def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model.pretrained_model is None and cfg.model.nemo_model is None:
raise ValueError(
"Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`"
)
if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None:
raise ValueError(
"Cannot set `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only."
)
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
if cfg.model.pretrained_model is not None:
model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model,
return_config=True)
update_encoder_config_to_support_adapter(model_cfg)
model = ASRModel.from_pretrained(cfg.model.pretrained_model,
override_config_path=model_cfg,
trainer=trainer)
else:
model_cfg = ASRModel.restore_from(cfg.model.nemo_model,
return_config=True)
update_encoder_config_to_support_adapter(model_cfg)
model = ASRModel.restore_from(cfg.model.nemo_model,
override_config_path=model_cfg,
trainer=trainer)
# Setup model for finetuning (train and validation only)
cfg.model.test_ds = update_model_cfg(model.cfg.test_ds, cfg.model.test_ds)
# Call the dataloaders and optimizer + scheduler
model.setup_multiple_test_data(cfg.model.test_ds)
# Setup adapters
with open_dict(cfg.model.adapter):
adapter_name = cfg.model.adapter.pop("adapter_name", None)
# Disable all other adapters, enable just the current adapter.
model.set_enabled_adapters(
enabled=False) # disable all adapters prior to training
if adapter_name is not None:
model.set_enabled_adapters(
adapter_name,
enabled=True) # enable just one adapter by name if provided
# First, Freeze all the weights of the model (not just encoder, everything)
model.freeze()
# Finally, train model
trainer.test(model)
def main(cfg: ParallelAlignmentConfig):
if cfg.model.endswith(".nemo"):
logging.info("Attempting to initialize from .nemo file")
model = ASRModel.restore_from(restore_path=cfg.model, map_location="cpu")
elif cfg.model.endswith(".ckpt"):
logging.info("Attempting to initialize from .ckpt file")
model = ASRModel.load_from_checkpoint(checkpoint_path=cfg.model, map_location="cpu")
else:
logging.info(
"Attempting to initialize from a pretrained model as the model name does not have the extension of .nemo or .ckpt"
)
model = ASRModel.from_pretrained(model_name=cfg.model, map_location="cpu")
trainer = ptl.Trainer(**cfg.trainer)
cfg.predict_ds.return_sample_id = True
cfg.return_predictions = False
cfg.use_cer = False
cfg.predict_ds = match_train_config(predict_ds=cfg.predict_ds, train_ds=model._cfg.train_ds)
data_loader = model._setup_dataloader_from_config(cfg.predict_ds)
os.makedirs(cfg.output_path, exist_ok=True)
# trainer.global_rank is not valid before predict() is called. Need this hack to find the correct global_rank.
global_rank = trainer.node_rank * trainer.num_devices + int(os.environ.get("LOCAL_RANK", 0))
output_file = os.path.join(cfg.output_path, f"predictions_{global_rank}.json")
output_ctm_dir = os.path.join(cfg.output_path, "ctm")
predictor_writer = ASRCTMPredictionWriter(
dataset=data_loader.dataset,
output_file=output_file,
output_ctm_dir=output_ctm_dir,
time_per_frame=cfg.model_stride * model._cfg.preprocessor['window_stride'],
)
trainer.callbacks.extend([predictor_writer])
aligner_wrapper = AlignerWrapperModel(model=model, cfg=cfg.aligner_args)
trainer.predict(model=aligner_wrapper, dataloaders=data_loader, return_predictions=cfg.return_predictions)
samples_num = predictor_writer.close_output_file()
logging.info(
f"Prediction on rank {global_rank} is done for {samples_num} samples and results are stored in {output_file}."
)
if torch.distributed.is_initialized():
torch.distributed.barrier()
samples_num = 0
if is_global_rank_zero():
output_file = os.path.join(cfg.output_path, f"predictions_all.json")
logging.info(f"Prediction files are being aggregated in {output_file}.")
with open(output_file, 'tw', encoding="utf-8") as outf:
for rank in range(trainer.world_size):
input_file = os.path.join(cfg.output_path, f"predictions_{rank}.json")
with open(input_file, 'r', encoding="utf-8") as inpf:
lines = inpf.readlines()
samples_num += len(lines)
outf.writelines(lines)
logging.info(
f"Prediction is done for {samples_num} samples in total on all workers and results are aggregated in {output_file}."
