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Update app.py

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  1. app.py +122 -122
app.py CHANGED
@@ -5,134 +5,134 @@ import torchaudio
5
  from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
6
  from transformers.models.speecht5 import SpeechT5HifiGan
7
 
8
- processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
9
- model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
10
- vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
11
-
12
- device = "cuda" if torch.cuda.is_available() else "cpu"
13
- model = model.to(device)
14
- vocoder = vocoder.to(device)
15
-
16
- speaker_embedding = torch.zeros(1, 512).to(device)
17
-
18
- # Load model and processor
19
- # processor = SpeechT5Processor.from_pretrained("nambn0321/TTS_british_2nd_attempt")
20
- # model = SpeechT5ForTextToSpeech.from_pretrained(
21
- # "nambn0321/TTS_british_2nd_attempt",
22
- # use_safetensors=True,
23
- # trust_remote_code=True
24
- # )
25
  # vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
26
 
27
- # # Move to CUDA if available
28
  # device = "cuda" if torch.cuda.is_available() else "cpu"
29
  # model = model.to(device)
30
  # vocoder = vocoder.to(device)
31
 
32
- # # # Dummy speaker embedding (or load your real one here)
33
- # speaker_embedding = torch.tensor([[-8.4146e-02, -9.6771e-03, 2.6844e-02, 2.2544e-02, -1.8455e-03,
34
- # -2.5492e-02, -6.2846e-02, -6.6044e-02, 3.1312e-02, 1.9042e-02,
35
- # -7.3039e-02, -8.0007e-02, 4.7080e-02, 3.7199e-02, 3.8119e-02,
36
- # 5.2214e-02, 1.1470e-02, 3.5968e-02, 8.1480e-03, 1.9952e-02,
37
- # 3.5186e-02, 1.3260e-02, -1.4271e-02, -3.8798e-02, -6.8658e-02,
38
- # -1.0121e-02, -5.8041e-02, 7.0087e-03, 2.4180e-02, 2.5093e-02,
39
- # -2.9513e-03, 4.1835e-02, 3.4997e-02, -4.7138e-02, 5.1652e-02,
40
- # -6.1358e-02, 8.9115e-03, 6.5912e-02, -6.1133e-02, -6.3178e-02,
41
- # 2.6463e-02, -6.4097e-02, 2.5865e-02, 3.6212e-02, 1.1775e-02,
42
- # -1.1237e-01, -1.9422e-02, 1.1771e-02, -5.1472e-02, 6.1495e-02,
43
- # 1.8622e-02, 3.6634e-02, 4.0088e-02, 2.4596e-02, -8.8999e-02,
44
- # -7.0578e-03, 4.2418e-02, 3.0962e-02, 3.6715e-02, 9.8362e-03,
45
- # 2.2486e-02, -8.9123e-03, -1.5486e-02, 9.6940e-03, 2.6662e-02,
46
- # 3.0941e-02, 9.7256e-03, -3.7924e-02, -6.1841e-02, -5.8947e-02,
47
- # 1.9625e-02, 1.0872e-02, 2.3126e-02, 9.8068e-03, 4.3716e-02,
48
- # 2.3031e-02, 1.6826e-02, 1.5088e-02, -7.1926e-02, -9.1217e-02,
49
- # -7.4439e-02, -3.6920e-02, -6.2430e-02, -5.1998e-02, -3.4543e-02,
50
- # -7.5438e-02, -4.9039e-02, 6.2118e-02, 1.7150e-02, 5.1786e-02,
51
- # 2.8916e-02, -7.0776e-02, 4.1283e-03, -7.0833e-02, 7.7827e-02,
52
- # -1.3024e-03, 2.8799e-02, 2.4766e-02, -5.6856e-02, -4.9455e-02,
53
- # 4.2941e-02, -6.8559e-02, -8.7198e-02, 5.3497e-02, 3.1263e-02,
54
- # -4.4196e-02, 4.5291e-02, 3.5016e-02, 2.7069e-02, 1.5925e-02,
