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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. 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 json
from nemo.collections.asr.metrics.wer import word_error_rate
from nemo.collections.tts.models.g2p_ctc import CTCG2PModel
from nemo.collections.tts.models.g2p_t5 import T5G2PModel
from nemo.utils import logging
def get_model(cfg, trainer):
"""
Get model instance
Args:
cfg: model's config file
trainer: trainer
Return:
G2PModel instance
"""
if "CTC" in cfg.name:
model = CTCG2PModel(cfg=cfg.model, trainer=trainer)
elif cfg.name == "T5G2P":
model = T5G2PModel(cfg=cfg.model, trainer=trainer)
else:
raise ValueError(f"{cfg.name} is not supported. Choose from [G2P-Conformer-CTC, T5G2P]")
return model
def get_metrics(manifest: str, pred_field="pred_text", phoneme_field="text", grapheme_field="text_graphemes"):
"""
Calculates WER and PER metrics (for duplicated grapheme entries with multiple reference values,
the best matching prediction will be used for evaluation.)
Args:
manifest: Path to .json manifest file
pred_field: name of the field in the output_file to save predictions
phoneme_field: name of the field in manifest_filepath for ground truth phonemes
grapheme_field: name of the field in manifest_filepath for input grapheme text
Returns: WER and PER values
"""
all_preds = []
all_references = []
all_graphemes = {}
with open(manifest, "r") as f:
for i, line in enumerate(f):
line = json.loads(line)
all_preds.append(line[pred_field])
all_references.append(line[phoneme_field])
if line[grapheme_field] not in all_graphemes:
all_graphemes[line[grapheme_field]] = []
all_graphemes[line[grapheme_field]].append(i)
# collect all examples with multiple phoneme options and same grapheme form, choose the one with min PER
all_graphemes = {k: v for k, v in all_graphemes.items() if len(v) > 1}
lines_to_drop = []
for phon_amb_indices in all_graphemes.values():
refs, preds = [], []
for phon_amb_indices_ in phon_amb_indices:
refs.append(all_references[phon_amb_indices_])
preds.append(all_preds[phon_amb_indices_])
pers = []
for ref_, pred_ in zip(refs, preds):
pers.append(word_error_rate(hypotheses=[pred_], references=[ref_], use_cer=True))
min_idx = pers.index(min(pers))
phon_amb_indices.pop(min_idx)
lines_to_drop.extend(phon_amb_indices)
# drop duplicated examples, only keep with min PER
all_preds = [x for i, x in enumerate(all_preds) if i not in lines_to_drop]
all_references = [x for i, x in enumerate(all_references) if i not in lines_to_drop]
wer = word_error_rate(hypotheses=all_preds, references=all_references)
per = word_error_rate(hypotheses=all_preds, references=all_references, use_cer=True)
logging.info(f"{manifest}: PER: {per * 100:.2f}%, WER: {wer * 100:.2f}%, lines: {len(all_references)}")
return wer, per
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