)
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model", type=str, default="QuartzNet15x5Base-En", required=False, help="Pass: '******'",
)
parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--out_dir", type=str, required=True, help="Destination dir for output files")
parser.add_argument("--sctk_dir", type=str, required=False, default="", help="Path to sctk root dir")
parser.add_argument("--glm", type=str, required=False, default="", help="Path to glm file")
args = parser.parse_args()
torch.set_grad_enabled(False)
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir, exist_ok=True)
use_sctk = os.path.exists(args.sctk_dir)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model = ASRModel.restore_from(restore_path=args.asr_model, map_location='cpu')
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model = ASRModel.from_pretrained(model_name=args.asr_model, map_location='cpu')
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
manifest_data = read_manifest(args.dataset)
references = [data['text'] for data in manifest_data]
audio_filepaths = [data['audio_filepath'] for data in manifest_data]
with autocast():
hypotheses = asr_model.transcribe(audio_filepaths, batch_size=args.batch_size)
# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
if type(hypotheses) == tuple and len(hypotheses) == 2:
hypotheses = hypotheses[0]
info_list = get_utt_info(args.dataset)
hypfile = os.path.join(args.out_dir, "hyp.trn")
reffile = os.path.join(args.out_dir, "ref.trn")
with open(hypfile, "w") as hyp_f, open(reffile, "w") as ref_f:
for i in range(len(hypotheses)):
utt_id = os.path.splitext(os.path.basename(info_list[i]['audio_filepath']))[0]
# rfilter in sctk likes each transcript to have a space at the beginning
hyp_f.write(" " + hypotheses[i] + " (" + utt_id + ")" + "\n")
ref_f.write(" " + references[i] + " (" + utt_id + ")" + "\n")
if use_sctk:
score_with_sctk(args.sctk_dir, reffile, hypfile, args.out_dir, glm=args.glm)
def test_constructor_pretrained(self):
# Check to/from config_dict:
cfg = ASRModel.from_pretrained('stt_en_citrinet_256',
map_location='cpu',
return_config=True)
adapter_metadata = get_registered_adapter(cfg.encoder._target_)
if adapter_metadata is not None:
cfg.encoder._target_ = adapter_metadata.adapter_class_path
model = ASRModel.from_pretrained('stt_en_citrinet_256',
override_config_path=cfg)
assert isinstance(model, AdapterModuleMixin)
assert hasattr(model, 'encoder')
assert isinstance(model.encoder, AdapterModuleMixin)
model.add_adapter(
'adapter_0',
cfg=get_adapter_cfg(in_features=cfg.encoder.jasper[0].filters,
dim=5))
assert model.is_adapter_available()
model.freeze()
model.unfreeze_enabled_adapters()
assert model.num_weights < 1e5
def get_asr_model(asr_model):
"""
Returns ASR Model
Args:
asr_model: NeMo ASR model
"""
if os.path.exists(args.model):
asr_model = ASRModel.restore_from(asr_model)
elif args.model in ASRModel.get_available_model_names():
asr_model = ASRModel.from_pretrained(asr_model)
else:
raise ValueError(
f'Provide path to the pretrained checkpoint or choose from {ASRModel.get_available_model_names()}'
)
return asr_model
def _asr_on_filepaths(
audio_filepaths, audio_dir, pretrained_model, asr_batch_size, asr_ckpt_path,
device, asr_model, logprobs, **kw
):
# Load acustic model
if asr_model is None:
if asr_ckpt_path:
warnings.warn("Models loaded from a .ckpt run on CPU")
# TODO: Infer this kind of model in GPU
asr_model = EncDecCTCModel.load_from_checkpoint(
checkpoint_path=asr_ckpt_path,
map_location=device
)
else:
asr_model = ASRModel.from_pretrained(
model_name=pretrained_model,
map_location=device
)
trainer = pl.Trainer(gpus=int(device != "cpu"))
asr_model.set_trainer(trainer)
asr_model = asr_model.eval()
# Transcribe
if audio_dir:
full_filepaths = [os.path.join(audio_dir, p) for p in audio_filepaths]
else:
full_filepaths = audio_filepaths
@contextlib.contextmanager
def autocast():
yield
with autocast():
with torch.no_grad():
preds = asr_model.transcribe(
full_filepaths,
batch_size=asr_batch_size,
logprobs=logprobs
)
filepath_to_pred = {}
for filepath, pred in zip(audio_filepaths, preds):
filepath_to_pred[filepath] = pred
return filepath_to_pred, asr_model
def main(cfg: TranscriptionConfig):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model_path is None and cfg.pretrained_name is None:
raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None !")
