app.py

# !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

import subprocess


from TTS.api import TTS
tts = TTS(model_name="tts_models/zh-CN/baker/tacotron2-DDC-GST", progress_bar=False, gpu=True)
import whisper
model = whisper.load_model("small")
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."}
]

res = []


'''
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(apikey, ta, audio, choice1):

    openai.api_key = apikey

    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # detect the spoken language
    _, probs = model.detect_language(mel)
    print(f"Detected language: {max(probs, key=probs.get)}")

    # decode the audio
    options = whisper.DecodingOptions()
    result = whisper.decode(model, mel, options)
    res.append(result.text)

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

    # chatgpt
    n = len(res)
    content = res[n-1]
    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})   

    tts.tts_to_file(chat_response, file_path="output.wav")

    target_audio = "target.wav"
    reference_audio = "output.wav"
    driving_audio = "output.wav"

    ra = "output.wav"
    da = "output.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

    files = [target_audio, reference_audio, driving_audio]

    for file in files:
        subprocess.run(["ffmpeg-normalize", file, "-nt", "rms", "-t=-27", "-o", file, "-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))

    voicefixer.restore(input=ref_wav_voc, # 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, or 2 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"]

c1=gr.Interface(
    fn=voice_conversion, 
    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", label = "和您的专属AI聊天吧！", type="filepath"),
        gr.Radio(["TOEFL", "Therapist", "Alice"], label="TOEFL Examiner, Therapist Tina, or Assistant Alice?"),
    ],
    outputs=[
        gr.Textbox(label="Speech to Text"), gr.Textbox(label="ChatGPT Output"), gr.Audio(label="Audio with Custom Voice"),
    ],
    #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！",
    )

c2=gr.Interface(    
    fn=voice_conversion, 
    inputs=[
        gr.Textbox(lines=1, label = "请填写您的OpenAI-API-key"),
        gr.Audio(source="microphone", label = "请上传您喜欢的声音，并尽量避免噪音", type="filepath"),
        gr.Audio(source="microphone", label = "和您的专属AI聊天吧！", type="filepath"),
        gr.Radio(["TOEFL", "Therapist", "Alice"], label="TOEFL Examiner, Therapist Tina, or Assistant Alice?"),
    ],
    outputs=[
        gr.Textbox(label="Speech to Text"), gr.Textbox(label="ChatGPT Output"), gr.Audio(label="Audio with Custom Voice"),
    ], 
    #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！",
    )

demo = gr.TabbedInterface([c1, c2], ["wav文件上传", "麦克风上传"], title = '🥳💬💕 - TalktoAI，随时随地，谈天说地！')
demo.launch()