Create app.py

app.py

@@ -0,0 +1,47 @@
+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import argparse
+from glob import glob
+import torchaudio
+import subprocess
+import gradio as gr
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+def get_filename(wav_file):
+    filename_local = wav_file.split('/')[-1][:-4]
+    filename_new = '/tmp/'+filename_local+'_16.wav'
+    
+    
+    subprocess.call(["sox {} -r {} -b 16 -c 1 {}".format(wav_file, str(16000), filename_new)], shell=True)
+    return filename_new
+
+def parse_transcription(wav_file):
+    # load pretrained model
+    processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+    model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+
+    # load audio
+
+    
+    wav_file = get_filename(wav_file.name)
+    audio_input, sample_rate = sf.read(wav_file)
+    #test_file = resampler(test_file[0])
+
+    # pad input values and return pt tensor
+    input_values = processor(audio_input, sampling_rate=16_000, return_tensors="pt").input_values
+
+    # INFERENCE
+    # retrieve logits & take argmax
+    logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+
+    # transcribe
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return transcription
+    
+    
+input = gr.inputs.Audio(source="microphone", type="file") 
+gr.Interface(parse_transcription, inputs = input,  outputs="text", 
+             analytics_enabled=False, show_tips=False).launch(inline=False);