update img

.gitignore

@@ -17,7 +17,5 @@
 *.sh
 synthesizer/saved_models/*
 vocoder/saved_models/*
-encoder/saved_models/*
 cp_hifigan/*
-!vocoder/saved_models/pretrained/*
+!vocoder/saved_models/pretrained/*
-!encoder/saved_models/pretrained.pt

.vscode/launch.json

@@ -17,7 +17,7 @@
       "request": "launch",
       "program": "vocoder_preprocess.py",
       "console": "integratedTerminal",
-      "args": ["..\\audiodata"]
+      "args": ["..\\..\\chs1"]
     },
     {
       "name": "Python: Vocoder Train",
@@ -25,23 +25,15 @@
       "request": "launch",
       "program": "vocoder_train.py",
       "console": "integratedTerminal",
-      "args": ["dev", "..\\audiodata"]
+      "args": ["dev", "..\\..\\chs1"]
     },
     {
-      "name": "Python: Demo Box",
+      "name": "Python: demo box",
       "type": "python",
       "request": "launch",
       "program": "demo_toolbox.py",
       "console": "integratedTerminal",
-      "args": ["-d","..\\audiodata"]
+      "args": ["-d", "..\\..\\chs"]
-    },
+    }
-    {
-      "name": "Python: Synth Train",
-      "type": "python",
-      "request": "launch",
-      "program": "synthesizer_train.py",
-      "console": "integratedTerminal",
-      "args": ["my_run", "..\\"]
-    },
   ]
 }

README-CN.md

@@ -5,7 +5,7 @@
 
 ### [English](README.md)  | 中文
 
-### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/)
+### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
 
 ## 特性
 🌍 **中文** 支持普通话并使用多种中文数据集进行测试：aidatatang_200zh, magicdata, aishell3， biaobei，MozillaCommonVoice 等
@@ -119,20 +119,15 @@
 
 | URL | Designation | 标题 | 实现源码 |
 | --- | ----------- | ----- | --------------------- |
-| [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | 本代码库 |
 | [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | 本代码库 |
-|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | SV2TTS | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis | This repo |
+|[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo |
 |[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
 |[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN)
 |[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | 本代码库 |
 
 ## 常見問題(FQ&A)
 #### 1.數據集哪裡下載?
-| 数据集 | OpenSLR地址 | 其他源 (Google Drive, Baidu网盘等) |
+[aidatatang_200zh](http://www.openslr.org/62/)、[magicdata](http://www.openslr.org/68/)、[aishell3](http://www.openslr.org/93/)
-| --- | ----------- | ---------------|
-| aidatatang_200zh | [OpenSLR](http://www.openslr.org/62/) | [Google Drive](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing) |
-| magicdata | [OpenSLR](http://www.openslr.org/68/) | [Google Drive (Dev set)](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing) |
-| aishell3 | [OpenSLR](https://www.openslr.org/93/) | [Google Drive](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing) |
 > 解壓 aidatatang_200zh 後，還需將 `aidatatang_200zh\corpus\train`下的檔案全選解壓縮
 
 #### 2.`<datasets_root>`是什麼意思?

README.md

@@ -16,7 +16,7 @@
 
 🌍 **Webserver Ready** to serve your result with remote calling
 
-### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/)
+### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
 
 ## Quick Start
 
@@ -77,7 +77,6 @@ You can then try the toolbox:
 
 | URL | Designation | Title | Implementation source |
 | --- | ----------- | ----- | --------------------- |
-| [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | This repo |
 | [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | This repo |
 |[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo |
 |[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |
@@ -86,11 +85,7 @@ You can then try the toolbox:
 
 ## F Q&A
 #### 1.Where can I download the dataset?
-| Dataset | Original Source | Alternative Sources |
+[aidatatang_200zh](http://www.openslr.org/62/)、[magicdata](http://www.openslr.org/68/)、[aishell3](http://www.openslr.org/93/)
-| --- | ----------- | ---------------|
-| aidatatang_200zh | [OpenSLR](http://www.openslr.org/62/) | [Google Drive](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing) |
-| magicdata | [OpenSLR](http://www.openslr.org/68/) | [Google Drive (Dev set)](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing) |
-| aishell3 | [OpenSLR](https://www.openslr.org/93/) | [Google Drive](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing) |
 > After unzip aidatatang_200zh, you need to unzip all the files under `aidatatang_200zh\corpus\train`
 
