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import os |
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import torch |
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import librosa |
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import numpy as np |
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import gradio as gr |
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import pyopenjtalk |
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from util import preprocess_input, postprocess_phn, get_tokenizer, load_pitch_dict, get_pinyin |
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from espnet_model_zoo.downloader import ModelDownloader |
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from espnet2.bin.svs_inference import SingingGenerate |
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singer_embeddings = { |
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"Model①(Chinese)-zh": { |
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"singer1 (male)": 1, |
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"singer2 (female)": 12, |
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"singer3 (male)": 23, |
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"singer4 (female)": 29, |
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"singer5 (male)": 18, |
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"singer6 (female)": 8, |
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"singer7 (male)": 25, |
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"singer8 (female)": 5, |
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"singer9 (male)": 10, |
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"singer10 (female)": 15, |
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}, |
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"Model②(Multilingual)-zh": { |
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"singer1 (male)": "resource/singer/singer_embedding_ace-1.npy", |
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"singer2 (female)": "resource/singer/singer_embedding_ace-2.npy", |
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"singer3 (male)": "resource/singer/singer_embedding_ace-3.npy", |
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"singer4 (female)": "resource/singer/singer_embedding_ace-8.npy", |
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"singer5 (male)": "resource/singer/singer_embedding_ace-7.npy", |
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"singer6 (female)": "resource/singer/singer_embedding_itako.npy", |
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"singer7 (male)": "resource/singer/singer_embedding_ofuton.npy", |
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"singer8 (female)": "resource/singer/singer_embedding_kising_orange.npy", |
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"singer9 (male)": "resource/singer/singer_embedding_m4singer_Tenor-1.npy", |
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"singer10 (female)": "resource/singer/singer_embedding_m4singer_Alto-4.npy", |
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}, |
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"Model②(Multilingual)-jp": { |
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"singer1 (male)": "resource/singer/singer_embedding_ace-1.npy", |
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"singer2 (female)": "resource/singer/singer_embedding_ace-2.npy", |
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"singer3 (male)": "resource/singer/singer_embedding_ace-3.npy", |
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"singer4 (female)": "resource/singer/singer_embedding_ace-8.npy", |
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"singer5 (male)": "resource/singer/singer_embedding_ace-7.npy", |
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"singer6 (female)": "resource/singer/singer_embedding_itako.npy", |
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"singer7 (male)": "resource/singer/singer_embedding_ofuton.npy", |
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"singer8 (female)": "resource/singer/singer_embedding_kising_orange.npy", |
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"singer9 (male)": "resource/singer/singer_embedding_m4singer_Tenor-1.npy", |
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"singer10 (female)": "resource/singer/singer_embedding_m4singer_Alto-4.npy", |
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} |
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} |
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model_dict = { |
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"Model①(Chinese)-zh": "espnet/aceopencpop_svs_visinger2_40singer_pretrain", |
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"Model②(Multilingual)-zh": "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained", |
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"Model②(Multilingual)-jp": "espnet/mixdata_svs_visinger2_spkembed_lang_pretrained", |
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} |
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total_singers = list(singer_embeddings["Model②(Multilingual)-zh"].keys()) |
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langs = { |
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"zh": 2, |
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"jp": 1, |
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} |
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predictor = torch.hub.load("South-Twilight/SingMOS:v0.2.0", "singing_ssl_mos", trust_repo=True) |
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exist_model = "Null" |
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svs = None |
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def gen_song(model_name, spk, texts, durs, pitchs): |
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fs = 44100 |
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tempo = 120 |
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lang = model_name.split("-")[-1] |
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PRETRAIN_MODEL = model_dict[model_name] |
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if texts is None: |
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return (fs, np.array([0.0])), "Error: No Text provided!" |
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if durs is None: |
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return (fs, np.array([0.0])), "Error: No Dur provided!" |
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if pitchs is None: |
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return (fs, np.array([0.0])), "Error: No Pitch provided!" |
