App Code updated

app.py

@@ -1,85 +1,152 @@
-import gradio as gr
-from transformers import pipeline, AutoModelForImageSegmentation 
-from gradio_imageslider import ImageSlider
-import torch
-from torchvision import transforms
-import spaces
-from PIL import Image
-
-import numpy as np
-import time
-
-birefnet = AutoModelForImageSegmentation.from_pretrained(
-    "ZhengPeng7/BiRefNet", trust_remote_code=True
-)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-print("Using device:", device)
-
-birefnet.to(device)
-transform_image = transforms.Compose(
-    [
-        transforms.Resize((1024, 1024)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]
-)
+# import gradio as gr
+# from transformers import pipeline, AutoModelForImageSegmentation 
+# from gradio_imageslider import ImageSlider
+# import torch
+# from torchvision import transforms
+# import spaces
+# from PIL import Image
+
+# import numpy as np
+# import time
+
+# birefnet = AutoModelForImageSegmentation.from_pretrained(
+#     "ZhengPeng7/BiRefNet", trust_remote_code=True
+# )
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# print("Using device:", device)
+
+# birefnet.to(device)
+# transform_image = transforms.Compose(
+#     [
+#         transforms.Resize((1024, 1024)),
+#         transforms.ToTensor(),
+#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+#     ]
+# )
+
+# # @spaces.GPU
+# # def PreProcess(image):
+# #     size = image.size
+# #     image = transform_image(image).unsqueeze(0).to(device)
+
+# #     with torch.no_grad():
+# #         preds = birefnet(image)[-1].sigmoid().cpu()
+# #     pred = preds[0].squeeze()
+# #     pred = transforms.ToPILImage()(pred)
+# #     mask = pred.resize(size)
+# #     # image.putalpha(mask)
+# #     return image
 
 # @spaces.GPU
 # def PreProcess(image):
-#     size = image.size
-#     image = transform_image(image).unsqueeze(0).to(device)
+#     size = image.size  # Save original size
+#     image_tensor = transform_image(image).unsqueeze(0).to(device)  # Transform the image into a tensor
 
 #     with torch.no_grad():
-#         preds = birefnet(image)[-1].sigmoid().cpu()
+#         preds = birefnet(image_tensor)[-1].sigmoid().cpu()  # Get predictions
 #     pred = preds[0].squeeze()
-#     pred = transforms.ToPILImage()(pred)
-#     mask = pred.resize(size)
-#     # image.putalpha(mask)
-#     return image
 
-@spaces.GPU
-def PreProcess(image):
-    size = image.size  # Save original size
-    image_tensor = transform_image(image).unsqueeze(0).to(device)  # Transform the image into a tensor
+#     # Convert the prediction tensor to a PIL image
+#     pred_pil = transforms.ToPILImage()(pred)
 
-    with torch.no_grad():
-        preds = birefnet(image_tensor)[-1].sigmoid().cpu()  # Get predictions
-    pred = preds[0].squeeze()
+#     # Resize the mask to match the original image size
+#     mask = pred_pil.resize(size)
 
-    # Convert the prediction tensor to a PIL image
-    pred_pil = transforms.ToPILImage()(pred)
+#     # Convert the original image (passed as input) to a PIL image
+#     image_pil = image.convert("RGBA")  # Ensure the image has an alpha channel
 
-    # Resize the mask to match the original image size
-    mask = pred_pil.resize(size)
+#     # Apply the alpha mask to the image
+#     image_pil.putalpha(mask)
 
-    # Convert the original image (passed as input) to a PIL image
-    image_pil = image.convert("RGBA")  # Ensure the image has an alpha channel
+#     return image_pil
 
-    # Apply the alpha mask to the image
-    image_pil.putalpha(mask)
+# def segment_image(image):
+#     start = time.time()
+#     image = Image.fromarray(image)
+#     image = image.convert("RGB")
+#     org = image.copy()
+#     image = PreProcess(image)
+#     time_taken = np.round((time.time() - start),2)
+#     return (image, org), time_taken
 
-    return image_pil
+# slider = ImageSlider(label='birefnet', type="pil")
+# image = gr.Image(label="Upload an Image")
 
-def segment_image(image):
-    start = time.time()
-    image = Image.fromarray(image)
-    image = image.convert("RGB")
-    org = image.copy()
-    image = PreProcess(image)
-    time_taken = np.round((time.time() - start),2)
-    return (image, org), time_taken
+# butterfly = Image.open("butterfly.png")
+# Dog = Image.open('Dog.jpg')
 
-slider = ImageSlider(label='birefnet', type="pil")
-image = gr.Image(label="Upload an Image")
+# time_taken = gr.Textbox(label="Time taken", type="text")
 
-butterfly = Image.open("butterfly.png")
-Dog = Image.open('Dog.jpg')
+# demo = gr.Interface(
+#     segment_image, inputs=image, outputs=[slider,time_taken], examples=[butterfly,Dog], api_name="BiRefNet")
 
-time_taken = gr.Textbox(label="Time taken", type="text")
+# if __name__ == '__main__' :
+#     demo.launch()
+
+import requests
+import gradio as gr
+import tempfile
+import os
+from transformers import pipeline
+from huggingface_hub import InferenceClient
+
+model_id = "openai/whisper-large-v3"
+client = InferenceClient(model_id)
+pipe = pipeline("automatic-speech-recognition", model=model_id)
+
+# def transcribe(inputs, task):
+#     if inputs is None:
+#         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+#     text = pipe(inputs, chunk_length_s=30)["text"]
+#     return text
+
+def transcribe(inputs, task):
+    if inputs is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+    try:
+  
+        res = client.automatic_speech_recognition(inputs).text
+        return res
+    
+    except Exception as e:
+        return fr'Error: {str(e)}'
+        
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="microphone", type="filepath"),
+        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+    ],
+    outputs="text",
+    title="Whisper Large V3: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button!"
+    ),
+    allow_flagging="never",
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="upload", type="filepath", label="Audio file"),
+        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+    ],
+    outputs="text",
+    title="Whisper Large V3: Transcribe Audio",
+    description=(
+        "Transcribe long-form microphone or audio inputs with the click of a button!"
+    ),
+    allow_flagging="never",
+)
 
-demo = gr.Interface(
-    segment_image, inputs=image, outputs=[slider,time_taken], examples=[butterfly,Dog], api_name="BiRefNet")
+with demo:
+    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
 
-if __name__ == '__main__' :
-    demo.launch()
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -1,13 +1,4 @@
-torch
-accelerate
-opencv-python
-spaces
-torchvision
-pillow
-numpy
-huggingface-hub
-gradio
-gradio-imageslider
+git+https://github.com/openai/whisper.git
 transformers
-timm
-kornia
+requests
+huggingface_hub