Initial commit

2026-02-06 20:31:14 +08:00
parent a0b51be095
commit c589bcb837
145 changed files with 28773 additions and 0 deletions
@@ -0,0 +1,111 @@
+import numpy as np
+import torch
+
+def regulate_real_note_itv(note_itv, note_bd, word_bd, word_durs, hop_size, audio_sample_rate):
+    # regulate note_itv in seconds according to the correspondence between note_bd and word_bd
+    assert note_itv.shape[0] == np.sum(note_bd) + 1
+    assert np.sum(word_bd) <= np.sum(note_bd)
+    assert word_durs.shape[0] == np.sum(word_bd) + 1, f"{word_durs.shape[0]} {np.sum(word_bd) + 1}"
+    word_bd = np.cumsum(word_bd) * word_bd  # [0,1,0,0,1,0,0,0] -> [0,1,0,0,2,0,0,0]
+    word_itv = np.zeros((word_durs.shape[0], 2))
+    word_offsets = np.cumsum(word_durs)
+    note2words = np.zeros(note_itv.shape[0], dtype=int)
+    for idx in range(len(word_offsets) - 1):
+        word_itv[idx, 1] = word_itv[idx + 1, 0] = word_offsets[idx]
+    word_itv[-1, 1] = word_offsets[-1]
+    note_itv_secs = note_itv * hop_size / audio_sample_rate
+    for idx, itv in enumerate(note_itv):
+        start_idx, end_idx = itv
+        if word_bd[start_idx] > 0:
+            word_dur_idx = word_bd[start_idx]
+            note_itv_secs[idx, 0] = word_itv[word_dur_idx, 0]
+            note2words[idx] = word_dur_idx
+        if word_bd[end_idx] > 0:
+            word_dur_idx = word_bd[end_idx] - 1
+            note_itv_secs[idx, 1] = word_itv[word_dur_idx, 1]
+            note2words[idx] = word_dur_idx
+    note2words += 1  # mel2ph fashion: start from 1
+    return note_itv_secs, note2words
+
+def regulate_ill_slur(notes, note_itv, note2words):
+    res_note2words = []
+    res_note_itv = []
+    res_notes = []
+    note_idx = 0
+    note_idx_end = 0
+    while True:
+        if note_idx > len(notes) - 1:
+            break
+        while note_idx <= note_idx_end < len(notes) and note2words[note_idx] == note2words[note_idx_end]:
+            note_idx_end += 1
+        res_note2words.append(note2words[note_idx])
+        res_note_itv.append(note_itv[note_idx].tolist())
+        res_notes.append(notes[note_idx])
+        for idx in range(note_idx+1, note_idx_end):
+            if notes[idx] == notes[idx-1]:
+                res_note_itv[-1][1] = note_itv[idx][1]
+            else:
+                res_note_itv.append(note_itv[idx].tolist())
+                res_note2words.append(note2words[idx])
+                res_notes.append(notes[idx])
+        note_idx = note_idx_end
+    res_notes = np.array(res_notes, dtype=notes.dtype)
+    res_note_itv = np.array(res_note_itv, dtype=note_itv.dtype)
+    res_note2words = np.array(res_note2words, dtype=note2words.dtype)
+    return res_notes, res_note_itv, res_note2words
+
+def bd_to_idxs(bd):
+    # bd [T]
+    idxs = []
+    for idx in range(len(bd)):
+        if bd[idx] == 1:
+            idxs.append(idx)
+    return idxs
+
+def bd_to_durs(bd):
+    # bd [T]
+    last_idx = 0
+    durs = []
+    for idx in range(len(bd)):
+        if bd[idx] == 1:
+            durs.append(idx - last_idx)
+            last_idx = idx
+    durs.append(len(bd) - last_idx)
+    return durs
+
+def get_mel_len(wav_len, hop_size):
+    return (wav_len + hop_size - 1) // hop_size
+
+def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
+    is_torch = isinstance(mel2token, torch.Tensor)
+    has_batch_dim = True
+    if not is_torch:
+        mel2token = torch.LongTensor(mel2token)
+    if T_txt is None:
+        T_txt = mel2token.max()
+    if len(mel2token.shape) == 1:
+        mel2token = mel2token[None, ...]
+        has_batch_dim = False
+    B, _ = mel2token.shape
+    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
+    dur = dur[:, 1:]
+    if max_dur is not None:
+        dur = dur.clamp(max=max_dur)
+    if not is_torch:
+        dur = dur.numpy()
+    if not has_batch_dim:
+        dur = dur[0]
+    return dur
+
+def align_word(word_durs, mel_len, hop_size, audio_sample_rate):
+    mel2word = np.zeros([mel_len], int)
+    start_time = 0
+    for i_word in range(len(word_durs)):
+        start_frame = int(start_time * audio_sample_rate / hop_size + 0.5)
+        end_frame = int((start_time + word_durs[i_word]) * audio_sample_rate / hop_size + 0.5)
+        mel2word[start_frame:end_frame] = i_word + 1
+        start_time = start_time + word_durs[i_word]
+
+    dur_word = mel2token_to_dur(mel2word)
+
+    return mel2word, dur_word.tolist()