big change!

1 files changed, 0 insertions, 203 deletions

diff --git a/swr2_asr/loss_scores.py b/swr2_asr/loss_scores.py
deleted file mode 100644
index 80285f6..0000000
--- a/swr2_asr/loss_scores.py
+++ /dev/null
@@ -1,203 +0,0 @@
-"""Methods for determining the loss and scores of the model."""
-import numpy as np
-
-
-def avg_wer(wer_scores, combined_ref_len) -> float:
-    """Calculate the average word error rate (WER).
-
-    Args:
-        wer_scores: word error rate scores
-        combined_ref_len: combined length of reference sentences
-
-    Returns:
-        average word error rate (float)
-
-    Usage:
-        >>> avg_wer([0.5, 0.5], 2)
-        0.5
-    """
-    return float(sum(wer_scores)) / float(combined_ref_len)
-
-
-def _levenshtein_distance(ref, hyp) -> int:
-    """Levenshtein distance.
-
-    Args:
-        ref: reference sentence
-        hyp: hypothesis sentence
-
-    Returns:
-        distance: levenshtein distance between reference and hypothesis
-
-    Usage:
-        >>> _levenshtein_distance("hello", "helo")
-        2
-    """
-    len_ref = len(ref)
-    len_hyp = len(hyp)
-
-    # special case
-    if ref == hyp:
-        return 0
-    if len_ref == 0:
-        return len_hyp
-    if len_hyp == 0:
-        return len_ref
-
-    if len_ref < len_hyp:
-        ref, hyp = hyp, ref
-        len_ref, len_hyp = len_hyp, len_ref
-
-    # use O(min(m, n)) space
-    distance = np.zeros((2, len_hyp + 1), dtype=np.int32)
-
-    # initialize distance matrix
-    for j in range(0, len_hyp + 1):
-        distance[0][j] = j
-
-    # calculate levenshtein distance
-    for i in range(1, len_ref + 1):
-        prev_row_idx = (i - 1) % 2
-        cur_row_idx = i % 2
-        distance[cur_row_idx][0] = i
-        for j in range(1, len_hyp + 1):
-            if ref[i - 1] == hyp[j - 1]:
-                distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]
-            else:
-                s_num = distance[prev_row_idx][j - 1] + 1
-                i_num = distance[cur_row_idx][j - 1] + 1
-                d_num = distance[prev_row_idx][j] + 1
-                distance[cur_row_idx][j] = min(s_num, i_num, d_num)
-
-    return distance[len_ref % 2][len_hyp]
-
-
-def word_errors(
-    reference: str, hypothesis: str, ignore_case: bool = False, delimiter: str = " "
-) -> tuple[float, int]:
-    """Compute the levenshtein distance between reference sequence and
-    hypothesis sequence in word-level.
-
-    Args:
-        reference: The reference sentence.
-        hypothesis: The hypothesis sentence.
-        ignore_case: Whether case-sensitive or not.
-        delimiter: Delimiter of input sentences.
-
-    Returns:
-        Levenshtein distance and length of reference sentence.
-
-    Usage:
-        >>> word_errors("hello world", "hello")
-        1, 2
-    """
-    if ignore_case:
-        reference = reference.lower()
-        hypothesis = hypothesis.lower()
-
-    ref_words = reference.split(delimiter)
-    hyp_words = hypothesis.split(delimiter)
-
-    edit_distance = _levenshtein_distance(ref_words, hyp_words)
-    return float(edit_distance), len(ref_words)
-
-
-def char_errors(
-    reference: str,
-    hypothesis: str,
-    ignore_case: bool = False,
-    remove_space: bool = False,
-) -> tuple[float, int]:
-    """Compute the levenshtein distance between reference sequence and
-    hypothesis sequence in char-level.
-    Args:
-        reference: The reference sentence.
-        hypothesis: The hypothesis sentence.
-        ignore_case: Whether case-sensitive or not.
-        remove_space: Whether remove internal space characters
-
-    Returns:
-        Levenshtein distance and length of reference sentence.
-
-    Usage:
-        >>> char_errors("hello world", "hello")
-        1, 10
-    """
-    if ignore_case:
-        reference = reference.lower()
-        hypothesis = hypothesis.lower()
-
-    join_char = " "
-    if remove_space:
-        join_char = ""
-
-    reference = join_char.join(filter(None, reference.split(" ")))
-    hypothesis = join_char.join(filter(None, hypothesis.split(" ")))
-
-    edit_distance = _levenshtein_distance(reference, hypothesis)
-    return float(edit_distance), len(reference)
-
-
-def wer(reference: str, hypothesis: str, ignore_case=False, delimiter=" ") -> float:
-    """Calculate word error rate (WER). WER compares reference text and
-    hypothesis text in word-level.
-    WER is defined as:
-        WER = (Sw + Dw + Iw) / Nw
-    with:
-        Sw is the number of words subsituted,
-        Dw is the number of words deleted,
-        Iw is the number of words inserted,
-        Nw is the number of words in the reference
-
-    Args:
-        reference: The reference sentence.
-        hypothesis: The hypothesis sentence.
-        ignore_case: Whether case-sensitive or not.
-        delimiter: Delimiter of input sentences.
-
-    Returns:
-        Word error rate (float)
-
-    Usage:
-        >>> wer("hello world", "hello")
-        0.5
-    """
-    edit_distance, ref_len = word_errors(reference, hypothesis, ignore_case, delimiter)
-
-    if ref_len == 0:
-        raise ValueError("Reference's word number should be greater than 0.")
-
-    word_error_rate = float(edit_distance) / ref_len
-    return word_error_rate
-
-
-def cer(reference, hypothesis, ignore_case=False, remove_space=False):
-    """Calculate charactor error rate (CER). CER compares reference text and
-    hypothesis text in char-level. CER is defined as:
-        CER = (Sc + Dc + Ic) / Nc
-    with
-        Sc is the number of characters substituted,
-        Dc is the number of characters deleted,
-        Ic is the number of characters inserted
-        Nc is the number of characters in the reference
-
-    Args:
-        reference: The reference sentence.
-        hypothesis: The hypothesis sentence.
-        ignore_case: Whether case-sensitive or not.
-        remove_space: Whether remove internal space characters
-
-    Returns:
-        Character error rate (float)
-
-    Usage:
-        >>> cer("hello world", "hello")
-        0.2727272727272727
-    """
-    edit_distance, ref_len = char_errors(reference, hypothesis, ignore_case, remove_space)
-
-    if ref_len == 0:
-        raise ValueError("Length of reference should be greater than 0.")
-
-    char_error_rate = float(edit_distance) / ref_len
-    return char_error_rate