overlap)
dtype=np.float64
overlap (int) : Timesteps for both xfade and rnn warmup
Return:
(ndarry) : audio samples in a 1d array
shape=(total_len)
dtype=np.float64
Details:
y = [[seq1],
[seq2],
[seq3]]
Apply a gain envelope at both ends of the sequences
y = [[seq1_in, seq1_target, seq1_out],
[seq2_in, seq2_target, seq2_out],
[seq3_in, seq3_target, seq3_out]]
Stagger and add up the groups of samples:
[seq1_in, seq1_target, (seq1_out + seq2_in), seq2_target, ...]
'''
num_folds, length = y.shape
target = length - 2 * overlap
total_len = num_folds * (target + overlap) + overlap
# Need some silence for the rnn warmup
silence_len = overlap // 2
fade_len = overlap - silence_len
silence = np.zeros((silence_len), dtype=np.float64)
# Equal power crossfade
t = np.linspace(-1, 1, fade_len, dtype=np.float64)
fade_in = np.sqrt(0.5 * (1 + t))
fade_out = np.sqrt(0.5 * (1 - t))
# Concat the silence to the fades
fade_in = np.concatenate([silence, fade_in])
fade_out = np.concatenate([fade_out, silence])
# Apply the gain to the overlap samples
y[:, :overlap] *= fade_in
y[:, -overlap:] *= fade_out
unfolded = np.zeros((total_len), dtype=np.float64)
# Loop to add up all the samples
for i in range(num_folds):
start = i * (target + overlap)
end = start + target + 2 * overlap
unfolded[start:end] += y[i]
return unfolded
def get_step(self) :
return self.step.data.item()
def checkpoint(self, model_dir, optimizer) :
k_steps = self.get_step() // 1000
self.save(model_dir.joinpath("checkpoint_%dk_steps.pt" % k_steps), optimizer)
def log(self, path, msg) :
with open(path, 'a') as f:
print(msg, file=f)
def load(self, path, optimizer) :
checkpoint = torch.load(path)
if "optimizer_state" in checkpoint:
self.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
else:
# Backwards compatibility
self.load_state_dict(checkpoint)
def save(self, path, optimizer) :
torch.save({
"model_state": self.state_dict(),
"optimizer_state": optimizer.state_dict(),
}, path)
def num_params(self, print_out=True):
parameters = filter(lambda p: p.requires_grad, self.parameters())
parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
if print_out :
print('Trainable Parameters: %.3fM' % parameters)

127
vocoder/train.py Normal file
View File

@ -0,0 +1,127 @@
from vocoder.models.fatchord_version import WaveRNN
from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
from vocoder.distribution import discretized_mix_logistic_loss
from vocoder.display import stream, simple_table
from vocoder.gen_wavernn import gen_testset
from torch.utils.data import DataLoader
from pathlib import Path
from torch import optim
import torch.nn.functional as F
import vocoder.hparams as hp
import numpy as np
import time
import torch
def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool,
save_every: int, backup_every: int, force_restart: bool):
# Check to make sure the hop length is correctly factorised
assert np.cumprod(hp.voc_upsample_factors)[-1] == hp.hop_length
# Instantiate the model
print("Initializing the model...")
model = WaveRNN(
rnn_dims=hp.voc_rnn_dims,
fc_dims=hp.voc_fc_dims,
bits=hp.bits,
pad=hp.voc_pad,
upsample_factors=hp.voc_upsample_factors,
feat_dims=hp.num_mels,
compute_dims=hp.voc_compute_dims,
res_out_dims=hp.voc_res_out_dims,
res_blocks=hp.voc_res_blocks,
hop_length=hp.hop_length,
sample_rate=hp.sample_rate,
mode=hp.voc_mode
)
if torch.cuda.is_available():
model = model.cuda()
device = torch.device('cuda')
else:
device = torch.device('cpu')
# Initialize the optimizer
optimizer = optim.Adam(model.parameters())
for p in optimizer.param_groups:
p["lr"] = hp.voc_lr
loss_func = F.cross_entropy if model.mode == "RAW" else discretized_mix_logistic_loss
# Load the weights
model_dir = models_dir.joinpath(run_id)
model_dir.mkdir(exist_ok=True)
weights_fpath = model_dir.joinpath(run_id + ".pt")
if force_restart or not weights_fpath.exists():
print("\nStarting the training of WaveRNN from scratch\n")
model.save(weights_fpath, optimizer)
else:
print("\nLoading weights at %s" % weights_fpath)
model.load(weights_fpath, optimizer)
print("WaveRNN weights loaded from step %d" % model.step)
# Initialize the dataset
metadata_fpath = syn_dir.joinpath("train.txt") if ground_truth else \
voc_dir.joinpath("synthesized.txt")
mel_dir = syn_dir.joinpath("mels") if ground_truth else voc_dir.joinpath("mels_gta")
wav_dir = syn_dir.joinpath("audio")
dataset = VocoderDataset(metadata_fpath, mel_dir, wav_dir)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=True,
pin_memory=True)
# Begin the training
simple_table([('Batch size', hp.voc_batch_size),
('LR', hp.voc_lr),
('Sequence Len', hp.voc_seq_len)])
for epoch in range(1, 350):
data_loader = DataLoader(dataset,
collate_fn=collate_vocoder,
batch_size=hp.voc_batch_size,
num_workers=2,
shuffle=True,
pin_memory=True)
start = time.time()
running_loss = 0.
for i, (x, y, m) in enumerate(data_loader, 1):
if torch.cuda.is_available():
x, m, y = x.cuda(), m.cuda(), y.cuda()
# Forward pass
y_hat = model(x, m)
if model.mode == 'RAW':
y_hat = y_hat.transpose(1, 2).unsqueeze(-1)
elif model.mode == 'MOL':
y = y.float()
y = y.unsqueeze(-1)
# Backward pass
loss = loss_func(y_hat, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
speed = i / (time.time() - start)
avg_loss = running_loss / i
step = model.get_step()
k = step // 1000
if backup_every != 0 and step % backup_every == 0 :
model.checkpoint(model_dir, optimizer)
if save_every != 0 and step % save_every == 0 :
model.save(weights_fpath, optimizer)
msg = f"| Epoch: {epoch} ({i}/{len(data_loader)}) | " \
f"Loss: {avg_loss:.4f} | {speed:.1f} " \
f"steps/s | Step: {k}k | "
stream(msg)
gen_testset(model, test_loader, hp.voc_gen_at_checkpoint, hp.voc_gen_batched,
hp.voc_target, hp.voc_overlap, model_dir)
print("")

