| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| def init_transcription_pipeline(): | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model_path = "c:/Users/vhits/Documents/Speect2Text/model/whisper-hindi-large-v2" | |
| transcribe_pipeline = pipeline( | |
| task = "automatic-speech-recognition", | |
| model = model_path, | |
| chunk_length_s = 30, | |
| device = device | |
| ) | |
| transcribe_pipeline.model.config.forced_decoder_ids = transcribe_pipeline.tokenizer.get_decoder_prompt_ids(language="gu", task="transcribe") | |
| return transcribe_pipeline | |
| transcribe_pipeline = init_transcription_pipeline() | |
| def transcribe_audio(audio_file_path): | |
| transcription_result = transcribe_pipeline(audio_file_path)["text"] | |
| return transcription_result | |
| iface = gr.Interface( | |
| fn = transcribe_audio, | |
| inputs = gr.Audio(label="Upload your audio file", type="filepath"), | |
| outputs=gr.Textbox(label="Transcription"), | |
| title = "Gujarati Audio VH Test" | |
| ).launch() |