This section describes the NeMo configuration file setup that is specific to models in the TTS collection. For general information about how to set up and run experiments that is common to all NeMo models (e.g. Experiment Manager and PyTorch Lightning trainer parameters), see the NeMo Models section.
The model section of the NeMo TTS configuration files generally requires information about the dataset(s) being used, the preprocessor for audio files, parameters for any augmentation being performed, as well as the model architecture specification. The sections on this page cover each of these in more detail.
Example configuration files for all of the NeMo TTS scripts can be found in the config directory of the examples.
Training, validation, and test parameters are specified using the model.train_ds, model.validation_ds, and model.test_ds sections in the configuration file, respectively. Depending on the task, there may be arguments specifying the sample rate of the audio files, supplementary data such as speech/text alignment priors and speaker IDs, etc., the threshold to trim leading and trailing silence from an audio signal, pitch normalization parameters, and so on. You may also decide to leave fields such as the manifest_filepath blank, to be specified via the command-line at runtime.
Any initialization parameter that is accepted for the class nemo.collections.tts.data.dataset.TTSDataset can be set in the config file. Refer to the Dataset Processing Classes section of the API for a list of datasets classes and their respective parameters. An example TTS train and validation configuration should look similar to the following:
model:
train_ds:
dataset:
_target_: nemo.collections.tts.data.dataset.TTSDataset
manifest_filepath: ???
sample_rate: 44100
sup_data_path: ???
sup_data_types: ["align_prior_matrix", "pitch"]
n_fft: 2048
win_length: 2048
hop_length: 512
window: hann
n_mels: 80
lowfreq: 0
highfreq: null
max_duration: null
min_duration: 0.1
ignore_file: null
trim: false
pitch_fmin: 65.40639132514966
pitch_fmax: 2093.004522404789
pitch_norm: true
pitch_mean: 212.35873413085938
pitch_std: 68.52806091308594
use_beta_binomial_interpolator: true
dataloader_params:
drop_last: false
shuffle: true
batch_size: 32
num_workers: 12
pin_memory: true
If you are loading audio files for your experiment, you will likely want to use a preprocessor to convert from the raw audio signal to features (e.g. mel-spectrogram or MFCC). The preprocessor section of the config specifies the audio preprocessor to be used via the _target_ field, as well as any initialization parameters for that preprocessor. An example of specifying a preprocessor is as follows. Refer to the Audio Preprocessors API section for the preprocessor options, expected arguments, and defaults.
model:
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
features: 80
lowfreq: 0
highfreq: null
n_fft: 2048
n_window_size: 2048
window_size: false
n_window_stride: 512
window_stride: false
pad_to: 1
pad_value: 0
sample_rate: 44100
window: hann
normalize: null
preemph: null
dither: 0.0
frame_splicing: 1
log: true
log_zero_guard_type: add
log_zero_guard_value: 1e-05
mag_power: 1.0
Text normalization (TN) converts text from written form into its verbalized form, and it is an essential preprocessing step before text-to-speech Synthesis. TN ensures that TTS can handle all input texts without skipping unknown symbols. For example, “$123” is converted to “one hundred and twenty three dollars”. Currently, NeMo supports text normalizers for English, German, Spanish, and Chinese. Refer to the previous Section (Inverse) Text Normalization for more details. Below shows an example of specifying text normalizer for English.
model:
text_normalizer:
_target_: nemo_text_processing.text_normalization.normalize.Normalizer
lang: en
input_case: cased
text_normalizer_call_kwargs:
verbose: false
punct_pre_process: true
punct_post_process: true
Tokenization converts input text string to a list of integer tokens. It may pad leading and/or trailing whitespaces to a string. NeMo tokenizer supports grapheme-only inputs, phoneme-only inputs, or a mixer of grapheme and phoneme inputs to disambiguate pronunciations of heteronyms for English, German, and Spanish. It also utilizes a grapheme-to-phoneme (G2P) tool to transliterate out-of-vocabulary (OOV) words. Please refer to the G2P section and TTS tokenizer collection for more details. Note that G2P integration to NeMo TTS tokenizers pipeline is upcoming soon. The following example sets up a EnglishPhonemesTokenizer with a mixer of grapheme and phoneme inputs where each word shown in the heteronym list is transliterated into graphemes or phonemes by a 50% chance.