 #### 2.What is`<datasets_root>`?

archived_untest_files/encoder_preprocess.py

@@ -1,4 +1,4 @@
-from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2, preprocess_aidatatang_200zh
+from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2
 from utils.argutils import print_args
 from pathlib import Path
 import argparse
@@ -10,7 +10,17 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
                     "writes them to the disk. This will allow you to train the encoder. The "
-                    "datasets required are at least one of LibriSpeech, VoxCeleb1, VoxCeleb2, aidatatang_200zh. ",
+                    "datasets required are at least one of VoxCeleb1, VoxCeleb2 and LibriSpeech. "
+                    "Ideally, you should have all three. You should extract them as they are "
+                    "after having downloaded them and put them in a same directory, e.g.:\n"
+                    "-[datasets_root]\n"
+                    "  -LibriSpeech\n"
+                    "    -train-other-500\n"
+                    "  -VoxCeleb1\n"
+                    "    -wav\n"
+                    "    -vox1_meta.csv\n"
+                    "  -VoxCeleb2\n"
+                    "    -dev",
         formatter_class=MyFormatter
     )
     parser.add_argument("datasets_root", type=Path, help=\
@@ -19,7 +29,7 @@ if __name__ == "__main__":
         "Path to the output directory that will contain the mel spectrograms. If left out, "
         "defaults to <datasets_root>/SV2TTS/encoder/")
     parser.add_argument("-d", "--datasets", type=str, 
-                        default="librispeech_other,voxceleb1,aidatatang_200zh", help=\
+                        default="librispeech_other,voxceleb1,voxceleb2", help=\
         "Comma-separated list of the name of the datasets you want to preprocess. Only the train "
         "set of these datasets will be used. Possible names: librispeech_other, voxceleb1, "
         "voxceleb2.")
@@ -53,7 +63,6 @@ if __name__ == "__main__":
         "librispeech_other": preprocess_librispeech,
         "voxceleb1": preprocess_voxceleb1,
         "voxceleb2": preprocess_voxceleb2,
-        "aidatatang_200zh": preprocess_aidatatang_200zh,
     }
     args = vars(args)
     for dataset in args.pop("datasets"):

encoder/preprocess.py

@@ -117,15 +117,6 @@ def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir,
     logger.finalize()
     print("Done preprocessing %s.\n" % dataset_name)
 
-def preprocess_aidatatang_200zh(datasets_root: Path, out_dir: Path, skip_existing=False):
-    dataset_name = "aidatatang_200zh"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return 
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("corpus", "train").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                                skip_existing, logger)
 
 def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
     for dataset_name in librispeech_datasets["train"]["other"]:

requirements.txt

@@ -19,5 +19,4 @@ flask
 flask_wtf
 flask_cors
 gevent==21.8.0
-flask_restx
+flask_restx
-tensorboard

synthesizer/gst_hyperparameters.py

@@ -1,13 +0,0 @@
-class GSTHyperparameters():
-    E = 512
-
-    # reference encoder
-    ref_enc_filters = [32, 32, 64, 64, 128, 128]
-
-    # style token layer
-    token_num = 10
-    # token_emb_size = 256
-    num_heads = 8
-
-    n_mels = 256  # Number of Mel banks to generate
-    

synthesizer/hparams.py

@@ -49,15 +49,12 @@ hparams = HParams(
                                                     # frame that has all values < -3.4
 