# setup gpu
if cfg.cuda is None:
cfg.cuda = torch.cuda.is_available()
if type(cfg.cuda) == int:
device_id = int(cfg.cuda)
else:
device_id = 0
device = torch.device(f'cuda:{device_id}' if cfg.cuda else 'cpu')
# setup model
if cfg.model_path is not None:
# restore model from .nemo file path
model_cfg = ASRModel.restore_from(restore_path=cfg.model_path, return_config=True)
classpath = model_cfg.target # original class path
imported_class = model_utils.import_class_by_path(classpath) # type: ASRModel
logging.info(f"Restoring model : {imported_class.__name__}")
asr_model = imported_class.restore_from(restore_path=cfg.model_path, map_location=device) # type: ASRModel
else:
# restore model by name
asr_model = ASRModel.from_pretrained(model_name=cfg.pretrained_name, map_location=device) # type: ASRModel
trainer = pl.Trainer(gpus=int(cfg.cuda))
asr_model.set_trainer(trainer)
asr_model = asr_model.eval()
# Setup decoding strategy
if hasattr(asr_model, 'change_decoding_strategy'):
asr_model.change_decoding_strategy(cfg.rnnt_decoding)
# load paths to audio
filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"*.{cfg.audio_type}")))
logging.info(f"\nTranscribing {len(filepaths)} files...\n")
# setup AMP (optional)
if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
logging.info("AMP enabled!\n")
autocast = torch.cuda.amp.autocast
else:
@contextlib.contextmanager
def autocast():
yield
# transcribe audio
with autocast():
with torch.no_grad():
transcriptions = asr_model.transcribe(filepaths, batch_size=cfg.batch_size)
logging.info(f"Finished transcribing {len(filepaths)} files !")
logging.info(f"Writing transcriptions into file: {cfg.output_filename}")
with open(cfg.output_filename, 'w', encoding='utf-8') as f:
for line in transcriptions:
f.write(f"{line}\n")
logging.info("Finished writing predictions !")
def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model_path is None and cfg.pretrained_name is None:
raise ValueError(
"Both cfg.model_path and cfg.pretrained_name cannot be None!")
if cfg.audio_dir is None and cfg.dataset_manifest is None:
raise ValueError(
"Both cfg.audio_dir and cfg.dataset_manifest cannot be None!")
