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"""Based on https://pytorch.org/audio/stable/tutorials/ctc_forced_alignment_api_tutorial.html"""
import csv
import os
import re
import sys
import typing
import sqlalchemy
import torch
import torchaudio
import torchaudio.functional as F
import tqdm
from kalpy.fstext.lexicon import CtmInterval as KalpyCtmInterval
from kalpy.fstext.lexicon import HierarchicalCtm, WordCtmInterval
from montreal_forced_aligner import config
from montreal_forced_aligner.command_line.mfa import mfa_cli
from montreal_forced_aligner.corpus.acoustic_corpus import AcousticCorpus
from montreal_forced_aligner.data import CtmInterval
from montreal_forced_aligner.db import File, Utterance, Word, WordInterval
from montreal_forced_aligner.exceptions import TextGridParseError
from montreal_forced_aligner.helper import mfa_open
from praatio import textgrid as tgio
root_dir = r"D:\Data\experiments\alignment_benchmarking"
mfa10_dir = r"D:\Data\models\1.0_archived"
mfa20_dir = r"D:\Data\models\2.0_archived"
mfa20a_dir = r"D:\Data\models\2.0.0a_archived"
mfa21_dir = r"D:\Data\models\2.1_trained"
mfa22_dir = r"D:\Data\models\2.2_trained"
mfa30_dir = r"D:\Data\models\3.0_trained"
trained22_dir = r"D:\Data\models\2.2_trained\buckeye"
trained30_dir = r"D:\Data\models\3.0_trained\buckeye"
mapping_directory = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "mapping_files"
)
nemo_tools_path = r"C:\Users\micha\Documents\Dev\NeMo\tools"
config.CLEAN = True
config.USE_POSTGRES = True
root_dir = r"D:\Data\experiments\alignment_benchmarking"
corpus_directories = {
"timit": r"D:\Data\speech\benchmark_datasets\timit",
"buckeye": r"D:\Data\speech\Buckeye",
}
conditions = {
"torchaudio_mms_fa": (None, None),
"nemo_forced_aligner": (None, None),
"whisperx": (None, None),
"arpa_1.0": (
os.path.join(mfa10_dir, "english.dict"),
os.path.join(mfa10_dir, "english.zip"),
),
"arpa_2.0": (
os.path.join(mfa20_dir, "english_us_arpa.dict"),
os.path.join(mfa20_dir, "english_us_arpa.zip"),
),
"arpa_2.0a": (
os.path.join(mfa20a_dir, "english_us_arpa.dict"),
os.path.join(mfa20a_dir, "english_us_arpa.zip"),
),
"mfa_2.0": (
os.path.join(mfa20_dir, "english_us_mfa.dict"),
os.path.join(mfa20_dir, "english_mfa.zip"),
),
"mfa_2.0a": (
os.path.join(mfa20a_dir, "english_us_mfa.dict"),
os.path.join(mfa20a_dir, "english_mfa.zip"),
),
"mfa_2.1": (
os.path.join(mfa21_dir, "english_us_mfa.dict"),
os.path.join(mfa21_dir, "english_mfa.zip"),
),
"mfa_2.2": (
os.path.join(mfa22_dir, "english_us_mfa.dict"),
os.path.join(mfa22_dir, "english_mfa.zip"),
),
"mfa_3.0": (
os.path.join(mfa30_dir, "english_us_mfa.dict"),
os.path.join(mfa30_dir, "english_mfa.zip"),
),
"arpa_3.0": (
os.path.join(mfa30_dir, "english_us_arpa.dict"),
os.path.join(mfa30_dir, "english_us_arpa.zip"),
),
"trained_2.2": (
os.path.join(trained22_dir, "english_us_mfa.dict"),
os.path.join(trained22_dir, "english_mfa.zip"),
),
"trained_3.0": (
os.path.join(trained30_dir, "english_us_mfa.dict"),
os.path.join(trained30_dir, "english_mfa.zip"),
),
"arpa_2.2": (
os.path.join(mfa20a_dir, "english_us_arpa.dict"),
os.path.join(mfa20a_dir, "english_us_arpa.zip"),
),
}
def align(emission, tokens):
targets = torch.tensor([tokens], dtype=torch.int32, device=device)
alignments, scores = F.forced_align(emission, targets, blank=0)
alignments, scores = (
alignments[0],
scores[0],
) # remove batch dimension for simplicity
scores = scores.exp() # convert back to probability
return alignments, scores
def unflatten(list_, lengths):
assert len(list_) == sum(lengths)
i = 0
ret = []
for l in lengths:
ret.append(list_[i : i + l])
i += l
return ret
def generate_ctm(