         ### Tacotron Training
-        tts_schedule = [(2,  1e-3,  10_000,  12),   # Progressive training schedule
+        tts_schedule = [(2,  1e-3,  20_000,  24),   # Progressive training schedule
-                        (2,  5e-4,  15_000,  12),   # (r, lr, step, batch_size)
+                        (2,  5e-4,  40_000,  24),   # (r, lr, step, batch_size)
-                        (2,  2e-4,  20_000,  12),   # (r, lr, step, batch_size)
+                        (2,  2e-4,  80_000,  24),   #
-                        (2,  1e-4,  30_000,  12),   #
+                        (2,  1e-4, 160_000,  24),   # r = reduction factor (# of mel frames
-                        (2,  5e-5,  40_000,  12),   #
+                        (2,  3e-5, 320_000,  24),   #     synthesized for each decoder iteration)
-                        (2,  1e-5,  60_000,  12),   #
+                        (2,  1e-5, 640_000,  24)],  # lr = learning rate
-                        (2,  5e-6, 160_000,  12),   # r = reduction factor (# of mel frames
-                        (2,  3e-6, 320_000,  12),   #     synthesized for each decoder iteration)
-                        (2,  1e-6, 640_000,  12)],  # lr = learning rate
 
         tts_clip_grad_norm = 1.0,                   # clips the gradient norm to prevent explosion - set to None if not needed
         tts_eval_interval = 500,                    # Number of steps between model evaluation (sample generation)

synthesizer/inference.py

@@ -70,7 +70,7 @@ class Synthesizer:
 
     def synthesize_spectrograms(self, texts: List[str],
                                 embeddings: Union[np.ndarray, List[np.ndarray]],
-                                return_alignments=False, style_idx=0):
+                                return_alignments=False):
         """
         Synthesizes mel spectrograms from texts and speaker embeddings.
 
@@ -125,7 +125,7 @@ class Synthesizer:
             speaker_embeddings = torch.tensor(speaker_embeds).float().to(self.device)
 
             # Inference
-            _, mels, alignments = self._model.generate(chars, speaker_embeddings, style_idx=style_idx)
+            _, mels, alignments = self._model.generate(chars, speaker_embeddings)
             mels = mels.detach().cpu().numpy()
             for m in mels:
                 # Trim silence from end of each spectrogram

