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!git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS
import os
import shutil
import gradio as gr
import sys
import string
import time
import argparse
import json
import numpy as np
import IPython
from IPython.display import Audio
import torch
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
try:
from TTS.utils.audio import AudioProcessor
except:
from TTS.utils.audio import AudioProcessor
from TTS.tts.models import setup_model
from TTS.config import load_config
from TTS.tts.models.vits import *
from TTS.tts.utils.speakers import SpeakerManager
from pydub import AudioSegment
from google.colab import files
import librosa
from scipy.io.wavfile import write, read
'''
from google.colab import drive
drive.mount('/content/drive')
src_path = os.path.join(os.path.join(os.path.join(os.path.join(os.getcwd(), 'drive'), 'MyDrive'), 'Colab Notebooks'), 'best_model_latest.pth.tar')
dst_path = os.path.join(os.getcwd(), 'best_model.pth.tar')
shutil.copy(src_path, dst_path)
'''
TTS_PATH = "TTS/"
# add libraries into environment
sys.path.append(TTS_PATH) # set this if TTS is not installed globally
# Paths definition
OUT_PATH = 'out/'
# create output path
os.makedirs(OUT_PATH, exist_ok=True)
# model vars
MODEL_PATH = 'best_model.pth.tar'
CONFIG_PATH = 'config.json'
TTS_LANGUAGES = "language_ids.json"
TTS_SPEAKERS = "speakers.json"
USE_CUDA = torch.cuda.is_available()
# load the config
C = load_config(CONFIG_PATH)
# load the audio processor
ap = AudioProcessor(**C.audio)
speaker_embedding = None
C.model_args['d_vector_file'] = TTS_SPEAKERS
C.model_args['use_speaker_encoder_as_loss'] = False
model = setup_model(C)
model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
# print(model.language_manager.num_languages, model.embedded_language_dim)
# print(model.emb_l)
cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
# remove speaker encoder
model_weights = cp['model'].copy()
for key in list(model_weights.keys()):
if "speaker_encoder" in key:
del model_weights[key]
model.load_state_dict(model_weights)
model.eval()
if USE_CUDA:
model = model.cuda()
# synthesize voice
use_griffin_lim = False
# Paths definition
# CONFIG_SE_PATH = "config_se.json"
# CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"
# Load the Speaker encoder
SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)
# Define helper function
def compute_spec(ref_file):
y, sr = librosa.load(ref_file, sr=ap.sample_rate)
spec = ap.spectrogram(y)
spec = torch.FloatTensor(spec).unsqueeze(0)
return spec
def voice_conversion(ta, ra, da):
target_audio = 'target.wav'
reference_audio = 'reference.wav'
driving_audio = 'driving.wav'
write(target_audio, ta[0], ta[1])
write(reference_audio, ra[0], ra[1])
write(driving_audio, da[0], da[1])
!ffmpeg-normalize $target_audio -nt rms -t=-27 -o $target_audio -ar 16000 -f
!ffmpeg-normalize $reference_audio -nt rms -t=-27 -o $reference_audio -ar 16000 -f
!ffmpeg-normalize $driving_audio -nt rms -t=-27 -o $driving_audio -ar 16000 -f
# ta_ = read(target_audio)
target_emb = SE_speaker_manager.compute_d_vector_from_clip([target_audio])
target_emb = torch.FloatTensor(target_emb).unsqueeze(0)
driving_emb = SE_speaker_manager.compute_d_vector_from_clip([reference_audio])
driving_emb = torch.FloatTensor(driving_emb).unsqueeze(0)
# Convert the voice
driving_spec = compute_spec(driving_audio)
y_lengths = torch.tensor([driving_spec.size(-1)])
if USE_CUDA:
ref_wav_voc, _, _ = model.voice_conversion(driving_spec.cuda(), y_lengths.cuda(), driving_emb.cuda(), target_emb.cuda())
ref_wav_voc = ref_wav_voc.squeeze().cpu().detach().numpy()
else:
ref_wav_voc, _, _ = model.voice_conversion(driving_spec, y_lengths, driving_emb, target_emb)
ref_wav_voc = ref_wav_voc.squeeze().detach().numpy()
# print("Reference Audio after decoder:")
# IPython.display.display(Audio(ref_wav_voc, rate=ap.sample_rate))
return (ap.sample_rate, ref_wav_voc)
demo = gr.Interface(
fn=voice_conversion,
inputs=["audio", "audio", "audio"],
outputs="audio"
)
demo.launch(debug=True, share=True)