Create app.py

app.py

@@ -0,0 +1,46 @@
+import torch
+import torchaudio
+import gradio as gr
+
+device="cpu"
+bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH
+processor = bundle.get_text_processor()
+tacotron2 = bundle.get_tacotron2().to(device)
+
+# Workaround to load model mapped on GPU
+# https://stackoverflow.com/a/61840832
+waveglow = torch.hub.load(
+    "NVIDIA/DeepLearningExamples:torchhub",
+    "nvidia_waveglow",
+    model_math="fp32",
+    pretrained=False,
+)
+checkpoint = torch.hub.load_state_dict_from_url(
+    "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth",  # noqa: E501
+    progress=False,
+    map_location=device,
+)
+state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()}
+
+waveglow.load_state_dict(state_dict)
+waveglow = waveglow.remove_weightnorm(waveglow)
+waveglow = waveglow.to(device)
+waveglow.eval()
+
+def inference(text):
+
+  with torch.inference_mode():
+      processed, lengths = processor(text)
+      processed = processed.to(device)
+      lengths = lengths.to(device)
+      spec, _, _ = tacotron2.infer(processed, lengths)
+
+
+
+  with torch.no_grad():
+      waveforms = waveglow.infer(spec)
+
+  torchaudio.save("_assets/output_waveglow.wav", waveforms[0:1].cpu(), sample_rate=22050)
+  return "output_waveglow.wav"
+  
+gr.Interface(inference,"text",gr.outputs.Audio(type="file")).launch(debug=True)