166 lines
6.9 KiB
Python
166 lines
6.9 KiB
Python
import os
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from enum import Enum
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from pathlib import Path
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from typing import Any, Tuple
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import librosa
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import matplotlib.pyplot as plt
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import torch
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from pydantic import BaseModel, Field
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from scipy.io.wavfile import write
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import models.ppg2mel as Convertor
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import models.ppg_extractor as Extractor
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from control.mkgui.base.components.types import FileContent
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from models.encoder import inference as speacker_encoder
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from models.synthesizer.inference import Synthesizer
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from models.vocoder.hifigan import inference as gan_vocoder
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# Constants
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AUDIO_SAMPLES_DIR = f'data{os.sep}samples{os.sep}'
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EXT_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}ppg_extractor'
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CONV_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}ppg2mel'
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VOC_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}vocoder'
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TEMP_SOURCE_AUDIO = f'wavs{os.sep}temp_source.wav'
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TEMP_TARGET_AUDIO = f'wavs{os.sep}temp_target.wav'
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TEMP_RESULT_AUDIO = f'wavs{os.sep}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(EXT_MODELS_DIRT):
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extractors = Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded extractor models: " + str(len(extractors)))
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else:
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raise Exception(f"Model folder {EXT_MODELS_DIRT} doesn't exist.")
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if os.path.isdir(CONV_MODELS_DIRT):
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convertors = Enum('convertors', list((file.name, file) for file in Path(CONV_MODELS_DIRT).glob("**/*.pth")))
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print("Loaded convertor models: " + str(len(convertors)))
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else:
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raise Exception(f"Model folder {CONV_MODELS_DIRT} doesn't exist.")
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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(vocoders)))
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else:
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raise Exception(f"Model folder {VOC_MODELS_DIRT} doesn't exist.")
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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(default=None, 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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local_audio_file_target: audio_input_selection = Field(
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..., alias="目标语音（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_target: FileContent = Field(default=None, alias="或上传目标语音",
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description="拖拽或点击上传.", mime_type="audio/wav")
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extractor: extractors = 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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convertor: convertors = Field(
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..., alias="转换模型",
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description="选择语音转换模型文件."
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)
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vocoder: vocoders = Field(
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..., alias="语音解码模型",
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description="选择语音解码模型文件(目前只支持HifiGan类型)."
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)
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class AudioEntity(BaseModel):
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content: bytes
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mel: Any
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class Output(BaseModel):
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__root__: Tuple[AudioEntity, AudioEntity, AudioEntity]
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def render_output_ui(self, streamlit_app, input) -> None: # type: ignore
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"""Custom output UI.
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If this method is implmeneted, it will be used instead of the default Output UI renderer.
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"""
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src, target, result = self.__root__
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streamlit_app.subheader("Synthesized Audio")
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streamlit_app.audio(result.content, format="audio/wav")
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fig, ax = plt.subplots()
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ax.imshow(src.mel, aspect="equal", interpolation="none")
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ax.set_title("mel spectrogram(Source Audio)")
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streamlit_app.pyplot(fig)
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fig, ax = plt.subplots()
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ax.imshow(target.mel, aspect="equal", interpolation="none")
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ax.set_title("mel spectrogram(Target Audio)")
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streamlit_app.pyplot(fig)
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fig, ax = plt.subplots()
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ax.imshow(result.mel, aspect="equal", interpolation="none")
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ax.set_title("mel spectrogram(Result Audio)")
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streamlit_app.pyplot(fig)
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def convert(input: Input) -> Output:
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"""convert(转换)"""
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# load models
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extractor = Extractor.load_model(Path(input.extractor.value))
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convertor = Convertor.load_model(Path(input.convertor.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 != None:
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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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src_wav, sample_rate = librosa.load(TEMP_SOURCE_AUDIO)
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else:
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src_wav, sample_rate = librosa.load(input.local_audio_file.value)
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write(TEMP_SOURCE_AUDIO, sample_rate, src_wav) #Make sure we get the correct wav
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if input.upload_audio_file_target != None:
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with open(TEMP_TARGET_AUDIO, "w+b") as f:
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f.write(input.upload_audio_file_target.as_bytes())
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f.seek(0)
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ref_wav, _ = librosa.load(TEMP_TARGET_AUDIO)
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else:
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ref_wav, _ = librosa.load(input.local_audio_file_target.value)
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write(TEMP_TARGET_AUDIO, sample_rate, ref_wav) #Make sure we get the correct wav
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ppg = extractor.extract_from_wav(src_wav)
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# Import necessary dependency of Voice Conversion
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from utils.f0_utils import (compute_f0, compute_mean_std, f02lf0,
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get_converted_lf0uv)
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ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav)))
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speacker_encoder.load_model(Path(f"data{os.sep}ckpt{os.sep}encoder{os.sep}pretrained_bak_5805000.pt"))
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embed = speacker_encoder.embed_utterance(ref_wav)
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lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True)
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min_len = min(ppg.shape[1], len(lf0_uv))
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ppg = ppg[:, :min_len]
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lf0_uv = lf0_uv[:min_len]
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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_, mel_pred, att_ws = convertor.inference(
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ppg,
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logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device),
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spembs=torch.from_numpy(embed).unsqueeze(0).to(device),
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)
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mel_pred= mel_pred.transpose(0, 1)
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breaks = [mel_pred.shape[1]]
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mel_pred= mel_pred.detach().cpu().numpy()
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# synthesize and vocode
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wav, sample_rate = gan_vocoder.infer_waveform(mel_pred)
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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_TARGET_AUDIO, "rb") as f:
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target_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(__root__=(AudioEntity(content=source_file, mel=Synthesizer.make_spectrogram(src_wav)), AudioEntity(content=target_file, mel=Synthesizer.make_spectrogram(ref_wav)), AudioEntity(content=result_file, mel=Synthesizer.make_spectrogram(wav)))) |