NeMo
English
audio
speech
forced-alignment
reference-text-alignment
speech-processing
mfa
whisperx
python
metric:mae
Eval Results (legacy)
Instructions to use vertox-ai/speech-text-data-aligners with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use vertox-ai/speech-text-data-aligners with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
File size: 1,344 Bytes
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# Created by: Patrick Lumbantobing, VertoX-AI
# Copyright (c) 2026 VertoX-AI. All rights reserved.
#
# This work is licensed under the Apache-2.0 License.
# To view a copy of this license, visit
# https://www.apache.org/licenses/LICENSE-2.0
"""Expose the stable backend-neutral public Python API."""
from speech_text_data_aligners.application.annotate import (
AnnotationOptions,
AnnotationRun,
annotate_metadata,
)
from speech_text_data_aligners.application.benchmark import (
BenchmarkOptions,
benchmark_backend,
benchmark_backends,
)
from speech_text_data_aligners.benchmarking.reports import BenchmarkReport
from speech_text_data_aligners.core.models import (
AlignmentFailure,
AlignmentOutcome,
AlignmentProvenance,
AlignmentRequest,
AlignmentResult,
Diagnostic,
LexicalWord,
WordAlignment,
)
from speech_text_data_aligners.core.protocols import AlignmentBackend
__all__ = [
"AlignmentBackend",
"AlignmentFailure",
"AlignmentOutcome",
"AlignmentProvenance",
"AlignmentRequest",
"AlignmentResult",
"AnnotationOptions",
"AnnotationRun",
"BenchmarkOptions",
"BenchmarkReport",
"Diagnostic",
"LexicalWord",
"WordAlignment",
"annotate_metadata",
"benchmark_backend",
"benchmark_backends",
]
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