synthesizer/models/global_style_token.py

@@ -1,135 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-import torch.nn.functional as tFunctional
-from synthesizer.gst_hyperparameters import GSTHyperparameters as hp
-
-
-class GlobalStyleToken(nn.Module):
-
-    def __init__(self):
-
-        super().__init__()
-        self.encoder = ReferenceEncoder()
-        self.stl = STL()
-
-    def forward(self, inputs):
-        enc_out = self.encoder(inputs)
-        style_embed = self.stl(enc_out)
-
-        return style_embed
-
-
-class ReferenceEncoder(nn.Module):
-    '''
-    inputs --- [N, Ty/r, n_mels*r]  mels
-    outputs --- [N, ref_enc_gru_size]
-    '''
-
-    def __init__(self):
-
-        super().__init__()
-        K = len(hp.ref_enc_filters)
-        filters = [1] + hp.ref_enc_filters
-        convs = [nn.Conv2d(in_channels=filters[i],
-                           out_channels=filters[i + 1],
-                           kernel_size=(3, 3),
-                           stride=(2, 2),
-                           padding=(1, 1)) for i in range(K)]
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList([nn.BatchNorm2d(num_features=hp.ref_enc_filters[i]) for i in range(K)])
-
-        out_channels = self.calculate_channels(hp.n_mels, 3, 2, 1, K)
-        self.gru = nn.GRU(input_size=hp.ref_enc_filters[-1] * out_channels,
-                          hidden_size=hp.E // 2,
-                          batch_first=True)
-
-    def forward(self, inputs):
-        N = inputs.size(0)
-        out = inputs.view(N, 1, -1, hp.n_mels)  # [N, 1, Ty, n_mels]
-        for conv, bn in zip(self.convs, self.bns):
-            out = conv(out)
-            out = bn(out)
-            out = tFunctional.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
-
-        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
-        T = out.size(1)
-        N = out.size(0)
-        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
-
-        self.gru.flatten_parameters()
-        memory, out = self.gru(out)  # out --- [1, N, E//2]
-
-        return out.squeeze(0)
-
-    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
-        for i in range(n_convs):
-            L = (L - kernel_size + 2 * pad) // stride + 1
-        return L
-
-
-class STL(nn.Module):
-    '''
-    inputs --- [N, E//2]
-    '''
-
-    def __init__(self):
-
-        super().__init__()
-        self.embed = nn.Parameter(torch.FloatTensor(hp.token_num, hp.E // hp.num_heads))
-        d_q = hp.E // 2
-        d_k = hp.E // hp.num_heads
-        # self.attention = MultiHeadAttention(hp.num_heads, d_model, d_q, d_v)
-        self.attention = MultiHeadAttention(query_dim=d_q, key_dim=d_k, num_units=hp.E, num_heads=hp.num_heads)
-
-        init.normal_(self.embed, mean=0, std=0.5)
-
-    def forward(self, inputs):
-        N = inputs.size(0)
-        query = inputs.unsqueeze(1)  # [N, 1, E//2]
-        keys = tFunctional.tanh(self.embed).unsqueeze(0).expand(N, -1, -1)  # [N, token_num, E // num_heads]
-        style_embed = self.attention(query, keys)
-
-        return style_embed
-
-
-class MultiHeadAttention(nn.Module):
-    '''
-    input:
-        query --- [N, T_q, query_dim]
-        key --- [N, T_k, key_dim]
-    output:
-        out --- [N, T_q, num_units]
-    '''
-
-    def __init__(self, query_dim, key_dim, num_units, num_heads):
-
-        super().__init__()
-        self.num_units = num_units
-        self.num_heads = num_heads
-        self.key_dim = key_dim
-
-        self.W_query = nn.Linear(in_features=query_dim, out_features=num_units, bias=False)
-        self.W_key = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
-        self.W_value = nn.Linear(in_features=key_dim, out_features=num_units, bias=False)
-
-    def forward(self, query, key):
-        querys = self.W_query(query)  # [N, T_q, num_units]
-        keys = self.W_key(key)  # [N, T_k, num_units]
-        values = self.W_value(key)
-
-        split_size = self.num_units // self.num_heads
-        querys = torch.stack(torch.split(querys, split_size, dim=2), dim=0)  # [h, N, T_q, num_units/h]
-        keys = torch.stack(torch.split(keys, split_size, dim=2), dim=0)  # [h, N, T_k, num_units/h]
-        values = torch.stack(torch.split(values, split_size, dim=2), dim=0)  # [h, N, T_k, num_units/h]
-
-        # score = softmax(QK^T / (d_k ** 0.5))
-        scores = torch.matmul(querys, keys.transpose(2, 3))  # [h, N, T_q, T_k]
-        scores = scores / (self.key_dim ** 0.5)
-        scores = tFunctional.softmax(scores, dim=3)
-
-        # out = score * V
-        out = torch.matmul(scores, values)  # [h, N, T_q, num_units/h]
-        out = torch.cat(torch.split(out, 1, dim=0), dim=3).squeeze(0)  # [N, T_q, num_units]
-
-        return out

synthesizer/models/tacotron.py

@@ -3,7 +3,8 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from synthesizer.models.global_style_token import GlobalStyleToken
+from pathlib import Path
+from typing import Union
 
 
 class HighwayNetwork(nn.Module):
@@ -337,7 +338,6 @@ class Tacotron(nn.Module):
         self.encoder = Encoder(embed_dims, num_chars, encoder_dims,
                                encoder_K, num_highways, dropout)
         self.encoder_proj = nn.Linear(encoder_dims + speaker_embedding_size, decoder_dims, bias=False)
-        self.gst = GlobalStyleToken()
         self.decoder = Decoder(n_mels, encoder_dims, decoder_dims, lstm_dims,
                                dropout, speaker_embedding_size)
         self.postnet = CBHG(postnet_K, n_mels, postnet_dims,
@@ -358,11 +358,11 @@ class Tacotron(nn.Module):
     def r(self, value):
         self.decoder.r = self.decoder.r.new_tensor(value, requires_grad=False)
 