# setup GPU
if cfg.cuda is None:
if torch.cuda.is_available():
cfg.cuda = 0 # use 0th CUDA device
else:
cfg.cuda = -1 # use CPU
device = torch.device(f'cuda:{cfg.cuda}' if cfg.cuda >= 0 else 'cpu')
# setup model
if cfg.model_path is not None:
# restore model from .nemo file path
model_cfg = ASRModel.restore_from(restore_path=cfg.model_path,
return_config=True)
classpath = model_cfg.target # original class path
imported_class = model_utils.import_class_by_path(
classpath) # type: ASRModel
logging.info(f"Restoring model : {imported_class.__name__}")
asr_model = imported_class.restore_from(
restore_path=cfg.model_path, map_location=device) # type: ASRModel
model_name = os.path.splitext(os.path.basename(cfg.model_path))[0]
else:
# restore model by name
asr_model = ASRModel.from_pretrained(
model_name=cfg.pretrained_name,
map_location=device) # type: ASRModel
model_name = cfg.pretrained_name
trainer = pl.Trainer(gpus=[cfg.cuda] if cfg.cuda >= 0 else 0)
asr_model.set_trainer(trainer)
asr_model = asr_model.eval()
# Setup decoding strategy
if hasattr(asr_model, 'change_decoding_strategy'):
asr_model.change_decoding_strategy(cfg.rnnt_decoding)
# get audio filenames
if cfg.audio_dir is not None:
filepaths = list(
glob.glob(os.path.join(cfg.audio_dir, f"*.{cfg.audio_type}")))
else:
# get filenames from manifest
filepaths = []
with open(cfg.dataset_manifest, 'r') as f:
for line in f:
item = json.loads(line)
filepaths.append(item['audio_filepath'])
logging.info(f"\nTranscribing {len(filepaths)} files...\n")
# setup AMP (optional)
if cfg.amp and torch.cuda.is_available() and hasattr(
torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
logging.info("AMP enabled!\n")
autocast = torch.cuda.amp.autocast
else:
@contextlib.contextmanager
def autocast():
yield
# Compute output filename
if cfg.output_filename is None:
# create default output filename
if cfg.audio_dir is not None:
cfg.output_filename = os.path.dirname(
os.path.join(cfg.audio_dir, '.')) + '.json'
else:
cfg.output_filename = cfg.dataset_manifest.replace(
'.json', f'_{model_name}.json')
# if transcripts should not be overwritten, and already exists, skip re-transcription step and return
if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename):
logging.info(
f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`"
f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text."
)
return cfg
# transcribe audio
with autocast():
with torch.no_grad():
transcriptions = asr_model.transcribe(filepaths,
batch_size=cfg.batch_size)
logging.info(f"Finished transcribing {len(filepaths)} files !")
logging.info(f"Writing transcriptions into file: {cfg.output_filename}")
# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
if type(transcriptions) == tuple and len(transcriptions) == 2:
transcriptions = transcriptions[0]
# write audio transcriptions
with open(cfg.output_filename, 'w', encoding='utf-8') as f:
if cfg.audio_dir is not None:
for idx, text in enumerate(transcriptions):
item = {'audio_filepath': filepaths[idx], 'pred_text': text}
f.write(json.dumps(item) + "\n")
else:
with open(cfg.dataset_manifest, 'r') as fr:
for idx, line in enumerate(fr):
item = json.loads(line)
item['pred_text'] = transcriptions[idx]
f.write(json.dumps(item) + "\n")
logging.info("Finished writing predictions !")
return cfg
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model.pretrained_model is None and cfg.model.nemo_model is None:
raise ValueError("Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`")
if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None:
raise ValueError("Cannot set both `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only.")
trainer = pl.Trainer(**cfg.trainer)
exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
if cfg.model.pretrained_model is not None:
model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model, return_config=True)
update_model_config_to_support_adapter(model_cfg, cfg)
model = ASRModel.from_pretrained(cfg.model.pretrained_model, override_config_path=model_cfg, trainer=trainer)
else:
model_cfg = ASRModel.restore_from(cfg.model.nemo_model, return_config=True)
update_model_config_to_support_adapter(model_cfg, cfg)
model = ASRModel.restore_from(cfg.model.nemo_model, override_config_path=model_cfg, trainer=trainer)
# Setup model for finetuning (train and validation only)
cfg.model.train_ds = update_model_cfg(model.cfg.train_ds, cfg.model.train_ds)
cfg.model.validation_ds = update_model_cfg(model.cfg.validation_ds, cfg.model.validation_ds)
# Call the dataloaders and optimizer + scheduler
model.setup_training_data(cfg.model.train_ds)
model.setup_multiple_validation_data(cfg.model.validation_ds)
# Setup optimizer
model.setup_optimization(cfg.model.optim)
# Setup spec augmentation
if 'spec_augment' in cfg.model:
model.spec_augmentation = model.from_config_dict(cfg.model.spec_augment)
else:
model.spec_augmentation = None
del model.cfg.spec_augment
# Setup adapters
with open_dict(cfg.model.adapter):
# Extract the name of the adapter (must be give for training)
adapter_name = cfg.model.adapter.pop("adapter_name")
adapter_module_name = cfg.model.adapter.pop("adapter_module_name", None)
adapter_state_dict_name = cfg.model.adapter.pop("adapter_state_dict_name", None)
# augment adapter name with module name, if not provided by user
if adapter_module_name is not None and ':' not in adapter_name:
adapter_name = f'{adapter_module_name}:{adapter_name}'
# Extract the global adapter config, if provided
adapter_global_cfg = cfg.model.adapter.pop(model.adapter_global_cfg_key, None)
if adapter_global_cfg is not None:
add_global_adapter_cfg(model, adapter_global_cfg)
model.add_adapter(adapter_name, cfg=cfg.model.adapter)
assert model.is_adapter_available()
