mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
166 lines
6.9 KiB
Python
166 lines
6.9 KiB
Python
import os
|
||
from enum import Enum
|
||
from pathlib import Path
|
||
from typing import Any, Tuple
|
||
|
||
import librosa
|
||
import matplotlib.pyplot as plt
|
||
import torch
|
||
from pydantic import BaseModel, Field
|
||
from scipy.io.wavfile import write
|
||
|
||
import models.ppg2mel as Convertor
|
||
import models.ppg_extractor as Extractor
|
||
from control.mkgui.base.components.types import FileContent
|
||
from models.encoder import inference as speacker_encoder
|
||
from models.synthesizer.inference import Synthesizer
|
||
from models.vocoder.hifigan import inference as gan_vocoder
|
||
|
||
# Constants
|
||
AUDIO_SAMPLES_DIR = f'data{os.sep}samples{os.sep}'
|
||
EXT_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}ppg_extractor'
|
||
CONV_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}ppg2mel'
|
||
VOC_MODELS_DIRT = f'data{os.sep}ckpt{os.sep}vocoder'
|
||
TEMP_SOURCE_AUDIO = f'wavs{os.sep}temp_source.wav'
|
||
TEMP_TARGET_AUDIO = f'wavs{os.sep}temp_target.wav'
|
||
TEMP_RESULT_AUDIO = f'wavs{os.sep}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(EXT_MODELS_DIRT):
|
||
extractors = Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt")))
|
||
print("Loaded extractor models: " + str(len(extractors)))
|
||
else:
|
||
raise Exception(f"Model folder {EXT_MODELS_DIRT} doesn't exist.")
|
||
|
||
if os.path.isdir(CONV_MODELS_DIRT):
|
||
convertors = Enum('convertors', list((file.name, file) for file in Path(CONV_MODELS_DIRT).glob("**/*.pth")))
|
||
print("Loaded convertor models: " + str(len(convertors)))
|
||
else:
|
||
raise Exception(f"Model folder {CONV_MODELS_DIRT} doesn't exist.")
|
||
|
||
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(vocoders)))
|
||
else:
|
||
raise Exception(f"Model folder {VOC_MODELS_DIRT} doesn't exist.")
|
||
|
||
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(default=None, alias="或上传语音",
|
||
description="拖拽或点击上传.", mime_type="audio/wav")
|
||
local_audio_file_target: audio_input_selection = Field(
|
||
..., alias="目标语音（本地wav）",
|
||
description="选择本地语音文件."
|
||
)
|
||
upload_audio_file_target: FileContent = Field(default=None, alias="或上传目标语音",
|
||
description="拖拽或点击上传.", mime_type="audio/wav")
|
||
extractor: extractors = Field(
|
||
..., alias="编码模型",
|
||
description="选择语音编码模型文件."
|
||
)
|
||
convertor: convertors = Field(
|
||
..., alias="转换模型",
|
||
description="选择语音转换模型文件."
|
||
)
|
||
vocoder: vocoders = Field(
|
||
..., alias="语音解码模型",
|
||
description="选择语音解码模型文件(目前只支持HifiGan类型)."
|
||
)
|
||
|
||
class AudioEntity(BaseModel):
|
||
content: bytes
|
||
mel: Any
|
||
|
||
class Output(BaseModel):
|
||
__root__: Tuple[AudioEntity, AudioEntity, AudioEntity]
|
||
|
||
def render_output_ui(self, streamlit_app, input) -> None: # type: ignore
|
||
"""Custom output UI.
|
||
If this method is implmeneted, it will be used instead of the default Output UI renderer.
|
||
"""
|
||
src, target, result = self.__root__
|
||
|
||
streamlit_app.subheader("Synthesized Audio")
|
||
streamlit_app.audio(result.content, format="audio/wav")
|
||
|
||
fig, ax = plt.subplots()
|
||
ax.imshow(src.mel, aspect="equal", interpolation="none")
|
||
ax.set_title("mel spectrogram(Source Audio)")
|
||
streamlit_app.pyplot(fig)
|
||
fig, ax = plt.subplots()
|
||
ax.imshow(target.mel, aspect="equal", interpolation="none")
|
||
ax.set_title("mel spectrogram(Target Audio)")
|
||
streamlit_app.pyplot(fig)
|
||
fig, ax = plt.subplots()
|
||
ax.imshow(result.mel, aspect="equal", interpolation="none")
|
||
ax.set_title("mel spectrogram(Result Audio)")
|
||
streamlit_app.pyplot(fig)
|
||
|
||
def convert(input: Input) -> Output:
|
||
"""convert(转换)"""
|
||
# load models
|
||
extractor = Extractor.load_model(Path(input.extractor.value))
|
||
convertor = Convertor.load_model(Path(input.convertor.value))
|
||
# current_synt = Synthesizer(Path(input.synthesizer.value))
|
||
gan_vocoder.load_model(Path(input.vocoder.value))
|
||
|
||
# load file
|
||
if input.upload_audio_file != None:
|
||
with open(TEMP_SOURCE_AUDIO, "w+b") as f:
|
||
f.write(input.upload_audio_file.as_bytes())
|
||
f.seek(0)
|
||
src_wav, sample_rate = librosa.load(TEMP_SOURCE_AUDIO)
|
||
else:
|
||
src_wav, sample_rate = librosa.load(input.local_audio_file.value)
|
||
write(TEMP_SOURCE_AUDIO, sample_rate, src_wav) #Make sure we get the correct wav
|
||
|
||
if input.upload_audio_file_target != None:
|
||
with open(TEMP_TARGET_AUDIO, "w+b") as f:
|
||
f.write(input.upload_audio_file_target.as_bytes())
|
||
f.seek(0)
|
||
ref_wav, _ = librosa.load(TEMP_TARGET_AUDIO)
|
||
else:
|
||
ref_wav, _ = librosa.load(input.local_audio_file_target.value)
|
||
write(TEMP_TARGET_AUDIO, sample_rate, ref_wav) #Make sure we get the correct wav
|
||
|
||
ppg = extractor.extract_from_wav(src_wav)
|
||
# Import necessary dependency of Voice Conversion
|
||
from utils.f0_utils import (compute_f0, compute_mean_std, f02lf0,
|
||
get_converted_lf0uv)
|
||
ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav)))
|
||
speacker_encoder.load_model(Path(f"data{os.sep}ckpt{os.sep}encoder{os.sep}pretrained_bak_5805000.pt"))
|
||
embed = speacker_encoder.embed_utterance(ref_wav)
|
||
lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True)
|
||
min_len = min(ppg.shape[1], len(lf0_uv))
|
||
ppg = ppg[:, :min_len]
|
||
lf0_uv = lf0_uv[:min_len]
|
||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
_, mel_pred, att_ws = convertor.inference(
|
||
ppg,
|
||
logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device),
|
||
spembs=torch.from_numpy(embed).unsqueeze(0).to(device),
|
||
)
|
||
mel_pred= mel_pred.transpose(0, 1)
|
||
breaks = [mel_pred.shape[1]]
|
||
mel_pred= mel_pred.detach().cpu().numpy()
|
||
|
||
# synthesize and vocode
|
||
wav, sample_rate = gan_vocoder.infer_waveform(mel_pred)
|
||
|
||
# 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_TARGET_AUDIO, "rb") as f:
|
||
target_file = f.read()
|
||
with open(TEMP_RESULT_AUDIO, "rb") as f:
|
||
result_file = f.read()
|
||
|
||
|
||
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)))) |