| import time | |
| from transformers import pipeline | |
| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| transcriber_hindi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-hindi") | |
| transcriber_bengali = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_bengali") | |
| transcriber_odia = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-odia") | |
| transcriber_gujarati = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_gujarati") | |
| transcriber_telugu = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_telugu") | |
| transcriber_sinhala = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_sinhala") | |
| transcriber_tamil = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_tamil") | |
| transcriber_nepali = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_nepali") | |
| transcriber_marathi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_marathi") | |
| def resample_to_16k(audio, orig_sr): | |
| y_resampled = librosa.resample(y=audio, orig_sr=orig_sr, target_sr=16000) | |
| return y_resampled | |
| def transcribe(audio): | |
| sr,y = audio | |
| y = y.astype(np.float32) | |
| y/= np.max(np.abs(y)) | |
| y_resampled = resample_to_16k(y,sr) | |
| pipe= eval(f'transcriber_{lang}') | |
| trans = pipe(y_resampled) | |
| return trans["text"] | |
| demo = gr.Interface( | |
| transcribe, | |
| inputs=["microphone",gr.Radio(["hindi","bangali","odia","gujarati","telugu","sinhala","tamil","nepali","marathi"],value="hindi")], | |
| outputs="text", | |
| examples=[["./Samples/Hindi_1.mp3","hindi"],["./Samples/Hindi_2.mp3","hindi"],["./Samples/Tamil_1.mp3","tamil"],["./Samples/Tamil_2.mp3","hindi"],["./Samples/Nepal_1.mp3","nepali"],["./Samples/Nepal_2.mp3","nepali"],["./Samples/Marathi_1.mp3","marathi"],["./Samples/Marathi_2.mp3","marathi"],["./Samples/climate ex short.wav","hindi"]]) | |
| demo.launch() |