| from transformers.pipelines.audio_utils import ffmpeg_read | |
| from transformers import WhisperForConditionalGeneration, WhisperProcessor, pipeline | |
| import gradio as gr | |
| import numpy as np | |
| def process_transcribe(file): | |
| audio_nparray = file[1] | |
| my_list = audio_nparray.tolist() | |
| endpoint = runpod.Endpoint("14ggfq6a17uim9") | |
| run_request = endpoint.run_sync( | |
| {"audio_list": my_list} | |
| ) | |
| raw_text = run_request | |
| return raw_text | |
| with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as iface: | |
| with gr.Tab("App"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_id = gr.Textbox(label="Audio") | |
| audio_file = gr.Audio(sources=["upload"], type="numpy") | |
| submit_btn = gr.Button("Submit", variant="primary") | |
| with gr.Column(): | |
| raw_transcript = gr.Textbox(label="Transcription") | |
| submit_btn.click(process_transcribe, inputs=[audio_file], outputs=[raw_transcript]) | |
| iface.launch(debug=True) |