Delete aap.py

aap.py

@@ -1,63 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[ ]:
-
-
-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("jonatasgrosman/wav2vec2-large-xlsr-53-english")
-    model = Wav2Vec2ForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-english")
-
-    # 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
-
-
-# In[ ]:
-
-
-import gradio as gr
-title = "Speech-to-Text-English"
-description = "Upload a English audio clip, and let AI do the hard work of transcribing."
-
-gr.Interface( 
-    parse_transcription,
-    title=title,
-    inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"),
-    description=description, outputs = "text").launch(inline = False)
-