Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
|
@@ -107,15 +107,32 @@ def _inference_forward_stream(
|
|
| 107 |
def get_model(name_model):
|
| 108 |
global models
|
| 109 |
if name_model in models:
|
| 110 |
-
|
| 111 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 112 |
models[name_model].decoder.apply_weight_norm()
|
| 113 |
# torch.nn.utils.weight_norm(self.decoder.conv_pre)
|
| 114 |
# torch.nn.utils.weight_norm(self.decoder.conv_post)
|
| 115 |
for flow in models[name_model].flow.flows:
|
| 116 |
torch.nn.utils.weight_norm(flow.conv_pre)
|
| 117 |
torch.nn.utils.weight_norm(flow.conv_post)
|
| 118 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 119 |
|
| 120 |
|
| 121 |
zero = torch.Tensor([0]).cuda()
|
|
@@ -124,10 +141,10 @@ import torch
|
|
| 124 |
TXT="""السلام عليكم ورحمة الله وبركاتة يا هلا وسهلا ومراحب بالغالي اخباركم طيبين ان شاء الله ارحبوا على العين والراس """
|
| 125 |
@spaces.GPU
|
| 126 |
def modelspeech(text=TXT,name_model="wasmdashai/vits-ar-sa-huba-v2",speaking_rate=16000):
|
| 127 |
-
|
| 128 |
|
| 129 |
inputs = tokenizer(text, return_tensors="pt")
|
| 130 |
-
|
| 131 |
model.speaking_rate=speaking_rate
|
| 132 |
with torch.no_grad():
|
| 133 |
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(
|
|
| 144 |
|
| 145 |
"wasmdashai/vits-ar-sa-A",
|
| 146 |
"wasmdashai/vits-ar-ye-sa",
|
| 147 |
-
"wasmdashai/vits-ar-sa-M-v1"
|
|
|
|
| 148 |
|
| 149 |
|
| 150 |
],
|
|
|
|
| 107 |
def get_model(name_model):
|
| 108 |
global models
|
| 109 |
if name_model in models:
|
| 110 |
+
if name_model=='wasmdashai/vits-en-v1':
|
| 111 |
+
tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
|
| 112 |
+
else:
|
| 113 |
+
tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
|
| 118 |
+
return models[name_model],tokenizer
|
| 119 |
+
models[name_model]=VitsModel.from_pretrained(name_model,token=token)
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
|
| 123 |
models[name_model].decoder.apply_weight_norm()
|
| 124 |
# torch.nn.utils.weight_norm(self.decoder.conv_pre)
|
| 125 |
# torch.nn.utils.weight_norm(self.decoder.conv_post)
|
| 126 |
for flow in models[name_model].flow.flows:
|
| 127 |
torch.nn.utils.weight_norm(flow.conv_pre)
|
| 128 |
torch.nn.utils.weight_norm(flow.conv_post)
|
| 129 |
+
|
| 130 |
+
if name_model=='wasmdashai/vits-en-v1':
|
| 131 |
+
tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
|
| 132 |
+
else:
|
| 133 |
+
tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
|
| 134 |
+
|
| 135 |
+
return models[name_model],tokenizer
|
| 136 |
|
| 137 |
|
| 138 |
zero = torch.Tensor([0]).cuda()
|
|
|
|
| 141 |
TXT="""السلام عليكم ورحمة الله وبركاتة يا هلا وسهلا ومراحب بالغالي اخباركم طيبين ان شاء الله ارحبوا على العين والراس """
|
| 142 |
@spaces.GPU
|
| 143 |
def modelspeech(text=TXT,name_model="wasmdashai/vits-ar-sa-huba-v2",speaking_rate=16000):
|
| 144 |
+
model,tokenizer=get_model(name_model)
|
| 145 |
|
| 146 |
inputs = tokenizer(text, return_tensors="pt")
|
| 147 |
+
|
| 148 |
model.speaking_rate=speaking_rate
|
| 149 |
with torch.no_grad():
|
| 150 |
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 |
|
| 162 |
"wasmdashai/vits-ar-sa-A",
|
| 163 |
"wasmdashai/vits-ar-ye-sa",
|
| 164 |
+
"wasmdashai/vits-ar-sa-M-v1",
|
| 165 |
+
'wasmdashai/vits-en-v1'
|
| 166 |
|
| 167 |
|
| 168 |
],
|