Merge pull request #95 from fgnt/greedy_ditcp

Add greedy DI-tcpWER

meeteval/wer/__main__.py

@@ -369,6 +369,34 @@ def greedy_dicpwer(
     _save_results(results, hypothesis, per_reco_out, average_out, wer_name='greedy-DI-cpWER')
 
 
+def greedy_ditcpwer(
+        reference, hypothesis,
+        average_out='{parent}/{stem}_greedy_ditcpwer.json',
+        per_reco_out='{parent}/{stem}_greedy_ditcpwer_per_reco.json',
+        regex=None,
+        collar=0,
+        hyp_pseudo_word_timing='character_based_points',
+        ref_pseudo_word_timing='character_based',
+        hypothesis_sort='segment',
+        reference_sort='segment',
+        uem=None,
+        partial=False,
+        normalizer=None,
+):
+    """Computes the time-constrained diarization-invariant cpWER (greedy DI-tcpWER)"""
+    results = meeteval.wer.api.greedy_ditcpwer(
+        reference, hypothesis, regex=regex,
+        collar=collar,
+        hyp_pseudo_word_timing=hyp_pseudo_word_timing,
+        ref_pseudo_word_timing=ref_pseudo_word_timing,
+        hypothesis_sort=hypothesis_sort,
+        reference_sort=reference_sort,
+        uem=uem, partial=partial,
+        normalizer=normalizer,
+    )
+    _save_results(results, hypothesis, per_reco_out, average_out, wer_name='greedy-DI-tcpWER')
+
+
 def _merge(
         files: 'list[str]',
         out: str = '-',
@@ -695,6 +723,7 @@ def cli():
     cli.add_command(tcpwer)
     cli.add_command(tcorcwer)
     cli.add_command(greedy_dicpwer)
+    cli.add_command(greedy_ditcpwer)
     cli.add_command(greedy_tcorcwer)
     cli.add_command(merge)
     cli.add_command(average)

meeteval/wer/api.py

@@ -15,6 +15,7 @@
     'tcorcwer',
     'greedy_tcorcwer',
     'greedy_dicpwer',
+    'greedy_ditcpwer',
 ]
 
 
@@ -361,6 +362,43 @@ def greedy_dicpwer(
     return results
 
 
+def greedy_ditcpwer(
+        reference, hypothesis,
+        regex=None,
+        collar=0,
+        hyp_pseudo_word_timing='character_based_points',
+        ref_pseudo_word_timing='character_based',
+        hypothesis_sort='segment',
+        reference_sort='segment',
+        uem=None,
+        partial=False,
+        normalizer=None,
+):
+    """Computes the Diarization Invariant cpWER (DI-cpWER) with a greedy
+    algorithm."""
+    from meeteval.wer.wer.di_cp import greedy_di_tcp_word_error_rate_multifile
+    reference, hypothesis = _load_texts(
+        reference, hypothesis, regex=regex,
+        uem=uem, normalizer=normalizer,
+    )
+    results = greedy_di_tcp_word_error_rate_multifile(
+        reference, hypothesis,
+        reference_pseudo_word_level_timing=ref_pseudo_word_timing,
+        hypothesis_pseudo_word_level_timing=hyp_pseudo_word_timing,
+        collar=collar,
+        reference_sort=reference_sort,
+        hypothesis_sort=hypothesis_sort,
+        partial=partial,
+    )
+    from meeteval.wer import combine_error_rates
+    average: ErrorRate = combine_error_rates(results)
+    if average.hypothesis_self_overlap is not None:
+        average.hypothesis_self_overlap.warn('hypothesis')
+    if average.reference_self_overlap is not None:
+        average.reference_self_overlap.warn('reference')
+    return results
+
+
 def greedy_tcorcwer(
         reference, hypothesis,
         regex=None,

meeteval/wer/matching/greedy_combination_matching.py

@@ -180,7 +180,7 @@ def initialize_assignment(
     >>> initialize_assignment(segments, streams, initialization='cp')
     [0, 0]
     """
-    if initialization == 'cp':
+    if initialization in ('cp', 'tcp'):
         # Special case when no streams are present
         if len(streams) == 0:
             return initialize_assignment(segments, streams, 'constant')
@@ -192,13 +192,14 @@ def initialize_assignment(
         # Compute cpWER to get a good starting point
         from meeteval.wer.wer.cp import _minimum_permutation_assignment
         from meeteval.wer.wer.siso import siso_levenshtein_distance
+        from meeteval.wer.wer.time_constrained import time_constrained_siso_levenshtein_distance
         if isinstance(streams, SegLST):
             streams = streams.groupby('speaker')
         speaker_grouped_segments = segments.groupby('speaker')
         assignment, _, cost_matrix = _minimum_permutation_assignment(
             speaker_grouped_segments,
             streams,
-            distance_fn=siso_levenshtein_distance,
+            distance_fn=siso_levenshtein_distance if initialization == 'cp' else time_constrained_siso_levenshtein_distance,
         )
         # Use integers for the assignment labels.
         counter = iter(itertools.count())

