Forced alignment suite. Includes English grapheme-to-phoneme (G2P) and phoneme alignment from the following forced alignment tools.
- RAD-TTS [1]
- Montreal Forced Aligner (MFA) [2]
- Penn Phonetic Forced Aligner (P2FA) [3]
RAD-TTS is used by default. Alignments can be saved to disk or accessed via the
pypar.Alignment phoneme alignment representation. See
pypar for more details.
pyfoal also includes the following
- Converting alignments to and from a categorical representation
suitable for training machine learning models (
pyfoal.convert) - Natural interpolation of forced alignments for time-stretching speech
(
pyfoal.interpolate)
pip install pyfoal
MFA and P2FA both require additional installation steps found below.
conda install -c conda-forge montreal-forced-aligner
P2FA depends on the
Hidden Markov Model Toolkit (HTK), which has been
tested on Mac OS and Linux using HTK version 3.4.0. There are known issues in
using version 3.4.1 on Linux. HTK is released under a license that prohibits
redistribution, so you must install HTK yourself and verify that the commands
HCopy and HVite are available as system-wide binaries. After downloading
HTK, I use the following for installation on Linux.
sudo apt-get install -y gcc-multilib libx11-dev
sudo chmod +x configure
./configure --disable-hslab
make all
sudo make install
For more help with HTK installation, see notes by Jaekoo Kang and Steve Rubin.
import pyfoal
# Load text
text = pyfoal.load.text(text_file)
# Load and resample audio
audio = pyfoal.load.audio(audio_file)
# Select an aligner. One of ['mfa', 'p2fa', 'radtts' (default)].
aligner = 'radtts'
# For RAD-TTS, select a model checkpoint
checkpoint = pyfoal.DEFAULT_CHECKPOINT
# Select a GPU to run inference on
gpu = 0
alignment = pyfoal.from_text_and_audio(
text,
audio,
pyfoal.SAMPLE_RATE,
aligner=aligner,
checkpoint=checkpoint,
gpu=gpu)"""Phoneme-level forced-alignment
Arguments
text : string
The speech transcript
audio : torch.tensor(shape=(1, samples))
The speech signal to process
sample_rate : int
The audio sampling rate
Returns
alignment : pypar.Alignment
The forced alignment
"""
"""Phoneme alignment from audio and text files
Arguments
text_file : Path
The corresponding transcript file
audio_file : Path
The audio file to process
aligner : str
The alignment method to use
checkpoint : Path
The checkpoint to use for neural methods
gpu : int
The index of the gpu to perform alignment on for neural methods
Returns
alignment : Alignment
The forced alignment
"""
"""Perform phoneme alignment from files and save to disk
Arguments
text_file : Path
The corresponding transcript file
audio_file : Path
The audio file to process
output_file : Path
The file to save the alignment
aligner : str
The alignment method to use
checkpoint : Path
The checkpoint to use for neural methods
gpu : int
The index of the gpu to perform alignment on for neural methods
"""
"""Perform parallel phoneme alignment from many files and save to disk
Arguments
text_files : list
The transcript files
audio_files : list
The corresponding speech audio files
output_files : list
The files to save the alignments
aligner : str
The alignment method to use
num_workers : int
Number of CPU cores to utilize. Defaults to all cores.
checkpoint : Path
The checkpoint to use for neural methods
gpu : int
The index of the gpu to perform alignment on for neural methods
"""
python -m pyfoal
[-h]
--text_files TEXT_FILES [TEXT_FILES ...]
--audio_files AUDIO_FILES [AUDIO_FILES ...]
--output_files OUTPUT_FILES [OUTPUT_FILES ...]
[--aligner ALIGNER]
[--num_workers NUM_WORKERS]
[--checkpoint CHECKPOINT]
[--gpu GPU]
Arguments:
-h, --help
show this help message and exit
--text_files TEXT_FILES [TEXT_FILES ...]
The speech transcript files
--audio_files AUDIO_FILES [AUDIO_FILES ...]
The speech audio files
--output_files OUTPUT_FILES [OUTPUT_FILES ...]
The files to save the alignments
--aligner ALIGNER
The alignment method to use
--num_workers NUM_WORKERS
Number of CPU cores to utilize. Defaults to all cores.
--checkpoint CHECKPOINT
The checkpoint to use for neural methods
--gpu GPU
The index of the GPU to use for inference. Defaults to CPU.
python -m pyfoal.data.download
Downloads and uncompresses the arctic and libritts datasets used for training.
python -m pyfoal.data.preprocess
Converts each dataset to a common format on disk ready for training.
python -m pyfoal.partition
Generates train valid, and test partitions for arctic and libritts.
Partitioning is deterministic given the same random seed. You do not need to
run this step, as the original partitions are saved in
pyfoal/assets/partitions.
python -m pyfoal.train --config <config> --gpus <gpus>
Trains a model according to a given configuration on the libritts
dataset. Uses a list of GPU indices as an argument, and uses distributed
data parallelism (DDP) if more than one index is given. For example,
--gpus 0 3 will train using DDP on GPUs 0 and 3.
Run tensorboard --logdir runs/. If you are running training remotely, you
must create a SSH connection with port forwarding to view Tensorboard.
This can be done with ssh -L 6006:localhost:6006 <user>@<server-ip-address>.
Then, open localhost:6006 in your browser.
python -m pyfal.evaluate \
--config <config> \
--checkpoint <checkpoint> \
--gpu <gpu>
Evaluate a model. <checkpoint> is the checkpoint file to evaluate and <gpu>
is the GPU index.
[1] R. Badlani, A. Łańcucki, K. J. Shih, R. Valle, W. Ping, and B. Catanzaro, "One TTS Alignment to Rule Them All," International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.
[2] J. Yuan and M. Liberman, “Speaker identification on the scotus corpus,” Journal of the Acoustical Society of America, vol. 123, p. 3878, 2008.
[3] M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, "Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi," Interspeech, vol. 2017, p. 498-502. 2017.
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