model:
text_tokenizer:
_target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer
punct: true
stresses: true
chars: true
apostrophe: true
pad_with_space: true
g2p:
_target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p
phoneme_dict: ${phoneme_dict_path}
heteronyms: ${heteronyms_path}
phoneme_probability: 0.5
Each configuration file should describe the model architecture being used for the experiment. Models in the NeMo TTS collection need several module sections with the _target_ field specifying which model architecture or component is used. Please refer to TTS module collection for details. Below shows an example of FastPitch model architecture,
model:
input_fft: #n_embed and padding_idx are added by the model
_target_: nemo.collections.tts.modules.transformer.FFTransformerEncoder
n_layer: 6
n_head: 1
d_model: 384
d_head: 64
d_inner: 1536
kernel_size: 3
dropout: 0.1
dropatt: 0.1
dropemb: 0.0
d_embed: 384
output_fft:
_target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder
n_layer: 6
n_head: 1
d_model: 384
d_head: 64
d_inner: 1536
kernel_size: 3
dropout: 0.1
dropatt: 0.1
dropemb: 0.0
alignment_module:
_target_: nemo.collections.tts.modules.aligner.AlignmentEncoder
n_text_channels: 384
duration_predictor:
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
input_size: 384
kernel_size: 3
filter_size: 256
dropout: 0.1
n_layers: 2
pitch_predictor:
_target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
input_size: 384
kernel_size: 3
filter_size: 256
dropout: 0.1
n_layers: 2
optim:
name: adamw
lr: 1e-3
betas: [0.9, 0.999]
weight_decay: 1e-6
sched:
name: NoamAnnealing
warmup_steps: 1000
last_epoch: -1
d_model: 1 # Disable scaling based on model dim
All TTS scripts support easy finetuning by partially/fully loading the pretrained weights from a checkpoint into the currently instantiated model. Note that the currently instantiated model should have parameters that match the pre-trained checkpoint (such that weights may load properly). In order to directly finetune a pre-existing checkpoint, please follow the tutorial of Finetuning FastPitch for a new speaker.
Pre-trained weights can be provided in multiple ways:
Providing a path to a NeMo model (via
init_from_nemo_model)Providing a name of a pretrained NeMo model (which will be downloaded via the cloud) (via
init_from_pretrained_model)Providing a path to a Pytorch Lightning checkpoint file (via
init_from_ptl_ckpt)
There are multiple TTS model finetuning scripts in examples/tts/<model> tag. An example of finetuning a HiFiGAN model is shown below.
Fine-tuning via a NeMo model
python examples/tts/hifigan_finetune.py \
--config-path=<path to dir of configs> \
--config-name=<name of config without .yaml>) \
model/train_ds=train_ds_finetune \
model/validation_ds=val_ds_finetune \
train_dataset="<path to manifest file>" \
validation_dataset="<path to manifest file>" \
model.optim.lr=0.00001 \
~model.optim.sched \
trainer.devices=-1 \
trainer.accelerator='gpu' \
trainer.max_epochs=50 \
+init_from_nemo_model="<path to .nemo model file>"
Fine-tuning via a NeMo pretrained model name
python examples/tts/hifigan_finetune.py \
--config-path=<path to dir of configs> \
--config-name=<name of config without .yaml>) \
model/train_ds=train_ds_finetune \
model/validation_ds=val_ds_finetune \
train_dataset="<path to manifest file>" \
validation_dataset="<path to manifest file>" \
model.optim.lr=0.00001 \
~model.optim.sched \
trainer.devices=-1 \
trainer.accelerator='gpu' \
trainer.max_epochs=50 \
+init_from_pretrained_model="<name of pretrained checkpoint>"
Fine-tuning via a Pytorch Lightning checkpoint
python examples/tts/hifigan_finetune.py \
--config-path=<path to dir of configs> \
--config-name=<name of config without .yaml>) \
model/train_ds=train_ds_finetune \
model/validation_ds=val_ds_finetune \
train_dataset="<path to manifest file>" \
validation_dataset="<path to manifest file>" \
model.optim.lr=0.00001 \
~model.optim.sched \
trainer.devices=-1 \
trainer.accelerator='gpu' \
trainer.max_epochs=50 \
+init_from_ptl_ckpt="<name of pytorch lightning checkpoint>"