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| # 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 # 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_tensor)[-1].sigmoid().cpu() # Get predictions | |
| # pred = preds[0].squeeze() | |
| # # Convert the prediction tensor to a PIL image | |
| # pred_pil = transforms.ToPILImage()(pred) | |
| # # Resize the mask to match the original image size | |
| # mask = pred_pil.resize(size) | |
| # # Convert the original image (passed as input) to a PIL image | |
| # image_pil = image.convert("RGBA") # Ensure the image has an alpha channel | |
| # # Apply the alpha mask to the image | |
| # image_pil.putalpha(mask) | |
| # return image_pil | |
| # 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 | |
| # slider = ImageSlider(label='birefnet', type="pil") | |
| # image = gr.Image(label="Upload an Image") | |
| # butterfly = Image.open("butterfly.png") | |
| # Dog = Image.open('Dog.jpg') | |
| # time_taken = gr.Textbox(label="Time taken", type="text") | |
| # demo = gr.Interface( | |
| # segment_image, inputs=image, outputs=[slider,time_taken], examples=[butterfly,Dog], api_name="BiRefNet") | |
| # if __name__ == '__main__' : | |
| # demo.launch() | |
| # import requests | |
| # import gradio as gr | |
| # import tempfile | |
| # import os | |
| # from transformers import pipeline | |
| # from huggingface_hub import InferenceClient | |
| # import time | |
| # import torch | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model_id = "openai/whisper-large-v3" | |
| # client = InferenceClient(model_id) | |
| # pipe = pipeline("automatic-speech-recognition", model=model_id, device=device) | |
| # # 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): | |
| # start = time.time() | |
| # 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 | |
| # end = time.time() - start | |
| # return res, end | |
| # except Exception as e: | |
| # return fr'Error: {str(e)}' | |
| # demo = gr.Blocks() | |
| # time_taken = gr.Textbox(label="Time taken", type="text") | |
| # mf_transcribe = gr.Interface( | |
| # fn=transcribe, | |
| # inputs=[ | |
| # gr.Audio(sources="microphone", type="filepath"), | |
| # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), | |
| # ], | |
| # outputs=["text", time_taken], | |
| # 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", time_taken], | |
| # title="Whisper Large V3: Transcribe Audio", | |
| # description=( | |
| # "Transcribe long-form microphone or audio inputs with the click of a button!" | |
| # ), | |
| # allow_flagging="never", | |
| # ) | |
| # with demo: | |
| # gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) | |
| # if __name__ == "__main__": | |
| # demo.queue().launch() | |
| import requests | |
| import gradio as gr | |
| import tempfile | |
| import os | |
| from transformers import pipeline | |
| from huggingface_hub import InferenceClient | |
| import time | |
| import torch | |
| # Ensure CUDA is available and set device accordingly | |
| # device = 0 if torch.cuda.is_available() else -1 | |
| model_id = "openai/whisper-large-v3" | |
| client = InferenceClient(model_id) | |
| pipe = pipeline("automatic-speech-recognition", model=model_id) #, device=device) | |
| def transcribe(inputs, task, use_api): | |
| start = time.time() | |
| if inputs is None: | |
| raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") | |
| try: | |
| if use_api: | |
| # Use InferenceClient (API) if checkbox is checked | |
| res = client.automatic_speech_recognition(inputs).text | |
| else: | |
| # Use local pipeline if checkbox is unchecked | |
| res = pipe(inputs, chunk_length_s=30)["text"] | |
| end = time.time() - start | |
| return res, end | |
| except Exception as e: | |
| return fr'Error: {str(e)}', None | |
| 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", "text"], # Placeholder for transcribed text and time taken | |
| 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", "text"], # Placeholder for transcribed text and time taken | |
| title="Whisper Large V3: Transcribe Audio", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button!" | |
| ), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| with gr.Row(): | |
| # with gr.Column(): | |
| # Group the tabs for microphone and file-based transcriptions | |
| gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) | |
| with gr.Column(): | |
| use_api_checkbox = gr.Checkbox(label="Use API", value=False) # Checkbox outside | |
| time_taken = gr.Textbox(label="Time taken", type="text") # Time taken outside the interfaces | |
| if __name__ == "__main__": | |
| demo.queue().launch() | |