# Copyright (c) 2020, 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 copy
import json
import os
import tempfile
from math import ceil
from typing import Dict, List, Optional, Union
import torch
from omegaconf import DictConfig, ListConfig, OmegaConf
from pytorch_lightning import Trainer
from nemo.collections.asr.data import audio_to_label_dataset
from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
from nemo.collections.asr.parts.features import WaveformFeaturizer
from nemo.collections.asr.parts.perturb import process_augmentations
from nemo.collections.common.losses import CrossEntropyLoss
from nemo.collections.common.metrics import TopKClassificationAccuracy
from nemo.core.classes.common import PretrainedModelInfo, typecheck
from nemo.core.neural_types import *
from nemo.utils import logging
__all__ = ['EncDecClassificationModel', 'MatchboxNet']
[docs]class EncDecClassificationModel(ASRModel, ExportableEncDecModel):
"""Encoder decoder Classification models."""
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
# Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
self.global_rank = 0
self.world_size = 1
self.local_rank = 0
if trainer is not None:
self.global_rank = (trainer.node_rank * trainer.num_gpus) + trainer.local_rank
self.world_size = trainer.num_nodes * trainer.num_gpus
self.local_rank = trainer.local_rank
super().__init__(cfg=cfg, trainer=trainer)
self._update_decoder_config(self._cfg.decoder)
self.preprocessor = EncDecClassificationModel.from_config_dict(self._cfg.preprocessor)
self.encoder = EncDecClassificationModel.from_config_dict(self._cfg.encoder)
self.decoder = EncDecClassificationModel.from_config_dict(self._cfg.decoder)
self.loss = CrossEntropyLoss()
if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None:
self.spec_augmentation = EncDecClassificationModel.from_config_dict(self._cfg.spec_augment)
else:
self.spec_augmentation = None
if hasattr(self._cfg, 'crop_or_pad_augment') and self._cfg.crop_or_pad_augment is not None:
self.crop_or_pad = EncDecClassificationModel.from_config_dict(self._cfg.crop_or_pad_augment)
else:
self.crop_or_pad = None
# Setup metric objects
self._accuracy = TopKClassificationAccuracy(dist_sync_on_step=True)
@torch.no_grad()
def transcribe(self, paths2audio_files: List[str], batch_size: int = 4, logprobs=False) -> List[str]:
"""
Generate class labels for provided audio files. Use this method for debugging and prototyping.
Args:
paths2audio_files: (a list) of paths to audio files. \
Recommended length per file is approximately 1 second.
batch_size: (int) batch size to use during inference. \
Bigger will result in better throughput performance but would use more memory.
logprobs: (bool) pass True to get log probabilities instead of class labels.
Returns:
A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files
"""
if paths2audio_files is None or len(paths2audio_files) == 0:
return {}
# We will store transcriptions here
labels = []
# Model's mode and device
mode = self.training
device = next(self.parameters()).device
dither_value = self.preprocessor.featurizer.dither
pad_to_value = self.preprocessor.featurizer.pad_to
try:
self.preprocessor.featurizer.dither = 0.0
self.preprocessor.featurizer.pad_to = 0
# Switch model to evaluation mode
self.eval()
logging_level = logging.get_verbosity()
logging.set_verbosity(logging.WARNING)
# Work in tmp directory - will store manifest file there
with tempfile.TemporaryDirectory() as tmpdir:
with open(os.path.join(tmpdir, 'manifest.json'), 'w') as fp:
