Upload inference_ms_cpu.py

inference_ms_cpu.py

@@ -0,0 +1,80 @@
+## VCTK
+import torch
+
+import commons
+import utils
+from models import SynthesizerTrn
+from text.symbols import symbols
+from text import text_to_sequence
+import time 
+
+from scipy.io.wavfile import write
+
+
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    print(text, text_norm)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+LANG = 'ru'
+CONFIG_PATH = f"./configs/{LANG}_base.json"
+MODEL_PATH = f"./logs/{LANG}_base/G_40000.pth"
+#TEXT = "I am artificial intelligent voice made by circulus."
+#TEXT = "저는 서큘러스의 AI Voice 모델입니다. 오늘도 즐거운하루 보내세요."
+TEXT = "привет. Я президент Путин, и мне нравятся советские лидеры Сталин и Ленин."
+#TEXT = "Xin chào. Tôi là Tổng thống Putin và tôi thích các nhà lãnh đạo Liên Xô Stalin và Lenin."
+#TEXT = "สวัสดี. ผมเป็นประธานาธิบดีปูติน และผมชอบผู้นำโซเวียตอย่างสตาลินและเลนิน"
+#TEXT = "Halo. Saya Presiden Putin, dan saya menyukai pemimpin Soviet Stalin dan Lenin."
+
+hps = utils.get_hparams_from_file(CONFIG_PATH)
+
+if (
+    "use_mel_posterior_encoder" in hps.model.keys()
+    and hps.model.use_mel_posterior_encoder == True
+):
+    print("Using mel posterior encoder for VITS2")
+    posterior_channels = 80  # vits2
+    hps.data.use_mel_posterior_encoder = True
+else:
+    print("Using lin posterior encoder for VITS1")
+    posterior_channels = hps.data.filter_length // 2 + 1
+    hps.data.use_mel_posterior_encoder = False
+
+net_g = SynthesizerTrn(
+    len(symbols),
+    posterior_channels,
+    hps.train.segment_size // hps.data.hop_length,
+    n_speakers=hps.data.n_speakers,
+    **hps.model
+)
+_ = net_g.eval()
+
+_ = utils.load_checkpoint(MODEL_PATH, net_g, None)
+
+stn_tst = get_text(TEXT, hps)
+
+with torch.no_grad():
+
+    for i in range(0,hps.data.n_speakers):
+        start = time.time()
+        x_tst = stn_tst.unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
+        sid = torch.LongTensor([i])
+        audio = (
+            net_g.infer(
+                x_tst,
+                x_tst_lengths,
+                sid=sid,
+                noise_scale=0.667,
+                noise_scale_w=0.8,
+                length_scale=1,
+            )[0][0, 0]
+            .data
+            .float()
+            .numpy()
+        )
+        print(i, time.time() - start)
+        write(data=audio, rate=hps.data.sampling_rate, filename=f"test_{LANG}_{i}.wav")