-    def forward(self, texts, mels, speaker_embedding):
+    def forward(self, x, m, speaker_embedding):
         device = next(self.parameters()).device  # use same device as parameters
 
         self.step += 1
-        batch_size, _, steps  = mels.size()
+        batch_size, _, steps  = m.size()
 
         # Initialise all hidden states and pack into tuple
         attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device)
@@ -383,12 +383,7 @@ class Tacotron(nn.Module):
 
         # SV2TTS: Run the encoder with the speaker embedding
         # The projection avoids unnecessary matmuls in the decoder loop
-        encoder_seq = self.encoder(texts, speaker_embedding)
+        encoder_seq = self.encoder(x, speaker_embedding)
-        # put after encoder 
-        if self.gst is not None:
-            style_embed = self.gst(speaker_embedding) 
-            style_embed = style_embed.expand_as(encoder_seq)
-            encoder_seq = encoder_seq + style_embed
         encoder_seq_proj = self.encoder_proj(encoder_seq)
 
         # Need a couple of lists for outputs
@@ -396,10 +391,10 @@ class Tacotron(nn.Module):
 
         # Run the decoder loop
         for t in range(0, steps, self.r):
-            prenet_in = mels[:, :, t - 1] if t > 0 else go_frame
+            prenet_in = m[:, :, t - 1] if t > 0 else go_frame
             mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \
                 self.decoder(encoder_seq, encoder_seq_proj, prenet_in,
-                             hidden_states, cell_states, context_vec, t, texts)
+                             hidden_states, cell_states, context_vec, t, x)
             mel_outputs.append(mel_frames)
             attn_scores.append(scores)
             stop_outputs.extend([stop_tokens] * self.r)
@@ -419,7 +414,7 @@ class Tacotron(nn.Module):
 
         return mel_outputs, linear, attn_scores, stop_outputs
 
-    def generate(self, x, speaker_embedding=None, steps=200, style_idx=0):
+    def generate(self, x, speaker_embedding=None, steps=2000):
         self.eval()
         device = next(self.parameters()).device  # use same device as parameters
 
@@ -445,18 +440,6 @@ class Tacotron(nn.Module):
         # SV2TTS: Run the encoder with the speaker embedding
         # The projection avoids unnecessary matmuls in the decoder loop
         encoder_seq = self.encoder(x, speaker_embedding)
-
-        # put after encoder 
-        if self.gst is not None and style_idx >= 0 and style_idx < 10:
-            gst_embed = self.gst.stl.embed.cpu().data.numpy()  #[0, number_token]
-            gst_embed = np.tile(gst_embed, (1, 8))
-            scale = np.zeros(512)
-            scale[:] = 0.3
-            speaker_embedding = (gst_embed[style_idx] * scale).astype(np.float32)
-            speaker_embedding = torch.from_numpy(np.tile(speaker_embedding, (x.shape[0], 1))).to(device)
-        style_embed = self.gst(speaker_embedding)
-        style_embed = style_embed.expand_as(encoder_seq)
-        encoder_seq = encoder_seq + style_embed
         encoder_seq_proj = self.encoder_proj(encoder_seq)
 
         # Need a couple of lists for outputs
@@ -511,7 +494,7 @@ class Tacotron(nn.Module):
         # Use device of model params as location for loaded state
         device = next(self.parameters()).device
         checkpoint = torch.load(str(path), map_location=device)
-        self.load_state_dict(checkpoint["model_state"], strict=False)
+        self.load_state_dict(checkpoint["model_state"])
 
         if "optimizer_state" in checkpoint and optimizer is not None:
             optimizer.load_state_dict(checkpoint["optimizer_state"])

toolbox/__init__.py

@@ -71,7 +71,6 @@ class Toolbox:
 