for audio_file in paths2audio_files:
entry = {'audio_filepath': audio_file, 'duration': 100000.0, 'label': self.cfg.labels[0]}
fp.write(json.dumps(entry) + '\n')
config = {'paths2audio_files': paths2audio_files, 'batch_size': batch_size, 'temp_dir': tmpdir}
temporary_datalayer = self._setup_transcribe_dataloader(config)
for test_batch in temporary_datalayer:
logits = self.forward(
input_signal=test_batch[0].to(device), input_signal_length=test_batch[1].to(device)
)
if logprobs:
# dump log probs per file
for idx in range(logits.shape[0]):
labels.append(logits[idx])
else:
labels_k = []
top_ks = self._accuracy.top_k
for top_k_i in top_ks:
# replace top k value with current top k
self._accuracy.top_k = top_k_i
labels_k_i = self._accuracy.top_k_predicted_labels(logits)
labels_k.append(labels_k_i)
# convenience: if only one top_k, pop out the nested list
if len(top_ks) == 1:
labels_k = labels_k[0]
labels += labels_k
# reset top k to orignal value
self._accuracy.top_k = top_ks
del test_batch
finally:
# set mode back to its original value
self.train(mode=mode)
self.preprocessor.featurizer.dither = dither_value
self.preprocessor.featurizer.pad_to = pad_to_value
logging.set_verbosity(logging_level)
return labels
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(
sample_rate=config['sample_rate'], int_values=config.get('int_values', False), augmentor=augmentor
)
shuffle = config['shuffle']
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config and config['manifest_filepath'] is None
):
logging.warning(
"Could not load dataset as `manifest_filepath` is None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
if 'vad_stream' in config and config['vad_stream']:
logging.warning("VAD inference does not support tarred dataset now")
return None
shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0
dataset = audio_to_label_dataset.get_tarred_classification_label_dataset(
featurizer=featurizer,
config=config,
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
)
shuffle = False
batch_size = config['batch_size']
collate_func = dataset.collate_fn
else:
if 'manifest_filepath' in config and config['manifest_filepath'] is None:
logging.warning(f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}")
return None
if 'vad_stream' in config and config['vad_stream']:
logging.info("Perform streaming frame-level VAD")
dataset = audio_to_label_dataset.get_speech_label_dataset(featurizer=featurizer, config=config)
batch_size = 1
collate_func = dataset.vad_frame_seq_collate_fn
else:
dataset = audio_to_label_dataset.get_classification_label_dataset(featurizer=featurizer, config=config)
batch_size = config['batch_size']
collate_func = dataset.collate_fn
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
collate_fn=collate_func,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
[docs] def setup_training_data(self, train_data_config: Optional[Union[DictConfig, Dict]]):
if 'shuffle' not in train_data_config:
train_data_config['shuffle'] = True
# preserve config
self._update_dataset_config(dataset_name='train', config=train_data_config)
self._train_dl = self._setup_dataloader_from_config(config=train_data_config)
# Need to set this because if using an IterableDataset, the length of the dataloader is the total number
# of samples rather than the number of batches, and this messes up the tqdm progress bar.
# So we set the number of steps manually (to the correct number) to fix this.
if 'is_tarred' in train_data_config and train_data_config['is_tarred']:
