wasmdashai commited on
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54547cf
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1 Parent(s): ef864f5

Update app.py

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  1. app.py +24 -6
app.py CHANGED
@@ -107,15 +107,32 @@ def _inference_forward_stream(
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  def get_model(name_model):
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  global models
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  if name_model in models:
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- return models[name_model]
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- models[name_model]=VitsModel.from_pretrained(name_model,token=token).cuda()
 
 
 
 
 
 
 
 
 
 
 
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  models[name_model].decoder.apply_weight_norm()
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  # torch.nn.utils.weight_norm(self.decoder.conv_pre)
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  # torch.nn.utils.weight_norm(self.decoder.conv_post)
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  for flow in models[name_model].flow.flows:
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  torch.nn.utils.weight_norm(flow.conv_pre)
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  torch.nn.utils.weight_norm(flow.conv_post)
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- return models[name_model]
 
 
 
 
 
 
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  zero = torch.Tensor([0]).cuda()
@@ -124,10 +141,10 @@ import torch
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  TXT="""السلام عليكم ورحمة الله وبركاتة يا هلا وسهلا ومراحب بالغالي اخباركم طيبين ان شاء الله ارحبوا على العين والراس """
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  @spaces.GPU
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  def modelspeech(text=TXT,name_model="wasmdashai/vits-ar-sa-huba-v2",speaking_rate=16000):
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-
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  inputs = tokenizer(text, return_tensors="pt")
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- model=get_model(name_model)
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  model.speaking_rate=speaking_rate
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  with torch.no_grad():
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  wav=list(_inference_forward_stream(model,input_ids=inputs.input_ids.cuda(),attention_mask=inputs.attention_mask.cuda(),speaker_embeddings= None,is_streaming=False))[0]
@@ -144,7 +161,8 @@ model_choices = gr.Dropdown(
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  "wasmdashai/vits-ar-sa-A",
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  "wasmdashai/vits-ar-ye-sa",
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- "wasmdashai/vits-ar-sa-M-v1"
 
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  ],
 
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  def get_model(name_model):
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  global models
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  if name_model in models:
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+ if name_model=='wasmdashai/vits-en-v1':
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+ tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
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+ else:
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+ tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
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+
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+
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+
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+
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+ return models[name_model],tokenizer
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+ models[name_model]=VitsModel.from_pretrained(name_model,token=token)
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+
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+
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+
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  models[name_model].decoder.apply_weight_norm()
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  # torch.nn.utils.weight_norm(self.decoder.conv_pre)
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  # torch.nn.utils.weight_norm(self.decoder.conv_post)
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  for flow in models[name_model].flow.flows:
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  torch.nn.utils.weight_norm(flow.conv_pre)
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  torch.nn.utils.weight_norm(flow.conv_post)
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+
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+ if name_model=='wasmdashai/vits-en-v1':
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+ tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
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+ else:
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+ tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
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+
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+ return models[name_model],tokenizer
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  zero = torch.Tensor([0]).cuda()
 
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  TXT="""السلام عليكم ورحمة الله وبركاتة يا هلا وسهلا ومراحب بالغالي اخباركم طيبين ان شاء الله ارحبوا على العين والراس """
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  @spaces.GPU
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  def modelspeech(text=TXT,name_model="wasmdashai/vits-ar-sa-huba-v2",speaking_rate=16000):
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+ model,tokenizer=get_model(name_model)
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  inputs = tokenizer(text, return_tensors="pt")
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+
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  model.speaking_rate=speaking_rate
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  with torch.no_grad():
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  wav=list(_inference_forward_stream(model,input_ids=inputs.input_ids.cuda(),attention_mask=inputs.attention_mask.cuda(),speaker_embeddings= None,is_streaming=False))[0]
 
161
 
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  "wasmdashai/vits-ar-sa-A",
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  "wasmdashai/vits-ar-ye-sa",
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+ "wasmdashai/vits-ar-sa-M-v1",
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+ 'wasmdashai/vits-en-v1'
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  ],