         # Initialize the events and the interface
         self.ui = UI()
-        self.style_idx = 0
         self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, seed)
         self.setup_events()
         self.ui.start()
@@ -234,7 +233,7 @@ class Toolbox:
         texts = processed_texts
         embed = self.ui.selected_utterance.embed
         embeds = [embed] * len(texts)
-        specs = self.synthesizer.synthesize_spectrograms(texts, embeds, style_idx=int(self.ui.style_idx_textbox.text()))
+        specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
         breaks = [spec.shape[1] for spec in specs]
         spec = np.concatenate(specs, axis=1)
         

toolbox/ui.py

@@ -574,14 +574,10 @@ class UI(QDialog):
         self.seed_textbox = QLineEdit()
         self.seed_textbox.setMaximumWidth(80)
         layout_seed.addWidget(self.seed_textbox, 0, 1)
-        layout_seed.addWidget(QLabel("Style#:(0~9)"), 0, 2)
-        self.style_idx_textbox = QLineEdit("-1")
-        self.style_idx_textbox.setMaximumWidth(80)
-        layout_seed.addWidget(self.style_idx_textbox, 0, 3)
         self.trim_silences_checkbox = QCheckBox("Enhance vocoder output")
         self.trim_silences_checkbox.setToolTip("When checked, trims excess silence in vocoder output."
             " This feature requires `webrtcvad` to be installed.")
-        layout_seed.addWidget(self.trim_silences_checkbox, 0, 4, 1, 2)
+        layout_seed.addWidget(self.trim_silences_checkbox, 0, 2, 1, 2)
         gen_layout.addLayout(layout_seed)
 
         self.loading_bar = QProgressBar()

utils/modelutils.py

@@ -11,6 +11,7 @@ def check_model_paths(encoder_path: Path, synthesizer_path: Path, vocoder_path:
 
     # If none of the paths exist, remind the user to download models if needed
     print("********************************************************************************")
-    print("Error: Model files not found. Please download the models")
+    print("Error: Model files not found. Follow these instructions to get and install the models:")
+    print("https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models")
     print("********************************************************************************\n")
     quit(-1)

web/__init__.py

@@ -9,7 +9,6 @@ from vocoder.wavernn import inference as rnn_vocoder
 import numpy as np
 import re
 from scipy.io.wavfile import write
-import librosa
 import io
 import base64
 from flask_cors import CORS
@@ -31,7 +30,6 @@ def webApp():
     synthesizers = list(Path(syn_models_dirt).glob("**/*.pt"))
     synthesizers_cache = {}
     encoder.load_model(Path("encoder/saved_models/pretrained.pt"))
-    # rnn_vocoder.load_model(Path("vocoder/saved_models/pretrained/pretrained.pt"))
     gan_vocoder.load_model(Path("vocoder/saved_models/pretrained/g_hifigan.pt"))
 
     def pcm2float(sig, dtype='float32'):
@@ -68,6 +66,7 @@ def webApp():
     @app.route("/api/synthesize", methods=["POST"])
     def synthesize():
         # TODO Implementation with json to support more platform
+
         # Load synthesizer
         if "synt_path" in request.form:
             synt_path = request.form["synt_path"]
@@ -81,16 +80,10 @@ def webApp():
             current_synt = synthesizers_cache[synt_path]
         print("using synthesizer model: " + str(synt_path))
         # Load input wav
-        if "upfile_b64" in request.form:
+        wav_base64 = request.form["upfile_b64"]
-            wav_base64 = request.form["upfile_b64"]
+        wav = base64.b64decode(bytes(wav_base64, 'utf-8'))
-            wav = base64.b64decode(bytes(wav_base64, 'utf-8'))
+        wav = pcm2float(np.frombuffer(wav, dtype=np.int16), dtype=np.float32)
-            wav = pcm2float(np.frombuffer(wav, dtype=np.int16), dtype=np.float32)
+        encoder_wav = encoder.preprocess_wav(wav, 16000)
-            sample_rate = Synthesizer.sample_rate
-        else:
-            wav, sample_rate,  = librosa.load(request.files['file'])
-        write("temp.wav", sample_rate, wav) #Make sure we get the correct wav
-        
-        encoder_wav = encoder.preprocess_wav(wav, sample_rate)
         embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
         
         # Load input text
@@ -107,7 +100,6 @@ def webApp():
         embeds = [embed] * len(texts)
         specs = current_synt.synthesize_spectrograms(texts, embeds)
         spec = np.concatenate(specs, axis=1)
-        # wav = rnn_vocoder.infer_waveform(spec)
         wav = gan_vocoder.infer_waveform(spec)
 