# We also need to check if limit_train_batches is already set.
# If it's an int, we assume that the user has set it to something sane, i.e. <= # training batches,
# and don't change it. Otherwise, adjust batches accordingly if it's a float (including 1.0).
if isinstance(self._trainer.limit_train_batches, float):
self._trainer.limit_train_batches = int(
self._trainer.limit_train_batches
* ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size'])
)
[docs] def setup_validation_data(self, val_data_config: Optional[Union[DictConfig, Dict]]):
if 'shuffle' not in val_data_config:
val_data_config['shuffle'] = False
# preserve config
self._update_dataset_config(dataset_name='validation', config=val_data_config)
self._validation_dl = self._setup_dataloader_from_config(config=val_data_config)
[docs] def setup_test_data(self, test_data_config: Optional[Union[DictConfig, Dict]]):
if 'shuffle' not in test_data_config:
test_data_config['shuffle'] = False
# preserve config
self._update_dataset_config(dataset_name='test', config=test_data_config)
self._test_dl = self._setup_dataloader_from_config(config=test_data_config)
[docs] def test_dataloader(self):
if self._test_dl is not None:
return self._test_dl
[docs] @classmethod
def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
results = []
model = PretrainedModelInfo(
pretrained_model_name="vad_telephony_marblenet",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_telephony_marblenet/versions/1.0.0rc1/files/vad_telephony_marblenet.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="vad_marblenet",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_marblenet/versions/1.0.0rc1/files/vad_marblenet.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v1",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v1",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v1.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v1",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v1",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v1.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2_subset_task",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2_subset_task",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2_subset_task.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2_subset_task",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2_subset_task",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2_subset_task.nemo",
)
results.append(model)
return results
@property
def input_types(self) -> Optional[Dict[str, NeuralType]]:
if hasattr(self.preprocessor, '_sample_rate'):
audio_eltype = AudioSignal(freq=self.preprocessor._sample_rate)
else:
audio_eltype = AudioSignal()
return {
"input_signal": NeuralType(('B', 'T'), audio_eltype),
"input_signal_length": NeuralType(tuple('B'), LengthsType()),
}
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {"outputs": NeuralType(('B', 'D'), LogitsType())}
[docs] @typecheck()
def forward(self, input_signal, input_signal_length):
processed_signal, processed_signal_len = self.preprocessor(
input_signal=input_signal, length=input_signal_length,
)
# Crop or pad is always applied
if self.crop_or_pad is not None:
processed_signal, processed_signal_len = self.crop_or_pad(
input_signal=processed_signal, length=processed_signal_len
)
# Spec augment is not applied during evaluation/testing
if self.spec_augmentation is not None and self.training:
processed_signal = self.spec_augmentation(input_spec=processed_signal)
encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_len)
logits = self.decoder(encoder_output=encoded)
return logits
# PTL-specific methods
[docs] def training_step(self, batch, batch_nb):
self.training_step_end()
audio_signal, audio_signal_len, labels, labels_len = batch
logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
loss_value = self.loss(logits=logits, labels=labels)
self.log('train_loss', loss_value)
self.log('learning_rate', self._optimizer.param_groups[0]['lr'])
self._accuracy(logits=logits, labels=labels)
topk_scores = self._accuracy.compute()
for top_k, score in zip(self._accuracy.top_k, topk_scores):
self.log('training_batch_accuracy_top@{}'.format(top_k), score)
return {
'loss': loss_value,
}
[docs] def validation_step(self, batch, batch_idx, dataloader_idx=0):
audio_signal, audio_signal_len, labels, labels_len = batch
logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
loss_value = self.loss(logits=logits, labels=labels)
acc = self._accuracy(logits=logits, labels=labels)
correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k
return {
'val_loss': loss_value,
'val_correct_counts': correct_counts,
'val_total_counts': total_counts,
'val_acc': acc,
}
[docs] def test_step(self, batch, batch_idx, dataloader_idx=0):
audio_signal, audio_signal_len, labels, labels_len = batch
logits = self.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
loss_value = self.loss(logits=logits, labels=labels)
acc = self._accuracy(logits=logits, labels=labels)
correct_counts, total_counts = self._accuracy.correct_counts_k, self._accuracy.total_counts_k
return {
'test_loss': loss_value,
'test_correct_counts': correct_counts,
'test_total_counts': total_counts,
'test_acc': acc,
}
[docs] def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean()
correct_counts = torch.stack([x['val_correct_counts'] for x in outputs]).sum(axis=0)
total_counts = torch.stack([x['val_total_counts'] for x in outputs]).sum(axis=0)
self._accuracy.correct_counts_k = correct_counts
self._accuracy.total_counts_k = total_counts
topk_scores = self._accuracy.compute()
tensorboard_log = {'val_loss': val_loss_mean}
for top_k, score in zip(self._accuracy.top_k, topk_scores):
tensorboard_log['val_epoch_top@{}'.format(top_k)] = score
return {'log': tensorboard_log}
[docs] def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
correct_counts = torch.stack([x['test_correct_counts'].unsqueeze(0) for x in outputs]).sum(axis=0)
total_counts = torch.stack([x['test_total_counts'].unsqueeze(0) for x in outputs]).sum(axis=0)
self._accuracy.correct_counts_k = correct_counts
self._accuracy.total_counts_k = total_counts
topk_scores = self._accuracy.compute()
tensorboard_log = {'test_loss': test_loss_mean}
for top_k, score in zip(self._accuracy.top_k, topk_scores):
tensorboard_log['test_epoch_top@{}'.format(top_k)] = score
return {'log': tensorboard_log}
[docs] def change_labels(self, new_labels: List[str]):
"""
Changes labels used by the decoder model. Use this method when fine-tuning on from pre-trained model.