55
- # -6.9111e-02, 2.8218e-02, 6.5694e-02, -2.9196e-03, 5.4257e-02,
56
- # 3.1966e-02, -5.5521e-02, -2.9675e-02, -5.7311e-02, -2.5660e-02,
57
- # 2.5544e-02, -6.7957e-02, 3.2566e-02, 5.1615e-02, -6.6451e-02,
58
- # 4.0387e-02, -5.5676e-02, 6.8813e-03, 4.2905e-02, 5.4461e-02,
59
- # 4.0467e-02, 9.1374e-03, 1.8873e-02, 4.0569e-02, 2.6208e-02,
60
- # -5.7306e-02, -4.2936e-02, 3.0970e-02, -5.9009e-02, -2.8001e-03,
61
- # 4.3110e-03, 3.9585e-03, -1.6110e-02, 3.8766e-02, -5.6695e-02,
62
- # 3.8432e-02, -8.8127e-03, -4.8471e-02, 6.2207e-02, -1.0577e-01,
63
- # 5.1350e-02, -3.8907e-02, -5.3652e-02, 3.5648e-02, 1.5506e-02,
64
- # 1.6576e-02, 5.0061e-03, -6.9767e-02, -4.8234e-02, 5.2357e-02,
65
- # 2.8605e-02, 3.0886e-03, 1.0158e-02, -5.5175e-02, -2.0156e-02,
66
- # 8.5321e-03, 1.9991e-02, 3.8980e-02, 7.9981e-03, 4.3244e-02,
67
- # 1.0584e-02, -8.0995e-02, 3.8089e-02, -3.5556e-02, -2.6731e-03,
68
- # 7.6711e-03, -4.6591e-02, 1.0168e-02, 2.5794e-02, -6.5743e-02,
69
- # 4.1506e-02, -5.1859e-02, 2.2370e-02, 2.5183e-02, -5.7133e-02,
70
- # -9.8171e-03, 4.1331e-02, 4.1121e-02, -3.7192e-02, -6.7827e-02,
71
- # -6.6048e-03, -5.5967e-03, -6.1243e-02, 1.8434e-02, 1.7639e-02,
72
- # 3.2567e-02, 4.3591e-02, 1.1444e-04, 2.1000e-02, -3.3589e-02,
73
- # 6.3952e-02, 3.2268e-02, -4.5664e-02, 6.0440e-03, 5.8870e-03,
74
- # 1.3482e-02, 3.1084e-02, -4.7126e-02, 4.0494e-02, -3.1627e-02,
75
- # 4.4450e-03, -8.1786e-02, -3.9848e-02, 5.6240e-02, -4.5132e-02,
76
- # 4.4740e-02, 5.3827e-02, -9.0746e-02, 2.2419e-02, 1.4306e-02,
77
- # -9.1269e-02, 2.7554e-02, -6.4524e-02, -2.8340e-02, 4.7677e-02,
78
- # 4.6020e-02, 6.4275e-02, 8.6387e-02, 6.4071e-02, 2.6549e-02,
79
- # -4.1220e-02, 2.2935e-02, 4.3711e-02, -4.0866e-02, -8.3878e-04,
80
- # -2.0668e-02, 4.3676e-02, 1.9026e-02, -5.8537e-04, 3.7715e-02,
81
- # -6.8229e-02, 2.5884e-02, -8.7095e-02, 9.5215e-03, -3.3552e-02,
82
- # 4.5210e-02, 4.0977e-02, 7.1891e-03, 4.1980e-02, 3.2498e-02,
83
- # 4.2608e-02, 3.9835e-02, 9.1516e-03, -4.8366e-02, 1.4572e-02,
84
- # 2.0647e-02, 3.6124e-02, 4.6580e-02, -7.7210e-02, 5.7704e-02,
85
- # 1.1030e-03, -5.8918e-02, 2.9356e-02, 3.4516e-02, 1.2991e-03,
86
- # -1.1909e-02, 4.8185e-02, 3.0447e-02, 4.7657e-03, -4.7582e-02,
87
- # 3.2026e-02, -1.8162e-02, 1.0278e-02, -3.2285e-02, -6.1007e-02,
88
- # 2.3500e-02, 3.6015e-02, 1.5098e-02, -6.6183e-02, -4.5165e-02,
89
- # -2.3547e-03, 2.5537e-02, 3.6404e-02, -7.5865e-02, -6.8920e-02,
90
- # 4.4063e-02, 2.2352e-02, 5.3221e-02, -1.1117e-01, 1.6892e-03,
91
- # 1.5308e-02, 2.4001e-02, -3.9773e-02, 3.4531e-02, -4.2249e-02,
92
- # 3.2091e-02, 1.8124e-02, -8.6720e-04, 1.1351e-02, 1.1819e-02,
93
- # 6.4638e-02, 2.8144e-02, 2.3477e-02, -6.5364e-02, 9.0942e-03,
94
- # 4.2016e-02, 1.3313e-02, 3.1852e-02, 4.7584e-02, 5.6397e-02,
95
- # 2.4306e-02, 2.4805e-02, -3.0570e-02, 5.5471e-02, -9.8115e-02,
