Update server.py

server.py

@@ -14,9 +14,9 @@ from scipy.io import wavfile
 # Flask Init
 app = Flask(__name__)
 app.config['JSON_AS_ASCII'] = False
+
 def get_text(text, language_str, hps):
     norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    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:
@@ -27,25 +27,36 @@ def get_text(text, language_str, hps):
             word2ph[i] = word2ph[i] * 2
         word2ph[0] += 1
     bert = get_bert(norm_text, word2ph, language_str)
+    del word2ph
+    assert bert.shape[-1] == len(phone), phone
 
-    assert bert.shape[-1] == len(phone)
-
+    if language_str=='ZH':
+        bert = bert
+        ja_bert = torch.zeros(768, len(phone))
+    elif language_str=="JA":
+        ja_bert = bert
+        bert = torch.zeros(1024, len(phone))
+    else:
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = torch.zeros(768, len(phone))
+    assert bert.shape[-1] == len(phone), (
+    bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
     phone = torch.LongTensor(phone)
     tone = torch.LongTensor(tone)
     language = torch.LongTensor(language)
+    return bert, ja_bert, phone, tone, 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,)
+def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
+    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, 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)
+        ja_bert = ja_bert.to(device).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
+        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_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
 
@@ -84,9 +95,8 @@ _ = net_g.eval()
 
 _ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
 
-@app.route("/",methods=['GET','POST'])
+@app.route("/")
 def main():
-    if request.method == 'GET':
     try:
         speaker = request.args.get('speaker')
         text = request.args.get('text').replace("/n","")
@@ -94,20 +104,23 @@ def main():
         noise = float(request.args.get("noise", 0.5))
         noisew = float(request.args.get("noisew", 0.6))
         length = float(request.args.get("length", 1.2))
+        language = request.args.get('language')
         if length >= 2:
             return "Too big length"
-            if len(text) >=200:
+        if len(text) >=250:
             return "Too long text"
         fmt = request.args.get("format", "wav")
         if None in (speaker, text):
             return "Missing Parameter"
         if fmt not in ("mp3", "wav", "ogg"):
             return "Invalid Format"
+        if language not in ("JA", "ZH")
+            return "Invalid language"
     except:
         return "Invalid Parameter"
 
     with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
+        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker,language = language)
 
     with BytesIO() as wav:
         wavfile.write(wav, hps.data.sampling_rate, audio)