         # Return cooked wav

web/config/default.py

@@ -5,4 +5,3 @@ PORT = 8080
 MAX_CONTENT_PATH =1024 * 1024 * 4  # mp3文件大小限定不能超过4M
 SECRET_KEY = "mockingbird_key"
 WTF_CSRF_SECRET_KEY = "mockingbird_key"
-TEMPLATES_AUTO_RELOAD = True

web/templates/index.html

@@ -38,37 +38,22 @@
 			</div>	
 
 			<div  style="margin-left: 5%;margin-top: 50px;width: 90%;">
-				<div style="font-size: larger;font-weight: bolder;">1. 请输入中文</div>
+				<div style="font-size: larger;font-weight: bolder;">请输入中文</div>
 				<textarea  id="user_input_text"
 					style="border:1px solid #ccc; width: 100%; height: 100px; font-size: 15px; margin-top: 10px;"></textarea>
 			</div>
-			<div class="pd btns" style="margin-left: 5%;margin-top: 20px;width: 90%; ">
+			
+			<div class="pd btns" style="margin-left: 5%;margin-top: 20px;width: 90%; text-align:right;">
 				<!-- <div>
 				<button onclick="recOpen()" style="margin-right:10px">打开录音,请求权限</button>
 				<button onclick="recClose()" style="margin-right:0">关闭录音,释放资源</button>
 			</div> -->
-				<div style="font-size: larger;font-weight: bolder;">2. 请直接录音，点击停止结束</div>
 				<button onclick="recStart()" >录制</button>
 				<button onclick="recStop()">停止</button>
 				<button onclick="recPlay()" >播放</button>
+				<button onclick="recUpload()" >上传</button>
 			</div>
-			<div class="pd btns" style="margin-left: 5%;margin-top: 20px;width: 90%; ">
+
-				<div style="font-size: larger;font-weight: bolder;">或上传音频</div>
-				<input type="file" id="fileInput" accept=".wav" />
-				<label for="fileInput">选择音频</label>
-				<div id="audio1"></div>
-			</div>
-			<div class="pd btns" style="margin-left: 5%;margin-top: 20px;width: 90%; ">
-				<div style="font-size: larger;font-weight: bolder;">3. 选择Synthesizer模型</div>
-				<span class="box">
-					<select id="select">
-					</select>
-				</span>
-			</div>
-			<div class="pd btns" style="margin-left: 5%;margin-top: 20px;width: 90%; text-align:right;">
-				<button id="upload" onclick="recUpload()">上传合成</button>
-			</div>
-			
 			<!-- 波形绘制区域 -->
 			<!-- <div class="pd recpower">
 				<div style="height:40px;width:100%;background:#fff;position:relative;">
@@ -91,37 +76,6 @@
 
 
 	<script>
-
-		$("#fileInput").change(function(){
-			var file = $("#fileInput").get(0).files;
-			if (file.length > 0) {
-				var path = URL.createObjectURL(file[0]);
-				var audio = document.createElement('audio');
-				audio.src = path;
-				audio.controls = true;
-				$('#audio1').empty().append(audio); 
-			}
-		});
-		
-		fetch("/api/synthesizers", {
-			method: 'get',
-			headers: {
-				"X-CSRFToken": "{{ csrf_token() }}"
-			}
-		}).then(function (res) {
-			if (!res.ok) throw Error(res.statusText);
-			return res.json();
-		}).then(function (data) {
-			for (var synt of data) {
-				var option = document.createElement('option');
-				option.text = synt.name
-				option.value = synt.path
-				$("#select").append(option);
-			}
-		}).catch(function (err) {
-			console.log('Error: ' + err.message);
-		})
-
 		var rec, wave, recBlob;
 		/**调用open打开录音请求好录音权限**/
 		var recOpen = function () {//一般在显示出录音按钮或相关的录音界面时进行此方法调用，后面用户点击开始录音时就能畅通无阻了
@@ -240,15 +194,9 @@
 