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if lang == "zh": |
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texts = preprocess_input(texts, "") |
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text_list = get_pinyin(texts) |
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elif lang == "jp": |
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texts = preprocess_input(texts, " ") |
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text_list = texts.strip().split() |
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durs = preprocess_input(durs, " ") |
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dur_list = durs.strip().split() |
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pitchs = preprocess_input(pitchs, " ") |
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pitch_list = pitchs.strip().split() |
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if len(text_list) != len(dur_list): |
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return (fs, np.array([0.0])), f"Error: len in text({len(text_list)}) mismatch with duration({len(dur_list)})!" |
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if len(text_list) != len(pitch_list): |
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return (fs, np.array([0.0])), f"Error: len in text({len(text_list)}) mismatch with pitch({len(pitch_list)})!" |
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tokenizer = get_tokenizer(model_name, lang) |
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sybs = [] |
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for text in text_list: |
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if text == "AP" or text == "SP": |
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rev = [text] |
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elif text == "-" or text == "——": |
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rev = [text] |
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else: |
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rev = tokenizer(text) |
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if rev == False: |
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return (fs, np.array([0.0])), f"Error: text `{text}` is invalid!" |
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rev = postprocess_phn(rev, model_name, lang) |
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phns = "_".join(rev) |
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sybs.append(phns) |
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pitch_dict = load_pitch_dict() |
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labels = [] |
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notes = [] |
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st = 0 |
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pre_phn = "" |
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for phns, dur, pitch in zip(sybs, dur_list, pitch_list): |
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if phns == "-" or phns == "——": |
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phns = pre_phn |
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if pitch not in pitch_dict: |
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return (fs, np.array([0.0])), f"Error: pitch `{pitch}` is invalid!" |
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pitch = pitch_dict[pitch] |
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phn_list = phns.split("_") |
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lyric = "".join(phn_list) |
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dur = float(dur) |
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note = [st, st + dur, lyric, pitch, phns] |
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st += dur |
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notes.append(note) |
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for phn in phn_list: |
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labels.append(phn) |
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pre_phn = labels[-1] |
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phns_str = " ".join(labels) |
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batch = { |
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"score": ( |
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int(tempo), |
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notes, |
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), |
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"text": phns_str, |
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} |
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print(batch) |
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global exist_model |
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global svs |
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if exist_model == "Null" or exist_model != model_name: |
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device = "cpu" |
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d = ModelDownloader() |
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pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL) |
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svs = SingingGenerate( |
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train_config = pretrain_downloaded["train_config"], |
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model_file = pretrain_downloaded["model_file"], |
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device = device |
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) |
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exist_model = model_name |
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if model_name == "Model①(Chinese)-zh": |
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sid = np.array([singer_embeddings[model_name][spk]]) |
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output_dict = svs(batch, sids=sid) |
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else: |
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lid = np.array([langs[lang]]) |
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spk_embed = np.load(singer_embeddings[model_name][spk]) |
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output_dict = svs(batch, lids=lid, spembs=spk_embed) |
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wav_info = output_dict["wav"].cpu().numpy() |
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global predictor |
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wav_mos = librosa.resample(wav_info, orig_sr=fs, target_sr=16000) |
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wav_mos = torch.from_numpy(wav_mos).unsqueeze(0) |
