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server.py

@@ -17,6 +17,8 @@ import utils
 from models import SynthesizerTrn
 from text.symbols import symbols
 from text import text_to_sequence
+from text import cleaned_text_to_sequence,_symbol_to_id, get_bert
+from text.cleaner import clean_text
 from scipy.io import wavfile
 
 # Get ffmpeg path
@@ -25,28 +27,60 @@ ffmpeg_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "ffmpeg")
 # Flask Init
 app = Flask(__name__)
 app.config['JSON_AS_ASCII'] = False
-# Text Preprocess
+def get_text(text, language_str, hps):
-def get_text(text, hps):
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    print([f"{p}{t}" for p, t in zip(phone, tone)])
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
     if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
+        phone = commons.intersperse(phone, 0)
-    text_norm = torch.LongTensor(text_norm)
+        tone = commons.intersperse(tone, 0)
-    return text_norm
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert = get_bert(norm_text, word2ph, language_str)
+
+    assert bert.shape[-1] == len(phone)
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+
+    return bert, phone, tone, language
+
+def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
+    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
+    with torch.no_grad():
+        x_tst=phones.to(dev).unsqueeze(0)
+        tones=tones.to(dev).unsqueeze(0)
+        lang_ids=lang_ids.to(dev).unsqueeze(0)
+        bert = bert.to(dev).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
+        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
+                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
+        return audio
+
+def replace_punctuation(text, i=2):
+    punctuation = "，。？！"
+    for char in punctuation:
+        text = text.replace(char, char * i)
+    return text
 
 # Load Generator
-hps_mt = utils.get_hparams_from_file("/GPUFS/sysu_hpcedu_123/vits/configs/genshin_xm37.json")
+hps = utils.get_hparams_from_file("./configs/config.json")
 
-net_g_mt = SynthesizerTrn(
+dev='cuda'
+net_g = SynthesizerTrn(
     len(symbols),
-    hps_mt.data.filter_length // 2 + 1,
+    hps.data.filter_length // 2 + 1,
-    hps_mt.train.segment_size // hps_mt.data.hop_length,
+    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps_mt.data.n_speakers,
+    n_speakers=hps.data.n_speakers,
-    **hps_mt.model).cuda()
+    **hps.model).to(dev)
-_ = net_g_mt.eval()
+_ = net_g.eval()
 
-_ = utils.load_checkpoint("/GPUFS/sysu_hpcedu_123/vits/logs/xm37/G_xm37_361200.pth", net_g_mt, None)
+_ = utils.load_checkpoint("logs/all_in_one/G_521000.pth", net_g, None)
-
-npcList = ['空', '荧', '派蒙', '纳西妲', '阿贝多', '温迪', '枫原万叶', '钟离', '荒泷一斗', '八重神子', '艾尔海森', '提纳里', '迪希雅', '卡维', '宵宫', '莱依拉', '赛诺', '诺艾尔', '托马', '凝光', '莫娜', '北斗', '神里绫华', '雷电将军', '芭芭拉', '鹿野院平藏', '五郎', '迪奥娜', '凯亚', '安柏', '班尼特', '琴', '柯莱', '夜兰', '妮露', '辛焱', '珐露珊', '魈', '香菱', '达达利亚', '砂糖', '早柚', '云堇', '刻晴', '丽莎', '迪卢克', '烟绯', '重云', '珊瑚宫心海', '胡桃', '可莉', '流浪者', '久岐忍', '神里绫人', '甘雨', '戴因斯雷布', '优菈', '菲谢尔', '行秋', '白术', '九条裟罗', '雷泽', '申鹤', '迪娜泽黛', '凯瑟琳', '多莉', '坎蒂丝', '萍姥姥', '罗莎莉亚', '留云借风真君', '绮良良', '瑶瑶', '七七', '奥兹', '米卡', '夏洛蒂', '埃洛伊', '博士', '女士', '大慈树王', '三月七', '娜塔莎', '希露瓦', '虎克', '克拉拉', '丹恒', '希儿', '布洛妮娅', '瓦尔特', '杰帕德', '佩拉', '姬子', '艾丝妲', '白露', '星', '穹', '桑博', '伦纳德', '停云', '罗刹', '卡芙卡', '彦卿', '史瓦罗', '螺丝咕姆', '阿兰', '银狼', '素裳', '丹枢', '黑塔', '景元', '帕姆', '可可利亚', '半夏', '符玄', '公输师傅', '奥列格', '青雀', '大毫', '青镞', '费斯曼', '绿芙蓉', '镜流', '信使', '丽塔', '失落迷迭', '缭乱星棘', '伊甸', '伏特加女孩', '狂热蓝调', '莉莉娅', '萝莎莉娅', '八重樱', '八重霞', '卡莲', '第六夜想曲', '卡萝尔', '姬子', '极地战刃', '布洛妮娅', '次生银翼', '理之律者', '真理之律者', '迷城骇兔', '希儿', '魇夜星渊', '黑希儿', '帕朵菲莉丝', '天元骑英', '幽兰黛尔', '德丽莎', '月下初拥', '朔夜观星', '暮光骑士', '明日香', '李素裳', '格蕾修', '梅比乌斯', '渡鸦', '人之律者', '爱莉希雅', '爱衣', '天穹游侠', '琪亚娜', '空之律者', '终焉之律者', '薪炎之律者', '云墨丹心', '符华', '识之律者', '维尔薇', '始源之律者', '芽衣', '雷之律者', '苏莎娜', '阿波尼亚', '陆景和', '莫弈', '夏彦', '左然', '标贝']
 
