def test_full_na():
""" A full Na integration test. """
# Pulls Na wavs from cloudstor.
NA_WAVS_LINK = "https://cloudstor.aarnet.edu.au/plus/s/LnNyNa20GQ8qsPC/download"
download_example_data(NA_WAVS_LINK)
na_dir = join(DATA_BASE_DIR, "na/")
os.rm_dir(na_dir)
os.makedirs(na_dir)
org_wav_dir = join(na_dir, "org_wav/")
os.rename(join(DATA_BASE_DIR, "na_wav/"), org_wav_dir)
tgt_wav_dir = join(na_dir, "wav/")
NA_REPO_URL = "https://github.com/alexis-michaud/na-data.git"
with cd(DATA_BASE_DIR):
subprocess.run(["git", "clone", NA_REPO_URL, "na/xml/"], check=True)
# Note also that this subdirectory only containts TEXTs, so this integration
# test will include only Na narratives, not wordlists.
na_xml_dir = join(DATA_BASE_DIR, "na/xml/TEXT/F4")
label_dir = join(DATA_BASE_DIR, "na/label")
label_type = "phonemes_and_tones"
na.prepare_labels(label_type, org_xml_dir=na_xml_dir, label_dir=label_dir)
tgt_feat_dir = join(DATA_BASE_DIR, "na/feat")
# TODO Make this fbank_and_pitch, but then I need to install kaldi on ray
# or run the tests on GPUs on slug or doe.
feat_type = "fbank"
na.prepare_feats(feat_type,
org_wav_dir=org_wav_dir,
tgt_wav_dir=tgt_wav_dir,
feat_dir=tgt_feat_dir,
org_xml_dir=na_xml_dir,
label_dir=label_dir)
from shutil import copyfile
copyfile("persephone/tests/test_sets/valid_prefixes.txt",
join(na_dir, "valid_prefixes.txt"))
copyfile("persephone/tests/test_sets/test_prefixes.txt",
join(na_dir, "test_prefixes.txt"))
na.make_data_splits(label_type, train_rec_type="text", tgt_dir=na_dir)
# Training with texts
exp_dir = experiment.prep_exp_dir(directory=EXP_BASE_DIR)
na_corpus = na.Corpus(feat_type,
label_type,
train_rec_type="text",
tgt_dir=na_dir)
na_corpus_reader = corpus_reader.CorpusReader(na_corpus)
model = rnn_ctc.Model(exp_dir,
na_corpus_reader,
num_layers=3,
hidden_size=400)
model.train(min_epochs=30)
# Ensure LER < 0.20
ler = get_test_ler(exp_dir)
assert ler < 0.2
def test_load_saver():
tgt_dir = Path(config.TEST_DATA_PATH) / "na"
na_corpus = na.Corpus("fbank_and_pitch",
"phonemes_and_tones",
tgt_dir=tgt_dir)
na_reader = corpus_reader.CorpusReader(na_corpus)
model_prefix_path = "/home/oadams/code/mam/exp/252/model/model_best.ckpt"
saver = tf.train.Saver()
def test_feed_batch():
tgt_dir = Path(config.TEST_DATA_PATH) / "na"
na_corpus = na.Corpus("fbank_and_pitch",
"phonemes_and_tones",
tgt_dir=tgt_dir)
na_reader = corpus_reader.CorpusReader(na_corpus)
model_path = "/home/oadams/code/mam/exp/252/model/model_best.ckpt"
graph = model.load_graph(model_path)
batch = next(na_reader.untranscribed_batch_gen())
print(model.decode(graph, batch))
def test_reuse_model(preprocess_na):
tgt_dir = Path(config.TEST_DATA_PATH) / "na"
na_corpus = na.Corpus("fbank_and_pitch",
"phonemes_and_tones",
tgt_dir=tgt_dir)
na_reader = corpus_reader.CorpusReader(na_corpus)
logging.info("na_corpus {}".format(na_corpus))
logging.info("na_corpus.get_untranscribed_fns():")
logging.info(pprint.pformat(na_corpus.get_untranscribed_fns()))