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len_mos = torch.tensor([wav_mos.shape[1]]) |
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score = predictor(wav_mos, len_mos) |
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return (fs, wav_info), "success!", round(score.item(), 2) |
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examples = [ |
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["Model①(Chinese)-zh", "singer1 (male)", "雨 淋 湿 了 SP 天 空 AP\n毁 的 SP 很 讲 究 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21\n0.3 0.12 0.12 0.25 0.5 0.48 0.34", "60 62 62 62 0 62 58 0\n58 58 0 58 58 63 0"], |
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["Model①(Chinese)-zh", "singer3 (male)", "雨 淋 湿 了 SP 天 空 AP\n毁 的 SP 很 讲 究 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21\n0.3 0.12 0.12 0.25 0.5 0.48 0.34", "C4 D4 D4 D4 rest D4 A#3 rest\nA#3 A#3 rest A#3 A#3 D#4 rest"], |
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["Model①(Chinese)-zh", "singer3 (male)", "雨 淋 湿 了 SP 天 空 AP\n毁 的 SP 很 讲 究 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21\n0.3 0.12 0.12 0.25 0.5 0.48 0.34", "C#4 D#4 D#4 D#4 rest D#4 B3 rest\nB3 B3 rest B3 B3 E4 rest"], |
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["Model①(Chinese)-zh", "singer3 (male)", "雨 淋 湿 了 SP 大 地 AP\n毁 的 SP 很 讲 究 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21\n0.3 0.12 0.12 0.25 0.5 0.48 0.34", "C4 D4 D4 D4 rest D4 A#3 rest\nA#3 A#3 rest A#3 A#3 D#4 rest"], |
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["Model②(Multilingual)-zh", "singer3 (male)", "你 说 你 不 SP 懂\n 为 何 在 这 时 牵 手 AP", "0.11 0.33 0.29 0.13 0.15 0.48\n0.24 0.18 0.34 0.15 0.27 0.28 0.63 0.44", "63 63 63 63 0 63\n62 62 62 63 65 63 62 0"], |
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["Model②(Multilingual)-zh", "singer3 (male)", "你 说 你 不 SP 懂\n 为 何 在 这 时 牵 手 AP", "0.23 0.66 0.58 0.27 0.3 0.97\n0.48 0.36 0.69 0.3 0.53 0.56 1.27 0.89", "63 63 63 63 0 63\n62 62 62 63 65 63 62 0"], |
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["Model①(Chinese)-zh", "singer3 (male)", "雨 淋 湿 了 SP 天 空 AP\n毁 的 SP 很 讲 究 AP\n你 说 你 不 SP 懂\n 为 何 在 这 时 牵 手 AP", "0.23 0.16 0.36 0.16 0.07 0.28 0.5 0.21\n0.3 0.12 0.12 0.25 0.5 0.48 0.34\n0.11 0.33 0.29 0.13 0.15 0.48\n0.24 0.18 0.34 0.15 0.27 0.28 0.63 0.44", "60 62 62 62 0 62 58 0\n58 58 0 58 58 63 0\n63 63 63 63 0 63\n62 62 62 63 65 63 62 0"], |
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["Model①(Chinese)-zh", "singer3 (male)", "修 炼 爱 情 的 心 酸 SP AP", "0.42 0.21 0.19 0.28 0.22 0.33 1.53 0.1 0.29", "68 70 68 66 63 68 68 0 0"], |
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["Model①(Chinese)-zh", "singer3 (male)", "学 会 放 好 以 前 的 渴 望 SP AP", "0.3 0.22 0.29 0.27 0.25 0.44 0.54 0.29 1.03 0.08 0.39", "68 70 68 66 61 68 68 65 66 0 0"], |
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["Model①(Chinese)-zh", "singer3 (male)", "SP 我 不 - 是 一 定 要 你 回 - 来 SP", "0.37 0.45 0.47 0.17 0.52 0.28 0.46 0.31 0.44 0.45 0.2 2.54 0.19", "0 51 60 61 59 59 57 57 59 60 61 59 0"], |
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["Model①(Chinese)-zh", "singer4 (female)", "AP 我 多 想 再 见 你\n哪 怕 匆 - 匆 一 AP 眼 就 别 离 AP", "0.13 0.24 0.68 0.78 0.86 0.4 0.94 0.54 0.3 0.56 0.16 0.86 0.26 0.22 0.28 0.78 0.68 1.5 0.32", "0 57 66 63 63 63 63 60 61 61 63 66 66 0 61 61 59 58 0"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "い じ ん さ ん に つ れ ら れ て", "0.6 0.3 0.3 0.3 0.3 0.6 0.6 0.3 0.3 0.6 0.23", "60 60 60 56 56 56 55 55 55 53 56"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "い じ ん さ ん に つ れ ら れ て", "0.6 0.3 0.3 0.3 0.3 0.6 0.6 0.3 0.3 0.6 0.23", "62 62 62 58 58 58 57 57 57 55 58"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "い じ ん さ ん に つ れ ら れ て", "1.2 0.6 0.6 0.6 0.6 1.2 1.2 0.6 0.6 1.2 0.45", "60 60 60 56 56 56 55 55 55 53 56"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "い じ ん さ ん に つ れ ら れ て", "0.3 0.15 0.15 0.15 0.15 0.3 0.3 0.15 0.15 0.3 0.11", "60 60 60 56 56 56 55 55 55 53 56"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "きっ と と べ ば そ ら ま で と ど く AP", "0.39 2.76 0.2 0.2 0.39 0.39 0.2 0.2 0.39 0.2 0.2 0.59 1.08", "64 71 68 69 71 71 69 68 66 68 69 68 0"], |
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["Model②(Multilingual)-jp", "singer8 (female)", "じゃ の め で お む か え う れ し い な", "0.43 0.14 0.43 0.14 0.43 0.14 0.43 0.14 0.43 0.14 0.43 0.14 0.65", "60 60 60 62 64 67 69 69 64 64 64 62 60"], |
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["Model②(Multilingual)-jp", "singer10 (female)", "お と め わ ら い か ふぁ い や ら い か ん な い す ぶ ろ うぃ ん ぶ ろ うぃ ん い ん ざ うぃ ん", "0.15 0.15 0.15 0.15 0.3 0.15 0.3 0.15 0.15 0.3 0.07 0.07 0.15 0.15 0.15 0.15 0.15 0.15 0.45 0.07 0.07 0.07 0.38 0.07 0.07 0.15 0.15 0.3 0.15 0.15", "67 67 67 67 67 67 69 67 67 69 67 67 64 64 64 64 64 64 62 64 64 62 62 64 64 62 62 59 59 59"], |
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] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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<h1 align="center"> Demo of Singing Voice Synthesis in Muskits-ESPnet </h1> |
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<div style="font-size: 20px;"> |
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This is the demo page of our toolkit <a href="https://arxiv.org/abs/2409.07226"><b>Muskits-ESPnet: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm</b></a>. |
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Singing Voice Synthesis (SVS) takes a music score as input and generates singing vocal with the voice of a specific singer. |
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Music score usually includes lyrics, as well as duration and pitch of each word in lyrics, |
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<h2>How to use:</h2> |
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<ol> |
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<li><b>Choose Model-Language</b>: |
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<ul> |
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<li>"zh" indicates lyrics input in Chinese, and "jp" indicates lyrics input in Japanese.</li> |
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<li>For example, "Model②(Mulitlingual)-zh" means model "Model②(Multilingual)" with lyrics input in Chinese.</li> |
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</ul> |
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</li> |
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<li><b>[Optional] Choose Singer</b>: Choose one singer you like from the drop-down list.</li> |
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<li><b>Input lyrics</b>: |
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<ul> |
