#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') os.system('pip install voicefixer --upgrade') from voicefixer import VoiceFixer voicefixer = VoiceFixer() import sys TTS_PATH = "TTS/" # add libraries into environment sys.path.append(TTS_PATH) # set this if TTS is not installed globally import string import time import argparse import json import numpy as np import IPython from IPython.display import Audio import torch import torchaudio from speechbrain.pretrained import SpectralMaskEnhancement import whisper model1 = whisper.load_model("small") import openai enhance_model = SpectralMaskEnhancement.from_hparams( source="speechbrain/metricgan-plus-voicebank", savedir="pretrained_models/metricgan-plus-voicebank", run_opts={"device":"cuda"}, ) mes = [ {"role": "system", "content": "You are my personal assistant. Try to be helpful."} ] res = [] 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 * 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(Text2, audio, Voicetoclone,VoiceMicrophone): openai.api_key = Text2 # 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(model1.device) # detect the spoken language _, probs = model1.detect_language(mel) print(f"Detected language: {max(probs, key=probs.get)}") # decode the audio options = whisper.DecodingOptions() result = whisper.decode(model1, mel, options) res.append(result.text) messages = mes # 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}) 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, 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"] gr.Interface( fn=greet, inputs=[gr.Textbox(label='请输入您的Openai-API-Key', type = "password"), gr.Audio(source="microphone", label='请在这里进行对话吧!随时随地,谈天说地!', type="filepath"), gr.Audio(type="filepath", source="upload",label='请上传您喜欢的声音(wav/mp3文件, max. 30mb)'), gr.Audio(source="microphone", type="filepath", label='请用麦克风上传您喜欢的声音,与文件上传二选一即可')], outputs=["text", "text", "audio"], title="🥳💬💕 - TalktoAI,随时随地,谈天说地!", 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!", article = "🎶🖼️🎡 - It’s the intersection of technology and liberal arts that makes our hearts sing. - Steve Jobs" ).launch()