96
- # -7.9501e-02, -2.4848e-02, 2.5320e-02, -6.8306e-02, 5.7311e-02,
97
- # -4.5671e-02, 5.5003e-02, -4.7802e-02, 1.1989e-02, 1.5714e-02,
98
- # -5.9569e-02, 2.2274e-02, 6.2575e-02, -6.7901e-02, -1.0904e-01,
99
- # -5.9256e-02, 3.2378e-02, 2.7536e-03, 4.5289e-02, -7.6812e-02,
100
- # 5.6575e-02, 4.3113e-02, -4.5169e-02, -1.9948e-02, -6.8110e-02,
101
- # 3.7796e-02, -5.1603e-02, 1.7293e-02, 3.9350e-04, -9.2822e-02,
102
- # 2.9600e-02, 1.8698e-02, 2.5137e-02, -4.4556e-02, -5.5333e-02,
103
- # -3.0218e-02, -3.8053e-02, -4.6037e-02, 9.3173e-02, 3.0859e-02,
104
- # -4.5461e-02, -6.4138e-03, 3.9415e-02, -6.0368e-02, 2.1082e-02,
105
- # -1.0852e-01, 3.1828e-02, -6.7432e-03, 2.7266e-02, 4.0655e-02,
106
- # -1.2353e-02, -5.5131e-02, 8.4986e-03, 7.7413e-03, 3.8210e-02,
107
- # 1.1482e-02, 4.4694e-02, -7.8239e-02, 2.7954e-02, -1.4465e-02,
108
- # -2.1566e-03, 4.4880e-02, 1.1331e-02, 4.6676e-02, 4.9100e-02,
109
- # -8.2094e-02, -1.0947e-01, 2.4079e-02, 3.3945e-02, 1.6590e-02,
110
- # -7.0771e-02, 4.7745e-02, -6.1351e-02, 3.1324e-02, 4.3053e-02,
111
- # 3.6573e-02, 1.3540e-02, -4.3418e-02, 2.7820e-02, 1.8301e-02,
112
- # 2.2243e-02, 2.3911e-02, -1.0137e-01, -1.4640e-02, 6.4367e-03,
113
- # 2.2727e-03, -5.7253e-02, 9.6726e-03, -2.1960e-02, 7.6282e-03,
114
- # 6.4420e-03, 5.0940e-03, -6.7982e-02, -3.8508e-02, -6.1518e-02,
115
- # -8.3228e-02, 2.2245e-02, 4.1082e-02, -1.2621e-02, 1.4017e-02,
116
- # -3.7110e-02, 2.4853e-03, -7.3434e-02, -1.5334e-01, -4.2328e-02,
117
- # -3.7094e-02, 5.9061e-03, 2.3480e-02, -4.8760e-02, -5.4049e-02,
118
- # 2.4981e-02, 3.9116e-02, 1.9743e-02, 3.5136e-02, -3.8281e-02,
119
- # -4.0026e-02, -1.6648e-02, -2.5015e-02, 4.2738e-02, 3.4282e-02,
120
- # 2.5185e-03, 3.5040e-02, 3.0350e-02, -6.4696e-02, -5.7804e-02,
121
- # 3.7872e-02, 4.9861e-02, 2.4467e-02, 2.2676e-03, 3.6383e-02,
122
- # 4.5123e-02, 2.6333e-02, 8.5096e-03, -6.5880e-03, -1.3365e-02,
123
- # 3.4063e-02, 6.1048e-03, 8.9429e-03, 1.9870e-02, -7.0909e-03,
124
- # 4.1589e-03, 2.4686e-02, -6.6816e-02, -7.1205e-02, 4.5023e-02,
125
- # -1.2020e-02, 1.7312e-02, 7.1944e-02, 2.5880e-02, -5.8930e-02,
126
- # 2.1297e-02, 3.8620e-02, -8.1744e-02, 1.2381e-02, 2.5355e-02,
127
- # -1.8926e-03, 1.7969e-02, 1.8478e-02, 4.2966e-03, -3.4873e-02,
128
- # -6.8647e-02, 6.3062e-03, -5.6329e-03, 5.0244e-02, 7.9961e-02,
129
- # -4.6829e-02, -5.0599e-02, 2.6159e-02, 3.4554e-02, 4.3835e-02,
130
- # 3.8469e-02, -4.2016e-02, 4.2099e-02, -1.9708e-02, -1.1056e-02,
131
- # -4.5883e-02, 1.9459e-02, -4.2683e-02, 2.8083e-02, 2.1005e-02,
132
- # -1.3023e-03, -4.5802e-02, -3.6830e-02, -7.2250e-02, -5.6689e-02,
133
- # 8.9729e-03, 2.5722e-02, -6.7266e-02, 3.6313e-02, -4.3734e-02,
134
- # -1.4877e-02, -6.9325e-02, 4.3192e-02, 5.8052e-02, -1.5820e-02,
135
- # 4.2151e-02, -6.9208e-02]]).to(device)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
136
 