 		/**上传**/
 		function recUpload() {
-			var blob
+			var blob = recBlob;
-			var loadedAudios = $("#fileInput").get(0).files
-			if (loadedAudios.length > 0) {
-				blob = loadedAudios[0];
-			} else {
-				blob = recBlob;
-			}
 			if (!blob) {
-				reclog("请先录音或选择音频，然后停止后再上传", 1);
+				reclog("请先录音，然后停止后再上传", 1);
 				return;
 			};
 
@@ -263,18 +211,15 @@
 				var csrftoken = "{{ csrf_token() }}";
 				var user_input_text = document.getElementById("user_input_text");
 				var input_text = user_input_text.value;
-				var postData = new FormData();
+				var postData = "";
-				postData.append("text", input_text)
+				postData += "mime=" + encodeURIComponent(blob.type);//告诉后端，这个录音是什么格式的，可能前后端都固定的mp3可以不用写
-				postData.append("file", blob)
+				postData += "&upfile_b64=" + encodeURIComponent((/.+;\s*base64\s*,\s*(.+)$/i.exec(reader.result) || [])[1]) //录音文件内容，后端进行base64解码成二进制
-				var sel = document.getElementById("select");
+				postData += "&text=" + encodeURIComponent(input_text);
-				var path = sel.options[sel.selectedIndex].value;
-				if (!!path) {
-					postData.append("synt_path", path);
-				}
 
 				fetch(api, {
 					method: 'post',
 					headers: {
+						"Content-type": "application/x-www-form-urlencoded; charset=UTF-8",
 						"X-CSRFToken": csrftoken
 					},
 					body: postData
@@ -393,6 +338,7 @@
 			padding: 12px;
 			border-radius: 6px;
 			background: #fff;
+			--border: 1px solid #327de8;
 			box-shadow: 2px 2px 3px #aaa;
 		}
 
@@ -402,7 +348,7 @@
 			cursor: pointer;
 			border: none;
 			border-radius: 3px;
-			background: #5698c3;
+			background: #327de8;
 			color: #fff;
 			padding: 0 15px;
 			margin: 3px 10px 3px 0;
@@ -413,13 +359,6 @@
 			vertical-align: middle;
 		}
 
-		.btns #upload {
-			background: #5698c3;
-			color: #fff;
-			width: 100px;
-			height: 42px;
-		}
-
 		.btns button:active {
 			background: #5da1f5
 		}
@@ -440,68 +379,6 @@
 			padding: 2px 8px;
 			border-radius: 99px;
 		}
-
-		#fileInput {
-			width: 0.1px;
-			height: 0.1px;
-			opacity: 0;
-			overflow: hidden;
-			position: absolute;
-			z-index: -1;
-		}
-		#fileInput + label {
-			padding: 0 15px;
-			border-radius: 4px;
-			color: white;
-			background-color: #5698c3;
-			display: inline-block;
-			width: 70px;
-			line-height: 36px;
-			height: 36px;
-		}
-		#fileInput + label {
-			cursor: pointer; /* "hand" cursor */
-		}
-		#fileInput:focus + label,
-		#fileInput + label:hover {
-			background-color: #5da1f5;
-		}
-
-		.box select {
-			background-color: #5698c3;
-			color: white;
-			padding: 8px;
-			width: 120px;
-			border: none;
-			border-radius: 4px;
-			font-size: 0.5em;
-			outline: none;
-			margin: 3px 10px 3px 0;
-		}
-
-		.box::before {
-			content: "\f13a";
-			position: absolute;
-			top: 0;
-			right: 0;
-			width: 20%;
-			height: 100%;
-			text-align: center;
-			font-size: 28px;
-			line-height: 45px;
-			color: rgba(255, 255, 255, 0.5);
-			background-color: rgba(255, 255, 255, 0.1);
-			pointer-events: none;
-		}
-
-		.box:hover::before {
-			color: rgba(255, 255, 255, 0.6);
-			background-color: rgba(255, 255, 255, 0.2);
-		}
-
-		.box select option {
-			padding: 30px;
-		}
 	</style>
 
 </body>