aap.py

#!/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)