137
  def tts_generate(text):
138
  print(f"📝 Input text: {text}")
 
5
  from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
6
  from transformers.models.speecht5 import SpeechT5HifiGan
7
 
8
+ # processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
9
+ # model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
10
  # vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
11
 
 
12
  # device = "cuda" if torch.cuda.is_available() else "cpu"
13
  # model = model.to(device)
14
  # vocoder = vocoder.to(device)
15
 
16
+ # speaker_embedding = torch.zeros(1, 512).to(device)
17
+
18
+ # Load model and processor
19
+ processor = SpeechT5Processor.from_pretrained("nambn0321/TTS_british_2nd_attempt")
20
+ model = SpeechT5ForTextToSpeech.from_pretrained(
21
+ "nambn0321/TTS_british_2nd_attempt",
22
+ use_safetensors=True,
23
+ trust_remote_code=True
24
+ )
25
+ vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
26
+
27
+ # Move to CUDA if available
28
+ device = "cuda" if torch.cuda.is_available() else "cpu"
29
+ model = model.to(device)
30
+ vocoder = vocoder.to(device)
31
+
32
+ # # Dummy speaker embedding (or load your real one here)
33
+ speaker_embedding = torch.tensor([[-8.4146e-02, -9.6771e-03, 2.6844e-02, 2.2544e-02, -1.8455e-03,
34
+ -2.5492e-02, -6.2846e-02, -6.6044e-02, 3.1312e-02, 1.9042e-02,
35
+ -7.3039e-02, -8.0007e-02, 4.7080e-02, 3.7199e-02, 3.8119e-02,
36
+ 5.2214e-02, 1.1470e-02, 3.5968e-02, 8.1480e-03, 1.9952e-02,
37
+ 3.5186e-02, 1.3260e-02, -1.4271e-02, -3.8798e-02, -6.8658e-02,
38
+ -1.0121e-02, -5.8041e-02, 7.0087e-03, 2.4180e-02, 2.5093e-02,
39
+ -2.9513e-03, 4.1835e-02, 3.4997e-02, -4.7138e-02, 5.1652e-02,
40
+ -6.1358e-02, 8.9115e-03, 6.5912e-02, -6.1133e-02, -6.3178e-02,
41
+ 2.6463e-02, -6.4097e-02, 2.5865e-02, 3.6212e-02, 1.1775e-02,
42
+ -1.1237e-01, -1.9422e-02, 1.1771e-02, -5.1472e-02, 6.1495e-02,
43
+ 1.8622e-02, 3.6634e-02, 4.0088e-02, 2.4596e-02, -8.8999e-02,
44
+ -7.0578e-03, 4.2418e-02, 3.0962e-02, 3.6715e-02, 9.8362e-03,
45
+ 2.2486e-02, -8.9123e-03, -1.5486e-02, 9.6940e-03, 2.6662e-02,
46
+ 3.0941e-02, 9.7256e-03, -3.7924e-02, -6.1841e-02, -5.8947e-02,
47
+ 1.9625e-02, 1.0872e-02, 2.3126e-02, 9.8068e-03, 4.3716e-02,
48
+ 2.3031e-02, 1.6826e-02, 1.5088e-02, -7.1926e-02, -9.1217e-02,
49
+ -7.4439e-02, -3.6920e-02, -6.2430e-02, -5.1998e-02, -3.4543e-02,
50
+ -7.5438e-02, -4.9039e-02, 6.2118e-02, 1.7150e-02, 5.1786e-02,
51
+ 2.8916e-02, -7.0776e-02, 4.1283e-03, -7.0833e-02, 7.7827e-02,
52
+ -1.3024e-03, 2.8799e-02, 2.4766e-02, -5.6856e-02, -4.9455e-02,
53
