mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
from asyncio.windows_events import NULL
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from pydantic import BaseModel, Field
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import os
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from pathlib import Path
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from enum import Enum
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from encoder import inference as encoder
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import librosa
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from scipy.io.wavfile import write
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import re
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import numpy as np
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from opyrator.components.types import FileContent
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from vocoder.hifigan import inference as gan_vocoder
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from synthesizer.inference import Synthesizer
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# Constants
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AUDIO_SAMPLES_DIR = 'samples\\'
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SYN_MODELS_DIRT = "synthesizer\\saved_models"
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ENC_MODELS_DIRT = "encoder\\saved_models"
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VOC_MODELS_DIRT = "vocoder\\saved_models"
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TEMP_SOURCE_AUDIO = "wavs/temp_source.wav"
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TEMP_RESULT_AUDIO = "wavs/temp_result.wav"
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# Load local sample audio as options TODO: load dataset
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if os.path.isdir(AUDIO_SAMPLES_DIR):
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audio_input_selection = Enum('samples', list((file.name, file) for file in Path(AUDIO_SAMPLES_DIR).glob("*.wav")))
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# Pre-Load models
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if os.path.isdir(SYN_MODELS_DIRT):
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synthesizers = Enum('synthesizers', list((file.name, file) for file in Path(SYN_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded synthesizer models: " + str(len(synthesizers)))
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if os.path.isdir(ENC_MODELS_DIRT):
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encoders = Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded encoders models: " + str(len(encoders)))
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if os.path.isdir(VOC_MODELS_DIRT):
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vocoders = Enum('vocoders', list((file.name, file) for file in Path(VOC_MODELS_DIRT).glob("**/*gan*.pt")))
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print("Loaded vocoders models: " + str(len(synthesizers)))
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class Input(BaseModel):
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local_audio_file: audio_input_selection = Field(
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..., alias="输入语音（本地wav）",
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description="选择本地语音文件."
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)
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upload_audio_file: FileContent = Field(..., alias="或上传语音",
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description="拖拽或点击上传.", mime_type="audio/wav")
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encoder: encoders = Field(
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..., alias="编码模型",
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description="选择语音编码模型文件."
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)
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synthesizer: synthesizers = Field(
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..., alias="合成模型",
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description="选择语音编码模型文件."
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)
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vocoder: vocoders = Field(
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..., alias="语音编码模型",
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description="选择语音编码模型文件(目前只支持HifiGan类型)."
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)
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message: str = Field(
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..., example="欢迎使用工具箱, 现已支持中文输入！", alias="输出文本内容"
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)
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class Output(BaseModel):
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result_file: FileContent = Field(
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...,
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mime_type="audio/wav",
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description="输出音频",
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)
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source_file: FileContent = Field(
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...,
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mime_type="audio/wav",
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description="原始音频.",
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)
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def mocking_bird(input: Input) -> Output:
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"""欢迎使用MockingBird Web 2"""
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# load models
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encoder.load_model(Path(input.encoder.value))
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current_synt = Synthesizer(Path(input.synthesizer.value))
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gan_vocoder.load_model(Path(input.vocoder.value))
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# load file
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if input.upload_audio_file != NULL:
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with open(TEMP_SOURCE_AUDIO, "w+b") as f:
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f.write(input.upload_audio_file.as_bytes())
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f.seek(0)
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wav, sample_rate = librosa.load(TEMP_SOURCE_AUDIO)
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else:
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wav, sample_rate = librosa.load(input.local_audio_file.value)
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write(TEMP_SOURCE_AUDIO, sample_rate, wav) #Make sure we get the correct wav
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# preprocess
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encoder_wav = encoder.preprocess_wav(wav, sample_rate)
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embed, _, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
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# Load input text
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texts = filter(None, input.message.split("\n"))
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punctuation = '！，。、,' # punctuate and split/clean text
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processed_texts = []
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for text in texts:
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for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
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if processed_text:
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processed_texts.append(processed_text.strip())
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texts = processed_texts
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# synthesize and vocode
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embeds = [embed] * len(texts)
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specs = current_synt.synthesize_spectrograms(texts, embeds)
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spec = np.concatenate(specs, axis=1)
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sample_rate = Synthesizer.sample_rate
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wav, sample_rate = gan_vocoder.infer_waveform(spec)
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# write and output
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write(TEMP_RESULT_AUDIO, sample_rate, wav) #Make sure we get the correct wav
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with open(TEMP_SOURCE_AUDIO, "rb") as f:
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source_file = f.read()
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with open(TEMP_RESULT_AUDIO, "rb") as f:
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result_file = f.read()
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return Output(source_file=source_file, result_file=result_file) |