84
vocoder/vocoder_dataset.py Normal file
View File

@ -0,0 +1,84 @@
from torch.utils.data import Dataset
from pathlib import Path
from vocoder import audio
import vocoder.hparams as hp
import numpy as np
import torch
class VocoderDataset(Dataset):
def __init__(self, metadata_fpath: Path, mel_dir: Path, wav_dir: Path):
print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, wav_dir))
with metadata_fpath.open("r") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
gta_fnames = [x[1] for x in metadata if int(x[4])]
gta_fpaths = [mel_dir.joinpath(fname) for fname in gta_fnames]
wav_fnames = [x[0] for x in metadata if int(x[4])]
wav_fpaths = [wav_dir.joinpath(fname) for fname in wav_fnames]
self.samples_fpaths = list(zip(gta_fpaths, wav_fpaths))
print("Found %d samples" % len(self.samples_fpaths))
def __getitem__(self, index):
mel_path, wav_path = self.samples_fpaths[index]
# Load the mel spectrogram and adjust its range to [-1, 1]
mel = np.load(mel_path).T.astype(np.float32) / hp.mel_max_abs_value
# Load the wav
wav = np.load(wav_path)
if hp.apply_preemphasis:
wav = audio.pre_emphasis(wav)
wav = np.clip(wav, -1, 1)
# Fix for missing padding # TODO: settle on whether this is any useful
r_pad = (len(wav) // hp.hop_length + 1) * hp.hop_length - len(wav)
wav = np.pad(wav, (0, r_pad), mode='constant')
assert len(wav) >= mel.shape[1] * hp.hop_length
wav = wav[:mel.shape[1] * hp.hop_length]
assert len(wav) % hp.hop_length == 0
# Quantize the wav
if hp.voc_mode == 'RAW':
if hp.mu_law:
quant = audio.encode_mu_law(wav, mu=2 ** hp.bits)
else:
quant = audio.float_2_label(wav, bits=hp.bits)
elif hp.voc_mode == 'MOL':
quant = audio.float_2_label(wav, bits=16)
return mel.astype(np.float32), quant.astype(np.int64)
def __len__(self):
return len(self.samples_fpaths)
def collate_vocoder(batch):
mel_win = hp.voc_seq_len // hp.hop_length + 2 * hp.voc_pad
max_offsets = [x[0].shape[-1] -2 - (mel_win + 2 * hp.voc_pad) for x in batch]
mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]
sig_offsets = [(offset + hp.voc_pad) * hp.hop_length for offset in mel_offsets]
mels = [x[0][:, mel_offsets[i]:mel_offsets[i] + mel_win] for i, x in enumerate(batch)]
labels = [x[1][sig_offsets[i]:sig_offsets[i] + hp.voc_seq_len + 1] for i, x in enumerate(batch)]
mels = np.stack(mels).astype(np.float32)
labels = np.stack(labels).astype(np.int64)
mels = torch.tensor(mels)
labels = torch.tensor(labels).long()
x = labels[:, :hp.voc_seq_len]
y = labels[:, 1:]
bits = 16 if hp.voc_mode == 'MOL' else hp.bits
x = audio.label_2_float(x.float(), bits)
if hp.voc_mode == 'MOL' :
y = audio.label_2_float(y.float(), bits)
return x, y, mels