# Disable all other adapters, enable just the current adapter.
model.set_enabled_adapters(enabled=False) # disable all adapters prior to training
model.set_enabled_adapters(adapter_name, enabled=True) # enable just one adapter by name
# First, Freeze all the weights of the model (not just encoder, everything)
model.freeze()
# Activate dropout() and other modules that depend on train mode.
model = model.train()
# Then, Unfreeze just the adapter weights that were enabled above (no part of encoder/decoder/joint/etc)
model.unfreeze_enabled_adapters()
# Update model config prior to training
model.cfg = model.cfg
# Finally, train model
trainer.fit(model)
# Save the adapter state dict
if adapter_state_dict_name is not None:
state_path = exp_log_dir if exp_log_dir is not None else os.getcwd()
ckpt_path = os.path.join(state_path, "checkpoints")
if os.path.exists(ckpt_path):
state_path = ckpt_path
state_path = os.path.join(state_path, adapter_state_dict_name)
# Save the adapter modules in a seperate file
model.save_adapters(str(state_path))
def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if is_dataclass(cfg):
cfg = OmegaConf.structured(cfg)
if cfg.model_path is None and cfg.pretrained_name is None:
raise ValueError(
"Both cfg.model_path and cfg.pretrained_name cannot be None!")
if cfg.audio_dir is None and cfg.dataset_manifest is None:
raise ValueError(
"Both cfg.audio_dir and cfg.dataset_manifest cannot be None!")
# setup GPU
if cfg.cuda is None:
if torch.cuda.is_available():
device = [0] # use 0th CUDA device
accelerator = 'gpu'
else:
device = 1
accelerator = 'cpu'
else:
device = [cfg.cuda]
accelerator = 'gpu'
map_location = torch.device('cuda:{}'.format(device[0]) if accelerator ==
'gpu' else 'cpu')
# setup model
if cfg.model_path is not None:
# restore model from .nemo file path
model_cfg = ASRModel.restore_from(restore_path=cfg.model_path,
return_config=True)
classpath = model_cfg.target # original class path
imported_class = model_utils.import_class_by_path(
classpath) # type: ASRModel
logging.info(f"Restoring model : {imported_class.__name__}")
asr_model = imported_class.restore_from(
restore_path=cfg.model_path,
map_location=map_location) # type: ASRModel
model_name = os.path.splitext(os.path.basename(cfg.model_path))[0]
else:
# restore model by name
asr_model = ASRModel.from_pretrained(
model_name=cfg.pretrained_name,
map_location=map_location) # type: ASRModel
model_name = cfg.pretrained_name
trainer = pl.Trainer(devices=device, accelerator=accelerator)
asr_model.set_trainer(trainer)
asr_model = asr_model.eval()
partial_audio = False
# Setup decoding strategy
if hasattr(asr_model, 'change_decoding_strategy'):
asr_model.change_decoding_strategy(cfg.rnnt_decoding)
# get audio filenames
if cfg.audio_dir is not None:
filepaths = list(
glob.glob(os.path.join(cfg.audio_dir, f"*.{cfg.audio_type}")))
else:
# get filenames from manifest
filepaths = []
if os.stat(cfg.dataset_manifest).st_size == 0:
logging.error(
f"The input dataset_manifest {cfg.dataset_manifest} is empty. Exiting!"
