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import time |
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from transformers import pipeline |
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import gradio as gr |
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import numpy as np |
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import librosa |
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transcriber_hindi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-hindi") |
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transcriber_bengali = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_bengali") |
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transcriber_odia = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-odia") |
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transcriber_gujarati = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_gujarati") |
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languages = ["hindi","bengali","odia","gujarati"] |
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def resample_to_16k(audio, orig_sr): |
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y_resampled = librosa.resample(y=audio, orig_sr=orig_sr, target_sr=16000) |
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return y_resampled |
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def transcribe(audio,lang="hindi"): |
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sr,y = audio |
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y = y.astype(np.float32) |
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y/= np.max(np.abs(y)) |
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y_resampled = resample_to_16k(y,sr) |
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if lang not in languages: |
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return "No Model","So Stay tuned!" |
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pipe= eval(f'transcriber_{lang}') |
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start_time = time.time() |
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trans = pipe(y_resampled) |
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end_time = time.time() |
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return trans["text"],(end_time-start_time) |
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demo = gr.Interface( |
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transcribe, |
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inputs=["microphone",gr.Radio(["hindi","bengali","odia","gujarati"],value="hindi")], |
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outputs=["text","text"], |
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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"]]) |
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demo.launch() |