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<li>Lyrics use Chinese characters when the language is 'zh' and hiragana when the language is 'jp'.</li> |
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<li>Special characters such as 'AP' (breath), 'SP' (silence), and '-' (slur, only for Chinese lyrics) can also be used.</li> |
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<li>Lyrics sequence should be separated by either a space (' ') or a newline ('\\n'), without the quotation marks.</li> |
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</ul> |
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</li> |
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<li><b>Input durations</b>: |
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<ul> |
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<li>Durations use float number as input.</li> |
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<li>Length of duration sequence should <b>be same as lyric sequence</b>, with each duration corresponding to the respective lyric.</li> |
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<li>Durations sequence should be separated by either a space (' ') or a newline ('\\n'), without the quotation marks.</li> |
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</ul> |
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</li> |
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<li><b>Input pitches</b>: |
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<ul> |
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<li>Pitches use MIDI note or MIDI note number as input. Specially, "69" in MIDI note number represents "A4" in MIDI note.</li> |
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<li>Length of pitch sequence should <b>be same as lyric sequence</b>, with each pitch corresponding to the respective lyric.</li> |
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<li>Pitches sequence should be separated by either a space (' ') or a newline ('\\n'), without the quotation marks.</li> |
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</ul> |
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</li> |
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<li><b>Hit "Generate" and listen to the result!</b></li> |
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</ol> |
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</div> |
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<h2>Notice:</h2> |
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<ul> |
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<li> Plenty of exmpales are provided. </li> |
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<li> Extreme values may result in suboptimal generation quality! </li> |
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</ul> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(variant="panel"): |
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model_name = gr.Radio( |
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label="Model-Language", |
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choices=[ |
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"Model①(Chinese)-zh", |
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"Model②(Multilingual)-zh", |
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"Model②(Multilingual)-jp", |
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], |
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) |
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with gr.Column(variant="panel"): |
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singer = gr.Dropdown( |
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label="Singer", |
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choices=total_singers, |
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) |
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with gr.Row(): |
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with gr.Column(variant="panel"): |
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lyrics = gr.Textbox(label="Lyrics") |
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duration = gr.Textbox(label="Duration") |
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pitch = gr.Textbox(label="Pitch") |
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generate = gr.Button("Generate") |
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with gr.Column(variant="panel"): |
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gened_song = gr.Audio(label="Generated Song", type="numpy") |
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run_status = gr.Textbox(label="Running Status") |
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pred_mos = gr.Textbox(label=" Pseudo MOS") |
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gr.Examples( |
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examples=examples, |
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inputs=[model_name, singer, lyrics, duration, pitch], |
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outputs=[singer], |
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label="Examples", |
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examples_per_page=20, |
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) |
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gr.Markdown(""" |
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<div style='margin:20px auto;'> |
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<p>References: <a href="https://arxiv.org/abs/2409.07226">Muskits-ESPnet paper</a> | |
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<a href="https://github.com/espnet/espnet">espnet</a> | |
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<a href="https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain">Model①(Chinese)</a> | |
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<a href="https://huggingface.co/espnet/mixdata_svs_visinger2_spkembed_lang_pretrained">Model②(Multilingual)</a></p> |
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</div> |
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""" |
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) |
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generate.click( |
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fn=gen_song, |
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inputs=[model_name, singer, lyrics, duration, pitch], |
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outputs=[gened_song, run_status, pred_mos], |
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) |
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demo.launch() |
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|