)
return None
with open(cfg.dataset_manifest, 'r') as f:
has_two_fields = []
for line in f:
item = json.loads(line)
if "offset" in item and "duration" in item:
has_two_fields.append(True)
else:
has_two_fields.append(False)
filepaths.append(item['audio_filepath'])
partial_audio = all(has_two_fields)
logging.info(f"\nTranscribing {len(filepaths)} files...\n")
# setup AMP (optional)
if cfg.amp and torch.cuda.is_available() and hasattr(
torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
logging.info("AMP enabled!\n")
autocast = torch.cuda.amp.autocast
else:
@contextlib.contextmanager
def autocast():
yield
# Compute output filename
if cfg.output_filename is None:
# create default output filename
if cfg.audio_dir is not None:
cfg.output_filename = os.path.dirname(
os.path.join(cfg.audio_dir, '.')) + '.json'
else:
cfg.output_filename = cfg.dataset_manifest.replace(
'.json', f'_{model_name}.json')
# if transcripts should not be overwritten, and already exists, skip re-transcription step and return
if not cfg.overwrite_transcripts and os.path.exists(cfg.output_filename):
logging.info(
f"Previous transcripts found at {cfg.output_filename}, and flag `overwrite_transcripts`"
f"is {cfg.overwrite_transcripts}. Returning without re-transcribing text."
)
return cfg
# transcribe audio
with autocast():
with torch.no_grad():
if partial_audio:
if isinstance(asr_model, EncDecCTCModel):
transcriptions = transcribe_partial_audio(
asr_model=asr_model,
path2manifest=cfg.dataset_manifest,
batch_size=cfg.batch_size,
num_workers=cfg.num_workers,
)
else:
logging.warning(
"RNNT models do not support transcribe partial audio for now. Transcribing full audio."
)
transcriptions = asr_model.transcribe(
paths2audio_files=filepaths,
batch_size=cfg.batch_size,
num_workers=cfg.num_workers,
)
else:
transcriptions = asr_model.transcribe(
paths2audio_files=filepaths,
batch_size=cfg.batch_size,
num_workers=cfg.num_workers,
)
logging.info(f"Finished transcribing {len(filepaths)} files !")
logging.info(f"Writing transcriptions into file: {cfg.output_filename}")
# if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
if type(transcriptions) == tuple and len(transcriptions) == 2:
transcriptions = transcriptions[0]
# write audio transcriptions
with open(cfg.output_filename, 'w', encoding='utf-8') as f:
if cfg.audio_dir is not None:
for idx, text in enumerate(transcriptions):
item = {'audio_filepath': filepaths[idx], 'pred_text': text}
f.write(json.dumps(item) + "\n")
else:
with open(cfg.dataset_manifest, 'r') as fr:
for idx, line in enumerate(fr):
item = json.loads(line)
item['pred_text'] = transcriptions[idx]
f.write(json.dumps(item) + "\n")
logging.info("Finished writing predictions !")
return cfg
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model.pretrained_model is None and cfg.model.nemo_model is None:
raise ValueError(
"Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`"
)
if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None:
raise ValueError(
"Cannot set `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only."
)
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
if cfg.model.pretrained_model is not None:
model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model,
return_config=True)
update_encoder_config_to_support_adapter(model_cfg)
model = ASRModel.from_pretrained(cfg.model.pretrained_model,
override_config_path=model_cfg,
trainer=trainer)
else:
model_cfg = ASRModel.restore_from(cfg.model.nemo_model,
return_config=True)
update_encoder_config_to_support_adapter(model_cfg)
model = ASRModel.restore_from(cfg.model.nemo_model,
override_config_path=model_cfg,
trainer=trainer)
# Setup model for finetuning (train and validation only)
cfg.model.train_ds = update_model_cfg(model.cfg.train_ds,
cfg.model.train_ds)
cfg.model.validation_ds = update_model_cfg(model.cfg.validation_ds,
cfg.model.validation_ds)
# Call the dataloaders and optimizer + scheduler
model.setup_training_data(cfg.model.train_ds)
model.setup_multiple_validation_data(cfg.model.validation_ds)
# Setup optimizer
cfg.model.optim = update_model_cfg(model.cfg.optim, cfg.model.optim)
model.setup_optimization(cfg.model.optim)
# Setup adapters
with open_dict(cfg.model.adapter):
# Extract the name of the adapter (must be give for training)
adapter_name = cfg.model.adapter.pop("adapter_name")
# Extract the global adapter config, if provided
adapter_global_cfg = cfg.model.adapter.pop(
model.adapter_global_cfg_key, None)
if adapter_global_cfg is not None:
add_global_adapter_cfg(model, adapter_global_cfg)
model.add_adapter(adapter_name, cfg=cfg.model.adapter)
assert model.is_adapter_available()