meeteval/wer/wer/__init__.py

@@ -4,4 +4,5 @@
 from .siso import siso_word_error_rate, siso_character_error_rate, siso_word_error_rate_multifile
 from .error_rate import ErrorRate, combine_error_rates
 from .time_constrained import time_constrained_minimum_permutation_word_error_rate, time_constrained_siso_word_error_rate, tcp_word_error_rate_multifile
-from .di_cp import greedy_di_cp_word_error_rate, DICPErrorRate, greedy_di_cp_word_error_rate_multifile
+from .di_cp import greedy_di_cp_word_error_rate, DICPErrorRate, greedy_di_cp_word_error_rate_multifile, greedy_di_tcp_word_error_rate, greedy_di_tcp_word_error_rate_multifile
+from .time_constrained_orc import time_constrained_orc_wer, time_constrained_orc_wer_multifile, greedy_time_constrained_orc_wer, greedy_time_constrained_orc_wer_multifile

meeteval/wer/wer/di_cp.py

@@ -11,6 +11,8 @@
     'DICPErrorRate',
     'greedy_di_cp_word_error_rate',
     'greedy_di_cp_word_error_rate_multifile',
+    'greedy_di_tcp_word_error_rate',
+    'greedy_di_tcp_word_error_rate_multifile',
     'apply_dicp_assignment',
 ]
 
@@ -111,6 +113,86 @@ def greedy_di_cp_word_error_rate_multifile(
     )
 
 
+def greedy_di_tcp_word_error_rate(
+        reference,
+        hypothesis,
+        reference_pseudo_word_level_timing='character_based',
+        hypothesis_pseudo_word_level_timing='character_based_points',
+        collar: int = 0,
+        reference_sort='segment',
+        hypothesis_sort='segment',
+):
+    """
+    Computes the DI-tcpWER (time-constrained DI-cpWER) with a greedy algorithm
+
+    >>> reference = SegLST([
+    ...     {'segment_index': 0, 'speaker': 'A', 'words': 'a', 'start_time': 0.0, 'end_time': 1.0},
+    ...     {'segment_index': 1, 'speaker': 'A', 'words': 'b', 'start_time': 1.0, 'end_time': 2.0},
+    ...     {'segment_index': 2, 'speaker': 'B', 'words': 'c', 'start_time': 2.0, 'end_time': 3.0},
+    ...     {'segment_index': 3, 'speaker': 'B', 'words': 'd', 'start_time': 3.0, 'end_time': 4.0},
+    ... ])
+    >>> greedy_di_tcp_word_error_rate(reference, reference)
+    DICPErrorRate(error_rate=0.0, errors=0, length=4, insertions=0, deletions=0, substitutions=0, reference_self_overlap=SelfOverlap(overlap_rate=0.0, overlap_time=0, total_time=6.0), hypothesis_self_overlap=SelfOverlap(overlap_rate=0.0, overlap_time=0, total_time=6.0), assignment=('A', 'A', 'B', 'B'))
+    >>> hypothesis = SegLST([
+    ...     {'segment_index': 0, 'speaker': 'A', 'words': 'a b', 'start_time': 0.0, 'end_time': 2.0},
+    ...     {'segment_index': 2, 'speaker': 'A', 'words': 'b c d', 'start_time': 1.0, 'end_time': 4.0},
+    ... ])
+    >>> greedy_di_tcp_word_error_rate(reference, hypothesis)
+    DICPErrorRate(error_rate=0.25, errors=1, length=4, insertions=1, deletions=0, substitutions=0, reference_self_overlap=SelfOverlap(overlap_rate=0.0, overlap_time=0, total_time=6.0), hypothesis_self_overlap=SelfOverlap(overlap_rate=0.25, overlap_time=1.0, total_time=4.0), assignment=('A', 'B'))
+    """
+
+    # The assignment of the DI-tcpWER is equal to the assignment of the tcORC-WER
+    # with swapped arguments (reference <-> hypothesis)
+    er = meeteval.wer.wer.time_constrained_orc.greedy_time_constrained_orc_wer(
+        hypothesis, reference,
+        hypothesis_pseudo_word_level_timing, reference_pseudo_word_level_timing,
+        collar,
+        hypothesis_sort, reference_sort
+    )
+
+    # The error rate object can be constructed just from the ORC-WER error rate
+    # object. Insertions and deletions are swapped, the length is different.
+    return DICPErrorRate(
+        er.errors, sum([len(s['words'].split()) for s in reference]),
+        insertions=er.deletions,
+        deletions=er.insertions,
+        substitutions=er.substitutions,
+        assignment=er.assignment,
+        reference_self_overlap=er.hypothesis_self_overlap,
+        hypothesis_self_overlap=er.reference_self_overlap,
+    )
+
+
+def greedy_di_tcp_word_error_rate_multifile(
+        reference,
+        hypothesis,
+        partial=False,
+        reference_pseudo_word_level_timing='character_based',
+        hypothesis_pseudo_word_level_timing='character_based_points',
+        collar: int = 0,
+        reference_sort='segment',
+        hypothesis_sort='segment',
+) -> 'dict[str, DICPErrorRate]':
+    """
+    Computes the (Greedy) DI-tcpWER for each example in the reference and hypothesis files.
+
+    To compute the overall WER, use
+    `sum(greedy_di_tcp_word_error_rate_multifile(r, h).values())`.
+    """
+    from meeteval.io.seglst import apply_multi_file
+    return apply_multi_file(
+        functools.partial(
+            greedy_di_tcp_word_error_rate,
+            reference_pseudo_word_level_timing=reference_pseudo_word_level_timing,
+            hypothesis_pseudo_word_level_timing=hypothesis_pseudo_word_level_timing,
+            collar=collar,
+            reference_sort=reference_sort,
+            hypothesis_sort=hypothesis_sort,
+        ), reference, hypothesis,
+        partial=partial
+    )
+
+
 def apply_dicp_assignment(
         assignment: 'list[int | str] | tuple[int | str]',
         reference: 'list[list[str]] | dict[str, list[str]] | SegLST',

