Upload app.py

app.py

@@ -1,85 +1,85 @@
-import gradio as gr
-import torch
-import numpy as np
-from torchaudio.functional import resample
-
-from processAudio import upscaleAudio
-
-class Object(object):
-    pass
-
-with gr.Blocks(theme=gr.themes.Default().set(body_background_fill="#CCEEFF")) as layout:
-    with gr.Row():
-        gr.Markdown("<h2>Broadcast Audio Upscaler</h2>")
-    with gr.Row():
-        with open("html/directions.html", "r") as directionsHtml:
-            gr.Markdown(directionsHtml.read())
-    with gr.Row():
-        modelSelect = gr.Dropdown(
-            [
-                ["FM Radio Super Resolution","FM_Radio_SR.th"],
-                ["AM Radio Super Resolution (Beta)","AM_Radio_SR.th"],
-                ["Telephone Super Resolution (Beta)","Telephone_SR.th"] 
-            ],
-            label="Select Model:",
-            value="FM_Radio_SR.th",
-        )
-    with gr.Row():
-        with gr.Column():
-            audioFileSelect = gr.Audio(label="Audio File (Mono or Stereo, Max 6 Minutes):",sources="upload", max_length=360)
-        with gr.Column():
-            audioOutput = gr.Audio(show_download_button=True, label="Restored Audio:", sources=[], max_length=360)
-    with gr.Row():
-        with gr.Column():
-            submit = gr.Button("Process Audio", variant="primary", interactive=False)
-    with gr.Row():
-        with gr.Accordion("More Information:", open=False):
-            with open("html/information.html", "r") as informationHtml:
-                gr.Markdown(informationHtml.read())    
-
-    @audioFileSelect.input(inputs=audioFileSelect, outputs=[submit, audioFileSelect])
-    def audioFileSelectChanged(audioData: gr.Audio):
-        #Audio exists and is mono or stereo
-        if audioData is None:
-            return gr.update(interactive=False), None
-        if len(audioData[1].shape) == 1:
-            return gr.update(interactive=True), audioData
-        if audioData[1].shape[1] > 2:
-            gr.Warning("Audio with more than 2 channels is not supported.")
-            return gr.update(interactive=False), None
-        return gr.update(interactive=True), audioData
-        
-
-    @submit.click(inputs=[modelSelect, audioFileSelect], outputs=audioOutput)
-    def processAudio(model: gr.Dropdown, audioData: gr.Audio):
-        if audioData is None:
-            raise gr.Error("Load an audio file.")
-            return None
-        elif len(audioData[1].shape) == 1: #Convert mono to stereo
-            lrAudio = torch.tensor(np.array([
-                audioData[1].copy().astype(np.float32)/32768, 
-                audioData[1].copy().astype(np.float32)/32768
-            ]))
-        elif audioData[1].shape[1] > 2:
-            raise gr.Error("Audio with more than 2 channels is not supported.")