59
vocoder_preprocess.py Normal file
View File

@ -0,0 +1,59 @@
from synthesizer.synthesize import run_synthesis
from synthesizer.hparams import hparams
from utils.argutils import print_args
import argparse
import os
if __name__ == "__main__":
class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
pass
parser = argparse.ArgumentParser(
description="Creates ground-truth aligned (GTA) spectrograms from the vocoder.",
formatter_class=MyFormatter
)
parser.add_argument("datasets_root", type=str, help=\
"Path to the directory containing your SV2TTS directory. If you specify both --in_dir and "
"--out_dir, this argument won't be used.")
parser.add_argument("--model_dir", type=str,
default="synthesizer/saved_models/pretrained/", help=\
"Path to the pretrained model directory.")
parser.add_argument("-i", "--in_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the mel spectrograms, the wavs and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("-o", "--out_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the output vocoder directory that will contain the ground truth aligned mel "
"spectrograms. Defaults to <datasets_root>/SV2TTS/vocoder/.")
parser.add_argument("--hparams", default="",
help="Hyperparameter overrides as a comma-separated list of name=value "
"pairs")
parser.add_argument("--no_trim", action="store_true", help=\
"Preprocess audio without trimming silences (not recommended).")
parser.add_argument("--cpu", action="store_true", help=\
"If True, processing is done on CPU, even when a GPU is available.")
args = parser.parse_args()
print_args(args, parser)
modified_hp = hparams.parse(args.hparams)
if not hasattr(args, "in_dir"):
args.in_dir = os.path.join(args.datasets_root, "SV2TTS", "synthesizer")
if not hasattr(args, "out_dir"):
args.out_dir = os.path.join(args.datasets_root, "SV2TTS", "vocoder")
if args.cpu:
# Hide GPUs from Pytorch to force CPU processing
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Verify webrtcvad is available
if not args.no_trim:
try:
import webrtcvad
except:
raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
"noise removal and is recommended. Please install and try again. If installation fails, "
"use --no_trim to disable this error message.")
del args.no_trim
run_synthesis(args.in_dir, args.out_dir, args.model_dir, modified_hp)

56
vocoder_train.py Normal file
View File

@ -0,0 +1,56 @@
from utils.argutils import print_args
from vocoder.train import train
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Trains the vocoder from the synthesizer audios and the GTA synthesized mels, "
"or ground truth mels.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the same run ID was previously "
"saved, the training will restart from there. Pass -f to overwrite saved states and "
"restart from scratch.")
parser.add_argument("datasets_root", type=str, help= \
"Path to the directory containing your SV2TTS directory. Specifying --syn_dir or --voc_dir "
"will take priority over this argument.")
parser.add_argument("--syn_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
"the wavs and the embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
parser.add_argument("--voc_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the vocoder directory that contains the GTA synthesized mel spectrograms. "
"Defaults to <datasets_root>/SV2TTS/vocoder/. Unused if --ground_truth is passed.")
parser.add_argument("-m", "--models_dir", type=str, default="vocoder/saved_models/", help=\
"Path to the directory that will contain the saved model weights, as well as backups "
"of those weights and wavs generated during training.")
parser.add_argument("-g", "--ground_truth", action="store_true", help= \
"Train on ground truth spectrograms (<datasets_root>/SV2TTS/synthesizer/mels).")
parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
"Number of steps between updates of the model on the disk. Set to 0 to never save the "
"model.")
parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model and restart from scratch.")
args = parser.parse_args()
# Process the arguments
if not hasattr(args, "syn_dir"):
args.syn_dir = Path(args.datasets_root, "SV2TTS", "synthesizer")
args.syn_dir = Path(args.syn_dir)
if not hasattr(args, "voc_dir"):
args.voc_dir = Path(args.datasets_root, "SV2TTS", "vocoder")
args.voc_dir = Path(args.voc_dir)
del args.datasets_root
args.models_dir = Path(args.models_dir)
args.models_dir.mkdir(exist_ok=True)
# Run the training
print_args(args, parser)
train(**vars(args))