meeteval/wer/wer/mimo.py

@@ -26,6 +26,13 @@ class MimoErrorRate(ErrorRate):
     """
     assignment: 'tuple[int, ...]'
 
+    def apply_assignment(self, reference, hypothesis):
+        return apply_mimo_assignment(
+            self.assignment,
+            reference=reference,
+            hypothesis=hypothesis,
+        )
+
 
 def mimo_error_rate(
         reference: 'list[list[Iterable]] | dict[Any, list[Iterable]]',

meeteval/wer/wer/time_constrained.py

@@ -350,8 +350,10 @@ def apply_collar(s: SegLST, collar: float):
     return s.map(
         lambda s: {
             **s,
-            'start_time': [t - collar for t in s['start_time']] if isinstance(s['start_time'], list) else s['start_time'] - collar,
-            'end_time': [t + collar for t in s['end_time']] if isinstance(s['end_time'], list) else s['end_time'] + collar,
+            'start_time': [t - collar for t in s['start_time']] if isinstance(s['start_time'], list) else s[
+                                                                                                              'start_time'] - collar,
+            'end_time': [t + collar for t in s['end_time']] if isinstance(s['end_time'], list) else s[
+                                                                                                        'end_time'] + collar,
         }
     )
 
@@ -549,6 +551,18 @@ def time_constrained_siso_levenshtein_distance(
 ) -> int:
     from meeteval.wer.matching.cy_levenshtein import time_constrained_levenshtein_distance
 
+    # Flatten words when segment_representation is 'segment'
+    def flatten_words(s):
+        if not isinstance(s['words'], list):
+            return [s]
+        return [
+            {'words': w, 'start_time': st, 'end_time': et}
+            for w, st, et in zip(s['words'], s['start_time'], s['end_time'])
+        ]
+
+    reference = reference.flatmap(flatten_words)
+    hypothesis = hypothesis.flatmap(flatten_words)
+
     # Ignore empty segments
     reference = reference.filter(lambda s: s['words'])
     hypothesis = hypothesis.filter(lambda s: s['words'])

meeteval/wer/wer/time_constrained_orc.py

@@ -142,7 +142,7 @@ def matching(reference, hypothesis):
             [list(zip(*r)) for r in reference.T['words', 'start_time', 'end_time']],
             [[w for words in stream.T['words', 'start_time', 'end_time'] for w in zip(*words)] for stream in
              hypothesis.values()],
-            initial_assignment=initialize_assignment(reference, hypothesis, initialization='cp'),
+            initial_assignment=initialize_assignment(reference, hypothesis, initialization='tcp'),
         )
         return distance, assignment
 

tests/test_cli.py

@@ -96,6 +96,12 @@ def test_burn_greedy_dicp():
     run(f'meeteval-wer greedy_dicpwer -h hyp.stm -r ref.stm')
 
