New GUI in order to combine web and toolbox in future

gui/app.py

@@ -0,0 +1,117 @@
+from asyncio.windows_events import NULL
+from pydantic import BaseModel, Field
+import os
+from pathlib import Path
+from enum import Enum
+from encoder import inference as encoder
+import librosa
+from scipy.io.wavfile import write
+import re
+import numpy as np
+from opyrator.components.types import FileContent
+from vocoder.hifigan import inference as gan_vocoder
+from synthesizer.inference import Synthesizer
+
+# Constants
+AUDIO_SAMPLES_DIR = 'samples\\'
+SYN_MODELS_DIRT = "synthesizer\\saved_models"
+ENC_MODELS_DIRT = "encoder\\saved_models"
+VOC_MODELS_DIRT = "vocoder\\saved_models"
+TEMP_SOURCE_AUDIO = "wavs/temp_source.wav"
+TEMP_RESULT_AUDIO = "wavs/temp_result.wav"
+
+# Load local sample audio as options TODO: load dataset 
+if os.path.isdir(AUDIO_SAMPLES_DIR):
+    audio_input_selection = Enum('samples', list((file.name, file) for file in Path(AUDIO_SAMPLES_DIR).glob("*.wav")))
+# Pre-Load models
+if os.path.isdir(SYN_MODELS_DIRT):    
+    synthesizers =  Enum('synthesizers', list((file.name, file) for file in Path(SYN_MODELS_DIRT).glob("**/*.pt")))
+    print("Loaded synthesizer models: " + str(len(synthesizers)))
+if os.path.isdir(ENC_MODELS_DIRT):    
+    encoders =  Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt")))
+    print("Loaded encoders models: " + str(len(encoders)))
+if os.path.isdir(VOC_MODELS_DIRT):    
+    vocoders =  Enum('vocoders', list((file.name, file) for file in Path(VOC_MODELS_DIRT).glob("**/*gan*.pt")))
+    print("Loaded vocoders models: " + str(len(synthesizers)))
+
+
+class Input(BaseModel):
+    local_audio_file: audio_input_selection = Field(
+        ..., alias="输入语音（本地wav）",
+        description="选择本地语音文件."
+    )
+    upload_audio_file: FileContent = Field(..., alias="或上传语音",
+        description="拖拽或点击上传.", mime_type="audio/wav")
+    encoder: encoders = Field(
+        ..., alias="编码模型", 
+        description="选择语音编码模型文件."
+    )
+    synthesizer: synthesizers = Field(
+        ..., alias="合成模型", 
+        description="选择语音编码模型文件."
+    )
+    vocoder: vocoders = Field(
+        ..., alias="语音编码模型", 
+        description="选择语音编码模型文件(目前只支持HifiGan类型)."
+    )
+    message: str = Field(
+        ..., example="欢迎使用工具箱, 现已支持中文输入！", alias="输出文本内容"
+    )
+
+class Output(BaseModel):
+    result_file: FileContent = Field(
+        ...,
+        mime_type="audio/wav",
+        description="输出音频",
+    )
+    source_file: FileContent = Field(
+        ...,
+        mime_type="audio/wav",
+        description="原始音频.",
+    )
+
+def mocking_bird(input: Input) -> Output:
+    """欢迎使用MockingBird Web 2"""
+    # load models
+    encoder.load_model(Path(input.encoder.value))
+    current_synt = Synthesizer(Path(input.synthesizer.value))
+    gan_vocoder.load_model(Path(input.vocoder.value))
+
+    # load file
+    if input.upload_audio_file != NULL:
+        with open(TEMP_SOURCE_AUDIO, "w+b") as f:
+            f.write(input.upload_audio_file.as_bytes())
+            f.seek(0)
+        wav, sample_rate = librosa.load(TEMP_SOURCE_AUDIO)
+    else:
+        wav, sample_rate  = librosa.load(input.local_audio_file.value)
+        write(TEMP_SOURCE_AUDIO, sample_rate, wav) #Make sure we get the correct wav
+
+    # preprocess
+    encoder_wav = encoder.preprocess_wav(wav, sample_rate)
+    embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
+
+    # Load input text
+    texts = filter(None, input.message.split("\n"))
+    punctuation = '！，。、,' # punctuate and split/clean text
+    processed_texts = []
+    for text in texts:
+        for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
+            if processed_text:
+                processed_texts.append(processed_text.strip())
+    texts = processed_texts
+
+    # synthesize and vocode
+    embeds = [embed] * len(texts)
+    specs = current_synt.synthesize_spectrograms(texts, embeds)
+    spec = np.concatenate(specs, axis=1)
+    sample_rate = Synthesizer.sample_rate
+    wav, sample_rate = gan_vocoder.infer_waveform(spec)
+
+    # write and output 
+    write(TEMP_RESULT_AUDIO, sample_rate, wav) #Make sure we get the correct wav
+    with open(TEMP_SOURCE_AUDIO, "rb") as f:
+        source_file = f.read()
+    with open(TEMP_RESULT_AUDIO, "rb") as f:
+        result_file = f.read()
+    return Output(source_file=source_file, result_file=result_file)

requirements.txt

@@ -21,3 +21,5 @@ flask_cors==3.0.10
 gevent==21.8.0
 flask_restx
 tensorboard
+opyrator
+streamlit==1.3.1