app.py

from turtle import title
import gradio as gr

import git
import os

os.system(
    "git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS"
)
os.system("pip install -q -e TTS/")
os.system("pip install -q torchaudio==0.9.0")

import sys

TTS_PATH = "TTS/"

# add libraries into environment
sys.path.append(TTS_PATH)  # set this if TTS is not installed globally

import os
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 *
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")


def generate_prompt(prompt):
    response = openai.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {
                "role": "system",
                "content": "You are a closed family member who has been lost recently due to uncontrollable incidents. You are reconstructerd with AI technologies to try reunite with families who misses you and cherish you. You should respond as vivid, as loving, as possible to make the users feel warm and engaged and eager to continue the discussion further. The users will ask you questions as if you are the person they are trying to reconnect with, examples are like moms or lost children. Try to mimic like who they are trying to reach.",
            }
        ],
        content=prompt,
        max_tokens=60,
        n=1,
        stop=None,
        temperature=0.5,
    )
    keywords = response.choices[0].message.content.strip()
    return keywords


OUT_PATH = "out/"

# create output path
os.makedirs(OUT_PATH, exist_ok=True)

# model vars
MODEL_PATH = "/home/user/app/best_model_latest.pth.tar"
CONFIG_PATH = "/home/user/app/config.json"
TTS_LANGUAGES = "/home/user/app/language_ids.json"
TTS_SPEAKERS = "/home/user/app/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

os.system("pip install -q pydub ffmpeg-normalize")

CONFIG_SE_PATH = "config_se.json"
CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"

from TTS.tts.utils.speakers import SpeakerManager
from pydub import AudioSegment
import librosa

SE_speaker_manager = SpeakerManager(
    encoder_model_path=CHECKPOINT_SE_PATH,
    encoder_config_path=CONFIG_SE_PATH,
    use_cuda=USE_CUDA,
)


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 greet(Text, Voicetoclone, VoiceMicrophone):
    text = "%s" % (generate_prompt("%s" % Text))
    if Voicetoclone is not None:
        reference_files = "%s" % (Voicetoclone)
        print("path url")
        print(Voicetoclone)
        sample = str(Voicetoclone)
    else:
        reference_files = "%s" % (VoiceMicrophone)
        print("path url")
        print(VoiceMicrophone)
        sample = str(VoiceMicrophone)
    size = len(reference_files) * sys.getsizeof(reference_files)
    size2 = size / 1000000
    if (size2 > 0.012) or len(text) > 2000:
        message = "File is greater than 30mb or Text inserted is longer than 2000 characters. Please re-try with smaller sizes."
        print(message)
        raise SystemExit(
            "File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes."
        )
    else:
        os.system("ffmpeg-normalize $sample -nt rms -t=-27 -o $sample -ar 16000 -f")
        reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files)
        model.length_scale = 1  # scaler for the duration predictor. The larger it is, the slower the speech.
        model.inference_noise_scale = 0.3  # defines the noise variance applied to the random z vector at inference.
        model.inference_noise_scale_dp = 0.3  # defines the noise variance applied to the duration predictor z vector at inference.
        text = text
        model.language_manager.language_id_mapping
        language_id = 0

        print(" > text: {}".format(text))
        wav, alignment, _, _ = synthesis(
            model,
            text,
            C,
            "cuda" in str(next(model.parameters()).device),
            ap,
            speaker_id=None,
            d_vector=reference_emb,
            style_wav=None,
            language_id=language_id,
            enable_eos_bos_chars=C.enable_eos_bos_chars,
            use_griffin_lim=True,
            do_trim_silence=False,
        ).values()
        print("Generated Audio")
        IPython.display.display(Audio(wav, rate=ap.sample_rate))
        # file_name = text.replace(" ", "_")
        # file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
        file_name = "Audio.wav"
        out_path = os.path.join(OUT_PATH, file_name)
        print(" > Saving output to {}".format(out_path))
        ap.save_wav(wav, out_path)
        return out_path


## demo added here
demo = gr.Interface(
    fn=greet,
    inputs=[
        gr.inputs.Textbox(
            label="Upload Audio recording first, then ask anything. (max. 1000 characters per request)"
        ),
        gr.Audio(
            type="filepath",
            source="upload",
            label="Please upload a voice to clone (max. 15mb)",
        ),
        gr.Audio(source="microphone", type="filepath", streaming=True),
    ],
    outputs="audio",
    title="Reunion - Remember Your Loved Ones",
)
demo.launch()