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import os
from montreal_forced_aligner.command_line.mfa import mfa_cli
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"
mfa31_dir = r"D:\Data\models\3.1_trained"
adapted_dir = r"D:\Data\models\adapted"
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"
)
corpus_directories = {
"timit": r"D:\Data\speech\benchmark_datasets\timit\benchmark",
"buckeye": r"D:\Data\speech\Buckeye\buckeye_corpus_benchmark",
}
reference_directories = {
"timit": r"D:\Data\speech\benchmark_datasets\timit\reference",
"buckeye": r"D:\Data\speech\Buckeye\buckeye_reference_alignments",
}
conditions = {
"arpa_1.0": (os.path.join(mfa10_dir, "english.dict"), os.path.join(mfa10_dir, "english.zip")),
"arpa_1.0_finetune": (
os.path.join(mfa10_dir, "english.dict"),
os.path.join(mfa10_dir, "english.zip"),
),
"arpa_1.0_adapted": (
os.path.join(mfa10_dir, "english.dict"),
os.path.join(mfa10_dir, "english.zip"),
),
"arpa_1.0_adapted_finetune": (
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"),
),
"mfa_3.0_finetune": (
os.path.join(mfa30_dir, "english_us_mfa.dict"),
os.path.join(mfa30_dir, "english_mfa.zip"),
),
"mfa_3.1": (
os.path.join(mfa31_dir, "english_us_mfa.dict"),
os.path.join(mfa31_dir, "english_mfa.zip"),
),
"mfa_3.1_finetune": (
os.path.join(mfa31_dir, "english_us_mfa.dict"),
os.path.join(mfa31_dir, "english_mfa.zip"),
),
"mfa_3.1_adapted": (
os.path.join(mfa31_dir, "english_us_mfa.dict"),
os.path.join(mfa31_dir, "english_mfa.zip"),
),
"mfa_3.1_adapted_finetune": (
os.path.join(mfa31_dir, "english_us_mfa.dict"),
os.path.join(mfa31_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"),
),
"arpa_3.0_finetune": (
os.path.join(mfa30_dir, "english_us_arpa.dict"),
os.path.join(mfa30_dir, "english_us_arpa.zip"),
),
"arpa_3.0_adapted": (
os.path.join(mfa30_dir, "english_us_arpa.dict"),
os.path.join(mfa30_dir, "english_us_arpa.zip"),
),
"arpa_3.0_adapted_finetune": (
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"),
),
"arpa_2.2_adapted": (
os.path.join(mfa20a_dir, "english_us_arpa.dict"),
os.path.join(mfa20a_dir, "english_us_arpa.zip"),
),
"arpa_2.2_adapted_finetune": (
os.path.join(mfa20a_dir, "english_us_arpa.dict"),
os.path.join(mfa20a_dir, "english_us_arpa.zip"),
),
}
mapping_files = {}
for k in conditions.keys():
for corpus in corpus_directories:
if "arpa" in k:
phone_set = "arpa"
else:
phone_set = "mfa"
mapping_files[(k, corpus)] = os.path.join(
mapping_directory, f"{phone_set}_{corpus}_mapping.yaml"
)
if __name__ == "__main__":
for condition, (dictionary_path, model_path) in conditions.items():
print(condition)
for corpus, root in corpus_directories.items():
output_directory = os.path.join(root_dir, "alignments", condition, corpus)
if os.path.exists(output_directory):
continue
if not os.path.exists(model_path):
continue
if not os.path.exists(dictionary_path):
continue
if "adapt" in condition:
os.makedirs(adapted_dir, exist_ok=True)
output_model_path = os.path.join(
adapted_dir, f"{condition.replace('_finetune', '')}.zip"
)
if not os.path.exists(output_model_path):
command = [
"adapt",
root,
str(dictionary_path),
str(model_path),
str(output_model_path),
"-j",
"10",
"--clean",
"--no_debug",
"--use_mp",
"--use_cutoff_model",
"--use_postgres",
"--beam",
"10",
"--retry_beam",
"40",
]
print(command)
mfa_cli(command, standalone_mode=False)
model_path = output_model_path
command = [
"align",
root,
dictionary_path,
model_path,
output_directory,
"-j",
"10",
"--clean",
"--no_debug",
"--use_mp",
"--use_cutoff_model",
"--use_postgres",
"--cleanup_textgrids",
"--reference_directory",
reference_directories[corpus],
"--custom_mapping_path",
mapping_files[(condition, corpus)],
"--beam",
"10",
"--retry_beam",
"40",
]
if "finetune" in condition:
command += ["--fine_tune"]
print(command)
mfa_cli(command, standalone_mode=False)
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