token_spans, transcript, ratio, sample_rate, utterance_begin, reference
) -> HierarchicalCtm:
i = 0
reference_index = 0
word_intervals = []
for word in transcript:
character_intervals = []
for j in range(len(word)):
span = token_spans[i]
begin = int(ratio * span.start) / sample_rate
begin += utterance_begin
end = int(ratio * span.end) / sample_rate
end += utterance_begin
character_intervals.append(
KalpyCtmInterval(begin, end, LABELS[span.token], span.token)
)
i += 1
if word == reference[reference_index].label:
reference_index += 1
elif (
word_intervals
and f"{word_intervals[-1].label}-{word}" in reference[reference_index].label
):
word_intervals[-1].label = f"{word_intervals[-1].label}-{word}"
word_intervals[-1].symbol = f"{word_intervals[-1].label}-{word}"
word_intervals[-1].phones.extend(character_intervals)
if reference[reference_index].label == word_intervals[-1].label:
reference_index += 1
continue
word_intervals.append(WordCtmInterval(word, word, character_intervals))
return HierarchicalCtm(word_intervals)
def align_words(
ref: typing.List[CtmInterval],
test: typing.List[CtmInterval],
):
try:
assert len(ref) == len(test)
except AssertionError:
print(ref)
print(test)
raise
error_sum = 0.0
boundary_count = 0
for i, r in enumerate(ref):
t = test[i]
# assert r.label == t.label
error_sum += abs(r.begin - t.begin)
error_sum += abs(r.end - t.end)
boundary_count += 2
return error_sum / boundary_count
def parse_aligned_textgrid(path: str, exclude_unknowns=True) -> typing.List[CtmInterval]:
"""
Load a TextGrid as a dictionary of speaker's phone tiers
Parameters
----------
path: :class:`~pathlib.Path`
TextGrid file to parse
Returns
-------
dict[str, list[:class:`~montreal_forced_aligner.data.CtmInterval`]]
Parsed phone tier
"""
tg = tgio.openTextgrid(path, includeEmptyIntervals=False, reportingMode="silence")
num_tiers = len(tg.tiers)
if num_tiers == 0:
raise TextGridParseError(path, "Number of tiers parsed was zero")
data = []
for tier_name in tg.tierNames:
ti = tg._tierDict[tier_name]
if not isinstance(ti, tgio.IntervalTier):
continue
if "words" not in tier_name:
continue
for begin, end, text in ti.entries:
text = text.lower().strip().replace("?", "")
if exclude_unknowns and re.match(r"^[\[(<].*", text):
continue
if not text:
continue
begin, end = round(begin, 4), round(end, 4)
# if end - begin < 0.01:
# continue
interval = CtmInterval(begin, end, text)
data.append(interval)
return data
if __name__ == "__main__":
csv_header = [
"file",
"begin",
"end",
"speaker",
"duration",
"normalized_text",
"filtered_text",
"alignment_score",
"alignment_likelihood",
"word_count",
"frames_per_second",
"words_per_second",
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
for condition, (dictionary_path, model_path) in conditions.items():
if condition == "torchaudio_mms_fa":
with torch.inference_mode():
for corpus, root in corpus_directories.items():
output_directory = os.path.join(root_dir, "alignments", condition, corpus)
if os.path.exists(output_directory):
continue
try:
bundle = torchaudio.pipelines.MMS_FA
except AttributeError:
print("Incorrect version of torchaudio, skipping torchaudio_mms_fa")
continue
model = bundle.get_model(with_star=False).to(device)
LABELS = bundle.get_labels(star=None)
DICTIONARY = bundle.get_dict(star=None)
corpus = AcousticCorpus(corpus_directory=os.path.join(root, "benchmark"))
corpus.delete_database()
corpus._load_corpus()
os.makedirs(output_directory, exist_ok=True)
csv_path = os.path.join(output_directory, "word_alignment.csv")
with (
corpus.session() as session,
mfa_open(csv_path, "w") as f,
tqdm.tqdm(total=corpus.num_utterances) as progress_bar,
):
writer = csv.DictWriter(f, fieldnames=csv_header)
