diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..1691f22a315762e154b4e48ff0b620d47d2e4003 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..90a702dc4f30ecbf510c62170892f580c642f472 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*cache* diff --git a/offline_process/create_features.py b/offline_process/create_features.py new file mode 100644 index 0000000000000000000000000000000000000000..cd00797b50251138250730f2eb43d643ee70b356 --- /dev/null +++ b/offline_process/create_features.py @@ -0,0 +1,62 @@ +from datasets import load_dataset, concatenate_datasets + +ds = load_dataset("espnet/ace-kising-segments", cache_dir="cache") + +combined = concatenate_datasets([ds["train"], ds["validation"], ds["test"]]) + +# 2. filter rows by singer: baber +combined = combined.filter(lambda x: x["singer"] == "barber") + +# 3. create a new column, which counts the nonzero numbers in the list in the note_midi column +combined = combined.map( + lambda x: {"note_midi_length": len([n for n in x["note_midi"] if n != 0])} +) + +# 4. sort by segment_id +combined = combined.sort("segment_id") + +# 5. iterate over rows +prev_songid = None +prev_song_segment_id = None +song2note_lengths = {} +for row in combined: + # segment_id: kising_barber_{songid}_{song_segment_id} + _, _, songid, song_segment_id = row["segment_id"].split("_") + if prev_songid != songid: + if prev_songid is not None: + assert ( + song_segment_id == "001" + ), f"prev_songid: {prev_songid}, songid: {songid}, song_segment_id: {song_segment_id}" + song2note_lengths[f"kising_{songid}"] = [row["note_midi_length"]] + else: + assert ( + int(song_segment_id) >= int(prev_song_segment_id) + 1 + ), f"prev_song_segment_id: {prev_song_segment_id}, song_segment_id: {song_segment_id}" + song2note_lengths[f"kising_{songid}"].append(row["note_midi_length"]) + prev_songid = songid + prev_song_segment_id = song_segment_id + +# 6. write to json +import json + +with open("song2note_lengths.json", "w") as f: + json.dump(song2note_lengths, f, indent=4) + +# 7. convert to pandas DataFrame +import pandas as pd + +df = pd.DataFrame.from_dict(combined) +df = df.drop(columns=["audio", "singer"]) +df["segment_id"] = df["segment_id"].str.replace("kising_barber_", "kising_") +# export to csv +df.to_csv("song_db.csv", index=False) + +# 8. push score segments to hub +# remove audio and singer columns +combined = combined.remove_columns(["audio", "singer"]) +# replace kising_barber_ with kising_ +combined = combined.map( + lambda x: {"segment_id": x["segment_id"].replace("kising_barber_", "kising_")} +) +# upload to hub +combined.push_to_hub("jhansss/kising_score_segments") diff --git a/resource/__init__.py b/resource/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/resource/all_plans.json b/resource/all_plans.json new file mode 100755 index 0000000000000000000000000000000000000000..94af8af22ad17cadfaf4505caae44c71fff6561e --- /dev/null +++ b/resource/all_plans.json @@ -0,0 +1,7660 @@ +{ + "jp_word2romaji": + { + "うぁ": "wa", + "うぉ": "wo", + "ぁ": "a", + "ぃ": "i", + "ぅ": "u", + "ぇ": "e", + "ぉ": "o", + "ゃ": "ya", + "ゅ": "yu", + "ょ": "yo", + "ウァ": "wa", + "ウォ": "wo", + "ァ": "a", + "ィ": "i", + "ゥ": "u", + "ェ": "e", + "ォ": "o", + "ャ": "ya", + "ュ": "yu", + "ョ": "yo", + "ｳｧ": "wa", + "ｳｫ": "wo", + "ｧ": "a", + "ｨ": "i", + "ｩ": "u", + "ｪ": "e", + "ｫ": "o", + "ｬ": "ya", + "ｭ": "yu", + "ｮ": "yo", + "よ": "yo", + "びゅ": "byu", + "ら": "ra", + "ジャ": "ja", + "り": "ri", + "ｷｬ": "kya", + "ﾂｪ": "tse", + "る": "ru", + "キュ": "kyu", + "フェ": "fe", + "れ": "re", + "びょ": "byo", + "ジュ": "ju", + "ろ": "ro", + "ｷｭ": "kyu", + "ﾘｪ": "rye", + "ﾂｫ": "tso", + "ﾘｬ": "rya", + "キョ": "kyo", + "フォ": "fo", + "わ": "wa", + "ｺ゚": "ngo", + "ジョ": "jo", + "ﾘｭ": "ryu", + "ｷｮ": "kyo", + "ﾘｮ": "ryo", + "を": "wo", + "ク゚": "ngu", + "ん": "N", + "キ゚ャ": "ngya", + "ｷ゚ｪ": "ngye", + "ｶﾞ": "ga", + "ふぁ": "fa", + "クァ": "kwa", + "キ゚ュ": "ngyu", + "ﾌﾞ": "bu", + "ふぃ": "fi", + "ｷ゚ｬ": "ngya", + "ﾌﾟ": "pu", + "クィ": "kwi", + "ア": "a", + "ふぅ": "fu", + "ズィ": "zi", + "きゃ": "kya", + "じゃ": "ja", + "ｽﾞｨ": "zi", + "イ": "i", + "キ゚ョ": "ngyo", + "ｷ゚ｭ": "ngyu", + "ﾌﾞｭ": "vyu", + "ｽｨ": "si", + "ふぇ": "fe", + "ウ": "u", + "きゅ": "kyu", + "じゅ": "ju", + "イェ": "ye", + "ｲｪ": "ye", + "ｷ゚ｮ": "ngyo", + "エ": "e", + "きょ": "kyo", + "ふぉ": "fo", + "じょ": "jo", + "オ": "o", + "カ": "ka", + "ガ": "ga", + "キ": "ki", + "ピャ": "pya", + "ギ": "gi", + "ク": "ku", + "ミャ": "mya", + "グ": "gu", + "リャ": "rya", + "ケ": "ke", + "ピュ": "pyu", + "ゲ": "ge", + "ミュ": "myu", + "コ": "ko", + "リュ": "ryu", + "ゴ": "go", + "ピョ": "pyo", + "サ": "sa", + "ミョ": "myo", + "ザ": "za", + "ﾄﾞｩ": "du", + "リョ": "ryo", + "シ": "shi", + "くぁ": "kwa", + "ジ": "ji", + "ス": "su", + 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], + "fyax": + [ + "fy", + "ax" + ], + "fix": + [ + "fy", + "ix" + ], + "fyux": + [ + "fy", + "ux" + ], + "fyex": + [ + "fy", + "ex" + ], + "fyox": + [ + "fy", + "ox" + ], + "vya": + [ + "vy", + "a" + ], + "vi": + [ + "vy", + "i" + ], + "vyu": + [ + "vy", + "u" + ], + "vye": + [ + "vy", + "e" + ], + "vyo": + [ + "vy", + "o" + ], + "vyax": + [ + "vy", + "ax" + ], + "vix": + [ + "vy", + "ix" + ], + "vyux": + [ + "vy", + "ux" + ], + "vyex": + [ + "vy", + "ex" + ], + "vyox": + [ + "vy", + "ox" + ], + "kwa": + [ + "kw", + "a" + ], + "kwi": + [ + "kw", + "i" + ], + "ku": + [ + "kw", + "u" + ], + "kwe": + [ + "kw", + "e" + ], + "kwo": + [ + "kw", + "o" + ], + "kwax": + [ + "kw", + "ax" + ], + "kwix": + [ + "kw", + "ix" + ], + "kux": + [ + "kw", + "ux" + ], + "kwex": + [ + "kw", + "ex" + ], + "kwox": + [ + "kw", + "ox" + ], + "gwa": + [ + "gw", + "a" + ], + "gwi": + [ + "gw", + "i" + ], + "gu": + [ + "gw", + "u" + ], + "gwe": + [ + "gw", + "e" + ], + "gwo": + [ + "gw", + "o" + ], + "gwax": + [ + "gw", + "ax" + ], + "gwix": + [ + "gw", + "ix" + ], + "gux": + [ + "gw", + "ux" + ], + "gwex": + [ + "gw", + "ex" + ], + "gwox": + [ + "gw", + "ox" + ] + }, + "phon_class": + { + "head": + [ + "k", + "s", + "sh", + "t", + "ch", + "ts", + "n", + "h", + "f", + "m", + "y", + "r", + "rr", + "w", + "g", + "z", + "j", + "d", + "b", + "p", + "ky", + "ty", + "ny", + "hy", + "my", + "ry", + "by", + "gy", + "py", + "dy", + "vy", + "fy", + "kw", + "gw", + "ng", + "ngy", + "v" + ], + "tail": + [ + "a", + "i", + "u", + "e", + "o", + "nv", + "mv", + "ax", + "ix", + "ux", + "ex", + "ox", + "cl" + ] + } + }, + { + "language": "eng", + "dict": + {}, + "phon_class": + { + "tail": + [ + "aa", + "ae", + "ah", + "ao", + "aw", + "ay", + "eh", + "er", + "ey", + "ih", + "iy", + "ow", + "oy", + "uh", + "uw", + "mv", + "nv", + "ngv" + ], + "head": + [ + "b", + "ch", + "d", + "dh", + "f", + "g", + "hh", + "jh", + "k", + "l", + "m", + "n", + "ng", + "p", + "r", + "s", + "sh", + "t", + "th", + "v", + "w", + "y", + "z", + "zh", + "dx", + "dr", + "tr" + ] + } + } + ] +} \ No newline at end of file diff --git a/resource/midi-note.scp b/resource/midi-note.scp new file mode 100755 index 0000000000000000000000000000000000000000..45113e930ae15c3145cf22df63108ffdea719743 --- /dev/null +++ b/resource/midi-note.scp @@ -0,0 +1,152 @@ +G9 127 +F#9 126 +Gb9 126 +F9 125 +E9 124 +D#9 123 +Eb9 123 +D9 122 +C#9 121 +Db9 121 +C9 120 +B8 119 +A#8 118 +Bb8 118 +A8 117 +G#8 116 +Ab8 116 +G8 115 +F#8 114 +Gb8 114 +F8 113 +E8 112 +D#8 111 +Eb8 111 +D8 110 +C#8 109 +Db8 109 +C8 108 +B7 107 +A#7 106 +Bb7 106 +A7 105 +G#7 104 +Ab7 104 +G7 103 +F#7 102 +Gb7 102 +F7 101 +E7 100 +D#7 99 +Eb7 99 +D7 98 +C#7 97 +Db7 97 +C7 96 +B6 95 +A#6 94 +Bb6 94 +A6 93 +G#6 92 +Ab6 92 +G6 91 +F#6 90 +Gb6 90 +F6 89 +E6 88 +D#6 87 +Eb6 87 +D6 86 +C#6 85 +Db6 85 +C6 84 +B5 83 +A#5 82 +Bb5 82 +A5 81 +G#5 80 +Ab5 80 +G5 79 +F#5 78 +Gb5 78 +F5 77 +E5 76 +D#5 75 +Eb5 75 +D5 74 +C#5 73 +Db5 73 +C5 72 +B4 71 +A#4 70 +Bb4 70 +A4 69 +G#4 68 +Ab4 68 +G4 67 +F#4 66 +Gb4 66 +F4 65 +E4 64 +D#4 63 +Eb4 63 +D4 62 +C#4 61 +Db4 61 +C4 60 +B3 59 +A#3 58 +Bb3 58 +A3 57 +G#3 56 +Ab3 56 +G3 55 +F#3 54 +Gb3 54 +F3 53 +E3 52 +D#3 51 +Eb3 51 +D3 50 +C#3 49 +Db3 49 +C3 48 +B2 47 +A#2 46 +Bb2 46 +A2 45 +G#2 44 +Ab2 44 +G2 43 +F#2 42 +Gb2 42 +F2 41 +E2 40 +D#2 39 +Eb2 39 +D2 38 +C#2 37 +Db2 37 +C2 36 +B1 35 +A#1 34 +Bb1 34 +A1 33 +G#1 32 +Ab1 32 +G1 31 +F#1 30 +Gb1 30 +F1 29 +E1 28 +D#1 27 +Eb1 27 +D1 26 +C#1 25 +Db1 25 +C1 24 +B0 23 +A#0 22 +Bb0 22 +A0 21 +rest 0 diff --git a/resource/pinyin_dict.py b/resource/pinyin_dict.py new file mode 100755 index 0000000000000000000000000000000000000000..535e11160d5d28a92be7b508ddb01ab5f6b008d1 --- /dev/null +++ b/resource/pinyin_dict.py @@ -0,0 +1,423 @@ +# Adapted from Opencpop's pinyin to phoneme mapping table: +# https://wenet.org.cn/opencpop/resources/annotationformat/ +PINYIN_DICT = { + "a": ("a",), + "ai": ("ai",), + "an": ("an",), + "ang": ("ang",), + "ao": ("ao",), + "ba": ("b", "a"), + "bai": ("b", "ai"), + "ban": ("b", "an"), + "bang": ("b", "ang"), + "bao": ("b", "ao"), + "bei": ("b", "ei"), + "ben": ("b", "en"), + "beng": ("b", "eng"), + "bi": ("b", "i"), + "bian": ("b", "ian"), + "biao": ("b", "iao"), + "bie": ("b", "ie"), + "bin": ("b", "in"), + "bing": ("b", "ing"), + "bo": ("b", "o"), + "bu": ("b", "u"), + "ca": ("c", "a"), + "cai": ("c", "ai"), + "can": ("c", "an"), + "cang": ("c", "ang"), + "cao": ("c", "ao"), + "ce": ("c", "e"), + "cei": ("c", "ei"), + "cen": ("c", "en"), + "ceng": ("c", "eng"), + "cha": ("ch", "a"), + "chai": ("ch", "ai"), + "chan": ("ch", "an"), + "chang": ("ch", "ang"), + "chao": ("ch", "ao"), + "che": ("ch", "e"), + "chen": ("ch", "en"), + "cheng": ("ch", "eng"), + "chi": ("ch", "i"), + "chong": ("ch", "ong"), + "chou": ("ch", "ou"), + "chu": ("ch", "u"), + "chua": ("ch", "ua"), + "chuai": ("ch", "uai"), + "chuan": ("ch", "uan"), + "chuang": ("ch", "uang"), + "chui": ("ch", "ui"), + "chun": ("ch", "un"), + "chuo": ("ch", "uo"), + "ci": ("c", "i"), + "cong": ("c", "ong"), + "cou": ("c", "ou"), + "cu": ("c", "u"), + "cuan": ("c", "uan"), + "cui": ("c", "ui"), + "cun": ("c", "un"), + "cuo": ("c", "uo"), + "da": ("d", "a"), + "dai": ("d", "ai"), + "dan": ("d", "an"), + "dang": ("d", "ang"), + "dao": ("d", "ao"), + "de": ("d", "e"), + "dei": ("d", "ei"), + "den": ("d", "en"), + "deng": ("d", "eng"), + "di": ("d", "i"), + "dia": ("d", "ia"), + "dian": ("d", "ian"), + "diao": ("d", "iao"), + "die": ("d", "ie"), + "ding": ("d", "ing"), + "diu": ("d", "iu"), + "dong": ("d", "ong"), + "dou": ("d", "ou"), + "du": ("d", "u"), + "duan": ("d", "uan"), + "dui": ("d", "ui"), + "dun": ("d", "un"), + "duo": ("d", "uo"), + "e": ("e",), + "ei": ("ei",), + "en": ("en",), + "eng": ("eng",), + "er": ("er",), + "fa": ("f", "a"), + "fan": ("f", "an"), + "fang": ("f", "ang"), + "fei": ("f", "ei"), + "fen": ("f", "en"), + "feng": ("f", "eng"), + "fo": ("f", "o"), + "fou": ("f", "ou"), + "fu": ("f", "u"), + "ga": ("g", "a"), + "gai": ("g", "ai"), + "gan": ("g", "an"), + "gang": ("g", "ang"), + "gao": ("g", "ao"), + "ge": ("g", "e"), + "gei": ("g", "ei"), + "gen": ("g", "en"), + "geng": ("g", "eng"), + "gong": ("g", "ong"), + "gou": ("g", "ou"), + "gu": ("g", "u"), + "gua": ("g", "ua"), + "guai": ("g", "uai"), + "guan": ("g", "uan"), + "guang": ("g", "uang"), + "gui": ("g", "ui"), + "gun": ("g", "un"), + "guo": ("g", "uo"), + "ha": ("h", "a"), + "hai": ("h", "ai"), + "han": ("h", "an"), + "hang": ("h", "ang"), + "hao": ("h", "ao"), + "he": ("h", "e"), + "hei": ("h", "ei"), + "hen": ("h", "en"), + "heng": ("h", "eng"), + "hm": ("h", "m"), + "hng": ("h", "ng"), + "hong": ("h", "ong"), + "hou": ("h", "ou"), + "hu": ("h", "u"), + "hua": ("h", "ua"), + "huai": ("h", "uai"), + "huan": ("h", "uan"), + "huang": ("h", "uang"), + "hui": ("h", "ui"), + "hun": ("h", "un"), + "huo": ("h", "uo"), + "ji": ("j", "i"), + "jia": ("j", "ia"), + "jian": ("j", "ian"), + "jiang": ("j", "iang"), + "jiao": ("j", "iao"), + "jie": ("j", "ie"), + "jin": ("j", "in"), + "jing": ("j", "ing"), + "jiong": ("j", "iong"), + "jiu": ("j", "iu"), + "ju": ("j", "v"), + "juan": ("j", "van"), + "jue": ("j", "ve"), + "jun": ("j", "vn"), + "ka": ("k", "a"), + "kai": ("k", "ai"), + "kan": ("k", "an"), + "kang": ("k", "ang"), + "kao": ("k", "ao"), + "ke": ("k", "e"), + "kei": ("k", "ei"), + "ken": ("k", "en"), + "keng": ("k", "eng"), + "kong": ("k", "ong"), + "kou": ("k", "ou"), + "ku": ("k", "u"), + "kua": ("k", "ua"), + "kuai": ("k", "uai"), + "kuan": ("k", "uan"), + "kuang": ("k", "uang"), + "kui": ("k", "ui"), + "kun": ("k", "un"), + "kuo": ("k", "uo"), + "la": ("l", "a"), + "lai": ("l", "ai"), + "lan": ("l", "an"), + "lang": ("l", "ang"), + "lao": ("l", "ao"), + "le": ("l", "e"), + "lei": ("l", "ei"), + "leng": ("l", "eng"), + "li": ("l", "i"), + "lia": ("l", "ia"), + "lian": ("l", "ian"), + "liang": ("l", "iang"), + "liao": ("l", "iao"), + "lie": ("l", "ie"), + "lin": ("l", "in"), + "ling": ("l", "ing"), + "liu": ("l", "iu"), + "lo": ("l", "o"), + "long": ("l", "ong"), + "lou": ("l", "ou"), + "lu": ("l", "u"), + "luan": ("l", "uan"), + "lun": ("l", "un"), + "luo": ("l", "uo"), + "lv": ("l", "v"), + "lve": ("l", "ve"), + "m": ("m",), + "ma": ("m", "a"), + "mai": ("m", "ai"), + "man": ("m", "an"), + "mang": ("m", "ang"), + "mao": ("m", "ao"), + "me": ("m", "e"), + "mei": ("m", "ei"), + "men": ("m", "en"), + "meng": ("m", "eng"), + "mi": ("m", "i"), + "mian": ("m", "ian"), + "miao": ("m", "iao"), + "mie": ("m", "ie"), + "min": ("m", "in"), + "ming": ("m", "ing"), + "miu": ("m", "iu"), + "mo": ("m", "o"), + "mou": ("m", "ou"), + "mu": ("m", "u"), + "n": ("n",), + "na": ("n", "a"), + "nai": ("n", "ai"), + "nan": ("n", "an"), + "nang": ("n", "ang"), + "nao": ("n", "ao"), + "ne": ("n", "e"), + "nei": ("n", "ei"), + "nen": ("n", "en"), + "neng": ("n", "eng"), + "ng": ("n", "g"), + "ni": ("n", "i"), + "nian": ("n", "ian"), + "niang": ("n", "iang"), + "niao": ("n", "iao"), + "nie": ("n", "ie"), + "nin": ("n", "in"), + "ning": ("n", "ing"), + "niu": ("n", "iu"), + "nong": ("n", "ong"), + "nou": ("n", "ou"), + "nu": ("n", "u"), + "nuan": ("n", "uan"), + "nun": ("n", "un"), + "nuo": ("n", "uo"), + "nv": ("n", "v"), + "nve": ("n", "ve"), + "o": ("o",), + "ou": ("ou",), + "pa": ("p", "a"), + "pai": ("p", "ai"), + "pan": ("p", "an"), + "pang": ("p", "ang"), + "pao": ("p", "ao"), + "pei": ("p", "ei"), + "pen": ("p", "en"), + "peng": ("p", "eng"), + "pi": ("p", "i"), + "pian": ("p", "ian"), + "piao": ("p", "iao"), + "pie": ("p", "ie"), + "pin": ("p", "in"), + "ping": ("p", "ing"), + "po": ("p", "o"), + "pou": ("p", "ou"), + "pu": ("p", "u"), + "qi": ("q", "i"), + "qia": ("q", "ia"), + "qian": ("q", "ian"), + "qiang": ("q", "iang"), + "qiao": ("q", "iao"), + "qie": ("q", "ie"), + "qin": ("q", "in"), + "qing": ("q", "ing"), + "qiong": ("q", "iong"), + "qiu": ("q", "iu"), + "qu": ("q", "v"), + "quan": ("q", "van"), + "que": ("q", "ve"), + "qun": ("q", "vn"), + "ran": ("r", "an"), + "rang": ("r", "ang"), + "rao": ("r", 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0000000000000000000000000000000000000000..e2c2948789fa6150373978498351a3260bc81a84 --- /dev/null +++ b/svs_utils.py @@ -0,0 +1,331 @@ +from util import preprocess_input, postprocess_phn, get_tokenizer, load_pitch_dict, get_pinyin +from espnet_model_zoo.downloader import ModelDownloader +from espnet2.bin.svs_inference import SingingGenerate +import librosa +import torch +import numpy as np +import random +import json +from datasets import load_dataset +# the code below should be in app.py than svs_utils.py +# espnet_model_dict = { +# "Model①(Chinese)-zh": "espnet/aceopencpop_svs_visinger2_40singer_pretrain", +# "Model②(Multilingual)-zh": "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained", +# "Model②(Multilingual)-jp": "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained", +# } + +espnet_downloader = ModelDownloader() + +singer_embeddings = { + "espnet/aceopencpop_svs_visinger2_40singer_pretrain": { + "singer1 (male)": 1, + "singer2 (female)": 12, + "singer3 (male)": 23, + "singer4 (female)": 29, + "singer5 (male)": 18, + "singer6 (female)": 8, + "singer7 (male)": 25, + "singer8 (female)": 5, + "singer9 (male)": 10, + "singer10 (female)": 15, + }, + "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained": { + "singer1 (male)": "resource/singer/singer_embedding_ace-1.npy", + "singer2 (female)": "resource/singer/singer_embedding_ace-2.npy", + "singer3 (male)": "resource/singer/singer_embedding_ace-3.npy", + "singer4 (female)": "resource/singer/singer_embedding_ace-8.npy", + "singer5 (male)": "resource/singer/singer_embedding_ace-7.npy", + "singer6 (female)": "resource/singer/singer_embedding_itako.npy", + "singer7 (male)": "resource/singer/singer_embedding_ofuton.npy", + "singer8 (female)": "resource/singer/singer_embedding_kising_orange.npy", + "singer9 (male)": "resource/singer/singer_embedding_m4singer_Tenor-1.npy", + "singer10 (female)": "resource/singer/singer_embedding_m4singer_Alto-4.npy", + }, +} + + +def svs_warmup(config): + """ + What: module loading, and model loading + Input: config dict/namespace (e.g., model path, cache dir, device, language, possibly speaker selection) + Return: the inference prototype function (which creates pitch/duration and runs model-specific inference) + """ + if config.model_path.startswith("espnet"): + downloaded = espnet_downloader.download_and_unpack( + config.model_path, config.cache_dir) + model = SingingGenerate( + train_config=downloaded["train_config"], + model_file=downloaded["model_file"], + device=config.device + ) + else: + raise NotImplementedError(f"Model {config.model_path} not supported") + return model + + +def svs_text_preprocessor(model_path, texts, lang): + ''' + Input: + - model_path (str), for getting the corresponding tokenizer + - texts (str), in Chinese character or