 
+def test_burn_greedy_ditcp():
+    # Normal test with stm files
+    run(f'python -m meeteval.wer greedy_ditcpwer -h hyp.stm -r ref.stm')
+    run(f'meeteval-wer greedy_ditcpwer -h hyp.stm -r ref.stm')
+
+
 def test_burn_mimo():
     run(f'python -m meeteval.wer mimower -h hyp.stm -r ref.stm')
     run(f"python -m meeteval.wer mimower -h 'hyp?.stm' -r 'ref?.stm'")

tests/test_di_cp.py

@@ -1,21 +1,26 @@
 from hypothesis import given, strategies as st, assume, settings
 import meeteval
 
-
 seglst = st.builds(
     meeteval.io.SegLST,
     st.lists(
         st.builds(
-            dict,
+            lambda **x: {
+                'end_time': x['start_time'] + x.pop('duration'),
+                **x,
+            },
             speaker=st.sampled_from(['spkA', 'spkB', 'spkC', 'spkD', 'spkE', 'spkF', 'spkG', 'spkH']),
             words=st.text(),
+            session_id=st.just('session1'),
+            start_time=st.floats(min_value=0, max_value=100),
+            duration=st.floats(min_value=0, max_value=10),
         ),
     )
 )
 
 
 @given(seglst, seglst)
-@settings(deadline=None)    # The tests take longer on the GitHub actions test servers
+@settings(deadline=None)  # The tests take longer on the GitHub actions test servers
 def test_greedy_di_cp_bound_by_cp(ref, hyp):
     cp = meeteval.wer.wer.cp.cp_word_error_rate(ref, hyp)
     dicp = meeteval.wer.wer.di_cp.greedy_di_cp_word_error_rate(ref, hyp)
@@ -24,7 +29,7 @@ def test_greedy_di_cp_bound_by_cp(ref, hyp):
 
 
 @given(seglst, seglst)
-@settings(deadline=None)    # The tests take longer on the GitHub actions test servers
+@settings(deadline=None)  # The tests take longer on the GitHub actions test servers
 def test_greedy_di_cp_vs_greedy_orc(ref, hyp):
     """
     Test that the total distance of the greedy di-cp algorithm is equal to the
@@ -37,3 +42,32 @@ def test_greedy_di_cp_vs_greedy_orc(ref, hyp):
     assert dicp.substitutions == orc.substitutions
     assert dicp.insertions == orc.deletions
     assert dicp.deletions == orc.insertions
+
+
+@given(seglst, seglst)
+@settings(deadline=None)  # The tests take longer on the GitHub actions test servers
+def test_greedy_di_tcp_bound_by_tcp(ref, hyp):
+    cp = meeteval.wer.wer.time_constrained.tcp_word_error_rate(ref, hyp)
+    dicp = meeteval.wer.wer.di_cp.greedy_di_tcp_word_error_rate(ref, hyp)
+
+    assert cp.error_rate is None and dicp.error_rate is None or cp.error_rate >= dicp.error_rate
+
+
+@given(seglst, seglst)
+@settings(deadline=None)  # The tests take longer on the GitHub actions test servers
+def test_greedy_di_tcp_vs_greedy_torc(ref, hyp):
+    """
+    Test that the total distance of the greedy di-tcp algorithm is equal to the
+    distance computed by the greedy tcorc algorithm with swapped arguments
+    """
+    dicp = meeteval.wer.wer.di_cp.greedy_di_tcp_word_error_rate(ref, hyp)
+    orc = meeteval.wer.wer.time_constrained_orc.greedy_time_constrained_orc_wer(
+        hyp, ref,
+        reference_pseudo_word_level_timing='character_based_points',
+        hypothesis_pseudo_word_level_timing='character_based',
+    )
+
+    assert dicp.errors == orc.errors
+    assert dicp.substitutions == orc.substitutions
+    assert dicp.insertions == orc.deletions
+    assert dicp.deletions == orc.insertions