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import os
import json
import math
import torch
import torchaudio
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
import commons
import utils
from data_utils import UnitAudioLoader, UnitAudioCollate
from models import SynthesizerTrn
import gradio
hubert = torch.hub.load("bshall/hubert:main", "hubert_soft")
hps = utils.get_hparams_from_file("configs/sovits_ow2.json")
net_g = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
_ = net_g.eval()
_ = utils.load_checkpoint("logs/ow2/G_195000.pth", net_g, None)
def infer(md, mic_audio, audio, speaker_id, pitch_shift, length_scale, noise_scale=.667, noise_scale_w=0.8):
source = None
sr = None
if mic_audio:
sr, source = mic_audio
source = torch.Tensor(source)
if source.dim() == 1:
source = source.unsqueeze(1)
source = source.T
if audio:
source, sr = torchaudio.load(audio)
source = torchaudio.functional.pitch_shift(source, sr, int(pitch_shift))#, n_fft=256)
source = torchaudio.functional.resample(source, sr, 16000)
source = torch.mean(source, dim=0).unsqueeze(0)
source = source.unsqueeze(0)
with torch.inference_mode():
# Extract speech units
unit = hubert.units(source)
unit_lengths = torch.LongTensor([unit.size(1)])
# for multi-speaker inference
sid = torch.LongTensor([speaker_id])
# Synthesize audio
audio_out = net_g.infer(unit, unit_lengths, sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.float().numpy()
return (22050, audio_out)
demo = gradio.Interface(
fn=infer,
inputs=[
gradio.Markdown(
"""
# SOVITS Any-to-Many VC | Overwatch 2
Upload any voice recording and turn it into a mangled approximation of any* Overwatch 2 Hero!
For a higher quality single-speaker model, check out my [soft-vc-widowmaker](https://huggingface.co/spaces/cjayic/soft-vc-widowmaker) space!
SOVITS doesn't really appear to adjust the pitch to the target speaker, so it helps to have your input voice at a similar pitch to the target voice.
I added a pitch shift option to preprocess the input voice, but it's slow and sometimes outright broken, use at your own risk.
( * up to Kiriko and without Bastion. Please forgive. )
"""),
gradio.Audio(label="Record Input Audio", source="microphone"),
gradio.Audio(label="Upload Input Audio", type="filepath"),
gradio.Dropdown(label="Target Voice", choices=["Ana", "Ashe", "Baptiste", "Brigitte", "Cassidy", "Doomfist", "D.Va", "Echo", "Genji", "Hanzo", "Junker Queen", "Junkrat", "Kiriko", "Lúcio", "Mei", "Mercy", "Moira", "Orisa", "Pharah", "Reaper", "Reinhardt", "Roadhog", "Sigma", "Sojourn", "Soldier_ 76", "Sombra", "Symmetra", "Torbjörn", "Tracer", "Widowmaker", "Winston", "Zarya", "Zenyatta"], type="index", value="Ana"),
gradio.Slider(label="Pitch Shift Input (+12 = up one octave, ⚠️ broken AF ⚠️)", minimum=-12.0, maximum=12.0, value=0, step=1),
gradio.Slider(label="Length Factor (higher = slower speech)", minimum=0.1, maximum=2.0, value=1.0),
gradio.Slider(label="Noise Scale (higher = more expressive and erratic)", minimum=0.0, maximum=2.0, value=.667),
gradio.Slider(label="Noise Scale W (higher = more variation in cadence)", minimum=0.0, maximum=2.0, value=.8)
],
outputs=[gradio.Audio(label="Audio as Target Voice")],
)
#demo.launch(share=True)
demo.launch(server_name="0.0.0.0")
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