Japanese character + - lang (str), language label jp/zh, input if is not espnet model + + Output: + - lyric_ls (lyric list), each element as 'k@zhe@zh' + - sybs (phn w/ _ list), each element as 'k@zh_e@zh' + - labels (phn w/o _ list), each element as 'k@zh' + + ''' + fs = 44100 + + if texts is None: + return (fs, np.array([0.0])), "Error: No Text provided!" + + # preprocess + if lang == "zh": + texts = preprocess_input(texts, "") + text_list = get_pinyin(texts) + elif lang == "jp": + texts = preprocess_input(texts, " ") + text_list = texts.strip().split() + + # text to phoneme + tokenizer = get_tokenizer(model_path, lang) + sybs = [] # phoneme list + for text in text_list: + if text == "AP" or text == "SP": + rev = [text] + elif text == "-" or text == "——": + rev = [text] + else: + rev = tokenizer(text) + if rev == False: + return (fs, np.array([0.0])), f"Error: text `{text}` is invalid!" + rev = postprocess_phn(rev, model_path, lang) + phns = "_".join(rev) + sybs.append(phns) + + lyric_ls = [] + labels = [] + pre_phn = "" + for phns in sybs: + if phns == "-" or phns == "——": + phns = pre_phn + + phn_list = phns.split("_") + lyric = "".join(phn_list) + for phn in phn_list: + labels.append(phn) + pre_phn = labels[-1] + lyric_ls.append(lyric) + + return lyric_ls, sybs, labels + + +def svs_get_batch(model_path, answer_text, lang, random_gen=True): + ''' + Input: + - answer_text (str), in Chinese character or Japanese character + - model_path (str), loaded pretrained model name + - lang (str), language label jp/zh, input if is not espnet model + Output: + - batch (dict) + + {'score': (75, [[0, 0.48057527844210024, 'n@zhi@zh', 66, 'n@zh_i@zh'], + [0.48057527844210024, 0.8049310140914353, 'k@zhe@zh', 57, 'k@zh_e@zh'], + [0.8049310140914353, 1.1905956333296641, 'm@zhei@zh', 64, 'm@zh_ei@zh']]), + 'text': 'n@zh i@zh k@zh e@zh m@zh ei@zh'} + ''' + tempo = 120 + lyric_ls, sybs, labels = svs_text_preprocessor( + model_path, answer_text, lang) + len_note = len(lyric_ls) + notes = [] + if random_gen: + # midi_range = (57,69) + st = 0 + for id_lyric in range(len_note): + pitch = random.randint(57, 69) + period = round(random.uniform(0.1, 0.5), 4) + ed = st+period + note = [st, ed, lyric_ls[id_lyric], pitch, sybs[id_lyric]] + st = ed + notes.append(note) + + phns_str = " ".join(labels) + batch = { + "score": ( + int(tempo), + notes, + ), + "text": phns_str, + } + + print(batch) + return batch + + +langs = { + "zh": 2, + "jp": 1, + "en": 2, +} + +exist_model = "Null" +svs = None + + +def svs_inference(model_name, model_svs, answer_text, lang, random_gen=True, fs=44100): + batch = svs_get_batch(model_name, answer_text, lang, + random_gen=random_gen) + + # Infer + spk = "singer1 (male)" + global exist_model + global svs + svs = model_svs + exist_model = model_name + # if exist_model == "Null" or exist_model != model_name: + # # device = "cpu" + # device = "cuda" if torch.cuda.is_available() else "cpu" + # d = ModelDownloader(cachedir="./cache") + # pretrain_downloaded = d.download_and_unpack(model_name) + # svs = SingingGenerate( + # train_config = pretrain_downloaded["train_config"], + # model_file = pretrain_downloaded["model_file"], + # device = device + # ) + # exist_model = model_name + if model_name == "Model①(Chinese)-zh": + sid = np.array([singer_embeddings[model_name][spk]]) + output_dict = svs(batch, sids=sid) + else: + lid = np.array([langs[lang]]) + spk_embed = np.load("resource/singer/singer_embedding_ace-1.npy") + output_dict = svs(batch, lids=lid, spembs=spk_embed) + wav_info = output_dict["wav"].cpu().numpy() + + return wav_info + + +def singmos_warmup(config): + predictor = torch.hub.load( + "South-Twilight/SingMOS:v0.2.0", "singing_ssl_mos", trust_repo=True) + return predictor, "South-Twilight/SingMOS:v0.2.0" + + +def singmos_evaluation(predictor, wav_info, fs): + wav_mos = librosa.resample(wav_info, orig_sr=fs, target_sr=16000) + wav_mos = torch.from_numpy(wav_mos).unsqueeze(0) + len_mos = torch.tensor([wav_mos.shape[1]]) + score = predictor(wav_mos, len_mos) + return score + + +def estimate_sentence_length(query, config, song2note_lengths): + if config.melody_source == "random_select": + # random select a song from database, and return its value in the phrase_length column + # return phrase_length column and song name + song_name = random.choice(list(song2note_lengths.keys())) + phrase_length = song2note_lengths[song_name] + metadata = {"song_name": song_name} + return phrase_length, metadata + else: + raise NotImplementedError( + f"melody source {config.melody_source} not supported") + + +def align_score_and_text(segment_iterator, lyric_ls, sybs, labels): + text = [] + lyric_idx = 0 + notes_info = [] + while lyric_idx < len(lyric_ls): + score = next(segment_iterator) + for note_start_time, note_end_time, reference_note_lyric, note_midi in zip(score['note_start_times'], score['note_end_times'], score["note_lyrics"], score['note_midi']): + if reference_note_lyric in ["", ""]: + notes_info.append([note_start_time, note_end_time, reference_note_lyric.strip("<>"), note_midi, reference_note_lyric.strip("<>")]) + text.append(reference_note_lyric.strip("<>")) + else: + notes_info.append([note_start_time, note_end_time, lyric_ls[lyric_idx], note_midi, sybs[lyric_idx]]) + text += sybs[lyric_idx].split("_") + lyric_idx += 1 + if lyric_idx >= len(lyric_ls): + break + batch = { + "score": ( + score['tempo'], # Assume the tempo is the same for all segments + notes_info + ), + "text": " ".join(text) + } + return batch + + +def song_segment_iterator(song_db, metadata): + song_name = metadata["song_name"] + if song_name.startswith("kising_"): + # return a iterator that load from song_name_{001} and increment + segment_id = 1 + while f"{song_name}_{segment_id:03d}" in song_db.index: + yield song_db.loc[f"{song_name}_{segment_id:03d}"] + segment_id += 1 + else: + raise NotImplementedError(f"song name {song_name} not supported") + + +def load_song_database(): + song_db = load_dataset("jhansss/kising_score_segments", + cache_dir="cache")["train"].to_pandas() + song_db.set_index("segment_id", inplace=True) + + with open("data/song2note_lengths.json", "r") as f: + song2note_lengths = json.load(f) + return song2note_lengths,song_db + +if __name__ == '__main__': + import argparse + + # -------- demo code for generate audio from randomly selected song ---------# + config = argparse.Namespace( + model_path="espnet/mixdata_svs_visinger2_spkembed_lang_pretrained", + cache_dir="cache", + device="cpu", + melody_source="random_select", + lang="zh", + ) + + # load model + model = svs_warmup(config) + + # load song database: jhansss/kising_score_segments + song2note_lengths, song_db = load_song_database() + + # get song_name and phrase_length + phrase_length, metadata = estimate_sentence_length( + None, config, song2note_lengths) + + # then, phrase_length info should be added to llm prompt, and get the answer lyrics from llm + # e.g. answer_text = "天气真好\n空气清新" + answer_text = "天气真好\n空气清新" + lyric_ls, sybs, labels = svs_text_preprocessor( + config.model_path, answer_text, config.lang) + segment_iterator = song_segment_iterator(song_db, metadata) + batch = align_score_and_text(segment_iterator, lyric_ls, sybs, labels) + singer_embedding = np.load( + singer_embeddings[config.model_path]["singer2 (female)"]) + lid = np.array([langs[config.lang]]) + output_dict = model(batch, lids=lid, spembs=singer_embedding) + wav_info = output_dict["wav"].cpu().numpy() + # write wav to output_retrieved.wav + import soundfile as sf + sf.write("output_retrieved.wav", wav_info, samplerate=44100) + + # -------- some other processes ---------# + # get answer text from LLM + answer_text = "你真是太美啦" + lang = "zh" + sample_rate = 44100 + model_path = "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained" # temp hard code + wav_info = svs_inference(config.model_path, model, answer_text, lang=config.lang, random_gen=True, fs=sample_rate) + wav_info = wav_info * 32767 + wav.write("output_random.wav", sample_rate, wav_info.astype('int16')) diff --git a/util.py b/util.py new file mode 100755 index 0000000000000000000000000000000000000000..c436a905f95dde58dc893f3b88588d712f340e9f --- /dev/null +++ b/util.py @@ -0,0 +1,93 @@ +import os +import json +import warnings +from typing import List +import re + +import pyopenjtalk +from resource.pinyin_dict import PINYIN_DICT +from pypinyin import lazy_pinyin + +def preprocess_input(src_str, seg_syb=" "): + src_str = src_str.replace("\n", seg_syb) + src_str = src_str.replace(" ", seg_syb) + return src_str + + +def postprocess_phn(phns, model_name, lang): + if "Chinese" in model_name: + return phns + return [phn + "@" + lang for phn in phns] + + +def pyopenjtalk_g2p(text) -> List[str]: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + # phones is a str object separated by space + phones = pyopenjtalk.g2p(text, kana=False) + if len(w) > 0: + for warning in w: + if "No phoneme" in str(warning.message): + return False + + phones = phones.split(" ") + return phones + + +def split_pinyin_ace(pinyin: str, zh_plan: dict) -> tuple[str]: + # load pinyin dict from local/pinyin.dict + pinyin = pinyin.lower() + if pinyin in zh_plan["dict"]: + return zh_plan["dict"][pinyin] + elif pinyin in zh_plan["syllable_alias"]: + return zh_plan["dict"][zh_plan["syllable_alias"][pinyin]] + else: + return False + + +def split_pinyin_py(pinyin: str) -> tuple[str]: + pinyin = pinyin.lower() + if pinyin in PINYIN_DICT: + return PINYIN_DICT[pinyin] + else: + return False + + +def get_tokenizer(model, lang): + if lang == "zh": + if "Chinese" in model: + print("hello") + return lambda text: split_pinyin_py(text) + else: + with open(os.path.join("resource/all_plans.json"), "r") as f: + all_plan_dict = json.load(f) + for plan in all_plan_dict["plans"]: + if plan["language"] == "zh": + zh_plan = plan + return lambda text: split_pinyin_ace(text, zh_plan) + elif lang == "jp": + return pyopenjtalk_g2p + + +def get_pinyin(texts): + pinyin_list = lazy_pinyin(texts) + text_list = [] + for text in pinyin_list: + if text[0] == "S" or text[0] == "A" or text[0] == '-': + sp_strs = re.findall(r'-|AP|SP', text) + for phn in sp_strs: + text_list.append(phn) + else: + text_list.append(text) + return text_list + + +def load_pitch_dict(file_path = "resource/midi-note.scp"): + pitch_dict = {} + with open(file_path, "r", encoding="utf-8") as f: + for line in f: + items = line.strip().split() + pitch_dict[items[0]] = int(items[1]) + pitch_dict[items[1]] = int(items[1]) + return pitch_dict +