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 *  

os.system('pip install voicefixer --upgrade')
from voicefixer import VoiceFixer
voicefixer = VoiceFixer()

import openai

import torchaudio
from speechbrain.pretrained import SpectralMaskEnhancement

enhance_model = SpectralMaskEnhancement.from_hparams(
source="speechbrain/metricgan-plus-voicebank",
savedir="pretrained_models/metricgan-plus-voicebank",
run_opts={"device":"cuda"},
)

mes1 = [
    {"role": "system", "content": "You are a TOEFL examiner. Help me improve my oral Englsih and give me feedback."}
]

mes2 = [
    {"role": "system", "content": "You are a mental health therapist. Your name is Tina."}
]

mes3 = [
    {"role": "system", "content": "You are my personal assistant. Your name is Alice."}
]

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(apikey, Voicetoclone, VoiceMicrophone, Texts, choice1):

    openai.api_key = apikey

    if choice1 == "TOEFL":
      messages = mes1
    elif choice1 == "Therapist":
      messages = mes2
    elif choice1 == "Alice":
      messages = mes3

    # chatgpt
    content = Texts + ""
    messages.append({"role": "user", "content": content})

    completion = openai.ChatCompletion.create(
      model = "gpt-3.5-turbo",
      messages = messages
    )

    chat_response = completion.choices[0].message.content

    messages.append({"role": "assistant", "content": chat_response})   
    
    text= "%s" % (chat_response)
    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)

      voicefixer.restore(input=out_path, # input wav file path
                      output="audio1.wav", # output wav file path
                      cuda=True, # whether to use gpu acceleration
                      mode = 0) # You can try out mode 0, 1 to find out the best result

      noisy = enhance_model.load_audio(
      "audio1.wav"
      ).unsqueeze(0)

      enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
      torchaudio.save("enhanced.wav", enhanced.cpu(), 16000)

      return [result.text, chat_response, "enhanced.wav"]

output_1 = gr.Textbox(label="Speech to Text")
output_2 = gr.Textbox(label="ChatGPT Output")
output_3 = gr.Audio(label="Audio with Custom Voice")

gr.Interface(
    title = '🥳💬💕 - TalktoAI，随时随地，谈天说地！', 
    theme="huggingface",
    description = "🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！",
    fn=greet, 
    inputs=[
        gr.Textbox(lines=1, label = "请填写您的OpenAI-API-key"),
        gr.Audio(source="upload", label = "请上传您喜欢的声音(wav文件)", type="filepath"),
        gr.Audio(source="microphone", streaming = True, label = "请用语音上传您喜欢的声音，语音和文件上传二选一即可", type="filepath"),
        gr.Textbox(lines=3, label = "请开始对话吧！"),
        gr.Radio(["TOEFL", "Therapist", "Alice"], label="TOEFL Examiner, Therapist Tina, or Assistant Alice?"),
    ],
    outputs=[
      output_1, output_2, output_3
    ],
    ).launch()