writer.writeheader()
file_query = (
session.query(File)
.options(
sqlalchemy.orm.joinedload(File.sound_file, innerjoin=True),
)
.order_by(File.name)
)
for file in file_query:
file_ctm = HierarchicalCtm([])
if file.relative_path:
output_path = os.path.join(output_directory, file.relative_path)
os.makedirs(output_path, exist_ok=True)
else:
output_path = output_directory
output_path = os.path.join(output_path, file.name + ".TextGrid")
query = (
session.query(Utterance)
.filter(Utterance.file_id == file.id)
.options(
sqlalchemy.orm.joinedload(
Utterance.file, innerjoin=True
).joinedload(File.sound_file, innerjoin=True),
)
.order_by(Utterance.begin)
)
reference_path = os.path.join(root, "reference")
if file.relative_path:
reference_path = os.path.join(reference_path, file.relative_path)
reference_path = os.path.join(reference_path, file.name + ".TextGrid")
reference_word_intervals = parse_aligned_textgrid(reference_path)
reference_interval_index = 0
for utterance in query:
kalpy_utterance = utterance.to_kalpy()
waveform = torch.unsqueeze(
torch.Tensor(kalpy_utterance.segment.load_audio()),
0,
)
text = utterance.text.lower().replace("-", " ")
transcript = [
re.sub(rf'[^{"".join(LABELS[1:])}]', "", x)
for x in text.split()
if not re.match(r"^[\[(<].*", x)
]
transcript = [x for x in transcript if x]
tokenized_transcript = [
DICTIONARY[c] for word in transcript for c in word
]
emission, _ = model(waveform.to(device))
aligned_tokens, alignment_scores = align(
emission, tokenized_transcript
)
token_spans = F.merge_tokens(aligned_tokens, alignment_scores)
alignment_likelihood = sum(alignment_scores) / len(
alignment_scores
)
word_spans = unflatten(
token_spans, [len(word) for word in transcript]
)
ratio = waveform.size(1) / emission.size(1)
frames_per_second = emission.size(1) / utterance.duration
reference = []
while True:
try:
interval = reference_word_intervals[
reference_interval_index
]
except IndexError:
break
if interval.begin >= utterance.end:
break
if interval.begin >= utterance.begin:
reference.append(interval)
reference_interval_index += 1
ctm = generate_ctm(
token_spans,
transcript,
ratio,
bundle.sample_rate,
utterance.begin,
reference,
)
file_ctm.word_intervals.extend(ctm.word_intervals)
try:
alignment_score = align_words(reference, ctm.word_intervals)
except AssertionError:
print(file.name)
raise
continue
words_per_second = len(reference) / utterance.duration
data = {
"file": file.name,
"begin": utterance.begin,
"end": utterance.end,
"duration": utterance.duration,
"speaker": utterance.speaker_name,
"normalized_text": text.replace(",", ""),
"filtered_text": " ".join(transcript),
"alignment_score": alignment_score,
"alignment_likelihood": float(alignment_likelihood),
"word_count": len(ctm.word_intervals),
"frames_per_second": frames_per_second,
"words_per_second": words_per_second,
}
writer.writerow(data)
progress_bar.update(1)
file_ctm.export_textgrid(
output_path,
file_duration=file.duration,
output_format="short_textgrid",
)
elif condition == "nemo_forced_aligner":
try:
from dataclasses import dataclass, field, is_dataclass
from nemo.collections.asr.models.ctc_models import EncDecCTCModel
from nemo.collections.asr.models.hybrid_rnnt_ctc_models import (
EncDecHybridRNNTCTCModel,
)
from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchASR
from nemo.collections.asr.parts.utils.transcribe_utils import setup_model
from nemo.core.config import hydra_runner
except ImportError:
raise
continue
sys.path.append(nemo_tools_path)
sys.path.insert(0, os.path.join(nemo_tools_path, "nemo_forced_aligner"))
import tempfile
from nemo_forced_aligner.align import AlignmentConfig, ASSFileConfig, CTMFileConfig