This method changes only decoder and leaves encoder and pre-processing modules unchanged. For example, you would
use it if you want to use pretrained encoder when fine-tuning on a data in another dataset.
If new_labels == self.decoder.vocabulary then nothing will be changed.
Args:
new_labels: list with new labels. Must contain at least 2 elements. Typically, \
this is set of labels for the dataset.
Returns: None
"""
if new_labels is not None and not isinstance(new_labels, ListConfig):
new_labels = ListConfig(new_labels)
if self._cfg.labels == new_labels:
logging.warning(
f"Old labels ({self._cfg.labels}) and new labels ({new_labels}) match. Not changing anything"
)
else:
if new_labels is None or len(new_labels) == 0:
raise ValueError(f'New labels must be non-empty list of labels. But I got: {new_labels}')
# Update config
self._cfg.labels = new_labels
decoder_config = self.decoder.to_config_dict()
new_decoder_config = copy.deepcopy(decoder_config)
self._update_decoder_config(new_decoder_config)
del self.decoder
self.decoder = EncDecClassificationModel.from_config_dict(new_decoder_config)
OmegaConf.set_struct(self._cfg.decoder, False)
self._cfg.decoder = new_decoder_config
OmegaConf.set_struct(self._cfg.decoder, True)
if 'train_ds' in self._cfg and self._cfg.train_ds is not None:
self._cfg.train_ds.labels = new_labels
if 'validation_ds' in self._cfg and self._cfg.validation_ds is not None:
self._cfg.validation_ds.labels = new_labels
if 'test_ds' in self._cfg and self._cfg.test_ds is not None:
self._cfg.test_ds.labels = new_labels
logging.info(f"Changed decoder output to {self.decoder.num_classes} labels.")
def _update_decoder_config(self, cfg):
"""
Update the number of classes in the decoder based on labels provided.
Args:
cfg: The config of the decoder which will be updated.
"""
OmegaConf.set_struct(cfg, False)
labels = self.cfg.labels
if 'params' in cfg:
cfg.params.num_classes = len(labels)
else:
cfg.num_classes = len(labels)
OmegaConf.set_struct(cfg, True)
def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLoader':
"""
Setup function for a temporary data loader which wraps the provided audio file.
Args:
config: A python dictionary which contains the following keys:
paths2audio_files: (a list) of paths to audio files. The files should be relatively short fragments. \
Recommended length per file is between 5 and 25 seconds.
batch_size: (int) batch size to use during inference. \
Bigger will result in better throughput performance but would use more memory.
temp_dir: (str) A temporary directory where the audio manifest is temporarily
stored.
Returns:
A pytorch DataLoader for the given audio file(s).
"""
dl_config = {
'manifest_filepath': os.path.join(config['temp_dir'], 'manifest.json'),
'sample_rate': self.preprocessor._sample_rate,
'labels': self.cfg.labels,
'batch_size': min(config['batch_size'], len(config['paths2audio_files'])),
'trim_silence': False,
'shuffle': False,
}
temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
return temporary_datalayer