+ 4.2941e-02, -6.8559e-02, -8.7198e-02, 5.3497e-02, 3.1263e-02,
54
+ -4.4196e-02, 4.5291e-02, 3.5016e-02, 2.7069e-02, 1.5925e-02,
55
+ -6.9111e-02, 2.8218e-02, 6.5694e-02, -2.9196e-03, 5.4257e-02,
56
+ 3.1966e-02, -5.5521e-02, -2.9675e-02, -5.7311e-02, -2.5660e-02,
57
+ 2.5544e-02, -6.7957e-02, 3.2566e-02, 5.1615e-02, -6.6451e-02,
58
+ 4.0387e-02, -5.5676e-02, 6.8813e-03, 4.2905e-02, 5.4461e-02,
59
+ 4.0467e-02, 9.1374e-03, 1.8873e-02, 4.0569e-02, 2.6208e-02,
60
+ -5.7306e-02, -4.2936e-02, 3.0970e-02, -5.9009e-02, -2.8001e-03,
61
+ 4.3110e-03, 3.9585e-03, -1.6110e-02, 3.8766e-02, -5.6695e-02,
62
+ 3.8432e-02, -8.8127e-03, -4.8471e-02, 6.2207e-02, -1.0577e-01,
63
+ 5.1350e-02, -3.8907e-02, -5.3652e-02, 3.5648e-02, 1.5506e-02,
64
+ 1.6576e-02, 5.0061e-03, -6.9767e-02, -4.8234e-02, 5.2357e-02,
65
+ 2.8605e-02, 3.0886e-03, 1.0158e-02, -5.5175e-02, -2.0156e-02,
66
+ 8.5321e-03, 1.9991e-02, 3.8980e-02, 7.9981e-03, 4.3244e-02,
67
+ 1.0584e-02, -8.0995e-02, 3.8089e-02, -3.5556e-02, -2.6731e-03,
68
+ 7.6711e-03, -4.6591e-02, 1.0168e-02, 2.5794e-02, -6.5743e-02,
69
+ 4.1506e-02, -5.1859e-02, 2.2370e-02, 2.5183e-02, -5.7133e-02,
70
+ -9.8171e-03, 4.1331e-02, 4.1121e-02, -3.7192e-02, -6.7827e-02,
71
+ -6.6048e-03, -5.5967e-03, -6.1243e-02, 1.8434e-02, 1.7639e-02,
72
+ 3.2567e-02, 4.3591e-02, 1.1444e-04, 2.1000e-02, -3.3589e-02,
73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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90
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92
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93
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95
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96
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100
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103
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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126
+ 2.1297e-02, 3.8620e-02, -8.1744e-02, 1.2381e-02, 2.5355e-02,
127
+ -1.8926e-03, 1.7969e-02, 1.8478e-02, 4.2966e-03, -3.4873e-02,
128
+ -6.8647e-02, 6.3062e-03, -5.6329e-03, 5.0244e-02, 7.9961e-02,
129
+ -4.6829e-02, -5.0599e-02, 2.6159e-02, 3.4554e-02, 4.3835e-02,
130
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131
+ -4.5883e-02, 1.9459e-02, -4.2683e-02, 2.8083e-02, 2.1005e-02,
132
+ -1.3023e-03, -4.5802e-02, -3.6830e-02, -7.2250e-02, -5.6689e-02,
133
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134
+ -1.4877e-02, -6.9325e-02, 4.3192e-02, 5.8052e-02, -1.5820e-02,
135
+ 4.2151e-02, -6.9208e-02]]).to(device)
136
 
137
  def tts_generate(text):
138
  print(f"📝 Input text: {text}")