# 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 os
from typing import Dict, List, Optional, Union
import torch
from lightning.pytorch import Trainer
from omegaconf import DictConfig, ListConfig, OmegaConf, open_dict
from nemo.collections.asr.data import audio_to_text_dataset
from nemo.collections.asr.data.audio_to_text import _AudioTextDataset
from nemo.collections.asr.data.audio_to_text_dali import AudioToBPEDALIDataset
from nemo.collections.asr.data.audio_to_text_lhotse import LhotseSpeechToTextBpeDataset
from nemo.collections.asr.losses.rnnt import RNNTLoss
from nemo.collections.asr.metrics.wer import WER
from nemo.collections.asr.models.rnnt_models import EncDecRNNTModel
from nemo.collections.asr.parts.mixins import ASRBPEMixin
from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTBPEDecodingConfig
from nemo.collections.asr.parts.utils.asr_batching import get_semi_sorted_batch_sampler
from nemo.collections.common.data.lhotse import get_lhotse_dataloader_from_config
from nemo.core.classes.common import PretrainedModelInfo
from nemo.utils import logging, model_utils
[docs]
class EncDecRNNTBPEModel(EncDecRNNTModel, ASRBPEMixin):
"""Base class for encoder decoder RNNT-based models with subword tokenization."""
[docs]
@classmethod
def list_available_models(cls) -> 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="stt_en_conformer_transducer_small",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_small",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_small/versions/1.6.0/files/stt_en_conformer_transducer_small.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_conformer_transducer_medium",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_medium",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_medium/versions/1.6.0/files/stt_en_conformer_transducer_medium.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_large/versions/1.10.0/files/stt_en_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_conformer_transducer_large_ls",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large_ls",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_large_ls/versions/1.8.0/files/stt_en_conformer_transducer_large_ls.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_conformer_transducer_xlarge",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_xlarge",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_xlarge/versions/1.10.0/files/stt_en_conformer_transducer_xlarge.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_conformer_transducer_xxlarge",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_xxlarge",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_xxlarge/versions/1.8.0/files/stt_en_conformer_transducer_xxlarge.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_de_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_conformer_transducer_large/versions/1.5.0/files/stt_de_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_fr_conformer_transducer_large",
description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_transducer_large/versions/1.5/files/stt_fr_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_es_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_conformer_transducer_large/versions/1.8.0/files/stt_es_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_enes_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_transducer_large/versions/1.0.0/files/stt_enes_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_ca_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_conformer_transducer_large/versions/1.11.0/files/stt_ca_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_rw_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_rw_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_rw_conformer_transducer_large/versions/1.11.0/files/stt_rw_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_enes_conformer_transducer_large_codesw",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_transducer_large_codesw",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_transducer_large_codesw/versions/1.0.0/files/stt_enes_conformer_transducer_large_codesw.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_kab_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_kab_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_kab_conformer_transducer_large/versions/1.12.0/files/stt_kab_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_be_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_be_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_be_conformer_transducer_large/versions/1.12.0/files/stt_be_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_hr_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_conformer_transducer_large/versions/1.11.0/files/stt_hr_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_it_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_conformer_transducer_large/versions/1.13.0/files/stt_it_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_ru_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_conformer_transducer_large/versions/1.13.0/files/stt_ru_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_eo_conformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_eo_conformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_eo_conformer_transducer_large/versions/1.14.0/files/stt_eo_conformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_fastconformer_transducer_large",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_large",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_large/versions/1.0.0/files/stt_en_fastconformer_transducer_large.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_fastconformer_transducer_large_ls",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_large_ls",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_large_ls/versions/1.0.0/files/stt_en_fastconformer_transducer_large_ls.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_fastconformer_transducer_xlarge",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_xlarge",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_xlarge/versions/1.20.1/files/stt_en_fastconformer_transducer_xlarge.nemo",
)
results.append(model)
model = PretrainedModelInfo(
pretrained_model_name="stt_en_fastconformer_transducer_xxlarge",
description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_transducer_xxlarge",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_transducer_xxlarge/versions/1.20.1/files/stt_en_fastconformer_transducer_xxlarge.nemo",
)
results.append(model)
return results
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
# Convert to Hydra 1.0 compatible DictConfig
cfg = model_utils.convert_model_config_to_dict_config(cfg)
cfg = model_utils.maybe_update_config_version(cfg)
# Tokenizer is necessary for this model
if 'tokenizer' not in cfg:
raise ValueError("`cfg` must have `tokenizer` config to create a tokenizer !")
