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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from hydra.utils import instantiate
from tqdm import tqdm
from nemo.core.config import hydra_runner
def get_pitch_stats(pitch_list):
pitch_tensor = torch.cat(pitch_list)
pitch_mean, pitch_std = pitch_tensor.mean().item(), pitch_tensor.std().item()
pitch_min, pitch_max = pitch_tensor.min().item(), pitch_tensor.max().item()
print(f"PITCH_MEAN={pitch_mean}, PITCH_STD={pitch_std}")
print(f"PITCH_MIN={pitch_min}, PITCH_MAX={pitch_max}")
def preprocess_ds_for_fastpitch_align(dataloader):
pitch_list = []
for batch in tqdm(dataloader, total=len(dataloader)):
audios, audio_lengths, tokens, tokens_lengths, align_prior_matrices, pitches, pitches_lengths = batch
pitch = pitches.squeeze(0)
pitch_list.append(pitch[pitch != 0])
get_pitch_stats(pitch_list)
def preprocess_ds_for_mixer_tts_x(dataloader):
pitch_list = []
for batch in tqdm(dataloader, total=len(dataloader)):
(
audios,
audio_lengths,
tokens,
tokens_lengths,
align_prior_matrices,
pitches,
pitches_lengths,
lm_tokens,
) = batch
pitch = pitches.squeeze(0)
pitch_list.append(pitch[pitch != 0])
get_pitch_stats(pitch_list)
CFG_NAME2FUNC = {
"ds_for_fastpitch_align": preprocess_ds_for_fastpitch_align,
"ds_for_mixer_tts": preprocess_ds_for_fastpitch_align,
"ds_for_mixer_tts_x": preprocess_ds_for_mixer_tts_x,
}
@hydra_runner(config_path='ljspeech/ds_conf', config_name='ds_for_fastpitch_align')
def main(cfg):
dataset = instantiate(cfg.dataset)
dataloader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
collate_fn=dataset._collate_fn,
num_workers=cfg.get("dataloader_params", {}).get("num_workers", 4),
)
print(f"Processing {cfg.manifest_filepath}:")
CFG_NAME2FUNC[cfg.name](dataloader)
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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