Extract get_text() and infer() from webui.py.

infer_utils.py

@@ -0,0 +1,91 @@
+"""
+@Author: Kasugano Sora
+@Github: https://github.com/jiangyuxiaoxiao
+@Date: 2023/10/08-18:01
+@Desc:
+@Ver : 1.0.0
+"""
+import torch
+import commons
+from text import cleaned_text_to_sequence, get_bert
+from text.cleaner import clean_text
+
+
+def get_text(text, language_str, hps, device):
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        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, device)
+    del word2ph
+    assert bert.shape[-1] == len(phone), phone
+
+    if language_str == "ZH":
+        bert = bert
+        ja_bert = torch.zeros(768, len(phone))
+    elif language_str == "JP":
+        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
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, phone, tone, language
+
+
+def infer(
+    text,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+    length_scale,
+    sid,
+    language,
+    hps,
+    net_g,
+    device,
+):
+    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps, device)
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        del phones
+        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
+        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()
+        )
+        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
+        torch.cuda.empty_cache()
+        return audio

server.py

@@ -3,83 +3,17 @@
 import torch
 from av import open as avopen
 
-import commons
 import utils
 from models import SynthesizerTrn
 from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
+from infer_utils import infer
 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)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        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, dev)
-    del word2ph
-    assert bert.shape[-1] == len(phone), 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
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-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(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,
-                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
-
-
 def replace_punctuation(text, i=2):
     punctuation = "，。？！"
     for char in punctuation:
@@ -141,7 +75,7 @@ def main():
             return "Missing Parameter"
         if fmt not in ("mp3", "wav", "ogg"):
             return "Invalid Format"
-        if language not in ("JA", "ZH"):
+        if language not in ("JP", "ZH"):
             return "Invalid language"
     except:
         return "Invalid Parameter"
@@ -155,6 +89,9 @@ def main():
             length_scale=length,
             sid=speaker,
             language=language,
+            hps=hps,
+            net_g=net_g,
+            device=dev,
         )
 
     with BytesIO() as wav:

webui.py

@@ -16,12 +16,10 @@
 
 import torch
 import argparse
-import commons
 import utils
 from models import SynthesizerTrn
 from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
+from infer_utils import infer
 import gradio as gr
 import webbrowser
 import numpy as np
@@ -35,76 +33,6 @@
     device = "cuda"
 
 
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        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, device)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JP":
-        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
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
-    with torch.no_grad():
-        x_tst = phones.to(device).unsqueeze(0)
-        tones = tones.to(device).unsqueeze(0)
-        lang_ids = lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        ja_bert = ja_bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        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()
-        )
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        torch.cuda.empty_cache()
-        return audio
-
-
 def tts_fn(
     text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
 ):
@@ -120,6 +48,9 @@ def tts_fn(
                 length_scale=length_scale,
                 sid=speaker,
                 language=language,
+                hps=hps,
+                net_g=net_g,
+                device=device,
             )
             audio_list.append(audio)
             silence = np.zeros(hps.data.sampling_rate)  # 生成1秒的静音