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import gradio as gr
from transformers import pipeline
import numpy as np
transcriber_hindi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-hindi")
transcriber_bang = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_bengali")
transcriber_odia = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-odia")
def transcribe(audio,lang = "hindi"):
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if lang == "hindi":
return transcriber_hindi({"sampling_rate": sr, "raw": y})["text"]
if lang == "bangali":
return transcriber_bang({"sampling_rate": sr, "raw": y})["text"]
if lang == "odia":
return transcriber_odia({"sampling_rate": sr, "raw": y})["text"]
demo = gr.Interface(
transcribe,
[gr.Audio(source="microphone"), gr.Radio(["hindi","bangali","odia"])]
"text")
demo.launch() |