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| 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") | |
| languages = ["hindi","bengali","odia","gujarati"] | |
| 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,lang="hindi"): | |
| sr,y = audio | |
| y = y.astype(np.float32) | |
| y/= np.max(np.abs(y)) | |
| y_resampled = resample_to_16k(y,sr) | |
| if lang not in languages: | |
| return "No Model","So Stay tuned!" | |
| pipe= eval(f'transcriber_{lang}') | |
| start_time = time.time() | |
| trans = pipe(y_resampled) | |
| end_time = time.time() | |
| return trans["text"],(end_time-start_time) | |
| demo = gr.Interface( | |
| transcribe, | |
| inputs=["microphone",gr.Radio(["hindi","bengali","odia","gujarati"],value="hindi")], | |
| # inputs=["microphone",gr.Radio(["hindi","bengali","odia","gujarati","telugu","sinhala","tamil","nepali","marathi"],value="hindi")], | |
| outputs=["text","text"], | |
| examples=[["./Samples/Hindi_1.mp3","hindi"],["./Samples/Hindi_2.mp3","hindi"],["./Samples/Hindi_3.mp3","hindi"],["./Samples/Hindi_4.mp3","hindi"],["./Samples/Hindi_5.mp3","hindi"],["./Samples/Tamil_2.mp3","hindi"],["./Samples/climate ex short.wav","hindi"],["./Samples/Gujarati_1.wav","gujarati"],["./Samples/Gujarati_2.wav","gujarati"],["./Samples/Bengali_1.wav","bengali"],["./Samples/Bengali_2.wav","bengali"]]) | |
| # 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() |