Create app.py

app.py

@@ -0,0 +1,38 @@
+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()