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| import os | |
| import warnings | |
| import gradio as gr | |
| from transformers import pipeline | |
| import re | |
| # Initialize the speech recognition pipeline and transliterator | |
| #p1 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-odia_v1") | |
| #p2 = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1") | |
| HF_TOKEN = os.getenv('HW_TOKEN') | |
| hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "save_audio") | |
| cur_line=0 | |
| def readFile(): | |
| f=open('prompt.txt') | |
| line_num=0 | |
| lines=f.readlines() | |
| line_num = len(lines) | |
| return line_num,lines | |
| totlines,file_content=readFile() | |
| callback = gr.CSVLogger() | |
| def readPromt(): | |
| global cur_line | |
| cur_line+=1 | |
| global file_content | |
| print (cur_line) | |
| return file_content[cur_line] | |
| def readNext(): | |
| global totlines | |
| print(totlines) | |
| global cur_line | |
| if cur_line<totlines-1: | |
| cur_line+=1 | |
| global file_content | |
| print (cur_line) | |
| return [file_content[cur_line],None] | |
| def readPrevious(): | |
| global cur_line | |
| if cur_line>=0: | |
| cur_line-=1 | |
| #cur_line=current_line | |
| global file_content | |
| print (cur_line) | |
| return [file_content[cur_line],None] | |
| demo = gr.Blocks() | |
| with demo: | |
| #dr=gr.Dropdown(["Hindi","Odiya"],value="Odiya",label="Select Language") | |
| #audio_file = gr.Audio(sources=["microphone","upload"],type="filepath") | |
| text = gr.Textbox(readPromt()) | |
| upfile = gr.Audio( | |
| sources=["microphone","upload"], type="filepath", label="Record" | |
| ) | |
| #upfile = gr.inputs.Audio(source="upload", type="filepath", label="Upload") | |
| with gr.Row(): | |
| b1 = gr.Button("Save") | |
| b2 = gr.Button("Next") | |
| b3 = gr.Button("Previous") | |
| #b4=gr.Button("Clear") | |
| b2.click(readNext,inputs=None,outputs=[text,upfile]) | |
| b3.click(readPrevious,inputs=None,outputs=[text,upfile]) | |
| #b4.click(lambda: None, outputs=upfile) | |
| # b1.click(sel_lng, inputs=[dr,mic,upfile], outputs=text) | |
| #b2.click(text_to_sentiment, inputs=text, outputs=label) | |
| #callback.setup([text, upfile], "flagged_data_points") | |
| #callback.setup([text, upfile], hf_writer) | |
| #b1.click(lambda *args: callback.flag(args), [text, upfile], None, preprocess=False) | |
| flagging_callback=hf_writer | |
| b1.click(lambda *args: flagging_callback, [text, upfile], None, preprocess=False) | |
| demo.launch() | |