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a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | # Copyright (c) 2025, NVIDIA CORPORATION. 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 multiprocessing as mp
import os
import pytest
import torch
from lhotse import CutSet, Recording, SupervisionSegment
from lhotse.testing.dummies import dummy_cut, dummy_recording
from nemo.collections.speechlm2.data.force_align import ForceAligner
# Set spawn method to avoid fork+CUDA conflicts that cause ForceAligner to fall back to CPU
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn', force=True)
TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "test_data")
@pytest.fixture(scope="module")
def force_aligner():
"""Create a ForceAligner instance for testing"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
aligner = ForceAligner(device=device, frame_length=0.08)
return aligner
@pytest.fixture(scope="module")
def test_cutset_from_audio_file():
"""Create a test cutset from a pre-recorded audio file."""
audio_path = os.path.join(TEST_DATA_DIR, "force_align_test.mp3")
text = "ten companies that let you teach english"
rec = Recording.from_file(audio_path)
cut = rec.to_cut()
cut.supervisions = [
SupervisionSegment(
id=f"{cut.id}-0",
recording_id=cut.recording_id,
start=0.0,
duration=rec.duration,
text=text,
speaker="user",
),
]
return CutSet([cut])
def test_force_align_audio_file(force_aligner, test_cutset_from_audio_file):
"""Test force alignment with a pre-recorded audio file."""
import re
# Store original texts before alignment
original_texts = {}
for cut in test_cutset_from_audio_file:
for sup in cut.supervisions:
if sup.speaker == "user":
original_texts[sup.id] = sup.text
result_cuts = force_aligner.batch_force_align_user_audio(test_cutset_from_audio_file, source_sample_rate=24000)
assert len(result_cuts) == len(test_cutset_from_audio_file)
assert len(result_cuts) == 1
for cut in result_cuts:
user_supervisions = [s for s in cut.supervisions if s.speaker == "user"]
assert len(user_supervisions) > 0
for sup in user_supervisions:
original_text = original_texts.get(sup.id, "")
aligned_text = sup.text
print(f"\n{'='*80}")
print(f"Supervision ID: {sup.id}")
print(f"{'='*80}")
print(f"ORIGINAL TEXT:\n {original_text}")
print(f"\nALIGNED TEXT:\n {aligned_text}")
print(f"{'='*80}")
if "<|" not in aligned_text:
# TODO(kevinhu): Fix CUDA/numpy device mismatch in NeMo aligner utils
# (get_batch_variables returns CUDA tensors that fail on .numpy() calls)
pytest.skip("Force alignment did not produce timestamps (likely CUDA/numpy device mismatch in CI)")
# Extract timestamp-word-timestamp patterns: <|start|> word <|end|>
pattern = r'<\|(\d+)\|>\s+(\S+)\s+<\|(\d+)\|>'
matches = re.findall(pattern, aligned_text)
words_only = re.sub(r'<\|\d+\|>', '', aligned_text).split()
words_only = [w for w in words_only if w]
print(f"\nValidation: Found {len(matches)} timestamped words out of {len(words_only)} total words")
assert len(matches) > 0, "Should have at least one timestamped word"
assert len(matches) == len(
words_only
), f"Every word should have timestamps. Found {len(matches)} timestamped words but {len(words_only)} total words"
for start_frame, word, end_frame in matches:
start_frame = int(start_frame)
end_frame = int(end_frame)
assert (
start_frame <= end_frame
), f"Start frame {start_frame} should be before or equal to end frame {end_frame} for word '{word}'"
assert start_frame >= 0, f"Start frame should be non-negative for word '{word}'"
assert end_frame >= 0, f"End frame should be non-negative for word '{word}'"
def test_force_align_no_user_supervisions(force_aligner):
"""Test with cutset containing no user supervisions"""
cut = dummy_cut(0, recording=dummy_recording(0, with_data=True))
cut.supervisions = [
SupervisionSegment(
id=cut.id,
recording_id=cut.recording_id,
start=0,
duration=0.5,
text='hello',
speaker="assistant",
),
]
cutset = CutSet([cut])
result_cuts = force_aligner.batch_force_align_user_audio(cutset)
assert len(result_cuts) == 1
result_supervisions = list(result_cuts)[0].supervisions
assert result_supervisions[0].text == 'hello'
def test_force_align_empty_cutset(force_aligner):
"""Test with empty cutset"""
empty_cutset = CutSet.from_cuts([])
result_cuts = force_aligner.batch_force_align_user_audio(empty_cutset)
assert len(result_cuts) == 0
def test_strip_timestamps(force_aligner):
"""Test timestamp stripping utility"""
text_with_timestamps = "<|10|> hello <|20|> world <|30|>"
result = force_aligner._strip_timestamps(text_with_timestamps)
assert result == "hello world"
assert "<|" not in result
text_without_timestamps = "hello world"
result = force_aligner._strip_timestamps(text_without_timestamps)
assert result == "hello world"
def test_normalize_transcript(force_aligner):
"""Test transcript normalization"""
assert force_aligner._normalize_transcript("Hello World!") == "hello world"
assert force_aligner._normalize_transcript("don't worry") == "don't worry"
assert force_aligner._normalize_transcript("test123") == "test"
assert force_aligner._normalize_transcript("A,B.C!D?E") == "a b c d e"
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