prepare_librispeech,
kwargs={
"data_folder": hparams["data_folder"],
"tr_splits": hparams["train_splits"],
"dev_splits": hparams["dev_splits"],
"te_splits": hparams["test_splits"],
"save_folder": hparams["data_folder"],
"merge_lst": hparams["train_splits"],
"merge_name": hparams["train_csv"],
},
)
# Create experiment directory
sb.create_experiment_directory(
experiment_directory=hparams["output_folder"],
hyperparams_to_save=hparams_file,
overrides=overrides,
)
# here we create the datasets objects as well as tokenization and encoding
train_data, valid_data, test_datasets, tokenizer = dataio_prepare(hparams)
# Trainer initialization
asr_brain = ASR(
modules=hparams["modules"],
opt_class=hparams["opt_class"],
hparams=hparams,
run_opts=run_opts,
checkpointer=hparams["checkpointer"],
)
def main(config):
### create Experiment Directory ###
# combine all hyperparameters into a single file
hparams = load_hparams(config.exp_config)
hparams["model_config"] = load_hparams(config.model_config)
# create exp dir
sb.create_experiment_directory(experiment_directory=config.output_folder,
hyperparams_to_save=config.exp_config,
overrides=None)
### Datasets and Tokenizer ###
train_data, valid_data, test_data, tokenizer = dataio_prepare(hparams)
# Trainer initialization
run_opts = {
"device": "cuda:0"
} # certain args from yaml file will autoamtically get picked as run_opts
# see https://github.com/speechbrain/speechbrain/blob/develop/recipes/LibriSpeech/ASR/transformer/train.py#L372
# see https://github.com/speechbrain/speechbrain/blob/d6adc40e742107c34ae38dc63484171938b4d237/speechbrain/core.py#L124
#print(type(hparams["model_config"]["modules"]))
#print(type(hparams))
#exit()
asr_brain = ASR(
modules=hparams["model_config"]["modules"],
opt_class=hparams["model_config"]["Adam"],
hparams=hparams["model_config"],
run_opts=run_opts,
checkpointer=hparams["model_config"]["checkpointer"],
)
# adding objects to trainer:
asr_brain.tokenizer = tokenizer # hparams["tokenizer"]
# Training
asr_brain.fit(
asr_brain.hparams.epoch_counter,
train_data,
valid_data,
train_loader_kwargs=hparams["model_config"]["train_dataloader_opts"],
valid_loader_kwargs=hparams["model_config"]["valid_dataloader_opts"],
)
raise NotImplementedError
### get Train Data ###
# list of {'audio__file': str, 'transcript_all_file': str, 'transcript_uid': str, 'filter_criteria': str}
# meaning that <audio__file>'s transcript is the one in the <transcript_all_file> with id <transcript_uid>
train_corpus = get_utterance_manifest_from_data_config(
config.train_data_config)
for x in train_corpus:
assert os.path.exists(
x["transcript_all_file"]
), "data transcript file {} does not exist! Exiting!".format(
x["transcript_all_file"])
### create json file for SpeechBrain-->SentencePiece ###
selected_transcripts_json, annotation_read = create_transcripts_json(
train_corpus)
### train custom SentencePiece Tokenizer ###
with tempfile.NamedTemporaryFile(mode="w+", suffix=".json") as f:
f.write(json.dumps(selected_transcripts_json))
f.seek(0)
SentencePiece(model_dir=config.output_folder,
vocab_size=config.vocab_size,
annotation_train=f.name,
annotation_read=annotation_read,
annotation_format="json",
model_type=config.model_type,
character_coverage=config.character_coverage,
annotation_list_to_check=config.annotation_list_to_check)