from nemo_forced_aligner.utils.data_prep import (
V_NEGATIVE_NUM,
Segment,
Token,
Word,
add_t_start_end_to_utt_obj,
get_utt_obj,
)
from nemo_forced_aligner.utils.viterbi_decoding import viterbi_decoding
cfg = AlignmentConfig()
cfg.pretrained_name = "stt_en_conformer_ctc_medium"
if cfg.transcribe_device is None:
transcribe_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
transcribe_device = torch.device(cfg.transcribe_device)
if cfg.viterbi_device is None:
viterbi_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
viterbi_device = torch.device(cfg.viterbi_device)
model, _ = setup_model(cfg, transcribe_device)
model.eval()
if isinstance(model, EncDecHybridRNNTCTCModel):
model.change_decoding_strategy(decoder_type="ctc")
if cfg.use_local_attention:
model.change_attention_model(
self_attention_model="rel_pos_local_attn", att_context_size=[64, 64]
)
with torch.inference_mode():
for corpus, root in corpus_directories.items():
output_directory = os.path.join(root_dir, "alignments", condition, corpus)
if os.path.exists(output_directory):
continue
corpus = AcousticCorpus(corpus_directory=os.path.join(root, "benchmark"))
corpus.delete_database()
corpus._load_corpus()
os.makedirs(output_directory, exist_ok=True)
csv_path = os.path.join(output_directory, "word_alignment.csv")
with (
corpus.session() as session,
mfa_open(csv_path, "w") as f,
tempfile.TemporaryDirectory() as tmpdir,
tqdm.tqdm(total=corpus.num_utterances) as progress_bar,
):
writer = csv.DictWriter(f, fieldnames=csv_header)
writer.writeheader()
file_query = (
session.query(File)
.options(
sqlalchemy.orm.joinedload(File.sound_file, innerjoin=True),
)
.order_by(File.name)
)
for file in file_query:
file_ctm = HierarchicalCtm([])
if file.relative_path:
output_path = os.path.join(output_directory, file.relative_path)
os.makedirs(output_path, exist_ok=True)
else:
output_path = output_directory
output_path = os.path.join(output_path, file.name + ".TextGrid")
query = (
session.query(Utterance)
.filter(Utterance.file_id == file.id)
.options(
sqlalchemy.orm.joinedload(
Utterance.file, innerjoin=True
).joinedload(File.sound_file, innerjoin=True),
)
.order_by(Utterance.begin)
)
reference_path = os.path.join(root, "reference")
if file.relative_path:
reference_path = os.path.join(reference_path, file.relative_path)
reference_path = os.path.join(reference_path, file.name + ".TextGrid")
reference_word_intervals = parse_aligned_textgrid(reference_path)
reference_interval_index = 0
for utterance in query:
kalpy_utterance = utterance.to_kalpy()
reference = []
while True:
try:
interval = reference_word_intervals[
reference_interval_index
]
except IndexError:
break
if interval.begin >= utterance.end:
break
if interval.begin >= utterance.begin:
reference.append(interval)
reference_interval_index += 1
text = " ".join(x.label for x in reference)
with torch.no_grad():
output_timestep_duration = None
path = os.path.join(tmpdir, f"sound_file.wav")
torchaudio.save(
path,
torch.unsqueeze(
torch.Tensor(kalpy_utterance.segment.load_audio()),
0,
),
16000,
)
hypotheses = model.transcribe(
[path],
return_hypotheses=True,
verbose=False,
batch_size=1,
)
# if hypotheses form a tuple (from Hybrid model), extract just "best" hypothesis
if type(hypotheses) == tuple and len(hypotheses) == 2:
hypotheses = hypotheses[0]
for hypothesis in hypotheses:
log_probs_list_batch = [hypothesis.y_sequence]
T_list_batch = [hypothesis.y_sequence.shape[0]]
pred_text_batch = [hypothesis.text]
utt_obj = get_utt_obj(
text,
model,
cfg.additional_segment_grouping_separator,
T_list_batch[0],
file.sound_file.sound_file_path,
utterance.kaldi_id,
)
