Create app.py

app.py

@@ -0,0 +1,165 @@
+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()