Create app.py

app.py

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+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import soundfile as sf
+import torch
+import gradio as gr
+
+
+# load model and processor
+processor = Wav2Vec2Processor.from_pretrained("h4d35/Wav2Vec2-hi")
+model = Wav2Vec2ForCTC.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()