alignment_likelihood = float(hypothesis.score)
y_list_batch = [utt_obj.token_ids_with_blanks]
U_list_batch = [len(utt_obj.token_ids_with_blanks)]
utt_obj_batch = [utt_obj]
T_max = max(T_list_batch)
U_max = max(U_list_batch)
if hasattr(model, "tokenizer"):
V = len(model.tokenizer.vocab) + 1
else:
V = len(model.decoder.vocabulary) + 1
T_batch = torch.tensor(T_list_batch)
U_batch = torch.tensor(U_list_batch)
log_probs_batch = V_NEGATIVE_NUM * torch.ones((1, T_max, V))
for b, log_probs_utt in enumerate(log_probs_list_batch):
t = log_probs_utt.shape[0]
log_probs_batch[b, :t, :] = log_probs_utt
y_batch = V * torch.ones((1, U_max), dtype=torch.int64)
for b, y_utt in enumerate(y_list_batch):
U_utt = U_batch[b]
y_batch[b, :U_utt] = torch.tensor(y_utt)
# calculate output_timestep_duration if it is None
if output_timestep_duration is None:
if not "window_stride" in model.cfg.preprocessor:
raise ValueError(
"Don't have attribute 'window_stride' in "
"'model.cfg.preprocessor' => cannot calculate "
" model_downsample_factor => stopping process"
)
if not "sample_rate" in model.cfg.preprocessor:
raise ValueError(
"Don't have attribute 'sample_rate' in "
"'model.cfg.preprocessor' => cannot calculate start "
" and end time of segments => stopping process"
)
audio_dur = utterance.duration
n_input_frames = (
audio_dur / model.cfg.preprocessor.window_stride
)
model_downsample_factor = round(
n_input_frames / int(T_batch[0])
)
output_timestep_duration = (
model.preprocessor.featurizer.hop_length
* model_downsample_factor
/ model.cfg.preprocessor.sample_rate
)
alignments_batch = viterbi_decoding(
log_probs_batch,
y_batch,
T_batch,
U_batch,
viterbi_device,
)
utt_obj, alignment_utt = (
utt_obj_batch[0],
alignments_batch[0],
)
utt_obj = add_t_start_end_to_utt_obj(
utt_obj, alignment_utt, output_timestep_duration
)
segment = [
x
for x in utt_obj.segments_and_tokens
if isinstance(x, Segment)
][0]
words = [
x for x in segment.words_and_tokens if isinstance(x, Word)
]
word_intervals = []
for word in words:
character_intervals = []
word_label = word.text
for token in word.tokens:
if token.text == "<b>":
continue
begin = token.t_start + utterance.begin
end = token.t_end + utterance.begin
if end > utterance.end:
end = utterance.end
label = token.text
character_intervals.append(
KalpyCtmInterval(begin, end, label, label)
)
word_intervals.append(
WordCtmInterval(
word_label, word_label, character_intervals
)
)
ctm = HierarchicalCtm(word_intervals)
frames_per_second = (
log_probs_list_batch[0].size(0) / utterance.duration
)
file_ctm.word_intervals.extend(ctm.word_intervals)
try:
alignment_score = align_words(reference, ctm.word_intervals)
except AssertionError:
print(file.name)
raise
continue
words_per_second = len(reference) / utterance.duration
data = {
"file": file.name,
"begin": utterance.begin,
"end": utterance.end,
"duration": utterance.duration,
"speaker": utterance.speaker_name,
"normalized_text": utterance.normalized_text,
"filtered_text": text,
"alignment_score": alignment_score,
"alignment_likelihood": float(alignment_likelihood),
"word_count": len(ctm.word_intervals),
"frames_per_second": frames_per_second,
"words_per_second": words_per_second,
}
writer.writerow(data)
progress_bar.update(1)
file_ctm.export_textgrid(
output_path,
file_duration=file.duration,
output_format="short_textgrid",
)
elif condition == "whisperx":
try:
import whisperx
except ImportError:
print("whisperx not installed, skipping")
continue
compute_type = "float16"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisperx.load_model(
"large-v2", device, compute_type=compute_type, language="en"
)
model_a, metadata = whisperx.load_align_model(language_code="en", device=device)