if not isinstance(cfg, DictConfig):
cfg = OmegaConf.create(cfg)
# Setup the tokenizer
self._setup_tokenizer(cfg.tokenizer)
# Initialize a dummy vocabulary
vocabulary = self.tokenizer.tokenizer.get_vocab()
# Set the new vocabulary
with open_dict(cfg):
cfg.labels = ListConfig(list(vocabulary))
with open_dict(cfg.decoder):
cfg.decoder.vocab_size = len(vocabulary)
with open_dict(cfg.joint):
cfg.joint.num_classes = len(vocabulary)
cfg.joint.vocabulary = ListConfig(list(vocabulary))
cfg.joint.jointnet.encoder_hidden = cfg.model_defaults.enc_hidden
cfg.joint.jointnet.pred_hidden = cfg.model_defaults.pred_hidden
super().__init__(cfg=cfg, trainer=trainer)
self.cfg.decoding = self.set_decoding_type_according_to_loss(self.cfg.decoding)
# Setup decoding object
self.decoding = RNNTBPEDecoding(
decoding_cfg=self.cfg.decoding,
decoder=self.decoder,
joint=self.joint,
tokenizer=self.tokenizer,
)
# Setup wer object
self.wer = WER(
decoding=self.decoding,
batch_dim_index=0,
use_cer=self._cfg.get('use_cer', False),
log_prediction=self._cfg.get('log_prediction', True),
dist_sync_on_step=True,
)
# Setup fused Joint step if flag is set
if self.joint.fuse_loss_wer:
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
[docs]
def change_vocabulary(
self,
new_tokenizer_dir: Union[str, DictConfig],
new_tokenizer_type: str,
decoding_cfg: Optional[DictConfig] = None,
):
"""
Changes vocabulary used during RNNT decoding process. 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 data in another language, or when you'd need model to learn capitalization, punctuation
and/or special characters.
Args:
new_tokenizer_dir: Directory path to tokenizer or a config for a new tokenizer
(if the tokenizer type is `agg`)
new_tokenizer_type: Type of tokenizer. Can be either `agg`, `bpe` or `wpe`.
decoding_cfg: A config for the decoder, which is optional. If the decoding type
needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here.
Returns: None
"""
if isinstance(new_tokenizer_dir, DictConfig):
if new_tokenizer_type == 'agg':
new_tokenizer_cfg = new_tokenizer_dir
else:
raise ValueError(
f'New tokenizer dir should be a string unless the tokenizer is `agg`, but this tokenizer \
type is: {new_tokenizer_type}'
)
else:
new_tokenizer_cfg = None
if new_tokenizer_cfg is not None:
tokenizer_cfg = new_tokenizer_cfg
else:
if not os.path.isdir(new_tokenizer_dir):
raise NotADirectoryError(
f'New tokenizer dir must be non-empty path to a directory. But I got: {new_tokenizer_dir}'
)
if new_tokenizer_type.lower() not in ('bpe', 'wpe'):
raise ValueError(f'New tokenizer type must be either `bpe` or `wpe`, got {new_tokenizer_type}')
tokenizer_cfg = OmegaConf.create({'dir': new_tokenizer_dir, 'type': new_tokenizer_type})
# Setup the tokenizer
self._setup_tokenizer(tokenizer_cfg)
# Initialize a dummy vocabulary
vocabulary = self.tokenizer.tokenizer.get_vocab()
joint_config = self.joint.to_config_dict()
new_joint_config = copy.deepcopy(joint_config)
if self.tokenizer_type == "agg":
new_joint_config["vocabulary"] = ListConfig(vocabulary)
else:
new_joint_config["vocabulary"] = ListConfig(list(vocabulary.keys()))
new_joint_config['num_classes'] = len(vocabulary)
del self.joint
self.joint = EncDecRNNTBPEModel.from_config_dict(new_joint_config)
decoder_config = self.decoder.to_config_dict()
new_decoder_config = copy.deepcopy(decoder_config)
new_decoder_config.vocab_size = len(vocabulary)
del self.decoder
self.decoder = EncDecRNNTBPEModel.from_config_dict(new_decoder_config)
del self.loss
self.loss = RNNTLoss(num_classes=self.joint.num_classes_with_blank - 1)
if decoding_cfg is None:
# Assume same decoding config as before
decoding_cfg = self.cfg.decoding
# Assert the decoding config with all hyper parameters
decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig)
decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls))
decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg)
decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg)
self.decoding = RNNTBPEDecoding(
decoding_cfg=decoding_cfg,
decoder=self.decoder,
joint=self.joint,
tokenizer=self.tokenizer,
)
self.wer = WER(
decoding=self.decoding,
batch_dim_index=self.wer.batch_dim_index,
use_cer=self.wer.use_cer,
log_prediction=self.wer.log_prediction,
dist_sync_on_step=True,
)
# Setup fused Joint step
if self.joint.fuse_loss_wer or (
self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0
):
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
# Update config
with open_dict(self.cfg.joint):
self.cfg.joint = new_joint_config
with open_dict(self.cfg.decoder):
self.cfg.decoder = new_decoder_config
with open_dict(self.cfg.decoding):
self.cfg.decoding = decoding_cfg
logging.info(f"Changed decoder to output to {self.joint.vocabulary} vocabulary.")