# Disable all other adapters, enable just the current adapter.
model.set_enabled_adapters(
enabled=False) # disable all adapters prior to training
model.set_enabled_adapters(adapter_name,
enabled=True) # enable just one adapter by name
# First, Freeze all the weights of the model (not just encoder, everything)
model.freeze()
# Activate dropout() and other modules that depend on train mode.
model = model.train()
# Then, Unfreeze just the adapter weights that were enabled above (no part of encoder/decoder/joint/etc)
model.unfreeze_enabled_adapters()
# Finally, train model
trainer.fit(model)
def main(cfg: TranscriptionConfig):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
if cfg.model_path is None and cfg.pretrained_name is None:
raise ValueError(
"Both cfg.model_path and cfg.pretrained_name cannot be None!")
if cfg.audio_dir is None and cfg.dataset_manifest is None:
raise ValueError(
"Both cfg.audio_dir and cfg.dataset_manifest cannot be None!")
# setup GPU
if cfg.cuda is None:
cfg.cuda = torch.cuda.is_available()
if type(cfg.cuda) == int:
device_id = int(cfg.cuda)
else:
device_id = 0
device = torch.device(f'cuda:{device_id}' if cfg.cuda else 'cpu')
# setup model
if cfg.model_path is not None:
# restore model from .nemo file path
model_cfg = ASRModel.restore_from(restore_path=cfg.model_path,
return_config=True)
classpath = model_cfg.target # original class path
imported_class = model_utils.import_class_by_path(
classpath) # type: ASRModel
logging.info(f"Restoring model : {imported_class.__name__}")
asr_model = imported_class.restore_from(
restore_path=cfg.model_path, map_location=device) # type: ASRModel
model_name = os.path.splitext(os.path.basename(cfg.model_path))[0]
else:
# restore model by name
asr_model = ASRModel.from_pretrained(
model_name=cfg.pretrained_name,
map_location=device) # type: ASRModel
model_name = cfg.pretrained_name
trainer = pl.Trainer(gpus=int(cfg.cuda))
asr_model.set_trainer(trainer)
asr_model = asr_model.eval()
# Setup decoding strategy
if hasattr(asr_model, 'change_decoding_strategy'):
asr_model.change_decoding_strategy(cfg.rnnt_decoding)
# get audio filenames
if cfg.audio_dir is not None:
filepaths = list(
glob.glob(os.path.join(cfg.audio_dir, f"*.{cfg.audio_type}")))
else:
# get filenames from manifest
filepaths = []
with open(cfg.dataset_manifest, 'r') as f:
for line in f:
item = json.loads(line)
filepaths.append(item['audio_filepath'])
logging.info(f"\nTranscribing {len(filepaths)} files...\n")
# setup AMP (optional)
if cfg.amp and torch.cuda.is_available() and hasattr(
torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
logging.info("AMP enabled!\n")
autocast = torch.cuda.amp.autocast
else:
@contextlib.contextmanager
def autocast():
yield
# transcribe audio
with autocast():
with torch.no_grad():
transcriptions = asr_model.transcribe(filepaths,
batch_size=cfg.batch_size)
logging.info(f"Finished transcribing {len(filepaths)} files !")
if cfg.output_filename is None:
# create default output filename
if cfg.audio_dir is not None:
cfg.output_filename = os.path.dirname(
os.path.join(cfg.audio_dir, '.')) + '.json'
else:
cfg.output_filename = cfg.dataset_manifest.replace(
'.json', f'_{model_name}.json')
logging.info(f"Writing transcriptions into file: {cfg.output_filename}")
with open(cfg.output_filename, 'w', encoding='utf-8') as f:
if cfg.audio_dir is not None:
for idx, text in enumerate(transcriptions):
item = {'audio_filepath': filepaths[idx], 'pred_text': text}
f.write(json.dumps(item) + "\n")
else:
with open(cfg.dataset_manifest, 'r') as fr:
for idx, line in enumerate(fr):
item = json.loads(line)
item['pred_text'] = transcriptions[idx]
f.write(json.dumps(item) + "\n")
logging.info("Finished writing predictions !")