 @app.route("/",methods=['GET','POST'])
 def main():
@@ -54,9 +88,10 @@ def main():
         try:
             speaker = request.args.get('speaker')
             text = request.args.get('text').replace("/n","")
+            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
             noise = float(request.args.get("noise", 0.5))
             noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.3))
+            length = float(request.args.get("length", 1.2))
             if length >= 2:
                 return "Too big length"
             if len(text) >=200:
@@ -69,16 +104,11 @@ def main():
         except:
             return "Invalid Parameter"
 
-        stn_tst_mt = get_text(text, hps_mt)
 
         with torch.no_grad():
-            x_tst_mt = stn_tst_mt.cuda().unsqueeze(0)
+            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-            x_tst_mt_lengths = torch.LongTensor([stn_tst_mt.size(0)]).cuda()
-            sid_mt = torch.LongTensor([npcList.index(speaker)]).cuda()
-            audio_mt = net_g_mt.infer(x_tst_mt, x_tst_mt_lengths, sid=sid_mt, noise_scale=noise, noise_scale_w=noisew, length_scale=length)[0][0,0].data.cpu().float().numpy()
-
         wav = BytesIO()
-        wavfile.write(wav, hps_mt.data.sampling_rate, audio_mt)
+        wavfile.write(wav, hps.data.sampling_rate, audio)
         torch.cuda.empty_cache()
         if fmt == "mp3":
             process = (
@@ -90,21 +120,3 @@ def main():
             out, _ = process.communicate(input=wav.read())
             return Response(out, mimetype="audio/mpeg")
         return Response(wav.read(), mimetype="audio/wav")
-    elif request.method == 'POST':
-        receive = request.get_data(as_text=True)
-        data = json.loads(receive)
-        speaker = data["speaker"]
-        text = data["text"].replace("/n","")
-        stn_tst_mt = get_text(text, hps_mt)
-
-        with torch.no_grad():
-            x_tst_mt = stn_tst_mt.cuda().unsqueeze(0)
-            x_tst_mt_lengths = torch.LongTensor([stn_tst_mt.size(0)]).cuda()
-            sid_mt = torch.LongTensor([npcList.index(speaker)]).cuda()
-            audio_mt = net_g_mt.infer(x_tst_mt, x_tst_mt_lengths, sid=sid_mt, noise_scale=0.667, noise_scale_w=0.8, length_scale=1.15)[0][0,0].data.cpu().float().numpy()
-
-        wav = BytesIO()
-        wavfile.write(wav, hps_mt.data.sampling_rate, audio_mt)
-        torch.cuda.empty_cache()
-        return Response(base64.b64encode(wav.read()))
-        #return Response(wav.read(), mimetype="audio/wav")