with torch.inference_mode():
for corpus, root in corpus_directories.items():
output_directory = os.path.join(root_dir, "alignments", condition, corpus)
if os.path.exists(output_directory):
continue
corpus = AcousticCorpus(corpus_directory=os.path.join(root, "benchmark"))
corpus.delete_database()
corpus._load_corpus()
os.makedirs(output_directory, exist_ok=True)
csv_path = os.path.join(output_directory, "word_alignment.csv")
with (
corpus.session() as session,
mfa_open(csv_path, "w") as f,
tqdm.tqdm(total=corpus.num_utterances) as progress_bar,
):
writer = csv.DictWriter(f, fieldnames=csv_header)
writer.writeheader()
file_query = (
session.query(File)
.options(
sqlalchemy.orm.joinedload(File.sound_file, innerjoin=True),
)
.order_by(File.name)
)
for file in file_query:
file_ctm = HierarchicalCtm([])
if file.relative_path:
output_path = os.path.join(output_directory, file.relative_path)
os.makedirs(output_path, exist_ok=True)
else:
output_path = output_directory
output_path = os.path.join(output_path, file.name + ".TextGrid")
query = (
session.query(Utterance)
.filter(Utterance.file_id == file.id)
.options(
sqlalchemy.orm.joinedload(
Utterance.file, innerjoin=True
).joinedload(File.sound_file, innerjoin=True),
)
.order_by(Utterance.begin)
)
reference_path = os.path.join(root, "reference")
if file.relative_path:
reference_path = os.path.join(reference_path, file.relative_path)
reference_path = os.path.join(reference_path, file.name + ".TextGrid")
reference_word_intervals = parse_aligned_textgrid(reference_path)
reference_interval_index = 0
for utterance in query:
kalpy_utterance = utterance.to_kalpy()
reference = []
while True:
try:
interval = reference_word_intervals[
reference_interval_index
]
except IndexError:
break
if interval.begin >= utterance.end:
break
if interval.begin >= utterance.begin:
reference.append(interval)
reference_interval_index += 1
text = " ".join(x.label for x in reference)
audio = kalpy_utterance.segment.load_audio()
# transcribe_result = model.transcribe(audio, batch_size=1)
# print(result["segments"])
segments = [
{
"text": text,
"start": 0.0,
"end": utterance.duration,
}
]
result = whisperx.align(
segments,
model_a,
metadata,
audio,
device,
return_char_alignments=True,
)
word_intervals = []
words = result["segments"][0]["words"]
chars = result["segments"][0]["chars"]
character_index = 0
score_sum = 0
for word in words:
character_intervals = []
word_label = word["word"]
while True:
try:
c = chars[character_index]
except IndexError:
break
if c["char"] == " ":
character_index += 1
continue
if c["start"] >= word["end"]:
break
begin = c["start"] + utterance.begin
end = c["end"] + utterance.begin
character_intervals.append(
KalpyCtmInterval(begin, end, c["char"], c["char"])
)
character_index += 1
word_intervals.append(
WordCtmInterval(
word_label, word_label, character_intervals
)
)
score_sum += word["score"]
ctm = HierarchicalCtm(word_intervals)
alignment_likelihood = score_sum / len(words)
file_ctm.word_intervals.extend(ctm.word_intervals)
try:
alignment_score = align_words(reference, ctm.word_intervals)
except AssertionError:
print(file.name)
raise
continue
words_per_second = len(reference) / utterance.duration
data = {
"file": file.name,
"begin": utterance.begin,
"end": utterance.end,
"duration": utterance.duration,
"speaker": utterance.speaker_name,
"normalized_text": utterance.normalized_text,
"filtered_text": text,
"alignment_score": alignment_score,
"alignment_likelihood": float(alignment_likelihood),
"word_count": len(ctm.word_intervals),
"frames_per_second": "NA",
"words_per_second": words_per_second,
}
writer.writerow(data)