[docs]
def change_decoding_strategy(self, decoding_cfg: DictConfig, verbose: bool = True):
"""
Changes decoding strategy used during RNNT decoding process.
Args:
decoding_cfg: A config for the decoder, which is optional. If the decoding type
needs to be changed (from say Greedy to Beam decoding etc), the config can be passed here.
verbose: A flag to enable/disable logging.
"""
if decoding_cfg is None:
# Assume same decoding config as before
logging.info("No `decoding_cfg` passed when changing decoding strategy, using internal config")
decoding_cfg = self.cfg.decoding
# Assert the decoding config with all hyper parameters
decoding_cls = OmegaConf.structured(RNNTBPEDecodingConfig)
decoding_cls = OmegaConf.create(OmegaConf.to_container(decoding_cls))
decoding_cfg = OmegaConf.merge(decoding_cls, decoding_cfg)
decoding_cfg = self.set_decoding_type_according_to_loss(decoding_cfg)
self.decoding = RNNTBPEDecoding(
decoding_cfg=decoding_cfg,
decoder=self.decoder,
joint=self.joint,
tokenizer=self.tokenizer,
)
self.wer = WER(
decoding=self.decoding,
batch_dim_index=self.wer.batch_dim_index,
use_cer=self.wer.use_cer,
log_prediction=self.wer.log_prediction,
dist_sync_on_step=True,
)
# Setup fused Joint step
if self.joint.fuse_loss_wer or (
self.decoding.joint_fused_batch_size is not None and self.decoding.joint_fused_batch_size > 0
):
self.joint.set_loss(self.loss)
self.joint.set_wer(self.wer)
self.joint.temperature = decoding_cfg.get('temperature', 1.0)
# Update config
with open_dict(self.cfg.decoding):
self.cfg.decoding = decoding_cfg
if verbose:
logging.info(f"Changed decoding strategy to \n{OmegaConf.to_yaml(self.cfg.decoding)}")
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if config.get("use_lhotse"):
return get_lhotse_dataloader_from_config(
config,
# During transcription, the model is initially loaded on the CPU.
# To ensure the correct global_rank and world_size are set,
# these values must be passed from the configuration.
global_rank=self.global_rank if not config.get("do_transcribe", False) else config.get("global_rank"),
world_size=self.world_size if not config.get("do_transcribe", False) else config.get("world_size"),
dataset=LhotseSpeechToTextBpeDataset(
tokenizer=self.tokenizer,
return_cuts=config.get("do_transcribe", False),
),
tokenizer=self.tokenizer,
)
dataset = audio_to_text_dataset.get_audio_to_text_bpe_dataset_from_config(
config=config,
local_rank=self.local_rank,
global_rank=self.global_rank,
world_size=self.world_size,
tokenizer=self.tokenizer,
preprocessor_cfg=self.cfg.get("preprocessor", None),
)
if dataset is None:
return None
if isinstance(dataset, AudioToBPEDALIDataset):
# DALI Dataset implements dataloader interface
return dataset
shuffle = config['shuffle']
if isinstance(dataset, torch.utils.data.IterableDataset):
shuffle = False
if hasattr(dataset, 'collate_fn'):
collate_fn = dataset.collate_fn
elif hasattr(dataset.datasets[0], 'collate_fn'):
# support datasets that are lists of entries
collate_fn = dataset.datasets[0].collate_fn
else:
# support datasets that are lists of lists
collate_fn = dataset.datasets[0].datasets[0].collate_fn
batch_sampler = None
if config.get('use_semi_sorted_batching', False):
if not isinstance(dataset, _AudioTextDataset):
raise RuntimeError(
"Semi Sorted Batch sampler can be used with AudioToCharDataset or AudioToBPEDataset "
f"but found dataset of type {type(dataset)}"
)
# set batch_size and batch_sampler to None to disable automatic batching
batch_sampler = get_semi_sorted_batch_sampler(self, dataset, config)
config['batch_size'] = None
config['drop_last'] = False
shuffle = False
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['batch_size'],
sampler=batch_sampler,
batch_sampler=None,
collate_fn=collate_fn,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
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).
"""
if 'manifest_filepath' in config:
manifest_filepath = config['manifest_filepath']
batch_size = config['batch_size']
else:
manifest_filepath = os.path.join(config['temp_dir'], 'manifest.json')
batch_size = min(config['batch_size'], len(config['paths2audio_files']))
dl_config = {
'use_lhotse': config.get('use_lhotse', True),
'manifest_filepath': manifest_filepath,
'sample_rate': self.preprocessor._sample_rate,
'batch_size': batch_size,
'shuffle': False,
'num_workers': config.get('num_workers', min(batch_size, os.cpu_count() - 1)),
'pin_memory': True,
'channel_selector': config.get('channel_selector', None),
'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False),
}
if config.get("augmentor"):
dl_config['augmentor'] = config.get("augmentor")
temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
return temporary_datalayer
