make vits a tts module

vits_tts/tts.py

@@ -0,0 +1,123 @@
+# coding=utf-8
+import sys
+import os
+run_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.append(run_dir)
+import re
+import argparse
+import utils
+import commons
+import json
+import torch
+from models import SynthesizerTrn
+from text import text_to_sequence, _clean_text
+from torch import no_grad, LongTensor
+import logging
+logging.getLogger('numba').setLevel(logging.WARNING)
+limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
+
+import scipy
+
+hps_ms = utils.get_hparams_from_file(f'{run_dir}/config/config.json')
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+tts_fn = None
+voice_opt = (0.6, 0.668, 1)
+
+def get_text(text, hps, is_symbol):
+    text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm, clean_text
+
+def create_tts_fn(net_g_ms, speaker_id):
+    def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol):
+        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
+        if limitation:
+            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
+            max_len = 100
+            if is_symbol:
+                max_len *= 3
+            if text_len > max_len:
+                return "Error: Text is too long", None
+        if not is_symbol:
+            if language == 0:
+                text = f"[ZH]{text}[ZH]"
+            elif language == 1:
+                text = f"[JA]{text}[JA]"
+            else:
+                text = f"{text}"
+        stn_tst, clean_text = get_text(text, hps_ms, is_symbol)
+        with no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(device)
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
+            sid = LongTensor([speaker_id]).to(device)
+            audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
+                                   length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
+
+        return "Success", (22050, audio)
+    return tts_fn
+
+def create_to_symbol_fn(hps):
+    def to_symbol_fn(is_symbol_input, input_text, temp_lang):
+        if temp_lang == 0:
+            clean_text = f'[ZH]{input_text}[ZH]'
+        elif temp_lang == 1:
+            clean_text = f'[JA]{input_text}[JA]'
+        else:
+            clean_text = input_text
+        return _clean_text(clean_text, hps.data.text_cleaners) if is_symbol_input else ''
+
+    return to_symbol_fn
+
+def _LoadCharacter(name):
+    with open(f"{run_dir}/pretrained_models/info.json", "r", encoding="utf-8") as f:
+        models_info = json.load(f)
+    for i, info in models_info.items():
+        sid = info['sid']
+        name_en = info['name_en']
+        name_zh = info['name_zh']
+        title = info['title']
+        cover = f"{run_dir}/pretrained_models/{i}/{info['cover']}"
+        example = info['example']
+        language = info['language']
+        if name == 'Any' or name == name_zh or name == name_en:
+            net_g_ms = SynthesizerTrn(
+                len(hps_ms.symbols),
+                hps_ms.data.filter_length // 2 + 1,
+                hps_ms.train.segment_size // hps_ms.data.hop_length,
+                n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0,
+                **hps_ms.model)
+            utils.load_checkpoint(f'{run_dir}/pretrained_models/{i}/{i}.pth', net_g_ms, None)
+            _ = net_g_ms.eval().to(device)
+            tts_fn = create_tts_fn(net_g_ms, sid)
+            to_symbol_fn = create_to_symbol_fn(hps_ms)
+            return True, tts_fn
+    return False, None
+
+def LoadCharacter(name):
+    global tts_fn
+    _, tts_fn = _LoadCharacter(name)
+
+def SetVoiceOption(ns, nsw, ls):
+    global voice_opt
+    voice_opt = (ns, nsw, ls)
+
+LoadCharacter("Any")
+
+def GenerateTTS(text):
+    if tts_fn != None and voice_opt != None:
+        (ns, nsw, ls) = voice_opt
+        symbol_input = False
+        result, (sampling_rate, output) = tts_fn(text, 0,  ns, nsw, ls, symbol_input)
+        if result == "Success":
+            save_path = f"{run_dir}/output.wav"
+            scipy.io.wavfile.write(save_path, rate=sampling_rate, data=output.T)
+            return True, save_path
+        else:
+            print(f'TTS: {result}')
+            return False, None
+
+__all__ = ['LoadCharacter', 'SetVoiceOption', 'GenerateTTS']