progress_bar.update(1)
file_ctm.export_textgrid(
output_path,
file_duration=file.duration,
output_format="short_textgrid",
)
else:
if not os.path.exists(model_path):
continue
for corpus, root in corpus_directories.items():
output_directory = os.path.join(root_dir, "alignments", condition, corpus)
csv_path = os.path.join(output_directory, "word_alignment.csv")
if os.path.exists(csv_path):
continue
command = [
"align",
os.path.join(root, "benchmark"),
dictionary_path,
model_path,
output_directory,
"-j",
"10",
"--clean",
"--debug",
"--use_mp",
"--use_cutoff_model",
"--use_postgres",
"--cleanup_textgrids",
"--beam",
"10",
"--retry_beam",
"40",
]
print(command)
mfa_cli(command, standalone_mode=False)
corpus = AcousticCorpus(corpus_directory=os.path.join(root, "benchmark"))
with (
corpus.session() as session,
mfa_open(csv_path, "w") as f,
tqdm.tqdm(total=corpus.num_utterances) as progress_bar,
):
writer = csv.DictWriter(f, fieldnames=csv_header)
writer.writeheader()
file_query = (
session.query(File)
.options(
sqlalchemy.orm.joinedload(File.sound_file, innerjoin=True),
)
.order_by(File.name)
)
for file in file_query:
query = (
session.query(Utterance)
.filter(Utterance.file_id == file.id)
.options(
sqlalchemy.orm.joinedload(
Utterance.file, innerjoin=True
).joinedload(File.sound_file, innerjoin=True),
)
.order_by(Utterance.begin)
)
reference_path = os.path.join(root, "reference")
if file.relative_path:
reference_path = os.path.join(reference_path, file.relative_path)
reference_path = os.path.join(reference_path, file.name + ".TextGrid")
reference_word_intervals = parse_aligned_textgrid(
reference_path, exclude_unknowns=True
)
reference_interval_index = 0
for utterance in query:
word_interval_query = (
session.query(WordInterval)
.join(WordInterval.word)
.filter(WordInterval.utterance_id == utterance.id)
.filter(Word.word.op("~")(r"^[^\[<{(]"))
.options(
sqlalchemy.orm.joinedload(WordInterval.word, innerjoin=True),
)
.order_by(WordInterval.begin)
)
reference = []
while True:
try:
interval = reference_word_intervals[reference_interval_index]
except IndexError:
break
if interval.begin >= utterance.end:
break
if interval.begin >= utterance.begin:
reference.append(interval)
reference_interval_index += 1
word_intervals = []
reference_index = 0
for i, wi in enumerate(word_interval_query):
if reference[reference_index].label == wi.word.word:
word_intervals.append(wi.as_ctm())
reference_index += 1
elif (
word_intervals
and reference[reference_index].label
== word_intervals[-1].label + wi.word.word
):
word_intervals[-1].label += wi.word.word
word_intervals[-1].end = wi.end
reference_index += 1
elif (
word_intervals
and word_intervals[-1].label + "-" + wi.word.word
in reference[reference_index].label
):
word_intervals[-1].label += "-" + wi.word.word
word_intervals[-1].end = wi.end
if (
word_intervals[-1].label
== reference[reference_index].label
):
reference_index += 1
else:
word_intervals.append(wi.as_ctm())
if not word_intervals:
continue
word_index = 0
alignment_score = align_words(reference, word_intervals)
words_per_second = len(reference) / utterance.duration
data = {
"file": file.name,
"begin": utterance.begin,
"end": utterance.end,
"duration": utterance.duration,
"speaker": utterance.speaker_name,
"normalized_text": utterance.normalized_text,
"filtered_text": " ".join(x.label for x in word_intervals),
"alignment_score": alignment_score,
"alignment_likelihood": utterance.alignment_log_likelihood,
"word_count": len(word_intervals),
"frames_per_second": 100,
"words_per_second": words_per_second,
}
writer.writerow(data)
progress_bar.update(1)
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