-            return None
-        else: #re-order channel data from [samples, 2] to [2, samples]
-            lrAudio = torch.tensor(audioData[1].copy().astype(np.float32)/32768).transpose(0,1)
-        if audioData[0] != 44100:
-            lrAudio = resample(lrAudio, audioData[0], 44100)
-        model_name, experiment_file = getModelInfo(model)
-        hrAudio=upscaleAudio(lrAudio, model, model_name=model_name, experiment_file=experiment_file)
-        hrAudio=hrAudio / max(hrAudio.abs().max().item(), 1)
-        outAudio=(hrAudio*32767).numpy().astype(np.int16).transpose(1,0)
-        return tuple([44100, outAudio])
-    
-    def getModelInfo(modelFilename: str):
-        if(modelFilename == "FM_Radio_SR.th"):
-            return "aero", "aero_441-441_512_256.yaml"
-        if(modelFilename == "AM_Radio_SR.th"):
-            return "aero", "aero_441-441_512_256.yaml"
-        if(modelFilename == "Telephone_SR.th"):
-            return "aero", "aero_441-441_512_256.yaml"
-        return "aero", "aero_441-441_512_256.yaml"
-
+import gradio as gr
+import torch
+import numpy as np
+from torchaudio.functional import resample
+
+from processAudio import upscaleAudio
+
+class Object(object):
+    pass
+
+with gr.Blocks(theme=gr.themes.Default().set(body_background_fill="#CCEEFF")) as layout:
+    with gr.Row():
+        gr.Markdown("<h2>Broadcast Audio Upscaler</h2>")
+    with gr.Row():
+        with open("html/directions.html", "r") as directionsHtml:
+            gr.Markdown(directionsHtml.read())
+    with gr.Row():
+        modelSelect = gr.Dropdown(
+            [
+                ["FM Radio Super Resolution","FM_Radio_SR.th"],
+                ["AM Radio Super Resolution (Beta v2)","AM_Radio_SR.th"],
+                ["Telephone Super Resolution (Beta)","Telephone_SR.th"] 
+            ],
+            label="Select Model:",
+            value="FM_Radio_SR.th",
+        )
+    with gr.Row():
+        with gr.Column():
+            audioFileSelect = gr.Audio(label="Audio File (Mono or Stereo, Max 6 Minutes):",sources="upload", max_length=360)
+        with gr.Column():
+            audioOutput = gr.Audio(show_download_button=True, label="Restored Audio:", sources=[], max_length=360)
+    with gr.Row():
+        with gr.Column():
+            submit = gr.Button("Process Audio", variant="primary", interactive=False)
+    with gr.Row():
+        with gr.Accordion("More Information:", open=False):
+            with open("html/information.html", "r") as informationHtml:
+                gr.Markdown(informationHtml.read())    
+
+    @audioFileSelect.input(inputs=audioFileSelect, outputs=[submit, audioFileSelect])
+    def audioFileSelectChanged(audioData: gr.Audio):
+        #Audio exists and is mono or stereo
+        if audioData is None:
+            return gr.update(interactive=False), None
+        if len(audioData[1].shape) == 1:
+            return gr.update(interactive=True), audioData
+        if audioData[1].shape[1] > 2:
+            gr.Warning("Audio with more than 2 channels is not supported.")
+            return gr.update(interactive=False), None
+        return gr.update(interactive=True), audioData
+        
+
+    @submit.click(inputs=[modelSelect, audioFileSelect], outputs=audioOutput)
+    def processAudio(model: gr.Dropdown, audioData: gr.Audio):
+        if audioData is None:
+            raise gr.Error("Load an audio file.")
+            return None
+        elif len(audioData[1].shape) == 1: #Convert mono to stereo
+            lrAudio = torch.tensor(np.array([
+                audioData[1].copy().astype(np.float32)/32768, 
+                audioData[1].copy().astype(np.float32)/32768
+            ]))
+        elif audioData[1].shape[1] > 2:
+            raise gr.Error("Audio with more than 2 channels is not supported.")
+            return None
+        else: #re-order channel data from [samples, 2] to [2, samples]
+            lrAudio = torch.tensor(audioData[1].copy().astype(np.float32)/32768).transpose(0,1)
+        if audioData[0] != 44100:
+            lrAudio = resample(lrAudio, audioData[0], 44100)
+        model_name, experiment_file = getModelInfo(model)
+        hrAudio=upscaleAudio(lrAudio, model, model_name=model_name, experiment_file=experiment_file)
+        hrAudio=hrAudio / max(hrAudio.abs().max().item(), 1)
+        outAudio=(hrAudio*32767).numpy().astype(np.int16).transpose(1,0)
+        return tuple([44100, outAudio])
+    
+    def getModelInfo(modelFilename: str):
+        if(modelFilename == "FM_Radio_SR.th"):
+            return "aero", "aero_441-441_512_256.yaml"
+        if(modelFilename == "AM_Radio_SR.th"):
+            return "aero", "aero_441-441_512_256.yaml"
+        if(modelFilename == "Telephone_SR.th"):
+            return "aero", "aero_441-441_512_256.yaml"
+        return "aero", "aero_441-441_512_256.yaml"
+
 layout.launch()