# TODO Currently assumes we're on slug. Need to package up the model and
# put it on cloudstor, then create a fixture to download it.
exp_dir = prep_exp_dir(directory=config.TEST_EXP_PATH)
model = rnn_ctc.Model(exp_dir, na_reader, num_layers=3, hidden_size=400)
model.transcribe(
restore_model_path="/home/oadams/code/mam/exp/252/model/model_best.ckpt"
)
def test_load_meta():
tgt_dir = Path(config.TEST_DATA_PATH) / "na"
na_corpus = na.Corpus("fbank_and_pitch",
"phonemes_and_tones",
tgt_dir=tgt_dir)
na_reader = corpus_reader.CorpusReader(na_corpus)
tf.reset_default_graph()
model_prefix_path = "/home/oadams/code/mam/exp/252/model/model_best.ckpt"
#model_prefix_path = "/home/oadams/code/persephone/testing/exp/39/model/model_best.ckpt"
#loaded_graph = model.load_graph(model_prefix_path)
metagraph = model.load_metagraph(model_prefix_path)
#imported_meta = tf.train.import_meta_graph(model_prefix_path + ".meta")
#print(type(imported_meta))
#print(dir(imported_meta))
#print(dir(imported_meta))
#print(dir(imported_meta.restore))
#exp_dir = prep_exp_dir(directory=config.TEST_EXP_PATH)
#new_mod = rnn_ctc.Model(exp_dir, na_reader)
#with tf.Session() as sess:
# sess.run(tf.global_variables_initializer())
# for v in tf.get_default_graph().get_collection("train_op"):
# print(v)
# return
#with tf.Session(graph=loaded_graph) as sess:
with tf.Session() as sess:
with tf.device("/cpu:0"):
metagraph.restore(sess, model_prefix_path)
#imported_meta.restore(sess, tf.train.latest_checkpoint('./'))
#imported_meta.restore(sess, model_prefix_path)
#print([x for x in tf.get_default_graph().get_operations() if "Placeholder" in x.type])
print([
x for x in sess.graph.get_operations() if "Placeholder" in x.type
])
print(dir(sess))
print(sess.graph)
print(dir(sess.graph))
for v in tf.get_default_graph().get_collection("variables"):
print(v)
for v in tf.get_default_graph().get_collection("trainable_variables"):
print(v)
for v in tf.get_default_graph().get_collection("train_op"):
print(v)
print(tf.get_default_graph().get_all_collection_keys())
pprint.pprint([
repr(op) for op in tf.get_default_graph().get_operations()
if "hyp" in op.name
])
pprint.pprint([
repr(op) for op in tf.get_default_graph().get_operations()
if "Placeholder" in op.type
])
pprint.pprint([
repr(op) for op in tf.get_default_graph().get_operations()
if "SparseToDense" in op.type
])
all_prefixes = []
all_hyps = []
for batch_i, batch in enumerate(na_reader.untranscribed_batch_gen()):
batch_x, batch_x_lens, feat_fn_batch = batch
prefixes = [
fn.split("/")[-1].split(".")[:2] for fn in feat_fn_batch
]
#ph_batch_x = tf.placeholder(
# tf.float32, [None, None, na_reader.corpus.num_feats])
#ph_batch_x_lens = tf.placeholder(tf.int32, [None])
#ph_batch_y = tf.sparse_placeholder(tf.int32)
#feed_dict = {"batch_x:0": batch_x,
# "batch_x_lens:0": batch_x_lens}
feed_dict = {
"Placeholder:0": batch_x,
"Placeholder_1:0": batch_x_lens
}
#[dense_decoded] = sess.run(["hyp_dense_decoded:0"],
# feed_dict=feed_dict)
[dense_decoded] = sess.run(["SparseToDense:0"],
feed_dict=feed_dict)
print(dense_decoded)
hyps = na_reader.human_readable(dense_decoded)
print(hyps)
print(na_reader.corpus.INDEX_TO_LABEL)
all_hyps.extend(["".join(hyp) for hyp in hyps])
all_prefixes.extend([".".join(prefix) for prefix in prefixes])
print(
results.fmt_latex_untranscribed(
all_hyps, all_prefixes,
Path("benevolence_and_funeral_custom.tex")))