add files

examples/biaobei/config.yaml

@@ -59,7 +59,7 @@ vocoder:
     config: ~/checkpoints/melgan/default.yaml
     device: cpu
   VocGan:
-    checkpoint: ~/checkpoints/vctk_pretrained_model_3180.pt #~/checkpoints/ljspeech_29de09d_4000.pt
+    checkpoint: checkpoints #~/checkpoints/ljspeech_29de09d_4000.pt
     denoise: True
     device: cpu
   HiFiGAN:

input.txt

@@ -0,0 +1 @@
+text1|sil ni3 qu4 zuo4 fan4 ba5 sil|sil 你 去 做 饭 吧 sil|0

mtts/models/vocoder/VocGAN/vocgan.py

@@ -1,3 +1,11 @@
+'''
+Author: wuxulong19950206 [email protected]
+Date: 2024-03-12 22:44:31
+LastEditors: wuxulong19950206 [email protected]
+LastEditTime: 2024-03-12 23:05:02
+FilePath: \text_to_speech\mtts\models\vocoder\VocGAN\vocgan.py
+Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
+'''
 import argparse
 import glob
 import os
@@ -16,13 +24,16 @@ from .download_utils import download_url
 url = 'https://zenodo.org/record/4743731/files/vctk_pretrained_model_3180.pt'
 class VocGan:
     def __init__(self, device='cuda:0',config=None, denoise=False):
-        home = os.environ['HOME']
-        checkpoint_path = os.path.join(home,'./.cache/vocgan')
+        # home = os.environ['HOME']
+        checkpoint_path = config["checkpoint"]
+        denoise = config["denoise"]
+        device =  config["device"]
+        # checkpoint_path = os.path.join(home,'./.cache/vocgan')
         os.makedirs(checkpoint_path,exist_ok=True)
         checkpoint_file = os.path.join(checkpoint_path,'vctk_pretrained_model_3180.pt')
         if not os.path.exists(checkpoint_file):
             download_url(url,checkpoint_path)
-         
+        config = None
         checkpoint = torch.load(checkpoint_file,map_location=device)
         if config is not None:
             hp = HParam(config)

synthesize.py

@@ -0,0 +1,114 @@
+import argparse
+import os
+import subprocess
+
+import numpy as np
+import torch
+import yaml
+from scipy.io import wavfile
+
+from mtts.models.fs2_model import FastSpeech2
+from mtts.models.vocoder import *
+from mtts.text import TextProcessor
+from mtts.utils.logging import get_logger
+
+logger = get_logger(__file__)
+
+
+def check_ffmpeg():
+    r, path = subprocess.getstatusoutput("which ffmpeg")
+    return r == 0
+
+
+with_ffmpeg = check_ffmpeg()
+
+
+def build_vocoder(device, config):
+    vocoder_name = config['vocoder']['type']
+    VocoderClass = eval(vocoder_name)
+    model = VocoderClass(config=config['vocoder'][vocoder_name])
+    return model
+
+
+def normalize(wav):
+    assert wav.dtype == np.float32
+    eps = 1e-6
+    sil = wav[1500:2000]
+    #wav = wav - np.mean(sil)
+    #wav = (wav - np.min(wav))/(np.max(wav)-np.min(wav)+eps)
+    wav = wav / np.max(np.abs(wav))
+    #wav = wav*2-1
+    wav = wav * 32767
+    return wav.astype('int16')
+
+
+def to_int16(wav):
+    wav = wav = wav * 32767
+    wav = np.clamp(wav, -32767, 32768)
+    return wav.astype('int16')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='input.txt')
+    parser.add_argument('--duration', type=float, default=1.0)
+    parser.add_argument('--output_dir', type=str, default='./outputs/')
+    parser.add_argument('--checkpoint', type=str, required=False, default='checkpoints\checkpoint_140000.pth.tar')
+    parser.add_argument('-c', '--config', type=str, default='./config.yaml')
+    parser.add_argument('-d', '--device', choices=['cuda', 'cpu'], type=str, default='cuda')
+    args = parser.parse_args()
+
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    with open(args.config) as f:
+        config = yaml.safe_load(f)
+        logger.info(f.read())
+
+    sr = config['fbank']['sample_rate']
+
+    vocoder = build_vocoder(args.device, config)
+    text_processor = TextProcessor(config)
+    model = FastSpeech2(config)
+
+    if args.checkpoint != '':
+        sd = torch.load(args.checkpoint, map_location=args.device)
+        if 'model' in sd.keys():
+            sd = sd['model']
+    model.load_state_dict(sd)
+    del sd  # to save mem
+    model = model.to(args.device)
+    torch.set_grad_enabled(False)
+
+    try:
+        lines = open(args.input).read().split('\n')
+    except:
+        print('Failed to open text file', args.input)
+        print('Treating input as text')
+        lines = [args.input]
+
+    for line in lines:
+        if len(line) == 0 or line.startswith('#'):
+            continue
+        logger.info(f'processing {line}')
+        name, tokens = text_processor(line)
+        tokens = tokens.to(args.device)
+        seq_len = torch.tensor([tokens.shape[1]])
+        tokens = tokens.unsqueeze(1)
+        seq_len = seq_len.to(args.device)
+        max_src_len = torch.max(seq_len)
+        output = model(tokens, seq_len, max_src_len=max_src_len, d_control=args.duration)
+        mel_pred, mel_postnet, d_pred, src_mask, mel_mask, mel_len = output
+
+        # convert to waveform using vocoder
+        mel_postnet = mel_postnet[0].transpose(0, 1).detach()
+        mel_postnet += config['fbank']['mel_mean']
+        wav = vocoder(mel_postnet)
+        if config['synthesis']['normalize']:
+            wav = normalize(wav)
+        else:
+            wav = to_int16(wav)
+        dst_file = os.path.join(args.output_dir, f'{name}.wav')
+        #np.save(dst_file+'.npy',mel_postnet.cpu().numpy())
+        logger.info(f'writing file to {dst_file}')
+        wavfile.write(dst_file, sr, wav)