def main(cfg: ParallelTranscriptionConfig):
if cfg.model.endswith(".nemo"):
logging.info("Attempting to initialize from .nemo file")
model = ASRModel.restore_from(restore_path=cfg.model,
map_location="cpu")
elif cfg.model.endswith(".ckpt"):
logging.info("Attempting to initialize from .ckpt file")
model = ASRModel.load_from_checkpoint(checkpoint_path=cfg.model,
map_location="cpu")
else:
logging.info(
"Attempting to initialize from a pretrained model as the model name does not have the extension of .nemo or .ckpt"
)
model = ASRModel.from_pretrained(model_name=cfg.model,
map_location="cpu")
trainer = ptl.Trainer(**cfg.trainer)
cfg.predict_ds.return_sample_id = True
cfg.predict_ds = match_train_config(predict_ds=cfg.predict_ds,
train_ds=model.cfg.train_ds)
data_loader = model._setup_dataloader_from_config(cfg.predict_ds)
os.makedirs(cfg.output_path, exist_ok=True)
# trainer.global_rank is not valid before predict() is called. Need this hack to find the correct global_rank.
global_rank = trainer.node_rank * trainer.num_gpus + int(
os.environ.get("LOCAL_RANK", 0))
output_file = os.path.join(cfg.output_path,
f"predictions_{global_rank}.json")
predictor_writer = ASRPredictionWriter(dataset=data_loader.dataset,
output_file=output_file)
trainer.callbacks.extend([predictor_writer])
predictions = trainer.predict(model=model,
dataloaders=data_loader,
return_predictions=cfg.return_predictions)
if predictions is not None:
predictions = list(itertools.chain.from_iterable(predictions))
samples_num = predictor_writer.close_output_file()
logging.info(
f"Prediction on rank {global_rank} is done for {samples_num} samples and results are stored in {output_file}."
)
if torch.distributed.is_initialized():
torch.distributed.barrier()
samples_num = 0
pred_text_list = []
text_list = []
if is_global_rank_zero():
output_file = os.path.join(cfg.output_path, f"predictions_all.json")
logging.info(
f"Prediction files are being aggregated in {output_file}.")
with open(output_file, 'w') as outf:
for rank in range(trainer.world_size):
input_file = os.path.join(cfg.output_path,
f"predictions_{rank}.json")
with open(input_file, 'r') as inpf:
lines = inpf.readlines()
for line in lines:
item = json.loads(line)
pred_text_list.append(item["pred_text"])
text_list.append(item["text"])
outf.write(json.dumps(item) + "\n")
samples_num += 1
wer_cer = word_error_rate(hypotheses=pred_text_list,
references=text_list,
use_cer=cfg.use_cer)
logging.info(
f"Prediction is done for {samples_num} samples in total on all workers and results are aggregated in {output_file}."
)
logging.info("{} for all predictions is {:.4f}.".format(
"CER" if cfg.use_cer else "WER", wer_cer))
raise ValueError(
f"ASR model must be provided to extract vocabulary for text processing"
)
elif os.path.exists(args.model):
model_cfg = ASRModel.restore_from(restore_path=args.model,
return_config=True)
classpath = model_cfg.target # original class path
imported_class = model_utils.import_class_by_path(
classpath) # type: ASRModel
print(f"Restoring model : {imported_class.__name__}")
asr_model = imported_class.restore_from(
restore_path=args.model) # type: ASRModel
model_name = os.path.splitext(os.path.basename(args.model))[0]
else:
# restore model by name
asr_model = ASRModel.from_pretrained(
model_name=args.model) # type: ASRModel
model_name = args.model
vocabulary = asr_model.cfg.decoder.vocabulary
if os.path.isdir(args.in_text):
text_files = Path(args.in_text).glob(("*.txt"))
else:
text_files.append(Path(args.in_text))
for text in text_files:
base_name = os.path.basename(text)[:-4]
out_text_file = os.path.join(args.output_dir, base_name + ".txt")
split_text(
text,
out_text_file,