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from pathlib import Path
import torchaudio
import gradio as gr
import numpy as np
import torch
from hifigan.config import v1
from hifigan.denoiser import Denoiser
from hifigan.env import AttrDict
from hifigan.models import Generator as HiFiGAN
from pflow.models.pflow_tts import pflowTTS
from pflow.text import text_to_sequence, sequence_to_text
from pflow.utils.utils import intersperse
from pflow.data.text_mel_datamodule import mel_spectrogram
from pflow.utils.model import normalize
PFLOW_MODEL_PATH = 'checkpoint_epoch=649.ckpt'
VOCODER_MODEL_PATH = 'g_00140000_m'
transform = torchaudio.transforms.Vol(gain=-32, gain_type="db")
wav, sr = torchaudio.load('prompt.wav')
prompt = mel_spectrogram(
transform(wav),
1024,
80,
22050,
256,
1024,
0,
8000,
center=False,
)[:,:,:264]
def process_text(text: str, device: torch.device):
x = torch.tensor(
intersperse(text_to_sequence(text, ["ukr_cleaners"]), 0),
dtype=torch.long,
device=device,
)[None]
x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
x_phones = sequence_to_text(x.squeeze(0).tolist())
return {"x_orig": text, "x": x, "x_lengths": x_lengths, 'x_phones':x_phones}
def load_hifigan(checkpoint_path, device):
h = AttrDict(v1)
hifigan = HiFiGAN(h).to(device)
hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"])
_ = hifigan.eval()
hifigan.remove_weight_norm()
return hifigan
def to_waveform(mel, vocoder, denoiser=None):
audio = vocoder(mel).clamp(-1, 1)
if denoiser is not None:
audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze()
return audio.cpu().squeeze()
def get_device():
if torch.cuda.is_available():
print("[+] GPU Available! Using GPU")
device = torch.device("cuda")
else:
print("[-] GPU not available or forced CPU run! Using CPU")
device = torch.device("cpu")
return device
device = get_device()
model = pflowTTS.load_from_checkpoint(PFLOW_MODEL_PATH, map_location=device)
_ = model.eval()
vocoder = load_hifigan(VOCODER_MODEL_PATH, device)
denoiser = Denoiser(vocoder, mode="zeros")
@torch.inference_mode()
def synthesise(text, temperature, speed):
if len(text) > 1000:
raise gr.Error("Текст повинен бути коротшим за 1000 символів.")
text_processed = process_text(text.strip(), device)
output = model.synthesise(
text_processed["x"].to(device),
text_processed["x_lengths"].to(device),
n_timesteps=40,
temperature=temperature,
length_scale=1/speed,
prompt=normalize(prompt, model.mel_mean, model.mel_std).to(device),
guidance_scale=1.0
)
waveform = to_waveform(output["mel"], vocoder, denoiser)
return text_processed['x_phones'][1::2], (22050, waveform.numpy())
description = f'''
# Експериментальна апка для генерації аудіо з тексту.
pflow checkpoint {PFLOW_MODEL_PATH}
vocoder: HIFIGAN(трейнутий на датасеті, з нуля) - {VOCODER_MODEL_PATH}
'''
if __name__ == "__main__":
i = gr.Interface(
fn=synthesise,
description=description,
inputs=[
gr.Text(label='Текст для синтезу:', lines=5, max_lines=10),
gr.Slider(minimum=0.0, maximum=1.0, label="Температура", value=0.4),
gr.Slider(minimum=0.6, maximum=2.0, label="Швидкість", value=1.0)
],
outputs=[
gr.Text(label='Фонемізований текст:', lines=5),
gr.Audio(
label="Згенероване аудіо:",
autoplay=False,
streaming=False,
type="numpy",
)
],
allow_flagging ='manual',
flagging_options=[("Якщо дуже погоне аудіо, тисни цю кнопку.", "negative")],
cache_examples=True,
title='',
# description=description,
# article=article,
# examples=examples,
)
i.queue(max_size=20, default_concurrency_limit=4)
i.launch(share=False, server_name="0.0.0.0")
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