Delete app.py

app.py

@@ -1,165 +0,0 @@
-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 *  
-
-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" % (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 = gr.Interface(
-    fn=greet, 
-    inputs=[gr.inputs.Textbox(label='What would you like the voice to say? (max. 2000 characters per request)'),gr.Audio(type="filepath",         source="upload",label='Please upload a voice to clone (max. 30mb)'),gr.Audio(source="microphone", type="filepath", streaming=True)],
-    outputs="audio",
-    title="Bilal's Voice Cloning Tool"
-    )
-demo.launch()