Update app.py

app.py

@@ -1,41 +1,10 @@
-#from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import soundfile as sf
-import torch
 import gradio as gr
-
-
-# load model and processor
-from transformers import AutoProcessor, AutoModelForCTC
-
-processor = AutoProcessor.from_pretrained("h4d35/Wav2Vec2-hi")
-
-model = AutoModelForCTC.from_pretrained("h4d35/Wav2Vec2-hi")
-
-# define function to read in sound file
-def map_to_array(file):
-   speech, _ = sf.read(file)
-   return speech
- 
-
-
-# tokenize
-def inference(audio):
-   input_values = processor(map_to_array(audio.name), return_tensors="pt", padding="longest").input_values  # Batch size 1
-
-   # retrieve logits
-   logits = model(input_values).logits
-
-   # take argmax and decode
-   predicted_ids = torch.argmax(logits, dim=-1)
-   transcription = processor.batch_decode(predicted_ids)
-   return transcription[0]
-
-inputs = gr.inputs.Audio(label="Input Audio", type="file")
-outputs =  gr.outputs.Textbox(label="Output Text")
-
 title = "HindiASR"
-description = "HindiASR using Wav2Vec2.0"
-
-
-#examples=[['poem.wav']]
-gr.Interface(inference, inputs, outputs, title=title, description=description).launch()
+description = "Gradio demo for HindiASR"
+# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.06678'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>"
+
+# examples = [['common_voice_en_18301577.mp3']]
+gr.Interface.load("huggingface/h4d35/Wav2Vec2-hi"", 
+    title=title,
+    description=description
+).launch()